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Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain, Edited by Chiang S. Jao p. cm. ISBN 978-953-307-258-6
free online editions of InTech Books and Journals can be found at www.intechopen.com
Contents Preface IX Part 1
Barriers, Challenges, Impacts, and Success Factors of System Adoption 1
Chapter 1
Challenges in Developing Effective Clinical Decision Support Systems 3 Kamran Sartipi, Norman P. Archer and Mohammad H. Yarmand
Chapter 2
Impacts and Risks of Adopting Clinical Decision Support Systems 21 Wilfred Bonney
Chapter 3
Success Factors and Barriers for Implementation of Advanced Clinical Decision Support Systems 31 Anne-Marie J.W. Scheepers-Hoeks, Rene J. Grouls, Cees Neef, Eric W. Ackerman and Erik H. Korsten
Part 2
Guideline-Based Clinical Decision Support System
45
Chapter 4
Information Extraction Approach for Clinical Practice Guidelines Representation in a Medical Decision Support System 47 Fernando Pech-May, Ivan Lopez-Arevalo and Victor J. Sosa-Sosa
Chapter 5
Guideline-Based Decision Support Systems for Prevention and Management of Chronic Diseases Niels Peek
Part 3 Chapter 6
67
Applications for Disease Management 87 Emerging Information Technologies to Provide Improved Decision Support for Surveillance, Prevention, and Control of Vector-Borne Diseases 89 Saul Lozano-Fuentes, Christopher M. Barker, Marlize Coleman, Michael Coleman, Bborie Park, William K. Reisen and Lars Eisen
VI
Contents
Chapter 7
Optimization Models, Statistical and DSS Tools for Prevention and Combat of Dengue Disease 115 Marcos Negreiros, Adilson E. Xavier, Airton F. S. Xavier, Nelson Maculan, Philippe Michelon, José Wellington O. Lima and Luis Odorico M. Andrade
Chapter 8
A Decision Support System Based on Artificial Neural Networks for Pulmonary Tuberculosis Diagnosis 151 Carmen Maidantchik, José Manoel de Seixas, Felipe F. Grael, Rodrigo C. Torres, Fernando G. Ferreira, Andressa S. Gomes, José Márcio Faier, Jose Roberto Lapa e Silva, Fernanda C. de Q Mello, Afrânio Kritski and João Baptista de Oliveira e Souza Filho
Part 4 Chapter 9
Applications for Medical Procedures
167
Temporal Knowledge Generation for Medical Procedures 179 Aida Kamišalić, David Riaño and Tatjana Welzer
Chapter 10
Predicting Pathology in Medical Decision Support Systems in Endoscopy of the Gastrointestinal Tract 195 Michael Liedlgruber and Andreas Uhl
Chapter 11
Workflow and Clinical Decision Support for Radiation Oncology Daniel L McShan
215
Chapter 12
Computerized Decision Support Systems for Mechanical Ventilation 227 Fleur T. Tehrani
Chapter 13
Decision Support Systems in Anesthesia, Emergency Medicine and Intensive Care Medicine 239 Thomas M. Hemmerling
Chapter 14
Decision Support by Visual Incidence Anamneses for Increased Patient Safety Kerstin Ådahl and Rune Gustavsson
Part 5 Chapter 15
Chapter 16
263
Miscellaneous Case Studies 287 Pharmacoepidemiological Studies Using the Veterans Affairs Decision Support System Benjamin Wolozin, Austin Lee, Nien-Chen Li and Lewis E. Kazis
289
Decision Support Systems in Animal Health 299 Nguyen Van Long, Mark Stevenson and Bryan O’Leary
Contents
Chapter 17
Development of an Image Retrieval Model for Biomedical Image Databases 311 Achimugu Philip, Babajide Afolabi, Adeniran Oluwaranti and Oluwagbemi Oluwatolani
VII
Preface Series Preface This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers. This book series is dedicated to support professionals and series readers in the emerging field of DSS. Preface Clinical decision support systems (CDSSs) are computer-based applications that can effectively assist clinical practitioners and healthcare providers in decision making to improve their clinical practice skills and reduce preventable medical errors. A popular example of CDSSs is computerized physician order entry (CPOE) systems that provide patient-specific recommendations, collaborate active problems on the problem list with prescribed medications on the medication list, attach care reminders and alerts to the charts of patients in electronic health records, link laboratory test data to alert physicians while atypical values are detected. Patient safety was once an emerging field when the Institute of Medicine's (IOM) report (“To Err is Human: Building a Safer Health System") was first released and captured the attention of the healthcare community in late 1999. Many of the errors in the biomedical domain result from a culture and a fragmented system. Evidences from research studies indicated that mistakes were not due to clinicians not trying hard
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Preface
enough; they resulted from inherent shortcomings in the health caring system. Appropriate design of CDSSs assists in reducing such kinds of mistakes and promoting patient safety. Book Volume 2 extends the concepts and methodology of decision support systems (DSS) mentioned in Book Volume 1 to the applications of CDSS in the biomedicalrelated domain. This book collects a variety of topics that cover design and development of CDSS applications. It can be used as a textbook in formal courses or a reference book for practitioners. The readers will gain in-depth knowledge about the applications of CDSSs to detect/prevent specific diseases, to facilitate medical procedures during operations, and to collaborate the knowledge of biomedical domain experts for making better decisions. Section 1, including Chapter 1 through 3, illustrates challenges, impacts, risks and success factors in developing and adopting a DSS in the clinical domain. Chapter 1 and 2 explore challenges, impacts and risks in developing and adopting effective CDSS. Chapter 3 presents potential success factors and barriers of implementing advanced CDSS. These findings assist decision makers in identifying potential bottlenecks about the development and assessment of a useful CDSS. It is evident that the appropriate use of CDSSs with emerging technologies could enhance the adoption and acceptance rate of CDSS in clinical practice. Section 2, including Chapter 4 and 5, illustrates the applications of CDSS based on clinical practice guidelines (CPG). Chapter 4 highlights the importance of CPG in documenting the clinical diagnosis, prognosis, and treatment of specific diseases. The information extraction approach can connect relevant information in the clinical documents and is critical in enhancing the knowledge acquisition on CPG in CDSS. An experiment was conducted to obtain an intermediate representation of actions from a textual CPG in XML format by means of an information extraction module. Chapter 5 presents a guideline-based CDSS for prevention and management of chronic diseases. The example of an evidence-based conceptual CDSS framework is illustrated how to identify that CDSS can improve CPG implementation by reducing guideline complexity. Section 3 and 4, including Chapter 6 throughout 14, present extensive applications of CDSS developing to diagnose/treat specific diseases (such as vector-borne diseases and pulmonary tuberculosis) or to operate medical procedures (such as endoscopy and radiation oncology) effectively. In each chapter, the readers are able to identify the use of appropriate CDSS models in individual specialty frameworks. It is noteworthy that Chapter 14 presents the visual incidence anamneses (VIA) tool to improve decision support and process transparency in diagnosing patients using the DSS so as to improve patient safety. The readers are able to recognize the deficiencies of patient safety in health care due to the invisibility of potential causes of incidents, injuries and deaths. The VIA can be supportive to screen out unnecessary alternatives and identify the cause of vulnerable events.
Preface
Section 5, including Chapter 15 through 17, present three case studies of CDSS Applications in the field of pharmaco-epidemiology, animal health, and image data retrieval. Chapter 15 introduces the design and execution of pharmacoepidemiological databases using the Veterans Affairs DSS. The authors employ data mining strategies applied to the DSS database. This study generates the DSS database that led to the outcomes of reducing incidents of given clinical problems related to the use of medications. It is significant that the DSS data can be cross-referenced to Medicare in USA, which can capture some off-plan elements such as nursing home utilization and use of health systems outside the system. Chapter 16 presents the infrastructure and implementation of an animal health DSS. This chapter covers the current situation of the animal health DSS in developing countries and discusses the issues when the DSS is used to detect emerging diseases in an animal population. Future directions in developing an animal health DSS are also suggested to reduce the cost and alleviate the obstacles for widespread update of this technology. This book concludes in Chapter 17 that presents an interesting topic about image retrieval for biomedical image databases that support communication among healthcare decision makers and communities at large. A neural network model with backward propagation algorithm is applied to generate expert rules and to improve predictive accuracy. The proposed methodology for image indexing can facilitate efficient retrieval of time-oriented medical images that have direct reliance on medical diagnosis and intervention. Chiang S. Jao Transformation, Inc. Rockville Maryland University of Illinois (Retired) Chicago Illinois
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Part 1 Barriers, Challenges, Impacts, and Success Factors of System Adoption
0 1 Challenges in Developing Effective Clinical Decision Support Systems Kamran Sartipi, Norman P. Archer and Mohammad H. Yarmand McMaster University Canada 1. Introduction to CDSS Decision making is one of the most important and frequent aspects of our daily activities. Personal decisions display our characteristics, behavior, successes, failures, and the nature of our personalities. These decisions affect us in different ways, such as reasoning style, relationships, education, purchasing, careers, investments, health, and entertainment. The effectiveness of such decisions is affected by our age, knowledge, environment, economic status, and regulations. Our business related decisions are influenced by our knowledge, experience, and the availability of supporting systems in terms of employed processes, standards and techniques. Due to the importance of decision making, different technologies have been developed to help humans make effective decisions in the shortest time. Current advances in Information and Communication Technology (ICT) have revolutionized the way people communicate, share information, and make effective decisions. Decision making is a complex intellectual task that uses assistance from different resources. In the past, such resources were restricted to personal knowledge, experience, logic, and human mentors. However, the norms of current society and existing technologies have enhanced critical decision making. Educational systems are not restricted to physical classrooms any more; on-line education is gradually taking over. Knowledge about a technical domain can be obtained easily using Internet search engines and free on-line scientific articles. Mentorship has expanded from colleagues and friends to a large community of domain experts through subject-specific social networking facilities. Moreover, due to ubiquitous wireless communication technologies, such facilities are also accessible from small and remote communities. As a result, technology and different web-based tools (browse and search, document sharing, data mining, maps, data bases, web services) can be utilized as computing support for people, to help them make more knowledgeable and effective decisions. The healthcare domain has recently embraced new information and communication technologies to improve the quality of healthcare delivery and medical services. This long overdue opportunity is expected to reduce high costs and medical errors in patient diagnosis and treatment; enhance the way healthcare providers interact; increase personal health knowledge of the public; improve the availability and quality of health services; and promote collaborative and patient-centric healthcare services. To meet demands arising from these improved services, new tools, methods, and business models must emerge. Clinical Decision Support Systems (CDSS) are defined as computer applications that assist practitioners and healthcare providers in decision making, through timely access to
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electronically stored medical knowledge, in order to improve their medical practices. For example, with the recent increased focus on the prevention of medical errors, Computer-based Physician Order Entry (CPOE) systems enhanced by CDSS have been proposed as a key element in improving patient safety (Berner, 2007). An “effective” CDSS must also take into consideration the working environment of the practitioners and care providers. Hence such a CDSS should: not interfere with professional authority; recognize the context of the user and adapt itself accordingly; manage different types of information and interruptions that may affect the physician; save operating cost and time; be easy to use; adhere to medical guidelines provided by evidence-based research and practice; and support a patient-centric and collaborative decision making environment. Clinical Decision Support Systems (CDSS) are specialized forms of general Decision Support Systems (DSS) that have been applied in many other domains. Research in decision support systems for organizational decision making began in the late 1950s, and research in the more technical aspects started in the 1960s (Keen & Morton, 1978). Scott Morton (1971) was one of the first researchers to coin the term decision support systems (Eom & Kim, 2006). Since then, major advances in computer technology have contributed to the application of DSS in many different disciplines and problem domains. In particular, advances in information technology infrastructure, data processing, microcomputers, networks, and human computer interactions have influenced DSS developments. Use of the Internet has enhanced DSS in terms of efficiency, widespread usage, and the employment of typical web browsers as user interface components. Recent advances in wireless communication technology and mobile devices have resulted in many new applications for decision support systems in daily activities (Shim et al., 2002). The structure of this chapter is as follows. Section 2 provides an overview of different techniques and standards for representing clinical knowledge and information, with an emphasis on international standards such as HL7. Section 3 explores the nature of data mining techniques in assisting clinicians to diagnose illnesses and communicating the results of data mining. Section 4 discusses the influence of modern technologies and ad hoc web-application integration techniques to make collaborative decisions. Section 5 discusses the importance of user context and customizable software agents at the client platform. Section 6 provides a set of approaches to evaluate the success or failure of existing techniques, with a focus on business aspects and user adoption. In Section 7 the authors propose some research ideas that might contribute to the future development of more effective and acceptable clinical decision support systems. Section 8 provides several models and techniques from different fields that are used to support CDSS. Finally, Section 9 summarizes conclusions from this chapter.
2. Clinical knowledge and information representation In a nutshell, a decision support system consists of the following components: i) knowledge base to store, maintain and retrieve knowledge from the relevant domain; ii) inference engine to retrieve the relevant knowledge from the knowledge base and interpret the knowledge to infer a decision; and iii) user support to interact with the user in a meaningful and natural way, with operations for data entry, representation, and result output. Such a system can also be improved by adding a history of the previous decisions, which is dynamically updated when new decisions are made. We will discuss knowledge representation in this section. The inference engine is represented by different models and techniques that will be discussed in Section 8. The user support component is mostly designed with web-based Graphical User Interfaces (GUI); however
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since this component has a major impact on the effectiveness of a CDSS, it requires particular attention from the research community. Knowledge should be represented formally so that it can be efficiently processed. Such representation should be: human and machine readable; accurate in specifying domain knowledge; and portable and reusable among organizations (Verlaene et al., 2007). In general, knowledge representation methods can be categorized as declarative (using propositions and sentences), and procedural (explicitly defining the actions to be taken) (Aleksovska-Stojkovska & Loskovska, 2010). According to (Kong et al., 2008) four categories of knowledge representation are: i) Logical conditions: where variables and their valid ranges are provided and variable values are verified against their ranges; Boolean operators are used to specify more complicated cases. ii) Rules: expressed by if-then-else statements which emulate human reasoning processes; nesting statements are used for more complex cases. iii) Graphs: including decision trees and artificial neural networks. iv) Structures: high level categorization of relevant knowledge that allows focused observation of each of the healthcare sub-domains. A major source of medical knowledge for decision making in a diagnosis or treatment process is the medical or clinical guidelines which have been used throughout the history of medicine. Modern clinical guidelines are developed based on rigorous studies of the medical literature and are based on consensus and evidence in medical research and practice. Such guidelines are represented as rules or flow charts and may include the computation algorithms to be followed. Guideline engines are used to execute the clinical guidelines in the context of Electronic Medical Record (EMR) systems. The GuideLine Interchange Format (GLIF) (Collaboratory, 2004) is a computer representation format for clinical guidelines that can be used for developing interoperable flow-based guidelines to be executed by such engines. Another source of medical knowledge is the clinical terminology systems which allow healthcare professionals to use widely agreed sets of terms and concepts for communicating clinical information among healthcare professionals around the world for the purposes of diagnosis, prognosis, and treatment of diseases. A clinical terminology system facilitates identifying and accessing information pertaining to the healthcare process and hence improves the provision of healthcare services by care providers. The (Systematized Nomenclature of Medicine Clinical Terminology (SNOMED CT), 2011) is a comprehensive clinical terminology system that provides clinical content and expressiveness for clinical documentation and reporting. It can be used to code, retrieve, and analyze clinical data. The terminology is comprised of concepts, terms and relationships with the objective of precisely representing clinical information across the scope of healthcare. SNOMED CT uses healthcare software applications that focus on the collection of clinical data, linking to clinical knowledge bases and information retrieval, as well as data aggregation and exchange. A number of tools exist that support knowledge construction during the CDSS development process. Four important tools are introduced here. UMLS - Unified Medical Language System is a repository of biomedical vocabularies which integrates over 2 million names for some 900 000 concepts from more than 60 families of biomedical vocabularies, as well as 12 million relations among these concepts (Bodenreider, 2004). Protege is an ontology editor and knowledge-base framework that provides a suite of tools to construct domain models using frames and the Web Ontology Language (OWL) (Protege website, 2011). GLARE is used to acquire, represent, and execute clinical guidelines. It provides consistency checking and temporal and hypothetical reasoning (Anselma et al., 2011). PROforma is a formal knowledge representation language for specifying clinical guidelines in a machine executable format.
