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BACKGROUND: Biomedical informatics is a broad discipline that borrows many methods and techniques from other disciplines. OBJECTIVE: To reflect a) on the character of biomedical informatics and to determine whether it is multi-disciplinary or inter-disciplinary; b) on the question whether biomedical informatics is more than the sum of its supporting disciplines and c) on the position of biomedical informatics with respect to related disciplines. METHOD: Inviting an international group of experts in biomedical informatics and related disciplines on the occasion of the 50th anniversary of Methods of Information in Medicine to present their viewpoints. RESULTS AND CONCLUSIONS: This paper contains the reflections of a number of the invited experts on the character of biomedical informatics. Most of the authors agree that biomedical informatics is an interdisciplinary field of study where researchers with different scientific backgrounds alone or in combination carry out research. Biomedical informatics is a very broad scientific field and still expanding, yet comprised of a constructive aspect (designing and building systems). One author expressed that the essence of biomedical informatics, as opposed to related disciplines, lies in the modelling of the biomedical content. Interdisciplinarity also has consequences for education. Maintaining rigid disciplinary structures does not allow for sufficient adaptability to capitalize on important trends nor to leverage the influences these trends may have on biomedical informatics. It is therefore important for students to become aware of research findings in related disciplines. In this respect, it was also noted that the fact that many scientific fields use different languages and that the research findings are stored in separate bibliographic databases makes it possible that potentially connected findings will never be linked, despite the fact that these findings were published. Bridges between the sciences are needed for the success of biomedical informatics.

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Factors contributing to low adherence to clinical guidelines by clinicians are not well understood. The user interface of ATHENA-HTN, a guideline-based decision support system (DSS) for hypertension, presents a novel opportunity to collect clinician feedback on recommendations displayed at the point of care. We analyzed feedback from 46 clinicians who received ATHENA advisories as part of a 15-month randomized trial to identify potential reasons clinicians may not intensify hypertension therapy when it is recommended. Among the 368 visits for which feedback was provided, clinicians commonly reported they did not follow recommendations because: recorded blood pressure was not representative of the patient's typical blood pressure; hypertension was not a clinical priority for the visit; or patients were nonadherent to medications. For many visits, current quality-assurance algorithms may incorrectly identify clinically appropriate decisions as guideline nonadherent due to incomplete capture of relevant information. We present recommendations for how automated DSSs may help identify "apparent" barriers and better target decision support.

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INTRODUCTION: Syndromic surveillance offers the potential to rapidly detect outbreaks resulting from terrorism. Despite considerable experience with implementing syndromic surveillance, limited evidence exists to describe the performance of syndromic surveillance systems in detecting outbreaks. OBJECTIVES: To describe a model for simulating cases that might result from exposure to inhalational anthrax and then use the model to evaluate the ability of syndromic surveillance to detect an outbreak of inhalational anthrax after an aerosol release. METHODS: Disease progression and health-care use were simulated for persons infected with anthrax. Simulated cases were then superimposed on authentic surveillance data to create test data sets. A temporal outbreak detection algorithm was applied to each test data set, and sensitivity and timeliness of outbreak detection were calculated by using syndromic surveillance. RESULTS: The earliest detection using a temporal algorithm was 2 days after a release. Earlier detection tended to occur when more persons were infected, and performance worsened as the proportion of persons seeking care in the prodromal disease state declined. A shorter median incubation state led to earlier detection, as soon as 1 day after release when the incubation state was < or =5 days. CONCLUSION: Syndromic surveillance of a respiratory syndrome using a temporal detection algorithm tended to detect an anthrax attack within 3-4 days after exposure if >10,000 persons were infected. The performance of surveillance (i.e., timeliness and sensitivity) worsened as the number of persons infected decreased.

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Measurement of provider adherence to a guideline-based decision support system (DSS) presents a number of important challenges. Establishing a causal relationship between the DSS and change in concordance requires consideration of both the primary intention of the guideline and different ways providers attempt to satisfy the guideline. During our work with a guideline-based decision support system for hypertension, ATHENA DSS, we document a number of subtle deviations from the strict hypertension guideline recommendations that ultimately demonstrate provider adherence. We believe that understanding these complexities is crucial to any valid evaluation of provider adherence. We also describe the development of an advisory evaluation engine that automates the interpretation of clinician adherence with the DSS on multiple levels, facilitating the high volume of complex data analysis that is created in a clinical trial of a guideline-based DSS.

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OBJECTIVE: To discuss unifying principles that can provide a theory for the diverse aspects of work in medical informatics. If medical informatics is to have academic credibility, it must articulate a clear theory that is distinct from that of computer science or of other related areas of study. RESULTS: The notions of reusable domain antologies and problem-solving methods provide the foundation for current work on second-generation knowledge-based systems. These abstractions are also attractive for defining the core contributions of basic research in informatics. We can understand many central activities within informatics in terms defining, refining, applying, and evaluating domain ontologies and problem-solving methods. CONCLUSION: Construing work in medical informatics in terms of actions involving ontologies and problem-solving methods may move us closer to a theoretical basis for our field.

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Compared to guideline representation formalisms, data and knowledge modeling for clinical guidelines is a relatively neglected area. Yet it has enormous impact on the format and expressiveness of decision criteria that can be written, on the inferences that can be made from patient data, on the ease with which guidelines can be formalized, and on the method of integrating guideline-based decision-support services into implementation sites' information systems. We clarify the respective roles that data and knowledge modeling play in providing patient-specific decision support based on clinical guidelines. We show, in the context of the EON guideline architecture, how we use the Protégé-2000 knowledge-engineering environment to build (1) a patient-data information model, (2) a medical-specialty model, and (3) a guideline model that formalizes the knowledge needed to generate recommendations regarding clinical decisions and actions. We show how the use of such models allows development of alternative decision-criteria languages and allows systematic mapping of the data required for guideline execution from patient data contained in electronic medical record systems.

