Identifying barriers to hypertension guideline adherence using clinician feedback at the point of care.

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.

Evaluating provider adherence in a trial of a guideline-based decision support system for hypertension.

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.

Medical informatics: searching for underlying components.

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.

Modeling data and knowledge in the EON guideline architecture.

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.

RASTA: a distributed temporal abstraction system to facilitate knowledge-driven monitoring of clinical databases.

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.

Building an explanation function for a hypertension decision-support system.

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.

Medical quality assessment by scoring adherence to guideline intentions.

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.

A formal method to resolve temporal mismatches in clinical databases.

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.

Patient safety in guideline-based decision support for hypertension management: ATHENA DSS.

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.

A virtual medical record for guideline-based decision support.

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.

Integration of textual guideline documents with formal guideline knowledge bases.

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.

Implementing clinical practice guidelines while taking account of changing evidence: ATHENA DSS, an easily modifiable decision-support system for managing hypertension in primary care.

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.

Ontology acquisition from on-line knowledge sources.

Electronic knowledge representation is becoming more and more pervasive both in the form of formal ontologies and less formal reference vocabularies, such as UMLS. The developers of clinical knowledge bases need to reuse these resources. Such reuse requires a new generation of tools for ontology development and management. Medical experts with little or no computer science experience need tools that will enable them to develop knowledge bases and provide capabilities for directly importing knowledge not only from formal knowledge bases but also from reference terminologies. The portions of knowledge bases that are imported from disparate resources then need to be merged or aligned to one another in order to link corresponding terms, to remove redundancies, to resolve logical conflicts. We discuss the requirements for ontology-management tools that will enable interoperability of disparate knowledge sources. Our group is developing a suite of tools for knowledge-base management based on the Protégé-2000 environment for ontology development and knowledge acquisition. We describe one such tool in detail here: an application for incorporating information from remote knowledge sources such as UMLS into a Protégé knowledge base.

Representation of temporal indeterminacy in clinical databases.

Temporal indeterminancy is common in clinical medicine because the time of many clinical events is frequently not precisely known. Decision support systems that reason with clinical data may need to deal with this indeterminancy. This indeterminacy support must have a sound foundational model so that other system components may take advantage of it. In particular, it should operate in concert with temporal abstraction, a feature that is crucial in several clinical decision support systems that our group has developed. We have implemented a temporal query system called Tzolkin that provides extensive support for the temporal indeterminancies found in clinical medicine, and have integrated this support with our temporal abstraction mechanism. The resulting system provides a simple, yet powerful approach for dealing with temporal indeterminancy and temporal abstraction.

Knowledge representation and tool support for critiquing clinical trial protocols.

The increasing complexities of clinical trials have led to increasing costs for investigators and organizations that author and administer those trials. The process of authoring a clinical trial protocol, the document that specifies the details of the study, is usually a manual task, and thus authors may introduce subtle errors in medical and procedural content. We have created a protocol inspection and critiquing tool (PICASSO) that evaluates the procedural aspects of a clinical trial protocol. To implement this tool, we developed a knowledge base for clinical trials that contains knowledge of the medical domain (diseases, drugs, lab tests, etc.) and of specific requirements for clinical trial protocols (eligibility criteria, patient treatments, and monitoring activities). We also developed a set of constraints, expressed in a formal language, that describe appropriate practices for authoring clinical trials. If a clinical trial designed with PICASSO violates any of these constraints, PICASSO generates a message to the user and a list of inconsistencies for each violated constraint. To test our methodology, we encoded portions of a hypothetical protocol and implemented designs consistent and inconsistent with known clinical trial practice. Our hope is that this methodology will be useful for standardizing new protocols and improving their quality.

The impact of displayed awards on the credibility and retention of Web site information.

Ratings systems and awards for medical Web sites have proliferated, but the validity and utility of the systems has not been well established. This study examined the effect of awards on the perceived credibility and retention of health information on a Web page. We recruited study participants from Internet newsgroups and presented them with information on the claimed health benefits of shark cartilage. Participants were randomized to receive health information with and without a medical award present on the page. We subsequently asked them to evaluate the credibility of the Web page and posed multiple-choice questions regarding the content of the pages. 137 completed responses were included for analysis. Our results show that the presentation of awards has no significant effect on the credibility or retention of health information on a Web page. Significantly, the highly educated participants in our study found inaccurate and misleading information on shark cartilage to be slightly believable.

From guideline modeling to guideline execution: defining guideline-based decision-support services.

We describe our task-based approach to defining the guideline-based decision-support services that the EON system provides. We categorize uses of guidelines in patient-specific decision support into a set of generic tasks--making of decisions, specification of work to be performed, interpretation of data, setting of goals, and issuance of alert and reminders--that can be solved using various techniques. Our model includes constructs required for representing the knowledge used by these techniques. These constructs form a toolkit from which developers can select modeling solutions for guideline task. Based on the tasks and the guideline model, we define a guideline-execution architecture and a model of interactions between a decision-support server and clients that invoke services provided by the server. These services use generic interfaces derived from guideline tasks and their associated modeling constructs. We describe two implementations of these decision-support services and discuss how this work can be generalized. We argue that a well-defined specification of guideline-based decision-support services will facilitate sharing of tools that implement computable clinical guidelines.