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Each guideline is expressed as a set of tasks, where a task can be of type: plan - contains any number of tasks; decision - taken at a point where options are presented; action: medical procedure; or inquiry - request for further information (Open Clinical: PROforma website, 2011). 2.1 HL7 v3 reference information modeling
Health Level Seven (HL7) (Health Level Seven official website, 2011) is an international community of healthcare experts and information scientists collaborating to create standards for the exchange, management and integration of electronic healthcare information. HL7 also refers to internationally accepted standards for healthcare information. Over the two decades since its inception, HL7 has undergone an evolutionary process starting from version 2.1 to its current version 3 (v3). HL7 v3 was a complete overhaul of its predecessor and was designed with consistency and comprehensive coverage in mind. It supports a wide range of areas such as patient care, patient administration, laboratory, pharmacy, diagnostic imaging, surgical procedures, insurance, accounting and clinical decision support systems. While all these topics are related, each of them has unique features and information requirements that need to be addressed by the standard. Furthermore, HL7 v3 uses several standard clinical terminology systems such as SNOMED and LOINC to represent information content. HL7 v3 uses Reference Information Model (RIM), a large class diagram representation of the clinical data. HL7 v3 applies object-oriented development methodology to RIM and its extensions to create standard message content. The HL7 refinement process uses RIM class diagrams, HL7-specified vocabulary domains, and data type specifications, and applies refinement rules to these base standards to generate information structures for HL7 v3 messages. The message development process consists of applying constraints to a pair of base specifications, i.e., HL7 RIM and HL7 Vocabulary Domains, and the extension of those specifications to create representations constrained to address a specific healthcare requirement. We now refer to the artifacts generated in the refinement process. The Domain Message Information Model (D-MIM) is a subset of RIM that includes a fully expanded set of class clones, attributes and relationships that are used to create messages for any particular domain. The Refined Message Information Model (R-MIM) is used to express the information content for one or more messages within a domain. Each R-MIM is a subset of the D-MIM and contains only those classes, attributes and associations required to compose the set of messages. Hierarchical Message Description (HMD) is a tabular representation of the sequence of elements (i.e., classes, attributes and associations) represented in an R-MIM. Each HMD produces a single base message template from which the specific HL7 v3 message types are drawn (Health Level Seven Ballot, 2011; HL7, 1999).
3. Data mining and interoperability in CDSS In this section, we describe a set of related techniques that demonstrate the role of data mining in discovering important hidden patterns among clinical data. In this case, association mining using concept lattice analysis discovers groups of diseases, symptoms, and signs that are highly associated. These groups assist the physician in disease diagnosis process. We further discuss how such important patterns of relationships (we call them mined-knowledge) can be transported to the point of use, where such knowledge can be incorporated into decision support systems to enhance physician decision making activity. We also present the supporting standards and infrastructure that allow such collaboration among heterogeneous systems. Data mining is the process of analyzing data from different perspectives to extract
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information and hidden patterns that are useful for planning and configuration purposes. Technologies from various domains such as statistics, data warehousing, and artificial intelligence support data mining activities. Chapter 3 in the book by (Berner, 2007) discusses the applications of data mining in CDSS. 3.1 Concept lattice analysis in diagnostic process
The following discussion is based on (Yousefi et al., 2009). The exploration nature of the association based data mining techniques (e.g., concept lattice analysis) in a diagnostic process simulates the normal process that a practitioner follows in clinical practice. On a daily basis, physicians encounter complex clinical scenarios where they compare a set of clinical observations in the form of symptoms and signs, with those they know based on their medical knowledge and experience, in order to make accurate disease diagnoses. In this context, the patient history from the EMR is also used as complementary information to clinical observations. The approach takes advantage of the automatic extraction of patterns from a patient EMR system using concept lattice analysis and uses a ranking mechanism to indicate the degree of relevancy of the clinical observation to each member of the identified group of diseases. Syndrome is a set of signs and symptoms which tend to occur together and reflect the presence of a particular disease. There are a large number of major clinical syndromes that can be modeled according to this technique. As a case study, the authors modeled a syndromic approach to Fever of Unknown Origin (FUO) due to its importance and complexity in the medical domain. There are a large number of cases with FUO which are undiagnosed despite hospitalization, costly paraclinic requests and invasive procedures. The authors have modeled FUO as an example of a major clinical syndrome. The case study modeled 45 diseases and 64 common symptoms and signs associated with FUO from a heavily cited medical reference by (Mandell et al., 2004). In this approach, a Concept Explorer tool (Formal concept analysis toolkit version 1.3, 2009) was used to illustrate the context table and concept lattice which present the relations among the diseases and their associated symptoms and signs in a large lattice consisting of 499 concept nodes. The concept lattice can be used as follows. During a patient visit, the physician observes and records the patient symptoms and signs, and consults the patient EMR to obtain other possible symptoms that may be relevant to the current visit. The tool then compares this set of symptoms and signs with those of different concepts in the concept lattice. The concepts with the highest overlap of symptoms and signs are then retrieved and a ranked list of the diseases within the concepts is presented to the physician. The physician uses his/her discretion to identify which disease within the provided concepts would be the best match with the patient’s situation. The authors discuss a case of an elderly female patient who had a fever for four days with other symptoms such as anorexia, malaise, non productive cough, night sweats, and chill. Using the approach described, four diseases were suggested to the physician: Tuberculosis, Sarcoidosis, Lymphoma, and Recurrent Pulmonary Emboli, where the latter was found to have the highest overlap with the patient’s symptoms. 3.2 Interoperability of mined-knowledge for CDSS
The details of this technique were presented in (Sherafat & K. Sartipi, 2010). Currently, decision making knowledge within most guideline modeling languages are represented by basic logical expressions. However, the results of data mining analyses from healthcare data can be employed as a source of knowledge to improve decision making. A CDSS can interact with practitioners and electronic medical records systems to receive patient data as
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input and provide reminders, alerts, or recommendations for patient diagnosis, treatment, long-term care planning, and the like. A CDSS requires access to healthcare data and knowledge that are stored in data and knowledge bases. Since these repositories normally have diverse internal representations, data and knowledge interoperability are major issues. To achieve data interoperability, two systems that participate in data communication must use the same vocabulary set, data model, and data interpretation mechanism. On the other hand, knowledge interoperability refers to the ability of healthcare information systems to incorporate and interpret the knowledge that is produced in other systems. Here, we focus on encoding, sharing, and using the results of data mining analyses for clinical decision making at the point of care. The proposed approach relies on adoption of standards to encode healthcare data and knowledge. In an off-line operation, existing healthcare databases (i.e., EMRs) are mined using different mining techniques to extract and store clinical mined-knowledge. In order to make this knowledge portable it is encoded in the form of a data mining model using a specialized XML-based standard, namely Predictive Model Markup Language (PMML) (DMG, 2010). Also, it is necessary for the patient data that are stored in EMR systems to be encoded using a specialized XML-based standard, namely Clinical Data Architecture (CDA) (HL7, 2005) so the data can be ported between heterogeneous systems. At the point of care, a decision module accesses and operates on both data and knowledge in order to make patient-specific interpretations of the knowledge available to the healthcare practitioner. Within the CDSS we adopt a flow-oriented clinical guideline modeling language GLIF3 (Collaboratory, 2004) to specify the overall decision making process. In this context, at different states of the flow-oriented guideline the CDSS accesses patient data by querying the EMR. Moreover, to perform knowledge-based decision making, the CDSS supplies patient data to the decision modules and receives the results of applying mined-knowledge to the patient data. Finally, healthcare personnel receive comments, recommendations, or alerts through interaction with the CDSS system, allowing them to make more knowledgeable decisions based on system-provided information. 3.3 Interoperability of clinical information and concept
The details of this technique were presented in (Jayaratna & Sartipi, 2009). A key objective of effective healthcare delivery is to facilitate seamless integration among heterogeneous applications, to provide a unified view of information to health practitioners and other stakeholders. Achieving such flawless integration requires interoperability among data sources serving the applications. This can only be achieved through standardization of information exchange and representation. The HL7 Reference Information Model (RIM) was introduced in Section 2.1. HL7 v3 based integration of systems requires an expert in medical domain who is also familiar with HL7 v3 standards and documentation. Hence, such an integration process is expensive, slow, and expert-based. In the following, we present a framework to support HL7 v3 message extraction for standard compliant integration projects, based on Semantic Web (SW) technologies. We have observed that the existing HL7 domain model does not facilitate efficient discovery of HL7 v3 messages due to overlaps and disconnects among the domains. Therefore, we developed a more intuitive and finer categorization for HL7 domains, namely contexts, which consist of 50 contexts to represent areas of healthcare that superimpose well with actual healthcare transactions. Each HL7 v3 message was associated with a single context. Context acts as a key piece of metadata in the search tool. Next, we classified HL7 messages into a
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hierarchy of classes based on the purpose of the messages they convey. This classification has been designed to be intuitive and general enough so that it could also be used to formally express a clinical transaction. In the following, the steps for the message extraction process are discussed. • Step 1: Integration Requirements Analysis. This step consists of: i) Storyboards which are a set of scenarios in the health domain written by health professionals in their own terminology. ii) Context extraction, where the tool searches storyboard text to create possible semantic maps between the Contexts and the words and phrases in the text. Within the tool, each Context has been annotated with Cognitive Synonyms that describe it. WordNet and SNOMED vocabularies were used to incorporate as many cognitive synonyms and phrases needed to describe each Context. iii) Identify transaction initiators, where each initiator starts a message in a sequence of messages that complete a transaction. Transaction initiators can be easily identified manually from storyboard text, while adhering to the obtained Contexts. • Step 2: Structured transaction generation. Each transaction initiator is then structured according to the proposed transaction schema so it is in machine readable format. • Step 3: Mapping. This step consists of: i) Message mapping, where structured transactions are entered into the tool, and the tool’s advanced semantic search feature searches the main artifact repository to find a matching message. ii) Vocabulary mapping, which converts local terms into standard terminology codes for transmission. The tool integrates with terminology systems SNOMED and LOINC to search for the most appropriate code for a particular legacy clinical term. Data fields extracted during Step 1 are used as search criteria.
4. Collaborative decision making In this section, we discuss the influence of modern technologies in social networks and ad hoc web application integration techniques to form focused groups of specialists to make collaborative decisions about a critically ill patient. Such an environment integrates different facilities for collecting patient records from the Electronic Health Record (EHR) system according to the case at hand and allows clinicians, nurses, and other support staff to communicate asynchronously through email, text messaging, and video conferencing. The platform will support conversations, collaborative decisions, etc. in a secure data center and will issue reminders and follow-ups to the group and the patient. In current healthcare systems, patients are often not active participants in their treatment; instead they rely on the practitioner who guides the diagnosis and treatment process. This process is not particularly effective as the patient may not be interested in the process, but just in a favorable outcome, i.e., a successful treatment. With the advent and popularity of social networking, people tend to form groups with special interests and share information and knowledge. This allows different patients with the same health related subject of interest to form small communities to share their experiences, augment their knowledge, and mutually encourage themselves to be more involved in their treatment process (Bos et al., 2008). Current Personal Health Record (PHR) systems help patients to be more aware of their treatment processes and to obtain information about their health. However, future PHR systems will allow more patient involvement through improved user interfaces to access their own healthcare data and sophisticated services through new features empowered by Web 2.0 technologies (Oreilly Web 2.0 Books, 2011).
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A Mashup (Abiteboul et al., 2008) is a Web 2.0 technology which is gaining popularity for developing complex applications by combining data, presentations, and applications from different sources to create a new web application. The approach is new and is still being enhanced. However, the need for fast and easy development of ad hoc web based applications has caused a large number of Mashups to be developed that are organized into different categories (ProgrammableWeb Web site, 2011). Such characteristics make Mashups an ideal tool for non-critical web-based data management applications, since they can be developed in a short time, with very little programming skill. The goal of a Mashup is to provide a means to utilize a large number of web based applications and heterogeneous data sources in a unified representation. However, Mashups do not meet the hard constraints imposed by application domains that incorporate sensitive data, real-time operations, or mission critical tasks. An example of a Mashup, namely MedickIT, is presented by (Abiteboul et al., 2008), and it consists of six components (or Mashlets). These Mashlets can be GUI-based components (i.e., widgets) or web services. The MedickIT consists of six Mashlets: an Electronic Health Record viewer, a map widget, a calendar, a medical search engine, an SMS, and a medical data analyzer. Such a Mashup allows a patient to access his/her medical data through the EMR; retrieve a doctor appointment from the calendar and drag it into the map to see the location of the doctor’s office, or drag it into the SMS widget so that a phone call is initiated to remind the patient about forthcoming doctor appointments. Such a Mashup will allow the patient to gain more control over his/her health information. Depending on the needs of the patient, other combinations of Mashlets are feasible. The (IBM Mash up Center, 2011) is an end-to-end enterprise Mashup platform that supports rapid assembly of dynamic web applications with management, security, and governance capabilities. It allows both nontechnical users and IT personnel to develop complex applications. In the case of collaborative healthcare delivery, an ad hoc case-specific team of local and remote professionals can be formed which consists of doctors, nurses, administrative and support staff. The team can communicate and brainstorm through the e-conferencing widget, access the patient’s EMR record, obtain reports of the patient’s risk factors by consulting with a CDSS tool, and make collaborative diagnostic decisions for the patient.
5. Challenges in future CDSS In this section, we discuss some challenges that designers of future clinical decision support systems face. Such challenges include better interactions with users to understand their work context, and utilizing customizable computer agents in the client platform. In general, user-centric and collaborative features of CDSS systems impose higher levels of abstraction and more intelligent service-based computing at both the application and middle-ware layers. 5.1 Customizable and context-based CDSS
The current state of CDSS web applications is represented by the services that require extra knowledge and expertise from a normal user to take advantage of the available features and operations of these services. Given the large variety of web applications as Mashups and the tight time schedules of users, they will have to limit themselves to a minimum set of available service features. This is also the case in using other types of computerized systems such as automobile gadgets, home appliances and entertainment centers. In other words, the proper and efficient use of computerized systems (embedded or software based) requires an extended level of knowledge in different application domains. The user interaction capabilities of these systems tend to be sophisticated and hence these systems act as effective user assistants by
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providing different types of information to assist users in performing their tasks. However, domain knowledge and expertise are still needed by users. The next generation of CDSS systems will become even more sophisticated when the required expertise is incorporated as part of the system’s functionality. For example, instead of expecting users of a collaborative CDSS that uses a Mashup service to understand the details and the operational steps needed to use the specialized web application, the web service itself should act as an expert. This expert would consult with the user to provide an effective and customized use of operations according to the user’s specific context information. This would provide an opportunity for the user to employ an expert software agent for managing web assets and performing the desired tasks with minimum effort and time. Such a software agent would support smart interactions with the system by the user. Such customizable software agents are resident at the client platform (as opposed to mobile agents that can move among different platforms) with a customizable architecture that receives a set of well-defined tasks in order to become expert and serve the user. The proposed customizable software agents would add to current traditional services, which receive a client’s request for a service, perform the service at the provider’s platform, and return the results to the user. In the following, the steps for using such a client-based customizable expert agent are presented: • Step 1: identifying user context. Context refers to any information that can be used to characterize the situation of a service requester or provider. We define a context as a tuple: <User, Role, User Location, Server Location, Time of Day, Team, Delegation, Requested Profile Status, Service Invocation Type, Requested Data Type, Login/Logout Event>. This context information is monitored dynamically to feed a database of context logs which will be used during the service selection. • Step 2: selecting the required task. The user (e.g., a physician) asks for a specific task and the required expertise needed for assistance. By mining the context logs (Step 1) and consulting with a web registry, a client proxy obtains a list of relevant services to perform the task, and generates a list that ranks them according to their capabilities and any associated charges. The user then selects an appropriate service, which best matches with the situation. In this context, the web registry must possess a list of application domains such as: banking, insurance, healthcare, telephone, airline, government, etc.; as well as a list of tasks within each domain, such as: PHR viewer, medication administrator, medical data analyzer, and medical search engine, within the healthcare domain. • Step 3: delegating expertise to the client. After selecting the required task, the client proxy retrieves the service descriptions of the selected service and invokes the service from the provider’s platform. Instead of performing the requested task for the client, the provider will send a set of instructions to the client where the customizable expert agent will customize itself to serve the physician in an interactive clinical decision activity. We have already applied the above architecture in several projects, including a customizable virtual remote nurse (Najafi et al., 2011), web service composition (Najafi & Sartipi, 2010), and web service selection tasks.