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The time dimension is very important for applications that reason with clinical data. Unfortunately, this task is inherently computationally expensive. As clinical decision support systems tackle increasingly varied problems, they will increase the demands on the temporal reasoning component, which may lead to slow response times. This paper addresses this problem. It describes a temporal reasoning system called RASTA that uses a distributed algorithm that enables it to deal with large data sets. The algorithm also supports a variety of configuration options, enabling RASTA to deal with a range of application requirements.

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ATHENA DSS is a decision-support system that provides recommendations for managing hypertension in primary care. ATHENA DSS is built on a component-based architecture called EON. User acceptance of a system like this one depends partly on how well the system explains its reasoning and justifies its conclusions. We addressed this issue by adapting WOZ, a declarative explanation framework, to build an explanation function for ATHENA DSS. ATHENA DSS is built based on a component-based architecture called EON. The explanation function obtains its information by tapping into EON's components, as well as into other relevant sources such as the guideline document and medical literature. It uses an argument model to identify the pieces of information that constitute an explanation, and employs a set of visual clients to display that explanation. By incorporating varied information sources, by mirroring naturally occurring medical arguments and by utilizing graphic visualizations, ATHENA DSS's explanation function generates rich, evidence-based explanations.

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Quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe an approach for evaluating and consistently scoring clinician adherence to medical guidelines using the intentions of guideline authors. We present the Quality Indicator Language (QUIL) that may be used to formally specify quality constraints on physician behavior and patient outcomes derived from medical guidelines. We present a modeling and scoring methodology for consistently evaluating multi-step and multi-choice guideline plans based on guideline intentions and their revisions.

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Overcoming data heterogeneity is essential to the transfer of decision-support programs to legacy databases and to the integration of data in clinical repositories. Prior methods have focused primarily on problems of differences in terminology and patient identifiers, and have not addressed formally the problem of temporal data heterogeneity, even though time is a necessary element in storing, manipulating, and reasoning about clinical data. In this paper, we present a method to resolve temporal mismatches present in clinical databases. This method is based on a foundational model of time that can formalize various temporal representations. We use this temporal model to define a novel set of twelve operators that can map heterogeneous time-stamped data into a uniform temporal scheme. We present an algorithm that uses these mapping operators, and we discuss our implementation and evaluation of the method as a software program called Synchronus.

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The Institute of Medicine recently issued a landmark report on medical error.1 In the penumbra of this report, every aspect of health care is subject to new scrutiny regarding patient safety. Informatics technology can support patient safety by correcting problems inherent in older technology; however, new information technology can also contribute to new sources of error. We report here a categorization of possible errors that may arise in deploying a system designed to give guideline-based advice on prescribing drugs, an approach to anticipating these errors in an automated guideline system, and design features to minimize errors and thereby maximize patient safety. Our guideline implementation system, based on the EON architecture, provides a framework for a knowledge base that is sufficiently comprehensive to incorporate safety information, and that is easily reviewed and updated by clinician-experts.

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A major obstacle in deploying computer-based clinical guidelines at the point of care is the variability of electronic medical records and the consequent need to adapt guideline modeling languages, guideline knowledge bases, and execution engines to idiosyncratic data models in the deployment environment. This paper reports an approach, developed jointly by researchers at Newcastle and Stanford, where guideline models are encoded assuming a uniform virtual electronic medical record and guideline-specific concept ontologies. For implementing a guideline-based decision-support system in multiple deployment environments, we created mapping knowledge bases to link terms in the concept ontology with the terminology used in the deployment systems. Mediation components use these mapping knowledge bases to map data in locally deployed medical record architectures to the virtual medical record. We discuss the possibility of using the HL7 Reference Information Model (RIM) as the basis for a standardized virtual medical record, showing how this approach also complies with the European pre-standard ENV13606 for electronic healthcare record communication.

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Numerous approaches have been proposed to integrate the text of guideline documents with guideline-based care systems. Current approaches range from serving marked up guideline text documents to generating advisories using complex guideline knowledge bases. These approaches have integration problems mainly because they tend to rigidly link the knowledge base with text. We are developing a bridge approach that uses an information retrieval technology. The new approach facilitates a versatile decision-support system by using flexible links between the formal structures of the knowledge base and the natural language style of the guideline text.

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This paper describes the ATHENA Decision Support System (DSS), which operationalizes guidelines for hypertension using the EON architecture. ATHENA DSS encourages blood pressure control and recommends guideline-concordant choice of drug therapy in relation to comorbid diseases. ATHENA DSS has an easily modifiable knowledge base that specifies eligibility criteria, risk stratification, blood pressure targets, relevant comorbid diseases, guideline-recommended drug classes for patients with comorbid disease, preferred drugs within each drug class, and clinical messages. Because evidence for best management of hypertension evolves continually, ATHENA DSS is designed to allow clinical experts to customize the knowledge base to incorporate new evidence or to reflect local interpretations of guideline ambiguities. Together with its database mediator Athenaeum, ATHENA DSS has physical and logical data independence from the legacy Computerized Patient Record System (CPRS) supplying the patient data, so it can be integrated into a variety of electronic medical record systems.

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