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Edward H Shortliffe

Publications and source records attributed to Edward H Shortliffe.

8 recordsLinked to original sources
PubMed2002

Representation primitives, process models and patient data in computer-interpretable clinical practice guidelines: a literature review of guideline representation models.

Representation of clinical practice guidelines in a computer-interpretable format is a critical issue for guideline development, implementation, and evaluation. We studied 11 types of guideline representation models that can be used to encode guidelines in computer-interpretable formats. We have consistently found in all reviewed models that primitives for representation of actions and decisions are necessary components of a guideline representation model. Patient states and execution states are important concepts that closely relate to each other. Scheduling constraints on representation primitives can be modeled as sequences, concurrences, alternatives, and loops in a guideline's application process. Nesting of guidelines provides multiple views to a guideline with different granularities. Integration of guidelines with electronic medical records can be facilitated by the introduction of a formal model for patient data. Data collection, decision, patient state, and intervention constitute four basic types of primitives in a guideline's logic flow. Decisions clarify our understanding on a patient's clinical state, while interventions lead to the change from one patient state to another.

Artificial Intelligence↗
PubMed2002

Support for guideline development through error classification and constraint checking.

Clinical guidelines aim to eliminate clinician errors, reduce practice variation, and promote best medical practices. Computer-interpretable guidelines (CIGs) can deliver patient-specific advice during clinical encounters, which makes them more likely to affect clinician behavior than narrative guidelines. To reduce the number of errors that are introduced while developing narrative guidelines and CIGs, we studied the process used by the ACP-ASIM to develop clinical algorithms from narrative guidelines. We analyzed how changes progressed between subsequent versions of an algorithm and between a narrative guideline and its derived clinical algorithm. We recommend procedures that could limit the number of errors produced when generating clinical algorithms. In addition, we developed a tool for authoring CIGs in GLIF3 format and validating their syntax, data type matches, cardinality constraints, and structural integrity constraints. We used this tool to author guidelines and to check them for errors.

Algorithms↗
PubMed2002

Extended attributes of event monitor systems for criteria-based notification modalities.

The efficacy of event monitors (EMs) at reducing morbidity and mortality of certain clinical conditions (CCs) is well established. In addition, studies have shown that user inverted exclamation mark s preferences on the modality of notification are correlated to the type of reminder or alert. Nonetheless, few institutions have implemented large scale automated monitoring of a considerable number of distinct CCs, and to our knowledge, none of these sizable projects also offer user-customizable communication modalities (CMs) over all monitored conditions. As both the numbers of CMs and CCs increase, the complexity of customizing user preferences amplifies following a geometric progression. This paper demonstrates an automated approach, based on generic notification attributes (NAs) and notification criteria (NC), which significantly simplifies the management and personalization of the CMs for institutions where the manual assignment of a CM for every alert is forbidding. The methods by which these NAs were developed, their significance for existing CCs and their implementation using the Arden Syntax and Guideline interchange format (GLIF) are described. The proposed Criteria-Based Notification is shown to improve two facets of the management of event monitors: 1) the assignment of CMs becomes independent from clinical conditions, de-facto removing institution-specific CMs from the knowledge bases of the event monitors and inserting CC-specific and institution-independent NAs, thus increasing their reusability and sharability; 2) knowledge-based independent NAs facilitate both institution-level management and user-level preference configuration.

Decision Making, Computer-Assisted↗
PubMed2002

GLEE--a model-driven execution system for computer-based implementation of clinical practice guidelines.

We have developed the GLEE system for execution of guidelines encoded in the GLIF3 format. This system can be integrated with a local clinical information system through standard interfaces to EMRs and clinical applications. The execution model of GLEE takes the "system suggests, user controls" approach. A tracing system is used to record the state of guideline steps and their transitions. GLEE provides an internal event-driven execution model that can be hooked up with the clinical event monitor in a local environment. We discuss the execution flexibility provided by GLEE and issues related to its integration in a local environment. Potential use of GLEE includes clinical decision support, quality assurance, guideline development and medical education.

Algorithms↗
PubMed2002

Toward a cognitive taxonomy of medical errors.

One critical step in addressing and resolving the problems associated with human errors is the development of a cognitive taxonomy of such errors. In the case of errors, such a taxonomy may be developed (1) to categorize all types of errors along cognitive dimensions, (2) to associate each type of error with a specific underlying cognitive mechanism, (3) to explain why, and even predict when and where, a specific error will occur, and (4) to generate intervention strategies for each type of error. Based on Reason's (1992) definition of human errors and Norman's (1986) cognitive theory of human action, we have developed a preliminary action-based cognitive taxonomy of errors that largely satisfies these four criteria in the domain of medicine. We discuss initial steps for applying this taxonomy to develop an online medical error reporting system that not only categorizes errors but also identifies problems and generates solutions.

Cognition↗
PubMedDate not supplied

Training synergies between medical informatics and health services research: successes and challenges.

Stanford's two decades of success in linking medical informatics and health services research in both training and investigational activities reflects advantageous geography and history as well as natural synergies in the two areas. Health services research and medical informatics at Stanford have long shared a quantitative, analytic orientation, along with linked administration, curriculum, and clinical activities. Both the medical informatics and the health services research curricula draw on diverse course offerings throughout the university, and both the training and research overlap in such areas as outcomes research, large database analysis, and decision analysis/decision support. The Stanford experience suggests that successful integration of programs in medical informatics and health services research requires areas of overlapping or synergistic interest and activity among the involved faculty and, hence, in time, among the students. This is enhanced by a mixture of casual and structured contact among students from both disciplines, including social interactions. The challenges to integration are how to overcome any geographic separation that may exist in a given institution; the proper management of relationships with those sub-areas of medical informatics that have less overlap with health services research; and the need to determine how best to exploit opportunities for collaboration that naturally occur.

California↗
PubMedDate not supplied

Comparing computer-interpretable guideline models: a case-study approach.

OBJECTIVES: Many groups are developing computer-interpretable clinical guidelines (CIGs) for use during clinical encounters. CIGs use "Task-Network Models" for representation but differ in their approaches to addressing particular modeling challenges. We have studied similarities and differences between CIGs in order to identify issues that must be resolved before a consensus on a set of common components can be developed. DESIGN: We compared six models: Asbru, EON, GLIF, GUIDE, PRODIGY, and PROforma. Collaborators from groups that created these models represented, in their own formalisms, portions of two guidelines: American College of Chest Physicians cough guidelines [correction] and the Sixth Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. MEASUREMENTS: We compared the models according to eight components that capture the structure of CIGs. The components enable modelers to encode guidelines as plans that organize decision and action tasks in networks. They also enable the encoded guidelines to be linked with patient data-a key requirement for enabling patient-specific decision support. RESULTS: We found consensus on many components, including plan organization, expression language, conceptual medical record model, medical concept model, and data abstractions. Differences were most apparent in underlying decision models, goal representation, use of scenarios, and structured medical actions. CONCLUSION: We identified guideline components that the CIG community could adopt as standards. Some of the participants are pursuing standardization of these components under the auspices of HL7.

Decision Support Systems, Clinical↗