6. Evaluation of techniques, adoption, and success of CDSS The four key functions of CDSS were outlined by (Perreault & Metzger, 1999), as follows: i) Administrative: supporting clinical coding and documentation, authorization of procedures, and referrals; ii) Managing clinical complexity and details: keeping patients on chemotherapy
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protocols, tracking orders, referrals follow-up, and preventive care; iii) Cost control: monitoring medication orders, avoiding duplicate or unnecessary tests; and iv) Decision support: supporting clinical diagnosis and treatment plan processes and promoting use of best practices, condition-specific guidelines, and population-based management. These functions are not necessarily logically separable, so they are addressed in a relatively all-inclusive manner in this section. In some cases, a CDSS may not need to be justified through improved patient outcomes because these systems are designed to influence healthcare providers and it is necessary only to demonstrate changes in clinician performance (Balas & Boren, 2007). But in many cases relationships between process and outcome is unclear (such as personal electronic medical records used by patients for self-management of certain chronic illnesses). However, in order to compete for scarce resources in the healthcare environment, developers must demonstrate the relevance of their systems to healthcare quality improvement and cost control. This requires evaluation approaches that are convincing to potential users, and focused on differences in the process or care outcomes involving the use of the CDSS (Balas & Boren, 2007). CDSS vendors often claim that their systems can directly improve clinical decisions. A wide variety of approaches and methodologies are available to assess these claims, ranging from controlled clinical trials to use of questionnaires and interviews with users. Techniques that are used should be based on fundamental principles and methods from cognitive science and usability engineering, where human computer interaction and usability in both laboratory and natural environments are examined. Methods can and should include the formative evaluation of systems during iterative development, and can also complement traditional summative assessment methods for completed systems (Kushniruk & Patel, 2004). CDSS designers may prefer to use benchmark tests, surveys, and historical control comparisons (before-after studies) to indicate improvements in quality due to the use of a new system. But benchmark tests only measure a system’s technical performance, and do not indicate the system’s impact on processes or outcomes of care. User opinion surveys can only provide indirect information about system impact. Before-after studies may provide useful information, but analysis of databases or historical control groups of patients cannot replace planned clinical experimentation. Randomized Controlled clinical Trials (RCTs) are generally recognized as the gold standard for determining the efficacy of computerized information systems in patient care. There are many types of randomized clinical trials but the basic principles are the same: prospective and contemporaneous monitoring of the effect of a randomly allocated intervention (Balas & Boren, 2007). One dissenter is (Kaplan, 2001) who suggests that Randomized Controlled clinical Trials (RCTs) are not suited to determining whether and/or how systems will be used. In particular, since CDSS are not yet widely used, it is important to develop evaluation techniques that will determine why this is the case, even for systems that seem to offer a great deal of promise for clinical support. Kaplan proposes a 4C approach that focuses on communication, control, care, and context, an approach that can be used for evaluating other types of clinical systems. For a fuller understanding of system operations, it is important to investigate social, cultural, organizational, cognitive, and other contextual concerns that can increase the understanding of other influences that affect systems application development and deployment. In a systematic review of controlled clinical trials of CDSS systems on physician performance and patient outcomes in 1998, (Johnston et al., 1994) studied 68 controlled trials, and found that 43 of the 65 that evaluated physician performance showed a benefit, and six of 14
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studies assessing patient outcomes found an improvement. Their basic conclusions were that CDSS can enhance physician clinical performance for drug dosing, preventive care, and other aspects of medical care, but not convincingly for diagnosis and that there was not yet sufficient evidence to determine the effects of CDSS on patient outcomes. In a 2005 followup review of 100 studies, (Garg et al., 2005) found that improved practitioner performance was associated with CDSS that automatically prompted users to activate the system, or when the study authors actually developed the CDSS. However, there were still not enough studies of patient outcomes available, so the impact on patient outcomes was not clear. RCTs do have limitations, since they can test only hypotheses about certain aspects of computer systems. RCT studies need to identify the conditions to be treated, interventions to be tested, and outcome variables to be measured. The results can then be regarded as specific, interpretable, and useful for practical purposes (Balas & Boren, 2007). Measuring and managing user attitudes toward various aspects of information systems is important in showing that computer systems are successful, since success is not possible without gaining the support of practitioners. Questionnaires can be used to measure user attitudes to the system. A critical success criterion for the usefulness of a system is how users react to various system aspects. Overall high satisfaction levels usually result in users adapting their activities to take advantage of the system. If satisfaction levels are low, users may actually become antagonistic and sabotage the system, or develop workarounds that avoid using the system. Randomized controlled trials have shown that there are four generic information interventions that can make a significant difference in patient care (patient education, treatment planning, physician and patient reminders) (Balas et al., 1996). It is therefore important to incorporate these information services into any CDSS that will be used for primary care, in order to improve its effectiveness. Following are three examples that demonstrate the diversity of methods for implementing and evaluating CDSS that all include the modern approach of using some form of a parallel development and evaluation process. • (Trafton et al., 2010) developed and implemented a CDSS using iterative evaluation throughout system analysis, design, development, implementation, including simulation and in-clinic assessments of usability for providers followed by targeted system revisions. Volunteers that evaluated the system at particular times provided detailed feedback that was used to guide improvements in the graphical user interface, system content, and design changes that increased clinical usefulness, understandability, clinical workflow fit, and ease of completing recommended practices according to specific guidelines. These revisions led to improved CDSS usability ratings over time, including attention to other practice concerns outside the scope of the CDSS. • One of the anticipated benefits from Computerized Physician Order Entry (CPOE) systems is the reduction of medication errors, but only a minority of hospitals have successfully implemented such systems. Physician resistance and frustration with such systems have been barriers to their use. An innovative approach to improve adoption and to realize the full benefits of such systems is to involve nurses in the order entry process in order to reduce physician data entry workload and resistance. (Kazemi et al., 2010) investigated whether a collaborative order entry method consisting of Nurse Order Entry (NOE) followed by physician verification and countersignature was as effective as a strictly Physician Order Entry (POE) method in reducing dose and frequency medication errors in a neonatal ward. They found a significant reduction in medication errors during the NOE
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period compared to the POE approach. The additional benefit to using such an approach is that physicians no longer have to participate in data entry, helping to overcome this barrier to CPOE use. • A good example of modern CDSS development is provided in a paper by (Leslie et al., 2005). This work focused on the development and evaluation of a CDSS to assist physicians in treating patients with chronic heart failure, and provides definitive support for the concept of iterative evaluation during CDSS development (Trafton et al., 2010) combined with a multidisciplinary approach to organizational and social aspects of the system environment (Kaplan, 2001). The CDSS was developed after discussions with a multidisciplinary panel, and evaluation took place during three stages over a 6 month period that involved an editorial check, interviews with potential users, and educational meetings with users. The process resulted in several changes to the CDSS at different stages of evaluation and development. Trends were found when comparing the CDSS with paper guidelines. GPs scored less well but junior doctors and medical students appeared to improve their scores. 70% of the users indicated that the CDSS was more useful than written guidelines. Implementation barriers included lower computer literacy among GPs, a lack of complexity within the CDSS that could address non-medical needs of patients, and medical staff reluctance to consult guidelines during patient consultations.
7. Research challenges in CDSS In this section, the authors propose some research ideas that they believe would contribute to the development of more effective and acceptable clinical decision support systems in future. Due to several limitations imposed by current economic and environmental situations world-wide, both the public and industry have raised their expectations in terms of the quality, performance and cost of future systems and equipment in different fields. Healthcare is no exception. In fact, given the recent increased adoption of IT by healthcare professionals we envision that promising changes in terms of efficiency and effectiveness of healthcare service delivery will happen in the near future. As suggested in this chapter, clinical decision support systems have already taken advantage of existing techniques and models from the computer science and information systems fields. However, the following research avenues need more exploration: • Human Computer Interaction (HCI): current computer interfaces seem to be restrictive and less flexible than expected for seamless and natural interactions that clinicians might expect to use in daily practice when using clinical decision support. Therefore, HCI research plays a key role in making CDSS systems more effective and acceptable. HCI involves inter-disciplinary research covering computer vision, machine learning, network security, database design, artificial intelligence, multimedia technology, embedded computation, ergonomics and cognitive psychology. It includes the design, implementation and evaluation of interactive computing systems for human use (ACM SIGCHI Curricula for Human-Computer Interaction, 2011). The goal of HCI is to achieve natural interaction between humans and computers. Actions of users can be captured as inputs, including vision (e.g., body movement and hand gestures), audio (e.g., speech), smell, touch, and taste. Advances in context awareness and context mining technologies have made it possible to extract and analyze implicit inputs. These implicit outputs can be integrated with the user environment instead of interrupting the user, allowing users to concentrate on their work (Schmidt, 2002).
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• Security and privacy: healthcare systems that operate with sensitive clinical data require particular attention and procedures to ensure authorized access in order to prevent privacy and security breaches of clinical data residing in the CDSS. There has been a dramatic increase in reports of security breaches. In particular, since most healthcare systems (including CDSS) are web and service-based (e.g., Mashups), preserving the integrity, security and privacy of patient data has utmost importance. Therefore, a research challenge is to target security and privacy issues. As an example of such research, (Sandell, 2007) secures personal health data by proposing a framework which breaks CDSS into data gathering, data management, and data delivery functions. It then provides vulnerability factors and the required measures to protect data. • Mobile CDSS: uses mobile devices or smart phones as the mediator between the healthcare provider (clinician, pharmacist, emergency staff) and the patient, so that the provider sends specific instructions and guidelines to the mediator. The mobile device then interacts with the patient to control his/her health condition according to the received instructions and guidelines. New advances in mobile communications allow remote areas and homecare to use a variety of remote health services. This opportunity has aroused much attention in the research and development of mobile healthcare (mHealth) and related services, such as Mobile CDSS (Tsumoto et al., 2005).
8. Related techniques in CDSS Several models and approaches have been proposed for decision support systems. In particular, management science and operations research models have been used frequently. In this section, we briefly present the techniques that have been applied in health and medical domains (Berner & Lande, 2007; Eom & Kim, 2006; Shim et al., 2002). These techniques can be categorized as follows. Deterministic models • Linear programming: a method for finding the best solution for a given mathematical model satisfying certain constraints by maximizing or minimizing an objective function, subject to linear equality and inequality constraints. (Hershey, 1991) uses linear programming to find optimal clinical strategies when event probabilities are not known but their value ranges are available. Linear programming has also been used for an operating room planning problem which employs 0-1 linear programming (Testi & Tànfani, 2009). • Inventory models: designed to minimize inventory costs by setting optimal values for time to place an order and order quantity. Placing an order can be performed either at fixed time spots or decisions can be based on the condition or level of the inventory (Oh & Hwang, 2005). • Integer programming: a mathematical optimization problem and a special form of linear programming problems where variables are restricted to be integer. In (Eben-Chaime & Pliskin, 1992) a mixed integer programming model is used to decide on the size and location of beds required in a given region, incorporating travel times into dialysis planning. • Nonlinear programming: used to maximize or minimize an objective function subject to a system of equality and inequality constraints where either the objective function or some of the constraints are nonlinear. (Aspden et al., 1981) use a non-linear programming model
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to study the problem of distributing available healthcare resources (such as hospital beds or nurses) to assist health services planing. • Dynamic programming: a problem solving method similar to divide-and-conquer with overlapping sub-problems that have optimal sub-structures. (Hall, 2010) uses dynamic programming models to investigate individual behaviors and their economic implications. This approach has been applied to healthcare spending, long-term care insurance, employment, entrepreneurial risk taking, and consumer debt. Stochastic models • Queuing: a technique for representing different types of queues to study their behavior. Certain performance measures are extracted as the result of queueing analysis. (Patrick & Puterman, 2006) use a queueing model to increase the utilization of Computed Tomography (CT) scanning devices and reduce waiting times of patients with several priority levels. • Markov: a stochastic model that is suitable for problems where the Markov assumption holds, i.e., problems that have memoryless properties. Problems may become intractable without this assumption. (Sonnenberg & Beck, 1993) discuss applications of Markov models in CDSS. They introduce the generic class of problems suitable for modeling with Markov models and different evaluation methods such as matrix algebra, cohort simulation, or Monte Carlo simulation. Artificial Intelligence • Artificial neural networks: an information processing paradigm that is inspired by the structure and functional aspects of biological neural networks and the way they process information. A neural network consists of an interconnected group of processing elements (artificial neurons). Adaptive learning, self-organizations, and fault tolerance are among dominant features of this paradigm. (Mangalampalli et al., 2006) used the concept of neural networks to develop a decision-support system, that suggests medications for a gynecological disease, based on the primary and secondary symptoms of the disease. • Genetic algorithms: an adaptive heuristic search algorithm that relies on the concept and process of natural evolution, selection, and genetics. This method is used in search spaces where little knowledge exists about the domain, the existing knowledge is difficult to encode, or mathematical models are not available. (Zellner et al., 2004) employed genetic algorithms to improve the performance of a logistic regression model in predicting the presence of brain neoplasia with magnetic resonance spectroscopy data. • Game theory: mathematical modeling for games in which a player attempts to achieve a certain goal which is dependent on the choices of other players. Game theory offers strategies for increasing the probability of success. Solutions can be extended to cases where several players seek goals with multiple criteria (Parsons et al., 2002). • Decision trees: visual and analytical tools that consist of three types of nodes: decision nodes, chance nodes, and end nodes. Decision trees are usually used for guidelines and are extensively used in CDSS. Examples can be found in (Critchfield & Willard, 1986; Hazen et al., 1998; Sonnenberg & Beck, 1993).
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Practical • Simulation: a generic term that encompasses approaches using simulation for decision making. (Critchfield & Willard, 1986) use Monte Carlo simulation techniques to model the uncertainty in the specification of decision tree probabilities. They apply their method to the clinical problem of anti-coagulation versus observation in combatting deep vein thrombosis during the first trimester of pregnancy. Their method provides decision analysts with tools to quantitatively evaluate the problem. In another work, simulation was used to determine the utilization of a cancer agency ambulatory care unit. This work analyzed the impact of operations, scheduling, and resource allocation on patient wait time, clinic overtime, and resource utilization (Santibanez et al., 2009). • Visual interactive modeling: provides animated graphics for target applications. This model offers interaction facilities so users can explore the dynamics of an application in order to gain an understanding of its features (Au & Paul, 1996).
9. Conclusion The healthcare system is witnessing major changes, in terms of reengineering healthcare processes and policies which will affect all stakeholders, ranging from healthcare providers to policy makers to consumers. These changes will bring technologists and medical experts closer together and will provide a more homogeneous environment for effective collaboration of these two groups. Effective and efficient decision making processes are the most critical aspect of any application domain but particularly in healthcare, as the costs of erroneous decisions are huge in terms of human quality of life, and the resulting damage in most cases is irreversible. In this chapter, we outlined different aspects and challenges in providing effective clinical decision support systems, mostly from a technical view point. We discussed different models and approaches, information and knowledge representations, using data mining techniques to provide patterns and trends as additional type of clinical knowledge, challenges in future systems through customization of expert agents, and evaluation and acceptance of CDSS by users. However, as clinical decision systems are heavily user-oriented systems and clinicians are in general less technically oriented and overwhelmed by different types of information and workplace interruptions, user acceptance and adoption is very important to the success of such systems. We have included a section to overview the challenges in system evaluation, user adoption, and other related topics that consider the impact of human factors in the success of clinical decision support systems.
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Kaplan, B. (2001). Evaluating informatics applications - some alternative approaches: Theory, social interactionism, and call for methodological pluralism, International Journal of Medical Informatics 64: 39–56. Kazemi, A., Fors, U. G., Tofighi, S., Tessma, M. & Ellenius, J. (2010). Physician order entry or nurse order entry? comparison of two implementation strategies for a computerized order entry system aimed at reducing dosing medication errors, Journal of Medical Internet Research 12(1): e5. Keen, P. & Morton, M. S. (1978). Decision Support Systems: An Organizational Perspective, Addison-Wesley. Kong, G., Xu, D.-L. & Yang, J.-B. (2008). Clinical decision support systems: A review on knowledge representation and inference under uncertainties, International Journal of Computational Intelligence Systems 1: 159–167. Kushniruk, A. W. & Patel, V. L. (2004). Cognitive and usability engineering methods for the evaluation of clinical information systems, Journal of Biomedical Informatics 37: 56–76. Leslie, S. J., Hartswood, M., Meurig, C., McKee, S. P., Slack, R., Procter, R. & Denvir, M. A. (2005). Clinical decision support software for management of chronic heart failure: Development and evaluation, Computers in Biology and Medicine 36: 495–506. Mandell, G., Bennett, J. & Dolin, R. (2004). Principles and Practice of Infectious Diseases: 2-Volume, Churchill Livingstone. Mangalampalli, A., Mangalampalli, S. M., Chakravarthy, R. & Jain, A. K. (2006). A neural network based clinical decision-support system for efficient diagnosis and fuzzy-based prescription of gynecological diseases using homoeopathic medicinal system, Expert Systems with Applications 30: 109–116. Najafi, M., Aghtar, S., Sartipi, K. & Archer, N. (2011). Virtual remote nursing system, IEEE International Workshop on Consumer eHealth Platforms, Services and Applications, pp. 1– 5. Najafi, M. & Sartipi, K. (2010). Client-side service composition using generic service representative, Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research, pp. 238–252. Oh, Y. H. & Hwang, H. (2005). Deterministic inventory models for recycling system, Intelligent Manufacturing 17(4): 423–428. Open Clinical: PROforma website (2011). http://www.openclinical.org/gmm-proforma.html. Oreilly Web 2.0 Books (2011). http://oreilly.com/web2/. Parsons, S., Gymtrasiewicz, P. & Wooldridge, M. (eds) (2002). Game Theory and Decision Theory in Agent-Based Systems, Springer. Collection of papers. Patrick, J. & Puterman, M. L. (2006). Improving resource utilization for diagnostic services through flexible inpatient scheduling: A method for improving resource utilization, Journal of Operational Research Society 58(2): 235–245. Perreault, L. & Metzger, J. (1999). A pragmatic framework for understanding clinical decision support, Journal of Healthcare Information Management 13: 5–21. ProgrammableWeb Web site (2011). http://www.programmableweb.com/. Protege website (2011). http://protege.stanford.edu/. Sandell, P. (2007). Framework for securing personal health data in clinical decision support systems, Journal of Healthcare Information Management 21(3): 34–40. Santibanez, P., Chow, V., French, J., Puterman, M. & Tyldesley, S. (2009). Reducing patient wait times and improving resource utilization at british columbia cancer agency’s
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ambulatory care unit through simulation, Journal of Health Care Management Science 12(4): 392–407. Schmidt, A. (2002). Ubiquitous computing-computing in context. PhD Thesis. Lancaster University, U.K. Sherafat, R. & K. Sartipi, P. J. (2010). A framework for data and mined-knowledge interoperability in clinical decision support systems, International Journal of Healthcare Information Systems and Informatics 5(1): 37 – 60. Shim, J. P., Warkentin, M., Courtney, J. F., Power, D. J., Sharda, R. & Carlsson, C. (2002). Past, present, and future of decision support technology, Decision Support Systems 33(2): 111 – 126. Sonnenberg, F. & Beck, J. (1993). Markov models in medical decision making: A practical guide, Medical Decision Making 13: 322–38. Systematized Nomenclature of Medicine Clinical Terminology (SNOMED CT) (2011). http://www. snomed.org/snomedct/index.html. Testi, A. & Tànfani, E. (2009). Tactical and operational decisions for operating room planning: Efficiency and welfare implications, Health Care Management Science 12: 363–373. Trafton, J., Martins, S., Michel, M., Lewis, E., Wang, D., Combs, A., Scates, N., Tu, S. & Goldstein, M. K. (2010). Evaluation of the acceptability and usability of a decision support system to encourage safe and effective use of opioid therapy for chronic, noncancer pain by primary care providers, Pain Medicine 11: 575–585. Tsumoto, S., Hirano, S., Abe, H., Nakakuni, H. & Hanada, E. (2005). Clinical decision support based on mobile telecommunication systems, Web Intelligence, IEEE / WIC / ACM International Conference on 0: 700–703. Verlaene, K., Joosen, W. & Verbaeten, P. (2007). Arriclides: An architecture integrating clinical decision support models, Proceedings of the 40th Annual Hawaii International Conference on System Sciences, pp. 1–10. Yousefi, A., Mastouri, N. & Sartipi, K. (2009). Scenario-oriented information extraction from electronic health records, IEEE International Symposium on Computer-Based Medical Systems, pp. 1 –5. Zellner, B., Rand, S., Prost, R., Krouwer, H. & Chetty, V. (2004). A cost-minimizing diagnostic methodology for discrimination between neoplastic and non-neoplastic brain lesions: utilizing a genetic algorithm, Academic Radiology 11(2): 169–177.
2 Impacts and Risks of Adopting Clinical Decision Support Systems Wilfred Bonney
Capella University/ HL7 International Canada 1. Background The development of interoperable Healthcare Information Systems such as Electronic Health Record (EHR), Electronic Medical Record (EMR), and Personal Health Record (PHR) has created a platform environment whereby massive data is collected, stored, shared, and analyzed at the point of care to support patient outcomes and efficient healthcare delivery. However, the analysis of structured and unstructured clinical datasets to support the decision-making process of healthcare providers is proving to be difficult in the healthcare industry as data is being captured in different and/or multiple formats. Clinical Decision Support Systems (CDSS) have emerged as toolkits for efficiently managing and analyzing the clinical datasets stored in the repositories of EHR, EMR, and PHR. In this book chapter, a literature review is used to explore and answer the following questions: What are clinical decision support systems (CDSS)? How does CDSS influence the decision-making process of clinicians in medical practice? What are the significant impacts and risks associated with the use and adoption of CDSS?
2. Introduction Increasing in computing power has provided a platform for developers to build numerous computing applications that “would have been impossible just a few years ago” (Seltzer, 2005, p. 50). Clinical Decision Support Systems (CDSS) are computer-based healthcare applications used to integrate clinical and patient information to provide support for decision-making in patient care as well as to generate case-specific advices (Bonney, 2009; Kotze & Brdaroska, 2004). CDSS aid in clinical decision-making not only by providing physicians and other healthcare stakeholders with computerized advice regarding drug doses, medications, laboratory results and diagnosis but also by enhancing a clinician’s ability to process data and information (Bonney, 2009; Kaushal, Shojania, & Bates, 2003; K. Kawamoto, Houlihan, Balas, & Lobach, 2005). However, there is growing evidence that when poorly designed, deployed and/or used, CDSS may lead to more harm than good (Coiera, Westbrook, & Wyatt, 2006; Kotze & Brdaroska, 2004; Toth-Pal, Wårdh, Strender, & Nilsson, 2008). This paper aims to explore the significant impacts and risks of adopting CDSS in clinical practice. Whereas the impact factors will explore how the use of CDSS has impacted clinical
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decision-making, clinical practice guidelines, efficiency of healthcare delivery, and patient safety and outcomes; the risk factors will focus on the CDSS dependence on repositories, knowledge management, misinterpretation of clinical datasets, and failure to fit routine works of clinicians.
3. Methodology A literature review is used to highlight the relevant impacts and risks of adopting CDSS in clinical practice. The methodology involves a systematic review of relevant publications, found and accessed with the help of ProQuest (with multiple databases option) and EBSCOhost databases. Additional sources were retrieved using the ScienceDirect, PubMed and ACM digital libraries. Whereas the impact factors explore how the use of CDSS has impacted clinical decision-making, clinical practice guidelines, efficiency of healthcare delivery, and patient outcomes and safety; the risk factors focus on the CDSS dependence on repositories, knowledge management, misinterpretation of clinical datasets, and failure to fit routine works of clinicians.
4. Overview of Clinical Decision Support Systems (CDSS) CDSS have been recognized as promising tools for influencing healthcare provider performance to improve and streamline the quality of healthcare delivery (Bassa et al., 2005; Pearson et al., 2009). CDSS originated from Decision Support Systems (DSS). According to Donzelli (2006), DSS simply “combine individuals’ and computers’ capabilities to improve the quality of decisions” (p. 67). These functionalities and capabilities of DSS have contributed to its popularity and use in the healthcare domain. Hwang, Chang, Hung, Sung, and Yen (2004) asserted that a “DSS that supports physicians with the potential to minimize practice variation and improve patient care” (p. 240) is known as CDSS. Throughout their inception in the medical arena in the early 1970s, CDSS have evolved immensely to support the workflow of clinicians and improved the effectiveness of decision outcomes (Bassa et al., 2005; Hwang et al., 2004; Pearson et al., 2009). Although several challenges are facing the use and adoption of CDSS in the healthcare setting, the technology still remains promising when it comes to its ability to support evidence-based practice and enhancing the clinical decision-making process of healthcare providers. It is in this regard that Kawamoto et al. (2005) noted that CDSS provide “clinicians with patient-specific assessments or recommendations to aid clinical decision making” (p. 765). Examples of CDSS include technologies such as Computerized Physician Order Entry (CPOE) systems that provide patient-specific recommendations as part of the order entry process; outpatient systems that attach care reminders to the charts of patients in need of specific preventive care services; and laboratory alerting systems that page physicians when critical laboratory values are detected (Kawamoto & Esler, 2006). The architecture components of CDSS consist of knowledge base, inference/reasoning engine, and user communication/interaction (Kola, n.d.; O'Kane et al., 2010). Figure 1 shows the architecture components of CDSS. Whereas the knowledge base is made up guidelines, rules, and probabilistic models, the inference/reasoning engine combines the data in the knowledge base with that of the patient data. The user communication component of the architecture consists of a simple way of getting data into the system and getting results to the user (O'Kane et al., 2010; Berner & La Lande, 2007). The fact that the architecture of the
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CDSS depends on knowledge bases means that inappropriate representation of data, information, and knowledge present enormous threats to the adoption of CDSS in clinical practice.
Fig. 1. Architecture components of CDSS (Kola, n.d.)
5. Impact factors The impact factors associated with the use and adoption of CDSS could be categorized under five broad themes: clinical decision-making, clinical practice guidelines, efficiency of healthcare delivery, and patient safety and outcomes. 5.1 Clinical decision-making CDSS has a significant impact on the quality of decision making by healthcare providers. According to Kawamoto et al. (2005), CDSS provide “clinicians with patient-specific assessments or recommendations to aid clinical decision making” (p. 765). However, this goal of achieving quality decision making is not an easy endeavour. Clinical decisionmaking is a “complex task requiring a knowledgeable practitioner, reliable informational inputs, and a supportive environment” (O'Neill, Dluhy, & Chin, 2005, p. 69). According to Buckingham (2002), clinical decision-making consists of classification tasks “where cues are used to assign patients to one of a number of potential categories” (p. 238). This complexity of achieving quality clinical decision making by healthcare providers is often facilitated with the use of CDSS as a supportive tool.
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Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain
In an attempt to improve the use of CDSS to support quality decision making in clinical practice, Buckingham (2002) proposed a gelatean model with the goal of linking “intuitive explanations of clinical expertise with empirical data analysis to enhance judgement accuracy” (p. 250). Buckingham (2002) identified this relation as a symbiotic relationship between clinicians and computers. Whereas the clinicians are responsible for using their psychological validity, the computers’ side of the symbiosis comes with its powers of data storage and analysis (Buckingham, 2002, p. 249). Enhancing judgement accuracy of clinicians is critical in ensuring that information emanating from the CDSS are interpreted well by the attending clinicians and not misinterpreted. Physicians can enhance their clinical judgement accuracy by combining their experiential knowledge with the use of CDSS so that a symbiotic relationship can be established. 5.2 Clinical practice guideline Many healthcare providers depend on clinical practice guidelines for quality and evidencebased healthcare delivery. Clinical practice guidelines are “systematically developed statements to assist practitioners and patient decisions about appropriate health care for specific clinical circumstances” (Kotze & Brdaroska, 2004, p. 361). According to Kotze and Brdaroska (2004), clinical practice guidelines have “little influence upon clinician practice and patient outcomes unless they are effectively implemented and integrated into the clinical setting” (p. 362). One approach for effectively integrating clinical practice guidelines into medical practice is the use of CDSS. The use of CDSS has facilitated clinicians’ adherence to clinical practice guidelines, thereby improving patient outcomes (Kotze & Brdaroska, 2004; Kwok, Dinh, Dinh, & Chu, 2009). Kotze and Brdaroska (2004) noted that the “ability of computers to store, search and sort large volumes of data rapidly, as well as the everexpanding knowledge, access and use of computers, have paved the way for the incorporation of clinical practice guidelines into computer-based decision support systems” (p. 362). This is because not only does the use of CDSS demand clinical practice guidelines but it also makes it easier for programmers to develop rule-based and/or case-based reasoning to support the advices emanating from the CDSS. The encoded rules in the clinical practice guidelines provide the framework in which programming rules are encoded and used in the development of CDSS. For example, Kwok et al. (2009) found that the use of an integrated and dynamic electronic decision support system (EDSS) at a single emergency department promoted strict adherence to asthma clinical guidelines and improved clinical documentation and discharge management plans for asthma management. It is in this regard that Kotze and Brdaroska (2004) indicated that CDSS are “crucial elements in long-term strategies for promoting the use of clinical practice guidelines” (p. 362). 5.3 Efficiency of healthcare delivery In a study conducted to assess the impact of CDSS on the management of patients with Hypercholesterolemia, Bassa et al. (2005) found that “it is possible to optimize the efficiency of the management of hypercholesterolemia in standard practice by the implementation of a CDSS” (p. 71). In a similar study, Cobos et al. (2005) found that the use and adoption of CDSS in clinical practice “was as effective as usual care and induced important savings in the management of hypercholesterolemia” (p. 431). The above two studies contribute to our
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understanding about the need to implement CDSS in clinical practice so as to support the efficiency of healthcare delivery. Healthcare providers stand to gain enormously and streamline the workflow of physicians by adopting CDSS. For example, Pomerleau (2008) noted that the use of CDSS allow “nurses to have information and unit policies at their fingertips, which help them adhere to standards while at the bedside” (p. 154). Successful implementation of CDSS in clinical settings will reduce waiting times, minimize the length of stay in hospitals, and enhance the efficiency of healthcare delivery. 5.4 Patient safety and outcomes Improving patient outcomes requires the use of efficient decision-making process and evidence-based practice that can only be best achieved through the utilization of CDSS. One of the ultimate uses of CDSS is to improve patient safety and outcomes. CDSS have consistently shown great promise for reducing medical errors and improving patient care, safety, and outcomes (K. Kawamoto et al., 2005; Mahoney, Berard-Collins, Coleman, Amaral, & Cotter, 2007; Pomerleau, 2008; Sintchenko, Coiera, Iredell, & Gilbert, 2004; Subramanian et al., 2007). When it comes to the use of medications and diagnostic testing in clinical settings, CDSS has emerged as a technology to reduce medication errors, “improve diagnostic accuracy, provide easier and more rapid access to patient information and more complete medical records” (Courtney, Alexander, & Demiris, 2008, p. 692). According to Mahoney et al. (2007), medication errors are deleterious, prevalence and costly. Hence the need to use robust healthcare information systems to monitor, track, and manage medications administered to patients is of prime concern to many healthcare providers. Mahoney et al. (2007) found that the use of integrated clinical information system technology “decreased selected types of medication errors throughout the medication-use process in a health care system and improved therapeutic drug monitoring in patients” (p. 1969). In the context of identifying the potential adverse drug events (ADEs) at the medication ordering stage, Roberts et al. (2010) noted that successful implementation of CPOE and other advanced CDSS tools “significantly increased the number of potential ADE alerts for pharmacist review and the number of true-positive ADE alerts identified per 1000 admissions” (p. 1845). Moreover, in a randomized control trial conducted to evaluate the effectiveness of CDSS in reducing potentially inappropriate prescribing to older adults, Terrell et al. (2009) found that CPOE with decision support “significantly reduced prescribing of potentially inappropriate medications for seniors” (p. 1389). In another study, Subramanian et al. (2007) found that the increasing use of CPOE has facilitated the “elimination of handwriting identification problems, reductions in error associated with similar drug names, faster delivery of orders to the pharmacy” (p. 1451). These studies and others from the literature affirm the significant impact of CDSS in reducing medical errors in clinical practice, thereby, improving the quality of care, patient safety, and patient outcomes (Pearson et al., 2009).
6. Risk factors The risk factors focus on the CDSS dependence on repositories, knowledge management, misinterpretation of clinical datasets, and failure to fit routine works of clinicians.
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6.1 Dependence on repositories One of the critical architecture components of all CDSS is the knowledge base. The knowledge base depends on a centralized clinical data repository (K. Kawamoto, Lobach, Willard, & Ginsburg, 2009; Roberts et al., 2010). The fact that CDSS depends on good quality clinical data repository reinforces the need for standardized data representation, storage, and retrieval that can be centrally managed in the knowledge base repositories. Lack of good clinical data warehouse could have significant impact on the quality of advices emanating from CDSS. Data mining algorithms require good quality clinical data repositories to be able to extract knowledge to support clinical decision-making. CDSS also depend profoundly on large volumes of readily-accessible, existing clinical datasets (Bonney, 2009). These large volumes of data are usually extracted from the repository content of EHR, EMR and PHR. Lack of standardized data capture by these systems will lead to corrupt datasets. When the entries in these data repository are not coded appropriately, there is tendency that the resulting datasets will not be a good representative of the patient population (Bonney, 2009). It is therefore essential that standardized data representation are used for leveraging the knowledge base repositories contained in the CDSS so as to facilitate the generation of patient-specific care recommendations at the point of care (K. Kawamoto et al., 2009). 6.2 Knowledge management CDSS depend on appropriate implementation of knowledge management. According to Kalkan (2008), the whole concepts of data, information and knowledge are generally misunderstood. Acknowledging the fact that information results from replacing data within some meaningful content, Kalkan (2008) noted that knowledge is an “organized and transformed combination of information, assimilated with a set of rules, procedures and operations learnt through experience and practice” (p. 391). This definition of knowledge emphasizes the need to manage knowledge appropriately. Without proper set of rules, guidelines and operations, knowledge cannot be assimilated. Thus the need for knowledge management in CDSS cannot be ignored. Knowledge management is defined as a “systematic management of knowledge-related activities, practices, programs and policies within the enterprise” (Kalkan, 2008, p. 392). Knowledge management has gain popularity in the IT industry because of its emphasis on how to articulate, capture and distribute explicit and tacit knowledge in different formats (Herschel & Jones, 2005; Kalkan, 2008). Knowledge management activities aim to “effectively apply an organization’s knowledge to create new knowledge to achieve and maintain competitive advantage” (Kalkan, 2008, p. 392). Creating new knowledge in the medical field is crucial in helping healthcare providers in combating new diseases and symptoms. However, when the newly created knowledge is based on poor quality data, the resulting outcome could be very devastating in clinical settings. The fact that CDSS have “become increasingly sophisticated by matching patient characteristics with computerised knowledge bases and using algorithms to generate patient-specific assessments or treatment recommendations” (Pearson et al., 2009, p. 155) demand that appropriate management of knowledge is implemented in the CDSS to ensure that the patient-specific assessments and/or treatment recommendations are not based on poor quality data. It is therefore important that narrative information emanating from the CDSS is further processed and analyzed by healthcare providers before clinical decisions are made (Pearson et al., 2009).
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6.3 Misinterpretation of clinical datasets Clinical information stored in the CDSS are often misrepresented and misinterpreted. This is partly due to the inconsistencies in data coding and extraction of poor quality data. According to Coiera et al. (2006), the use of CDSS can “improve the overall safety and quality of health care delivery, but may also introduce machine-related errors” (p. 20). Coiera et al. (2006) noted that the use of poor quality data could lead to wrong medications and misdiagnosis. Coiera et al. (2006) also noted that automation biases and using evidenceretrieval systems may generate decision errors that might not necessarily correlates with the experiential knowledge of the physicians. Acknowledging the fact that inference rules forms the basic building blocks of any given CDSS and are usually extracted by data mining existing clinical datasets, Bonney (2009) noted that the “trustworthy of CDSS is based on how effective the extracted inference rules correlates with the experiential knowledge of domain experts” (p. 116). Chaudhry (2008) also emphasized the misrepresentation of clinical datasets by noting that, “real clinical data from patient interviews or medical records are far less structured and would likely alter the performance of the system considerably” (p. 86), if not extracted appropriately. This has a significant effect on the quality of data used in developing CDSS. Poor quality data will lead to misinterpretation of clinical datasets. The use of health information standards such as ICD-10, SNOMED, LOINC and UMLS will ensure uniformity and consistency of the health datasets, used in generating the inference rules (Bonney, 2009). 6.4 Failure to fit routine works of clinicians According to Hwang et al. (2004), accessing CDSS in a computer by medical practitioners is not a smooth process for actual usage/implementation. Hwang et al. (2004) attributed the complexity of the process to the fact that in actual clinical settings, integrating CDSS with the routine work of clinicians will demand that the physicians “run back and forth from point of care to computer station to complete their diagnosis” (p. 240). This approach could be daunting considering the workload of average physicians. Moreover, the routine use of CDSS during consultation could alienate patients from the direct contact with their physicians. When it comes to the use and adoption of technology, medical practitioners with experiential knowledge are more likely to override the decisions and advices presented by CDSS. For example, Dowding et al. (2009) noted that nurses are “less likely to use CDSS for telephone triage decisions that they feel they have experience in making” (p. 1160). These attitudes of medical practitioners towards CDSS often impede their overall acceptance and adoption in clinical practice. Acknowledging the fact that perceived usefulness of medical information is a function of its relevance, validity, and the effort involved in searching for it, Sintchenko et al. (2004) noted that physicians often “choose not to use available evidence at the time of decision making but rely on what they know and choose the strategy requiring least effort” (p. 75). Hence clinicians’ attitudes and the environment in which decisions are made influence the overall acceptance and adoption of decision support tools (Sintchenko et al., 2004; Toth-Pal et al., 2008). It is therefore recommended that the development and deployment of the CDSS should fit the workflow of clinicians so as to ensure that the system is enabling without constraining (Ash, Gorman, Lavelle, & Payne, 2003; Bonney, 2009).
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7. Discussion and conclusion In a qualitative study conducted to explore general practitioners’ (GPs) handling of a CDSS during the implementation process, Toth-Pal et al. (2008) found that despite their benefits in medicine, CDSS are rarely used in clinical practice. Toth-Pal et al. (2008) attributed CDSS barriers to “limited computer skills, shortage of time during consultation, problems with interpreting the recommendations given, and the GPs’ concerns about patient reactions” (p. 40). Moreover, in an analysis of 70 randomized controlled trial, Kawamoto et al. (2005) found that successful implementation of CDSS should “(a) provide decision support automatically as part of clinician workflow, (b) deliver decision support at the time and location of decision making, (c) provide actionable recommendations, and (d) use a computer to generate the decision support” (p. 771). These four recommendations seem to support the overall use of CDSS in improving the quality of clinical care. They also make it easier for clinicians to use CDSS thereby minimising the effort required by clinicians to receive and act on system recommendations (Kawamoto et al., 2005). The development of CDSS should also utilize health information standards so as to ensure its interoperability with other legacy systems and support distributed computing (Bonney, 2009). This research has the potential to benefit healthcare providers and stakeholders in determining the significant impacts and risks of adopting CDSS in medical practice. With the impacts and risks presented in the paper, it is evident that the appropriate use CDSS with emerging technologies could enhance the adoption and acceptance rate of CDSS in clinical practice. Future research should therefore focus on how to integrate Business Intelligence (BI) into CDSS. This is because BI is emerging as the new frontier in data mining that will facilitate the extraction of both structured and unstructured datasets. It is also important that future research promote the rigorous testing of CDSS to provide high quality evidence about their clinical and economic impacts on healthcare delivery (Pearson et al., 2009).
8. References Ash, J. S., Gorman, P. N., Lavelle, M., & Payne, T. H. (2003). A cross-site qualitative study of physician order entry. Journal of the American Medical Informatics Association, 10(2), 188-200. Bassa, A., Miguel, d. V., Cobos, A., Torremadé, E., Bergoñón, S., Crespo, C., Espinosa, C. (2005). Impact of a clinical decision support system on the management of patients with hypercholesterolemia in the primary healthcare setting. Disease Management & Health Outcomes, 13(1), 65-72. Berner, E. S., & La Lande, T. J. (2007). Overview of clinical decision support systems. In Clinical decision support systems – theory and practice, (2nd ed.)., Berner E.S (ed.), Springer: New York. Bonney, W. (2009). Is it appropriate, or ethical, to use health data collected for the purpose of direct patient care to develop computerized predictive decision support tools? Studies in Health Technology and Informatics, 143, 115-121. Buckingham, C. (2002). Psychological cue use and implications for a clinical decision support system. Medical Informatics & the Internet in Medicine, 27(4), 237-251.
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Chaudhry, B. (2008). Computerized clinical decision support: Will it transform healthcare? doi:10.1007/s11606-007-0432-9 Cobos, A., Vilaseca, J., Asenjo, C., Pedro-Botet, J., Sáonchez, E., Val, A., Bergoñón, S. (2005). Cost effectiveness of a clinical decision support system based on the recommendations of the european society of cardiology and other societies for the management of hypercholesterolemia: Report of a cluster-randomized trial. Disease Management & Health Outcomes, 13(6), 421-432. Coiera, E., Westbrook, J. I., & Wyatt, J. C. (2006). The safety and quality of decision support systems. Methods of Information in Medicine, 45(Suppl. 1), 20-25. Courtney, K. L., Alexander, G. L., & Demiris, G. (2008). Information technology from novice to expert: Implementation implications. Journal of Nursing Management, 16(6), 692699. Donzelli, P. (2006). A decision support system for software project management. IEEE Software, 23(4), 67-75. Dowding, D., Mitchell, N., Randell, R., Foster, R., Lattimer, V., & Thompson, C. (2009). Nurses' use of computerised clinical decision support systems: A case site analysis. Journal of Clinical Nursing, 18(8), 1159-1167. Herschel, R. T., & Jones, N. E. (2005). Knowledge management and business intelligence: The importance of integration. Journal of Knowledge Management, 9(4), 45-55. Hwang, H., Chang, I., Hung, W., Sung, M., & Yen, D. (2004). The design and evaluation of clinical decision support systems in the area of pharmacokinetics. Medical Informatics & the Internet in Medicine, 29(3), 239-251. doi:10.1080/14639230400009158 Kalkan, V. D. (2008). An overall view of knowledge management challenges for global business. Business Process Management Journal, 14(3), 390-400. Kaushal, R., Shojania, K. G., & Bates, D. W. (2003). Effects of computerized physician order entry and clinical decision support systems on medication safety: A systematic review. Archives of Internal Medicine, 163(12), 1409-1416. Kawamoto, K., & Esler, B. (2006). HL7 service functional model specification - decision support services (DSS). Retrieved December 2, 2005, from http://www.hl7.org/documentcenter/ballots/2006SEP/support/AUDIT_SDO_C DS_DSS_R1_D1_2006SEP_20070129061919.pdf Kawamoto, K., Houlihan, C. A., Balas, E. A., & Lobach, D. F. (2005). Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success. British Medical Journal, 330(7494), 765-772. Kawamoto, K., Lobach, D. F., Willard, H. F., & Ginsburg, G. S. (2009). A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine. BMC Medical Informatics & Decision Making, 9(1), 17-30. doi:10.1186/1472-6947-9-17 Kola, J. (n.d.). Clinical decision support systems: Approach and use. Retrieved November, 21, 2010, from https://www.nibhi.org.uk/Health%20Informatics/Decision%20Support%20Syste ms%20Export.ppt Kotze, B., & Brdaroska, B. (2004). Clinical decision support systems in psychiatry in the information age. Australasian Psychiatry, 12(4), 361-364. doi:10.1111/j.14401665.2004.02127.x
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Kwok, R., Dinh, M., Dinh, D., & Chu, M. (2009). Improving adherence to asthma clinical guidelines and discharge documentation from emergency departments: Implementation of a dynamic and integrated electronic decision support system. Emergency Medicine Australasia, 21(1), 31-37. Mahoney, C. D., Berard-Collins, C., Coleman, R., Amaral, J. F., & Cotter, C. M. (2007). Effects of an integrated clinical information system on medication safety in a multihospital setting. American Journal of Health-System Pharmacy, 64(18), 1969-1977. O'Kane, T., O'Donoghue, J., Gallagher, J., Aftab, A., Casey, A., Angove, P., Courtney, G. (2010). MEWS to e-MEWS: From a paper-based to an electronic clinical decision support system. Proceedings of the European Conference on Information Management & Evaluation, , 301-311. O'Neill, E., Dluhy, N. M., & Chin, E. (2005). Modelling novice clinical reasoning for a computerized decision support system. Journal of Advanced Nursing, 49(1), 68-77. Pearson, S., Moxey, A., Robertson, J., Hains, I., Williamson, M., Reeve, J., & Newby, D. (2009). Do computerised clinical decision support systems for prescribing change practice? A systematic review of the literature (1990-2007). BMC Health Services Research, 9, 154-167. Pomerleau, M. (2008). Electronic health record: Are you ready for the next step? Nursing for Women's Health, 12(2), 151-156. Roberts, L. L., Ward, M. M., Brokel, J. M., Wakefield, D. S., Crandall, D. K., & Conlon, P. (2010). Impact of health information technology on detection of potential adverse drug events at the ordering stage. American Journal of Health-System Pharmacy, 67(21), 1838-1846. doi:10.2146/ajhp090637 Seltzer, M. (2005). Beyond relational databases. Queue, 3(3), 50-58. Sintchenko, V., Coiera, E., Iredell, J. R., & Gilbert, G. L. (2004). Comparative impact of guidelines, clinical data, and decision support on prescribing decisions: An interactive web experiment with simulated cases. Journal of the American Medical Informatics Association, 11(1), 71-77. Subramanian, S., Hoover, S., Gilman, B., Field, T. S., Mutter, R., & Gurwitz, J. H. (2007). Computerized physician order entry with clinical decision support in long-term care facilities: Costs and benefits to stakeholders. Journal of the American Geriatrics Society, 55(9), 1451-1457. Terrell, K. M., Perkins, A. J., Dexter, P. R., Hui, S. L., Callahan, C. M., & Miller, D. K. (2009). Computerized decision support to reduce potentially inappropriate prescribing to older emergency department patients: A randomized, controlled trial. Journal of the American Geriatrics Society, 57(8), 1388-1394. Toth-Pal, E., Wårdh, I., Strender, L., & Nilsson, G. (2008). Implementing a clinical decisionsupport system in practice: A qualitative analysis of influencing attitudes and characteristics among general practitioners. Informatics for Health & Social Care, 33(1), 39-54. doi:10.1080/17538150801956754
3 Success Factors and Barriers for Implementation of Advanced Clinical Decision Support Systems Anne-Marie J.W. Scheepers-Hoeks, Rene J. Grouls, Cees Neef, Eric W. Ackerman and Erik H. Korsten Catharina-hospital Eindhoven / University of Technology Eindhoven The Netherlands
1. Introduction Ten years ago, the US Institute of Medicine (IOM) called for a massive redesign of the healthcare delivery system.(Committee on Quality of Health Care in America, 2001) Today it is clear that one of the goals, the nationwide use of an electronic medical record (EMR) by 2010, has failed to be reached as the process of adoption has been slow. Some may consider an EMR as a final destination, although in fact it is only the start point of a revolution in healthcare: the implementation of clinical decision support systems (CDSS) that ‘make it easy to do it right’.(James, 2001) These systems are able to address the large, potential additional value of the implementation of an EMR. When an EMR is available, this is already a step in the right direction, to have an easy and structured access to all patient data available for all healthcare professionals that need them. However, this is still a huge amount of data, but one should also have the ability to integrate all these data and use these data in making the right choices in therapy. Practice has shown that despite the availability of an EMR, still many medication errors are made. Therefore, CDSS are designed to aid clinical decision-making by matching patient characteristics to a computerized knowledge or rule base to generate patient-specific recommendations.(Kawamoto et al., 2005) In the trendsetting IOM reports ‘To Err is Human’ (2000) and ‘Crossing the quality chasm’ (2001), a CDSS was endorsed as one of the most powerful tools available for improving patient safety and healthcare quality.(Kohn et al., 2000;Committee on Quality of Health Care in America, 2001) It is difficult to accept that despite multiple opportunities and promising results, these systems instead of being common practice, still remain ‘next-generation’.(James, 2001) During the last five years, research gave more insight in the success factors that could accelerate the idle process of CDSS adoption. (Kawamoto et al., 2005; Garg et al., 2005;Nies et al., 2006) The conclusions however are not univocal because the reviews included a wide variety of systems ranging from computerised to non-computerised CDSS as well as from basic to advanced systems. Basic decision support includes checking on drug-drug interactions, duplicate therapy, drug-allergies and generalized drug dosing. Advanced CDSS, used in addition to basic CDSS, includes for example checking on contra-indications (disease and drugs), individualized dosing support during renal impairment or guidance
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for medication-related laboratory testing.(Kuperman et al., 2007) Basic decision support is nowadays commonly available. In the Netherlands, this provision is used nationwide since 1980, integrated in a drug database (G-standaard, Royal Dutch Association for the Advancement of Pharmacy). Despite this support that is available for every physician and pharmacy, medication errors occur frequently which emphasizes the urge for advanced solutions.(Leendertse et al., 2008) In our 600-bed university-affiliated hospital, we have implemented an advanced CDSS in practice on a few departments already and are on the eve of hospital-wide expansion. A critical examination of the literature made clear which prerequisites are needed for optimal implementation of advanced CDSS. To accelerate the process of CDSS adoption, we present an overview of success factors and barriers for implementation of advanced CDSS in hospital practice. Subsequently, we present our own experienced success factors and barriers after implementing an advanced decision support system in 2008 in daily hospital practice and compare these results with literature findings.
2. Background Medication errors occur distressingly frequent due to deficiencies in the overall system of healthcare delivery despite use of current medication safety systems.(Kohn et al., 2000;Committee on Quality of Health Care in America, 2001;Schiff et al., 2003) Many reports call attention to the gaps between optimal and actual practice. (Kohn et al., 2000;Committee on Quality of Health Care in America, 2001;Aspden et al., 2006) The report ‘To Err is human’ indicated that 44.000 to 98.000 patients die in hospitals each year due to medical errors.(Kohn et al., 2000) The recent IOM report ‘Preventing Medication Errors’(Aspden et al., 2006) showed that in the USA medications harm at least 1.5 million people per year of which at least 400,000 preventable adverse drug events (ADE’s) in hospitals. The number of patients, dying from medical errors is probably a low estimate and the situation in Europe is not expected to be different. The Dutch statistics are, for example, not encouraging either. From the patients that died in Dutch hospitals in 2007, 10.7% experienced preventable medical complications; resulting in the death of 1735 patients (4.1%). Even more discouraging is the fact that the number of unnecessary deaths tends to increase. For the Netherlands these were 1745 deaths in 2004 and 1960 in 2008; an increase of 11.5%, despite advances in knowledge and IT-systems (Langelaan, 2010) The HARMreport (2006, NL) showed 19.000 preventable drug related admissions a year in Dutch hospitals; 5.6 % of all acute admissions, associated with a total cost of 86 million euros.(Leendertse et al., 2008) Medication errors occur due to the rapidly increasing complexity of evidence based medicine and error sensitivity of healthcare.(James, 2002) Physicians need to take many drug- and patient specific characteristics into account and literature shows that this is often omitted or not recognized in time.(Levy et al., 1999;Schiff et al., 2003;Denekamp, 2007) Beyond reminders, CDSS can integrate clinical data to support professionals managing an increasingly complex practice environment.(James, 2001) Integration of these specific parameters is necessary to guide patients through the complete clinical pathway from anamnesis to evaluation and fine-tuning of the therapy.
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Reviews of Kawamoto and Garg have shown that a CDSS is effective in decreasing medication errors and improving efficiency and quality of care.(Kawamoto et al., 2005;Garg et al., 2005) These reviews found that 64% respectively 68% of the decision support systems significantly improve clinical practice.(Kawamoto et al., 2005;Garg et al., 2005) In literature, basic and advanced decision support are both called CDSS and exact descriptions of the systems used are scarce, which makes these systems difficult to compare. This review will only include advanced CDSS, defined as a multi-purpose rule based expert system which contains complex electronic guidelines that can integrate data from different domains.(Sucher et al., 2008) Goals of implementing advanced CDSS are to decrease errors and improve patient safety, improve quality through adoption of best practices, increase cost-effectiveness and optimize the management of chronic diseases.(Greenes, 2007) In our hospital, research is performed with clinical decision support since 1998, in which we found that these goals can be achieved though structured development, validation and implementation. The objective of this chapter is to extend a practical overview of success factors and barriers of advanced CDSS found in literature and practice, before widespread hospital implementation, concentrating on these central aspects.
3. Success factors Right message
Right time
Right place Right system
Accurate content Reliable messages Easy and actionable messages Inclusion of references in the message Save time Integration in workflow High system’s speed Deliver message at the point of care Active alerting mechanism Electronic availability of data in the EMR Integration with other systems Maintenance of the system and content
Table 1. Overview of success factors for implementation of advanced CDSS (see text for references) 3.1 The right message 3.1.1 Accurate content To facilitate the adoption of evidence based medicine in practice, paper guidelines have to be translated into a computer-interpretable format, called clinical rules. This is a challenging task, because evidence based recommendations are often non-specific, multi-interpretable and either too complex to recall or too general to apply specifically.(Trivedi et al., 2002;Denekamp, 2007;Kuperman et al., 2007;Sucher et al., 2008) For example, according to guidelines, leukocyte counts should be measured frequent during lithium therapy, but the exact frequency, cut-off values and relevance is lacking.(Wessels-Basten et al., 2007) This problem can be solved by involving a multidisciplinary expert team to review and optimize the clinical rules.(Dexter et al., 2001;Trivedi et al., 2002;Osheroff et al., 2005;Leslie and Denvir, 2007;Wessels-Basten et al., 2007) One should compose the expert team carefully,
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being a reflection of end users and recognised experts in the particular field, as this determines the strength of the clinical rule developed. In fact, this is generally alike to the development of a new guideline. Benefits of this approach are that the specific subject gains renewed attention from the experts which may already increase compliance to these guidelines and user commitment is gained as the experts will be end-users of the system and motivate other users once implemented in practice.(de Clercq and Hasman, 2004;Osheroff et al., 2005;Wessels-Basten et al., 2007) 3.1.2 Reliable messages Current medication safety systems generate masses of irrelevant alerts leading to alert fatigue as a result of the often low specificity and clinical relevance.(van der Sijs et al., 2006) This is especially the case with basic, drug-oriented decision support, as we know in the Netherlands for about 30 years. Many alerts are generated, and the research of van der Sijs showed that more than 90% of alerts are overridden, without the physician even looking at the alert. The underlying conditions are: low alert specificity, information content unclear, low sensitivity, handling was insufficient and unsafe and the workflow was unnecessary disrupted. Also, physicians were not trained on the alerts and trusted the pharmacy in checking their medication orders. In summary, there are many failures of the systems used today, that can be solved with these new advanced decision support systems. Even, these current systems can be dangerous, which pleads for a rapid response. These basic systems increases the risk of overriding a potentially harmful alert.(Bates et al., 2003;van der Sijs et al., 2006;Denekamp, 2007) Even pharmacists have been found to override a third of life-threatening drug-drug interactions.(Bates et al., 2003) Therefore, it is vitally important to validate the clinical rules extensively before bringing these into practice to ensure reliable recommendations.(Dexter et al., 2001;Wetter, 2002;Wessels-Basten et al., 2007;Sucher et al., 2008;Varonen et al., 2008) The above mentioned obstacles need to be taken into account when designing en implementing these newer systems. Also, validation of the alerts is fundamental for success. Scheepers et al (2009) has described a strategy that can be used in various settings to create clinical rules of a high reliability. By measuring the positive and negative predictive value during the development process and afterwards, one can easily monitor if the quality of the clinical rules is high enough before implementation in practice. Since few studies have focused on validation of clinical rules, more research is needed. There is a need for other successful strategies to be described, to help others with these fundamental questions. Also, research may answer the question which minimal notification threshold is needed for effective alerting before bringing the alert into practice.(Osheroff et al., 2005) 3.1.3 Easy and actionable messages A CDSS is effective when it minimizes the effort required by physicians to receive and act on system recommendations.(Kawamoto et al., 2005;Varonen et al., 2008) This can be accomplished by giving clear advice and a straightforward way to perform the action indicated. (Bates et al., 2003;Osheroff et al., 2005;Varonen et al., 2008;Mollon et al., 2009) Besides that, applications must anticipate physicians needs for gathering and translating data into actionable recommendations.(Bates et al., 2003;Kawamoto et al., 2005;Mollon et al., 2009) For example, a clinical rule is developed to check if a patient needs a gastroprotective drug when using a NSAID or dosage adjustment because of renal insufficiency.
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(Field et al., 2008) If necessary, an automatic medication order pops-up and only one click on the ‘authorize’ button is needed to prevent the patient from having adverse drug events. This is all in line with the words of James (2001) to ‘Make it easy to do it right’ and emphasises the need of an expert/end users team that is well involved in the development process. 3.1.4 Inclusion of references in the message A valuable addition to the message is to present the source of the information and the explanation of the rationale for the guideline.(Maviglia et al., 2003;Kawamoto et al., 2005; Goud et al., 2008;Mollon et al., 2009) Many guidelines exist and these are continuously renewed, which makes it difficult to keep up with all recommendations. Although it appears that not all users use this option(Bates et al., 2003), it is a valuable addition that literature citations and web-based evidence are available when desired. (Bates et al., 2003) 3.2 The right time 3.2.1 Save time Physicians encounter a barrier to invest time in another computer system. All health professionals face time pressure and increasing data overload. The number of therapies is so large that physicians cannot effectively keep track of them all.(Bates et al., 2003) A CDSS can save time by making associations between different data domains, which physicians might miss because of the large amount of data. Time saving can be achieved by making it easy to do it right and is therefore a highly valued success factor.(Wetter, 2002; Mollon et al., 2009) It is important to find the right balance between over- and under reporting in accordance with the wishes of the end-users of the system and to convince users that the added value of the system compensates for the time required to learn and use the system.(Goud et al., 2008) 3.2.2 Integration in clinical workflow The most important success factor of CDSS is that the system is integrated in the clinical workflow.(Kawamoto et al., 2005;Osheroff et al., 2005;Mollon et al., 2009) A CDSS will otherwise have no beneficial effect and will not be used. Messages should be presented at the moment of decision making, though with as less disturbance for the physician as possible. Therefore different alert mechanisms (pop-up, automatic lab order, prescription order, email, etc) should be developed, suitable for different alerting priorities.(Osheroff et al., 2005) In this particular field, the research is very limited and needs to be expanded soon. Understanding the clinical workflow and user’s wishes thoroughly strongly increases the probability for success.(Bates et al., 2003) 3.2.3 High system’s speed As with every computer system, speed is a very important parameter for user’s acceptance. (Mollon et al., 2009) As explained above, recommendations should appear exactly at the right time of decision making. When the speed of an application is slow, user satisfaction declines markedly. Bates et al found that this parameter is valued most by users and therefore it should be a priority factor of the CDSS.(Bates et al., 2003)
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3.3 The right place 3.3.1 Point of care An important quality of advanced CDSS is the ability to deliver recommendations at the point of care.(Mollon et al., 2009) The question is who should receive the message. The point of care may vary, as patients therapy is guided by various health professionals in the process. The multidisciplinary expert team can identify the receiver of the recommendations (e.g. physician, pharmacist, nurse etc).(Wessels-Basten et al., 2007) 3.3.2 Active alert mechanism The performance of systems in which users are automatically prompted to use the system is significantly more effective, compared with studies in which users were required to actively initiate the system.(Dexter et al., 2001; Kawamoto et al., 2005; Garg et al., 2005; van Wyk et al., 2008) Also a larger positive impact is seen when a recommendation is prompting for an action and not ignorable.(Dexter et al., 2001; Bates et al., 2003) Dexter found that relatively small changes in the presentation of alerts made the difference between a significantly increase in preventive measurement rates and no effect at all.(Dexter et al., 2001) So it is important to select the alert mechanism carefully, as the type of alert will greatly affect the impact of the clinical rule. 3.4 The right system 3.4.1 Electronic availability of data A CDSS acts on electronically available patient data in the EMR (clinical data, pharmacy, laboratory, diagnoses, complications, microbiology etc).(Soman et al., 2008) Progress of implementation and adoption of EMR is slow, which inhibits adoption of CDSS. The most specific and advanced clinical rules can be developed if a CDSS is able to gather all mentioned data from the EMR, but at least a pharmacy-laboratory link is needed. (Schiff et al., 2003; Maviglia et al., 2003; Jha et al., 2008) The ability to integrate outpatient data in the CDSS can give additional improvements for more specific clinical rules.(Goud et al., 2008) 3.4.2 Integration with other systems Integrated systems (e.g. in CPOE) are significantly more likely to succeed than stand alone systems.(Kawamoto et al., 2005;Garg et al., 2005;Mollon et al., 2009) This provides relevant information to physicians at key times in decision making, enabling to prompt alerts during drug prescription or chart review.(Denekamp, 2007;Varonen et al., 2008) 3.4.3 Maintenance of system and content Once a CDSS is implemented it is essential for long term success that the system and content remains up-to-date.(Trivedi et al., 2009) Technical maintenance is important to guarantee a flawless link between the CDSS and the EMR. Maintenance concerning content is needed to ensure that the clinical rules remain applicable regarding the latest evidence-based guidelines.(Osheroff et al., 2005) Sometimes corrections are required after implementation when it turns out that the impact or physician’s satisfaction is not as expected.(Trivedi et al., 2002;Bates et al., 2003) Therefore Bates et al(Bates et al., 2003) assigned each area of decision support to an individual. For example, a cardiologist has to evaluate the clinical rules regarding his specialism periodically. Regarding the novelty of complex CDSS, maintenance of the system and content is hardly studied yet.
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4. Barriers Certain barriers found in literature may hamper implementation of advanced CDSS. Besides the lack of the described success factors, an often mentioned barrier to implementation is low computer skills among physicians.(Garg et al., 2005;Leslie and Denvir, 2007;Trivedi et al., 2009) This must be carefully taken into account within the design of the alerts. Newgeneration physicians, like medical students and junior doctors, may bring a higher level of computer literacy to clinical practice and stimulate implementation of a CDSS in practice. (Leslie and Denvir, 2007) Another barrier, brought forward by Bates(Bates et al., 2003), is the loss of physician’s autonomy with the use of CDSS. However, CDSS are able to present the best evidence-based practice automatically, without requiring extra thought or work. This allows the health professionals to focus on those areas of special need and adjust care to each individual patient.(James, 2002) This not only increases patient safety, but also physician’s safety by reducing the risk on malpractice claims. Even, the system may improve clinical skills through a learn effect of the corrective messages.(Varonen et al., 2008)
5. Own experienced success factors and barriers In our years of research we evolved a development and validation strategy for clinical rules. This three-step strategy (retrospective technical, retrospective therapeutic and prospective validation step), following a Plan-do-check-act-cycle in combination with an expert team, has led to high success rates.(Wessels-Basten et al., 2007;Helmons et al., 2009) We found that a well considered strategy, that creates rules with a high positive predictive value (PPV) is essential for accurate content and reliable messages. Application of this strategy has led to clinical rules with a reliability (PPV) of at least 94%, which indicates that more than 94% of the alerts are technically correct and clinically relevant as indicated by the expert team and end-users.(Wessels-Basten et al., 2007) This strategy mentioned is described in detail in another publication (Scheepers, 2009) and tries to solve important barriers described earlier, especially the ones regarding content and reliability. In our hospital we use the advanced CDSS Gaston, a commercially available system described in many publications. (de Clercq, 2004). Gaston is a software system – you could also call it an intelligent agent – that consists of two parts. The first part – the guideline-editor (Fig. 1) – allows the user to build the guidelines as flowcharts in a userfriendly environment. The steps in the flowchart contain the selection definitions based on the parameters that are available in the EHR. The second part, the decision support module (DCS) is the guideline execution engine. This engine translates the guideline from the guideline-editor into a routine that can be executed. As the expert team is the key quality of the strategy developed in our previous research, we experienced some specific factors that need to be taken into account. These aspects may help others to compose the right team in order to optimised outcomes: Define the objective clearly. The context of the clinical rule and how it will be applied in practice should be clear for all participants. Start by making a clear structure in which the clinical rule is developed. Some content covers only one drug, some covers a certain drug class, while others concern a complete (contra-)indication. One should try to classify the content correctly.
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Develop the clinical rule in a way that is clear for all participants before start and in a structured way with a pre-defined strategy. Compose the team of experts with experts from different departments and specialisms. Also, compose the team with people that can be trusted to have an active role in the discussion and reflect the opinion of others well. Communicate to the experts what effort is expected form them and how much time it will cost them. How will they be rewarded for their input. Communicate clearly how feedback will be given, in meetings or digital communication. Determine how decisions are made when not all experts agree. Which experts are identified as having the last word or should there be a majority with the same opinion. When opinions differ, stay objective as being the chairman of the session. Try to make a clear overview of all pro- and contra arguments and try to literature to create an agreement.
Fig. 1. Screenshot of the Gaston Guideline Editor, with an example of a clinical rule, build as a flowchart to cover the complete therapy of lithium. Integration of all computer systems in one environment is also found to be important to ease implementation. In our hospital, CPOE is integrated in the EMR. Only one link is needed between CDSS and EMR/CPOE which facilitates technical implementation, maintenance and costs.(de Clercq and Hasman, 2004) An example: we found that every time the EMR
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was upgraded to a new version, also adaptations in de CDSS were needed. Besides these two factors, we experienced that the success factors as described above are important for an effective foundation before widespread hospital implementation. 5.1 Barriers experienced in practice When the dependence on a CDSS is growing for the checking of guidelines in a more consistent and reliable way, it is necessary that the well-functioning of the CDSS is guaranteed in the hospital. During our research we experienced a lot of barriers. For the guarantee of well-functioning, the following ‘commandments’ need to be addressed: 1. Stability and performance of the CDSS: When the CDSS is increasingly used, it may impact the performance of the system itself as well as other systems. It is important that the position of the CDSS is well known amongst all and that these performance problems are known, controlled and monitored continuously by the ICT-department. 2. Maintenance of the CDSS: Maintenance can be divided in functional and technical maintenance, with different responsibilities. It should be indicated clearly who is responsible for the content of the system and all the parameters used in these clinical rules (functional). This is usually a task for the medical professional, who builds, tests and releases these clinical guidelines. Between function and technical maintenance is the maintenance of the data and parameters used (data dictionary). When a parameter, location or database structure are changed for the information systems that feed the CDSS with data (e.g. the EHR or laboratory information system), the data dictionary of the CDSS should be changed accordingly. The use of so-called ‘free-text’ in the data feeding information systems should be minimized for as much as possible. Typo-errors or different use of text for the same concept makes it impossible to guarantee the correct functioning of the CDSS. Technical maintenance includes the maintenance of the CDSS application, installing updates and new releases of the CDSS (and EHR) and testing the technical correct functioning of the CDSS. Because a CDSS uses data from other information systems like the EHR, it is important that technical testing of the CDSS is also performed after installing updates or new releases of other information systems like the EHR. This is usually done by the ICT-department. In addition, advanced reporting-systems should be able for signalling and solving errors of the CDSS. When one is not aware of an error or malfunctioning of the CDSS, the patient safety could be at risk without anybody noticing it. The responsibility of the functional and technical maintenance of the CDSS should be clearly defined and time and budget should be available for medical professionals and the hospital IT –department. 3. Skills and knowledge of personnel: When there are errors concerning the CDSS, one should be able to estimate the possible risks on patient safety and immediately act upon it. This requires effort from all personnel, medical as well as IT personnel. Documentation on the guidelines, possible errors and their risks and an emergency procedure, in case of malfunctioning of the CDSS is an important aspect of the safe use of a CDSS. 4. A generalised format: Agreements should be made between the hospital departments about the layout, priority and content of warnings of the CDSS, the way they should respond to them and the content of guidelines. The presence of a wide variety of warnings could cause that medical professionals lose interest for them. In that case the warnings of the CDSS are not used and their additional value declines.
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5.
Continuity and support of the CDSS vendor: This support is essential for continued technical quality of the system and the validated manner of application in practice. In every setting local adaptations are needed to fulfil the demands of the CDSS customer. 6. Comprehensive testing protocols: These should be available for the testing of guidelines before implementation and for the testing of guidelines after new releases and updates. Also testing protocols should be available for the technical testing of the CDSS. When the number of guidelines increases in a hospital, it will cost an increasing amount of time to test all the guidelines after a new release or update. One should develop testing strategies, which allow adequate testing within a limited amount of time. This list is not a complete list and not a completely new list: Most of the issues are also applicable for other information systems like the EHR itself. It is important that the issues on this list are addressed because downtime and malfunctioning of the EHR or the CDSS have severe implications for the hospital and its patients. One should be aware that malfunctioning of the CDSS is not immediately visible for the medical professional because it generally results in the absence of warnings. Warnings are, however, not always present, only in case of treatment differing from guidelines. The absence of warnings will therefore not automatically alarm a medical professional. Continuously monitoring of the CDSS by the IT department is therefore important, to prevent unreported malfunctioning CDSS, which is a potential risk for patient safety.
6. Discussion The primary finding in this chapter is that many factors determine the successful implementation of advanced decision support. Although several publications have addressed the need of detailed description of system design and implementation features, these are continuously poorly reported.(Kawamoto et al., 2005;Garg et al., 2005;Mollon et al., 2009) Also the definition of a CDSS is used confusingly, as they include a wide variety of computerized and non-computerized systems that apply basic and advanced decision support. This complicates to conclude which factors really contribute to success of advanced decision support and seriously hampers widespread implementation of these systems that are so urgently needed. The implementation of effective clinical decision support is a challenging task and in this complex process there are no easy solutions to guarantee success or to avoid failure. When starting with a CDSS, first certain prerequisites must be met. First, nationwide adoption of an EMR must be accelerated, as this is a prerequisite for CDSS adoption. Secondly, it is important to gather the right data and create a clinical rule with high reliability with technically correct and clinically relevant messages. Essential is that an effective system must minimize the effort required by clinicians to receive and act on systems recommendations.(Kawamoto et al., 2005) Last but not least, the commitment and participation of physicians as most frequent end-users is crucial for success.(Bates et al., 2003) It is important to identify stakeholders that support the implementation and a multidisciplinary team to achieve realisation, distribution and continuation.(Osheroff et al., 2005) When these people address the value and possibilities of these systems, time and money can be made available. It is very time consuming to add knowledge to a CDSS, continuously test and validate the clinical rules and recommendations. Consequently, high
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costs are associated with the development of clinical rules and the cost-effectiveness of CDSS has rarely been studied yet.(Field et al., 2008) Nevertheless, a CDSS is a powerful tool that can even fulfil ‘latent needs’. These needs, that are present but not yet realized, can be fulfilled by detecting errors that need prevention but are too time-consuming with current possibilities. An example is the clinical rule for renal function Helmons et al developed.(Helmons et al., 2009) This clinical rule checks when a patient has a decreased renal function and dose adjustments of the medication are necessary. Compared to chart review, this clinical rule decreases time needed for checking and intervention by 80%. This example illustrates that today many gaps exist between optimal and actual practice. Although a pharmacist should check all patients on the combination of drug dosing and decreased renal function, in the current situation it is impossible as time is limited. Our research shows that with every developed clinical rule, remarkable differences between guidelines and practice were found, even though guidelines are well known.(Wessels-Basten et al., 2007;Helmons et al., 2009) Prospective research must demonstrate the effect of these clinical rules in practice.
7. Conclusion and further recommendations This chapter demonstrates a practical overview of success factors which are crucial for successful implementation of advanced CDSS to facilitate widespread implementation in hospital practice. These success factors are focused on presenting the right message, at the right time, in the right place with the right system. More adequate descriptions of system design features and implementation are needed to translate these factors to effects on patient outcomes. CDSS are expected to unchain a revolution in healthcare and fill the existing information gap by bringing relevant data and knowledge to the point of care.(Bates et al., 2003) Despite the consciousness that change is indispensable and the years of research and effort worldwide, this has only led to slow adoption of EMR and CDSS. We believe it is now the time to bring these identified success factors in practice to accelerate the adoption of these systems and reap the benefits of this promising next-generation in medication safety. More research is needed in most areas concerning CDSS (Kuperman et al., 2007;van Wyk et al., 2008) The focus of this research should be which alerting mechanism (list, pop-ups, email, SMS, dedicated quality persons) results in the best adherence of physicians to previously agreed standards of care and how do these methods effect patient outcomes? Eventually this will give an answer to the question: how to optimally implement a CDSS in clinical practice in order to reduce errors and omissions so frequently noted.
8. References Aspden, P., J. Wolcott, J. Bootman, and L. Cronenwett, 2006,Aspden, P., J. Wolcott, J. Bootman, and L. Cronenwett Preventing Medication Errors: Quality Chasm Series: Washington, DC, National Academy Press. Bates, D. W. et al., 2003, Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality: J.Am.Med.Inform.Assoc., v. 10, no. 6, p. 523-530.
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Committee on Quality of Health Care in America, 2001,Committee on Quality of Health Care in America Crossing the quality chasm: a new health system for the 21st century: Washington, D.C, National Academy Press. de Clercq, P., and A. Hasman, 2004, Experiences with the development, implementation and evaluation of automated decision support systems: Stud.Health Technol.Inform, v. 107, no. Pt 2, p. 1033-1037. Denekamp, Y., 2007, Clinical decision support systems for addressing information needs of physicians: Isr.Med.Assoc.J., v. 9, no. 11, p. 771-776. Dexter, P. R., S. Perkins, J. M. Overhage, K. Maharry, R. B. Kohler, and C. J. McDonald, 2001, A computerized reminder system to increase the use of preventive care for hospitalized patients: The New England Journal of Medicine, v. 345, no. 13, p. 965970. Field, T. S., P. Rochon, M. Lee, L. Gavendo, S. Subramanian, S. Hoover, J. Baril, and J. Gurwitz, 2008, Costs Associated with Developing and Implementing a Computerized Clinical Decision Support System for Medication Dosing for Patients with Renal Insufficiency in the Long-term Care Setting: Journal of the American Medical Informatics Association, v. 15, no. 4, p. 466-472. Garg, A. X., N. K. J. Adhikari, H. McDonald, M. P. Rosas-Arellano, P. J. Devereaux, J. Beyene, J. Sam, and R. B. Haynes, 2005, Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes: A Systematic Review: JAMA: The Journal of the American Medical Association, v. 293, no. 10, p. 1223-1238. Goud, R., A. Hasman, and N. Peek, 2008, Development of a guideline-based decision support system with explanation facilities for outpatient therapy: Computer Methods and Programs in Biomedicine, v. 91, no. 2, p. 145-153. Greenes, RA., 2007,Greenes, RA. Clinical Decision Support. The Road Ahead: Boston, Elsevier Academic Press. Helmons, P., R. Grouls, A. Roos, A. Bindels, P. De Clercq, S. Wessels-Basten, E. Ackerman, and H. Korsten, 2009, The potential value on medication safety of a clinical decision support system in intensive care patients with renal insufficiency: QSHC, in press. James, B., 2001, Making It Easy to Do It Right: The New England Journal of Medicine, v. 345, no. 13, p. 991-993. James, B., 2002, Quality improvement opportunities in health care - Making it easy to do it right: J Manag.Care Pharm., v. 8, no. 5, p. 394-399. Jha, A. K., J. Laguette, A. Seger, and D. W. Bates, 2008, Can Surveillance Systems Identify and Avert Adverse Drug Events? A Prospective Evaluation of a Commercial Application: Journal of the American Medical Informatics Association, v. 15, no. 5, p. 647-653. Kawamoto, K., C. A. Houlihan, E. A. Balas, and D. F. Lobach, 2005, Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success: BMJ, v. 330, no. 7494, p. 765. Kohn, L., J. Corrigan, and M. Donaldson, 2000,Kohn, L., J. Corrigan, and M. Donaldson To Err Is Human: Building a Safer Health System: Washington, DC, National Academy Press. Kuperman, G. J., A. Bobb, T. H. Payne, A. J. Avery, T. K. Gandhi, G. Burns, D. C. Classen, and D. W. Bates, 2007, Medication-related Clinical Decision Support in
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Computerized Provider Order Entry Systems: A Review: Journal of the American Medical Informatics Association, v. 14, no. 1, p. 29-40. Leendertse, A. J., A. C. G. Egberts, L. J. Stoker, P. M. L. A. van den Bemt, and for the HARM Study Group, 2008, Frequency of and Risk Factors for Preventable MedicationRelated Hospital Admissions in the Netherlands: Archives of Internal Medicine, v. 168, no. 17, p. 1890-1896. Leslie, S. J., and M. A. Denvir, 2007, Clinical decision support software for chronic heart failure: Crit Pathw.Cardiol., v. 6, no. 3, p. 121-126. Levy, M., T. Azaz-Livshits, B. Sadan, M. Shalit, G. Geisslinger, and K. Brune, 1999, Computerized surveillance of adverse drug reactions in hospital: implementation: Eur J Clin Pharmacol, v. 54, no. 11, p. 887-892. Maviglia, S. M., R. D. Zielstorff, M. Paterno, J. M. Teich, D. W. Bates, and G. J. Kuperman, 2003, Automating Complex Guidelines for Chronic Disease: Lessons Learned: Journal of the American Medical Informatics Association, v. 10, no. 2, p. 154-165. Mollon, B., J. Chong, A. Holbrook, M. Sung, L. Thabane, and G. Foster, 2009, Features predicting the success of computerized decision support for prescribing: a systematic review of randomized controlled trials: BMC Medical Informatics and Decision Making, v. 9, no. 1, p. 11. Nies, J., I. Colombet, P. Degoulet, and P. Durieux, 2006, Determinants of success for computerized clinical decision support systems integrated in CPOE systems: a systematic review: AMIA Annu Symp Proc, p. 594-598. Osheroff, J., E. Pifer, J. Teich, D. Sittig, and R. Jenders, 2005,Osheroff, J., E. Pifer, J. Teich, D. Sittig, and R. Jenders Improving outcomes with clinical decision support: an implementer's guide.: Health Information Management and Systems Society. Schiff, G. D., D. Klass, J. Peterson, G. Shah, and D. W. Bates, 2003, Linking Laboratory and Pharmacy: Opportunities for Reducing Errors and Improving Care: Archives of Internal Medicine, v. 163, no. 8, p. 893-900. Soman, S., G. Zasuwa, and J. Yee, 2008, Automation, Decision Support, and Expert Systems in Nephrology: Advances in Chronic Kidney Disease, v. 15, no. 1, p. 42-55. Sucher, J. F., F. A. Moore, S. R. Todd, R. M. Sailors, and B. A. McKinley, 2008, Computerized clinical decision support: a technology to implement and validate evidence based guidelines: J.Trauma, v. 64, no. 2, p. 520-537. Trivedi, M. H., J. K. Kern, A. Marcee, B. Grannemann, B. Kleiber, T. Bettinger, K. Z. Altshuler, and A. McClelland, 2002, Development and implementation of computerized clinical guidelines: barriers and solutions: Methods Inf.Med, v. 41, no. 5, p. 435-442. Trivedi, M., E. Daly, J. Kern, B. Grannemann, P. Sunderajan, and C. Claassen, 2009, Barriers to implementation of a computerized decision support system for depression: an observational report on lessons learned in "real world" clinical settings: BMC Medical Informatics and Decision Making, v. 9, no. 1, p. 6. van der Sijs, H., J. Aarts, A. Vulto, and M. Berg, 2006, Overriding of Drug Safety Alerts in Computerized Physician Order Entry: Journal of the American Medical Informatics Association, v. 13, no. 2, p. 138-147. van Wyk, J. T., M. A. M. van Wijk, M. C. J. M. Sturkenboom, M. Mosseveld, P. W. Moorman, and J. van der Lei, 2008, Electronic Alerts Versus On-Demand Decision Support to
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Improve Dyslipidemia Treatment: A Cluster Randomized Controlled Trial: Circulation, v. 117, no. 3, p. 371-378. Varonen, H., T. Kortteisto, M. Kaila, and for the EBMeDS Study Group, 2008, What may help or hinder the implementation of computerized decision support systems (CDSSs): a focus group study with physicians: Family Practice, v. 25, no. 3, p. 162167. Wessels-Basten, S., A. Hoeks, R. Grouls, P. Helmons, E. Ackerman, and H. Korsten, 2007, Development strategy and potential impact on medication safety for clinical rules: the lithium case: Br J Clin Pharmacol, v. 63, no. 4, p. 507-508. Wetter, T., 2002, Lessons learnt from bringing knowledge-based decision support into routine use: Artificial Intelligence in Medicine, v. 24, no. 3, p. 195-203.
Part 2 Guideline-Based Clinical Decision Support System
4 Information Extraction Approach for Clinical Practice Guidelines Representation in a Medical Decision Support System Fernando Pech-May1, Ivan Lopez-Arevalo2 and Victor J. Sosa-Sosa2 1Instituto
Tecnológico Superior de los Ríos Balancan, Tabasco 2Information Technology Laboratory, Cinvestav – Tamaulipas Victoria, Tamaulipas Mexico 1. Introduction Errors in healthcare are a leading cause of death and injury. Kohn et al. (Kohn et al., 2000) mention that, for example, preventable adverse events are a leading cause of death in the United States. In their studies they state that at least 44,000 and perhaps as many as 98,000 americans die in hospitals each year as result of medical errors. Similar scenarios are for other countries. This situation has motivated the usage of Clinical Practice Guidelines (CPGs) to reduce the uncertainty of the clinical professional (nurses and physicians) when making decisions about the patient illness. Clinical Practice Guidelines (CPGs) are documents containing guidelines and structured recommendations that are defined by domain experts based on medical and scientific evidence (Teije et al., 2006; Twaddle, 2005). Thus CPGs provide guides and scientific evidence to clinical professional to make flexible recommendations about specific health circumstances (Field & Lohr, 1990). The main objective of CPGs is to offer to clinical staff a set of recommendations that are focused on helping in the diagnosis, prognosis, and treatment of specific illness. The goal is to enhance the medical attention to patients. Furthermore, a CPG is an important support for the patient itself and his/her family on understanding the efficiency of a treatment and an important tool to improve the quality in medical care. Because CPGs are largely documents in narrative form, sometimes are ambiguous and lack of a defined structure and internal consistency, which make them too complicated for being understood directly by a computer. The information usually contained in a CPG is plain texts, lists, diagrams, tables, and annotations in HTML, XHTML or PDF format. To make this information understandable for a computer, it is required the usage of CPGs formal representation languages (Clercq et al., 2004; Votruba et al., 2004). In this sense, many researchers have proposed different frameworks, approaches, representation languages, and tools for CPGs modelling, which can be interpreted by computers (Hripcsak et al., 2005; Isern & Moreno, 2008). Some of these tools and approaches provide orientation for a specific representation of CPGs, others are intended for a more general use in several representation languages.
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Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain
However, nowadays the formalization process is still carried out manually. Although there exist several formal languages, their usage represents a complex and time-consuming work for a manual formalization of CPGs. The usage of tools for the formalization of CPGs requires not only knowledge about formal methods, but also about the medical domain. This paper describes a basic Information Extraction (IE) approach to enhance the knowledge acquisition on Clinical Practice Guidelines. The aim is to support the CPG modeller during the formalization process facilitating the CPG interpretation by computers, becoming an important module in every Medical Decision Support System. The output of this approach can be used for a better understanding by non-clinical medical people of CPGs. The starting point of this work was motivated for a preliminary effort wherein a CPG interpreter was developed (Pech-May, 2010). The paper is organised as follows. In section 2 the contextualisation background about Clinical Practice Guidelines and Information Extraction is given. Section 3 describes the proposed Information Extraction approach. Section 4 shows the experiments carried out and the obtained results of a first prototype for the proposed approach. Finally, the section 5 presents some conclusions, remarks, and further work.
2. Background 2.1 Clinical Practice Guidelines For interpretation issues, the medical procedures within a CPG are translated into algorithms that describe such procedures for diagnosis, prognosis, and treatment. The representation of a CPG as algorithms allows the organization of the relevant information in a directly applicable manner. In consequence this representation can enhance and support the decisions making process (Patel et al., 2001; Lyng et al., 2008). Several languages for CPGs representation have been developed for different purposes, users, and applications. Shiffman et al. (Shifman et al., 2000) described the requirements for modelling the knowledge of CPGs taking into account issues as completeness, expressivity, usability, and reuse. Most of the representation languages use the XML format as readable-machine language. Some of the most used languages for representing CPGs are: Asbru (Young et al., 2007) is a task-specific and intention-based plan representation language. It was designed specifically for a set of management-task plans. Some tools that help the formalization of GPCs in Asbru are AsbruView1 and DELT/A2. GLIF (Wang et al., 2004) (the Guideline Interchange Format) defines an ontology for the representation of CPGs, as well as a medical ontology for representing medical data and concepts. GLIF (on its third version) includes a formal expression language for specifying decision criteria and patient state. A tool that allows modeling CPGs in GLIF is Protégé3. GEM (Ciccarese et al., 2004) (the Guideline Elements Model) is an XML-based guideline document model that can store and organize the heterogeneous information contained in practice guideline documents. A tool that supports the formalization of CPGs in GEM is GEM Cutter4. http://www.asgaard.tuwien.ac.at/asbruview/ http://gem.med.yale.edu/default.htm 3 http://protege.stanford.edu 4 http://gem.med.yale.edu/default.htm 1 2
Information Extraction Approach for Clinical Practice Guidelines Representation in a Medical Decision Support System
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EON (Tu et al., 2001) is a guideline modeling and execution system that is part of the EON architecture, a component-based suite of models and software components for the creation of guideline-based applications. PROforma (Sutton and Fox, 2003) allows the guideline to be modeled as a set of tasks and data items, it is designed to support the management of medical procedures and clinical decision making at the point of care. The PROforma task model divides a generic task (keystone) into four types: plans, decisions, actions, and enquiries. The following list includes some remarkable aspects that are considered in the most typical formal languages such as Asbru, PROforma, and GLIF. Organization of plans: Asbru as well PROforma use an isolate generic object class for modelling plans: the plan object. GLIF uses two types of plans: guides and macros. The guides cover the direction and flow control decisions. The macros are used to specify in a declarative way the procedure patterns for specific purposes by means of a set of implementation steps, which appear as a one block in the CPGs. Specification of goals/intentions: GLIF specifies goals as strings; Asbru represents the intentions of plans as temporal patterns depending on the context. Action model: Actions are the primitives for the modeling used to represent tasks in a CPG (for instance, prescription, clinical research). All the languages allow specifying medical actions, but just GLIF has special structured classes to do it. This modeling method has an efficient mechanism to map instances of medical actions to terms of a restricted vocabulary. Regarding to the effect of actions, Asbru and PROforma, unlike GLIF, support express effects allowing to reason about actions based on its effects. In Asbru the effects of a plan can be used to select between different alternative plans and express causal relations. In turn in PROforma the effect of actions are modelled as postconditions, which are semantically different to the effects on Asbru because they represent assertions when an action is completed. Representation of medical knowledge and patient data: PROforma model medical knowledge by means of relations between concepts (indications, conindications, interaction between drugs, etc.). Such relations are included as arguments in alternative decisions. In GLIF the medical knowledge is represented as instances of concept-relation. Asbru has not a explicit representation for this kind of knowledge as part of the CPG model; nevertheless, this knowledge can be accessed by means of functions calls. The use of any of the formal languages involves several additional tasks that depend on the particular language. To tackle this issue, several assistant tools have been developed to support the formalization process. They range from markup-based tools, such as DELT/A, Stepper, and GEM-Cutter, to graphical tools using symbols to model diagrams, such as Protégé or the plan body wizard of the DeGel framework. A brief description of these tools is given below. Stepper (Růzicka and Svatek, 2004) is a markup tool for the formalization of narrative CPGs. The formalization of the CPGs is done through user-defined stages and each stage transformed to XML. GEM Cutter (Karras et al., 2000) transforms CPGs into GEM format. The GEM Cutter interface shows the textual CPG and its XML representation, thereby facilitating the user interaction in the transformation of the CPG. DELT/A (Votruba et al., 2004) support the translation of HTML documents to XML. DELT/A provides two main features: (1) linking between a textual guideline and its formal representation, and (2) applying design patterns as macros forms.
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Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain
Uruz is part of the Degel framework (Shalom et al., 2003), it uses a markup mechanism that allows the user to introduce medical terms in the CPG. Such terms can come from some vocabulary as ICD-9-CM (ICD-9-CM, 2010)5. Protégé (Gennari et al., 2002) is a general purpose tool for knowledge acquisition. It is broadly used in several knowledge domain fields. This tool allows modelling CPGs in different representation formal languages. AsbruView (Kosara et al., 2002) is a graphical user interface for Asbru to support the development of CPGs and medical protocols. AsbruView is focused on visualising data and plans during the design and execution. According to the specialized literature, important work has been done trying to translate CPG documents into a readable-machine presentation (Pech-May, 2010). Dart et al. (Dart et al., 2001) proposed a generic model to represent any CPG in XML format. Moreover, they proved that CPGs can be modeled in a generic XML file. Bosse (Bosse, 2001) developed an interpreter capable of simulating CPGs written in Asbru language for one CPG. Geldof (Geldof, 2002) presented a methodology to formalize CPGs in several languages, from his understanding until computerizing in XML. Aguirre-Junco et al. (Aguirre-Junco, et al., 2004) described a knowledge specification method based on a structured and systematic analysis of text allowing a detailed specification of a decision tree for CPGs. Fuchsberger and Miksch (Fuchsberger & Miksch, 2002) presented an execution unit tailored for a particular CPG representation in Asbru plans. The main aim for all the above work is the reasoning with the extracted medical knowledge. A reasoning process over such knowledge is desirable by nurses and physicians. Following this tendency, some efforts have been done to develop Medical Decision Support Systems (Kaiser & Miksch, 2005). But, like in similar works about Decision Support Systems, the knowledge acquisition becomes a bottleneck, which is the main limitation for this kind of systems. In this sense, we are introducing an approach to extract knowledge from textual CPGs that integrates an innovative Information Extraction module that facilitates knowledge acquisition. 2.2 Information Extraction The Information Extraction (IE) is responsible for structuring information contained in plain texts, which can be relevant for a particular domain (called extraction domain) (Karras, et al., 2000; Lehnert et al., 1994). The IE is a research subject that covers many areas. The goal of an IE system is finding and linking relevant information while ignoring the strange and irrelevant information. Peshkin and Pfeffer (Peshkin & Pfeffer, 2003) define the Information Extraction as the task of filling template information from previously unseen text which belongs to a pre-defined domain. One of the main reasons to use IE is its role in the evaluation and comparison of different Natural Language Processing technologies in domains highly influenced by human interactions, like the medical domain. The IE systems can be classified based on two approaches: 5 International Classification of Diseases. This is a classification of diseases and procedures used in the coding of clinical information derived from medical assistance, mainly in the hospital environment and specialized medical care centers.
Information Extraction Approach for Clinical Practice Guidelines Representation in a Medical Decision Support System
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Knowledge Engineering (KE): This is focused on an empiric method or based on a domain corpus to develop efficient and robust Natural Language Processing systems (Kasabov 2006). Machine Learning (ML): This has a well-known set of documents and outputs and uses a set of patterns to extract knowledge by means of Machine Learning techniques (Ethem, 2004). Based on the ML approach, the IE can be seen as useful technique to extract information from Clinical Practice Guidelines (CPGs) with the aim of enhancing its formalization. Particularly one of the tools used in the medical domain is the Badger system (Soderland, et al., 1995), which is a text analysis tool to summarize medical patient records by extracting diagnoses, symptoms, physical findings, test results, and therapeutic treatments based on linguistic concepts. There are other IE systems based on Machine Learning techniques such as SRV -Sequence Rules with Validation- (Freitag, 1998), which transforms the patterns learning problem into a classification problem; RAPIER (Califf, 1998) that uses pairs of test documents and fills templates; and WHISK (Sonderland, 1999) that uses learning rules to extract a set of text styles. These tools can be adapted to several domains. Some authors consider the Information Extraction (IE) as a later stage in the Information Retrieval (IR) process (Marie-Francine, 2006), the main difference between both is that IE provides the exactly desired information, while IR is in charge of finding the documents wherein the desired information should appear. Some new technologies try to merge advantages from both, such as some web wrappers (XWRAP (Liu et al, 2000) or (Baumgartner et al., 2001)) that extract information from HTML documents and search answers (automatic response over punctual queries). In this sense, a wrapper is a program that retrieves information from different repositories, merging, and unifying them. The aim of a wrapper is to locate relevant information in a semi-structured data and put it into a selfdescribed representation for further processing (Kushmerick et al., 1997).
3. Approach Most representation languages for CPG are very powerful and complex. They can contain many different types of information and data. The main goal for the application of Information Extraction on CPG documents is to obtain the relevant text by means of natural language patterns which can be used in the formalization of the CPG. This approach is illustrated in Figure 1. The approach facilitates the formalization process by using several intermediate representations that are obtained by stepwise procedures. The idea is to obtain an intermediate representation of a CPG in XML format for reasoning. Such intermediate representation takes into account all the most important pieces from the CPG (such as actions, processes, sequences, etc.). The final output is a XML representation. This approach is an extension and adaptation of the work carried out by Cem Akkaya (Akkaya, 2005), which is a basic method for IE. The initial idea is to enhance the performance of a preliminary prototype to match patient data against CPGs (Pech-May et al., 2009) within a more general Medical Decision Support System. To make the extraction, some specific templates have been generated, which are filled by the desired information. To detect such information, a heuristic method is applied. The filled templates are later processed.
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Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain
Fig. 1. Proposed IE approach.
Fig. 2. General structure for CPGs.
Information Extraction Approach for Clinical Practice Guidelines Representation in a Medical Decision Support System
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The input CPGs are chosen from the National Guideline Clearinghouse6 (NGC) repository in XHTML format. Then, XHTML documents are analyzed to extract relevant information, and subsequently to obtain the intermediate representation. Such representation is displayed through templates in form of views by means of the Prefuse7 tool (Jeffrey et al., 2005). The approach considers that tested CPGs follow the structure of the NGC repository since these CPGs have a predefined structure. This approach works only for textual CPGs because in graphical or chart representation few text is included, relevant text (from medical experts) is mandatory for the approach. In general, a GPC has a structure consisting of separated sections for the treatment of a disease. For example, Figure 2 shows the general sections for diagnosis and treatment of a disease. A general flowchart for a CPG is depicted in Figure 3, based on the general structure of a CPG. In order to obtain information from CPGs, this approach is based on the transformation of multiple processes following three heuristic patterns for Information Extraction: a. Phrase pattern level (lexical level) b. Sentence pattern level (syntactic level) c. Speech pattern level (semantic level) It is necessary a parsing process to obtain the extraction rules. This is based on a knowledge engineering approach considering syntactic and semantic restrictions, and taking into account delimiters. In order for processing a large amount of documents and information, it is necessary specific heuristics for each type of information required, for example: a. Different types of information, in which each type of information needs specific methods for its processing (e.g. processes, parameters). b. Different representations of information, in which it should be taken into account that the information could be represented in different ways (structured, semi-structured, or plain text). c. Different types of guidelines, in which there may be CPGs for different diseases, diverse user groups, and several organizations that may contain similar CPGs.
Fig. 3. General flowchart for CPGs www.guideline.gov The Prefuse toolkit is a set of software tools for creating interactive data visualizations for Java language. 6 7
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Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain
The core of the approach is based on the atomic approach (Appelt & Israel, 1999), which basic idea is to assume that every noun phrase and verb of the right type, independently of the syntactic relations obtained among them, indicate an event/relationship of interest. It does not take into account the accuracy of the data extraction. Subsequently to the extracted data, a segmentation and filtering process is performed for its depuration. In this way, only the data concerning to the information of interest (diagnosis, treatment, drugs, etc.) is obtained. These data is stored in specific templates for further processing. The medical terms used in our prototype come from the Medical Subject Headings (MeSH)8 of the National Library of Medicine from United States. Next, each heuristic pattern is briefly described. 3.1 Phrase pattern level In this stage a lexical parser is used. It has the responsibility of splitting the text in paragraphs, tokens, and identifies important phrases in the CPG (e.g. administration of a drug, surgical procedures, dose of a drug, etc.). The lexical analyzer function is to identify the relevant information and then extract important data from the CPG. The lexical analyzer is in charge of filtering, for the second level of IE (sentence pattern level), the information that can be used by the syntactic level. They are defined by regular expressions as: Action terms (mainly verbs; e.g., “activate”, “perform”, “prescribe”, “treat”, “integrate”, “receive”, etc). Condition terms (regular expressions describing a condition, such as “if [, : \.]+”, “in case(s)? [, : \.]+”, “if [_,2 weeks]+”, etc.). Time Annotations (e.g. [ESS, LSS], [EFS, LFS], [MinDu, MaxDu], REFERENCE) Dose unit terms (e.g. “(m|d|c)?(l|g)(/kg/day)?”, “drop(s)?”, “teaspoon(s)?”, “tsp”). 3.2 Sentence pattern level In this level (syntactic level), the entire document is parsed and split into sentences. Then every sentence is processed with regard to its context within the document and its group affiliation. Thereby, the context is obtained by captions (e.g. “Acute Pharyngitis in children Algorithm Annotations | Treatment | Recommendations:”) and a group contains sentences from the same paragraph or the same list, if there are no sublists. Thus, each sentence is now checked for relevance. Useful medical terms and keywords to identify medical actions can be found. The words or groups of words are mainly verbs indicating the application of a therapy, administration of a drug or a surgical procedure. This level considers two groups of patterns: Free text pattern. It is used to identify paragraphs from a list of items. The pattern indicates therapy instruments (surgical procedures) combined with key terms (e.g. prescribe, indicate, execute, etc). Concise text pattern. It is used to detect specific defined patterns such as lists of items with incorrect grammar. In general, it denotes the right therapy to apply, instruments for the therapy or drugs. These can be merged with other detected labels in the sentence or phrase pattern level. Detecting relevant sentences is a challenging task, which is undertaken in two steps: 1. detecting irrelevant sentences to exclude them from further processing, and 2. detecting relevant sentences. 8
http://www.nlm.nih.gov/mesh/
Information Extraction Approach for Clinical Practice Guidelines Representation in a Medical Decision Support System
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In both steps, special keywords are used to detect whether a sentence is irrelevant or relevant. Keywords describing irrelevant sentences are “history”, “diagnosis”, “criteria”, “symptom”, “clinical assessment”, “risk factor”, “complicating factor”, “etiology”, and so on. These terms point out that the following paragraph does not describe treatment processes, but that it describes symptoms, demonstration of diagnoses, and so on. If such a term appears within a caption the corresponding section is removed. 3.3 Speech pattern level In this level semantic aspects are solved and the design and the structure of the final document XML are improved. In addition, this level is used to categorize sentences, actions, and to find their relationships. To accomplish the later task the following processes in the CPG are identified: a. processes with temporal dependencies (processes at some point depending on another process), b. sequential processes (the processes that are required to run with the authorization of other), c. processes containing a thread, d. selection process, and e. recurring processes. The application of the extraction rules gives as a result a well-structured XML document which can be represented by using specific templates or graphical forms (by using the Prefuse tool): Templates. It is the final representation of the Information Extraction module; these can be filled with specific CPG data. After collected all the relevant phrases from the CPG, the document is generated by using the representation of sentences, actions, relationships, and hierarchical structure. The representation is done through a document markup listing all relevant sentences and an identification of MeSH terms. Thus the document contains information for a dose, duration, actions, administration of a disease, etc. Graphical representation. The generated document is represented visually using Prefuse. In this way, it provides data to optimize table structures, tree graph design, visual encoding techniques, dynamic queries, integrated search, and database connectivity.
4. Experiments A first implementation of this approach was developed by using Java language. For a performance analysis, different CPGs corresponding to different specific diseases were employed, which are: Diagnosis and treatment of otitis media in children Diagnosis and treatment of diabetes-mellitus (type 2) Acute pharyngitis in children Diagnostics and treatment of jaundice Management and treatment of dengue hemorrhagic fever at first and second attention level Treatment of breast cancer Chronic cough in a child The CPGs were divided into two groups:
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Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain
1. Clinical guidelines to develop and improve the heuristics 2. Clinical guidelines to test the obtained heuristics The choice of these groups is not a trivial task because the organizations that develop CPGs do not regularly take care of following the same hierarchical structure. In this experiment, complex hierarchical structures were used as selection criteria, and distributed evenly to each group. Before applying the heuristics, some pre treatment was carried out (to verify that XHTML documents satisfy the structuring elements). This is achieved through the conversion of paragraphs/sections from the CPG and their corresponding items (according to the three pattern levels). Our test considered the following two tasks: Task 1: detection of relevant sentences, and Task 2: summarization of the detection types of sentence and the relationship between processes. The performance of the prototype was evaluated by using the precision and recall measures. The recall score measures the ratio of correct information extracted from the texts against all the available information present in the text. The precision score measures the ratio of correct information that was extracted against all the information that was extracted (Lehnert et al., 1994). The following summarizes the obtained results by task: Task 1: It obtains promising results (Table 1), even if it means lowering the precision punctuation. The lower recall score implies that detecting relevant sentences has to be improved. The high accuracy on precision score shows that irrelevant sentences were classified as relevant. Task 2: The entry for task 2 (Table 2) consists of sentences identified with very high punctuation in the previous task. The recall score is very high, which means that only few sentences were falsely not detected. The precision score implies that some slots were filled out incorrectly. The reason for this is that they do not always detect the correct type of sentence and specially when assigning annotations to their particular actions, situation that has to be improved. Table 3 presents an overall evaluation. For all the tables, the nomenclature for columns is: COR –Number of correct slots that were identified by our IE system MAT –Total number of slots that match a CPG template in the CPGs group IDE –Total number of slots that were identified by our IE system REC: Represents our system recall that is given by COR/IDE PRE: Represents our system precision that is given by COR/MAT At the phrase pattern level several regular expressions were necessary. Figure 4 shows a fragment for a pattern in this level. At the sentence pattern level the text free patterns were identified, such as
and
(to identify de paragraphs),
and
(to identify lists of items), and some additional labels. These labels are combined with the labels from de phrase pattern level like <dosage> or , <dose> or , etc. After each CPG was processed in the three analysis stages (phrase level, sentence level, and speech level), an intermediate representation was obtained. For this, two files were generated, the first one containing the list of relevant sentences (see Table 5) and a second one which is a mark-up document (see Table 6). The intermediate representation shown in Table 6 contains a set of actions and relations. An action contains sentences describing the action and annotation assigned by means of the DELT/A tool. It also contains the instrument for the treatment and an identifier within the MeSH dictionary. If the information is about a dose, duration of treatment or drug management, then a corresponding MeSH identifier is assigned to it. Table 7 partially shows
Information Extraction Approach for Clinical Practice Guidelines Representation in a Medical Decision Support System
57
the actions and its assigned MeSH identifiers for the CPG “Diagnosis and treatment of otitis media in children”. CPG Diagnosis and treatment of otitis media in children Diagnosis and treatment of diabetes-mellitus (type 2)
COR
MAT
IDE
REC
PRE
23
24
26
0.958
0.884
45
57
53
0.789
0.849
Acute pharyngitis in children
9
12
10
0.75
0.9
Diagnostics and treatment of jaundice
53
56
53
0.946
1
Management and treatment of dengue hemorrhagic fever at first and second attention level
65
68
75
0.955
0.866
Treatment of breast cancer
56
59
74
0.946
0.756
Chronic cough in a child
23
31
26
0.741
0.884
Table 1. Evaluation of Task 1 for each CPG. CPG Diagnosis and treatment of otitis media in children Diagnosis and treatment of diabetes-mellitus (type 2)
COR
MAT
IDE
REC
PRE
27
32
27
0.843
1
32
34
40
0.941
0.8
Acute pharyngitis in children
56
58
67
0.965
0.835
Diagnostics and treatment of jaundice
36
42
45
0.857
0.8
Management and treatment of dengue hemorrhagic fever at first and second attention level
73
75
75
0.973
0.973
Treatment of breast cancer
86
116
98
0.741
0.877
Chronic cough in a child
14
18
20
0.777
0.7
Table 2. Evaluation of Task 2 for each CPG. Task
COR
MAT
IDE
REC
PRE
Task 1
355
443
432
0.801
0.821
Task 2
849
865
978
0.981
0.868
Table 3. Overall evaluation results
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Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain
