On exemplary scientific conduct regarding submission of manuscripts to biomedical informatics journals.
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Biomedical Research↗
Biomedical subjects
Edward H Shortliffe
Publications and source records attributed to Edward H Shortliffe.
The public health informatics infrastructure: anticipating its role in cancer.
Cancer information and surveillance, historically conducted with manual data collection and submission, are viewed increasingly as inherently dependent on the effective application of information science. One challenge is to use information technology (IT) in a manner that improves cancer-related decision-making and ultimately the quality of care that is offered to patients with cancer. In this article we begin by envisioning a future view of IT-supported surveillance and care that can be made available for application in cancer and its management. We then ask what barriers need to be overcome and what forces are at work that may help us in our efforts to effect the necessary changes. Our future vision for surveillance and information, although appealing and widely shared, requires major cultural change, financial investment, and logistical planning. Competition in the medical marketplace, coupled with fiscal pressures affecting providers and health systems, suggests that leadership for regional and national coordination will need to come from elsewhere-and likely from governments.
Decision Support Techniques↗
CPOE and the facilitation of medication errors.
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Clinical Pharmacy Information Systems↗
Risky decisions despite counter evidence: modeling a culture of safer sexual practices.
To create a culture of safe practices, we need to understand how and under what conditions the public makes risky decisions about their health. Because risky sexual behaviors are known to be common in young adults, we investigated their decision making regarding sexual activities that could incur a high risk of HIV infection. Sixty young urban adults maintained journals for two weeks and were interviewed regarding condom use and sexual history. We characterized four patterns of condom use behavior: consistent (35.0%), inconsistent (16.7%), consistent to inconsistent (35.0%), and inconsistent to consistent (13.3%). Directionality of reasoning was analyzed in the explanations provided for condom use decisions. The consistent and inconsistent patterns were associated with data-driven heuristic reasoning, where behavior becomes automated and is associated with a high level of confidence in one's judgment. In the other two patterns, the shift in behavior was due to a significant event that influenced a change in directionality to explanation-based reasoning. We discuss these results within the framework of identifying potentially high-risk groups for whom customized intervention strategies (such as computer-based educational programs) can be used to reduce risk, thereby creating a culture of safer sexual practices.
Adult↗
Training the next generation of informaticians: the impact of "BISTI" and bioinformatics--a report from the American College of Medical Informatics.
In 2002-2003, the American College of Medical Informatics (ACMI) undertook a study of the future of informatics training. This project capitalized on the rapidly expanding interest in the role of computation in basic biological research, well characterized in the National Institutes of Health (NIH) Biomedical Information Science and Technology Initiative (BISTI) report. The defining activity of the project was the three-day 2002 Annual Symposium of the College. A committee, comprised of the authors of this report, subsequently carried out activities, including interviews with a broader informatics and biological sciences constituency, collation and categorization of observations, and generation of recommendations. The committee viewed biomedical informatics as an interdisciplinary field, combining basic informational and computational sciences with application domains, including health care, biological research, and education. Consequently, effective training in informatics, viewed from a national perspective, should encompass four key elements: (1). curricula that integrate experiences in the computational sciences and application domains rather than just concatenating them; (2). diversity among trainees, with individualized, interdisciplinary cross-training allowing each trainee to develop key competencies that he or she does not initially possess; (3). direct immersion in research and development activities; and (4). exposure across the wide range of basic informational and computational sciences. Informatics training programs that implement these features, irrespective of their funding sources, will meet and exceed the challenges raised by the BISTI report, and optimally prepare their trainees for careers in a field that continues to evolve.
Computational Biology↗
GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines.
The Guideline Interchange Format (GLIF) is a model for representation of sharable computer-interpretable guidelines. The current version of GLIF (GLIF3) is a substantial update and enhancement of the model since the previous version (GLIF2). GLIF3 enables encoding of a guideline at three levels: a conceptual flowchart, a computable specification that can be verified for logical consistency and completeness, and an implementable specification that is intended to be incorporated into particular institutional information systems. The representation has been tested on a wide variety of guidelines that are typical of the range of guidelines in clinical use. It builds upon GLIF2 by adding several constructs that enable interpretation of encoded guidelines in computer-based decision-support systems. GLIF3 leverages standards being developed in Health Level 7 in order to allow integration of guidelines with clinical information systems. The GLIF3 specification consists of an extensible object-oriented model and a structured syntax based on the resource description framework (RDF). Empirical validation of the ability to generate appropriate recommendations using GLIF3 has been tested by executing encoded guidelines against actual patient data. GLIF3 is accordingly ready for broader experimentation and prototype use by organizations that wish to evaluate its ability to capture the logic of clinical guidelines, to implement them in clinical systems, and thereby to provide integrated decision support to assist clinicians.
Artificial Intelligence↗
A cognitive taxonomy of medical errors.
OBJECTIVE: Propose a cognitive taxonomy of medical errors at the level of individuals and their interactions with technology. DESIGN: Use cognitive theories of human error and human action to develop the theoretical foundations of the taxonomy, develop the structure of the taxonomy, populate the taxonomy with examples of medical error cases, identify cognitive mechanisms for each category of medical error under the taxonomy, and apply the taxonomy to practical problems. MEASUREMENTS: Four criteria were used to evaluate the cognitive taxonomy. The taxonomy should be able (1) to categorize major types of errors at the individual level along cognitive dimensions, (2) to associate each type of error with a specific underlying cognitive mechanism, (3) to describe how and explain why a specific error occurs, and (4) to generate intervention strategies for each type of error. RESULTS: The proposed cognitive taxonomy largely satisfies the four criteria at a theoretical and conceptual level. CONCLUSION: Theoretically, the proposed cognitive taxonomy provides a method to systematically categorize medical errors at the individual level along cognitive dimensions, leads to a better understanding of the underlying cognitive mechanisms of medical errors, and provides a framework that can guide future studies on medical errors. Practically, it provides guidelines for the development of cognitive interventions to decrease medical errors and foundation for the development of medical error reporting system that not only categorizes errors but also identifies problems and helps to generate solutions. To validate this model empirically, we will next be performing systematic experimental studies.
Cognition↗
Design and implementation of the GLIF3 guideline execution engine.
We have developed the GLIF3 Guideline Execution Engine (GLEE) as a tool for executing guidelines encoded in the GLIF3 format. In addition to serving as an interface to the GLIF3 guideline representation model to support the specified functions, GLEE provides defined interfaces to electronic medical records (EMRs) and other clinical applications to facilitate its integration with the clinical information system at a local institution. The execution model of GLEE takes the "system suggests, user controls" approach. A tracing system is used to record an individual patient's state when a guideline is applied to that patient. GLEE can also support an event-driven execution model once it is linked to the clinical event monitor in a local environment. Evaluation has shown that GLEE can be used effectively for proper execution of guidelines encoded in the GLIF3 format. When using it to execute each guideline in the evaluation, GLEE's performance duplicated that of the reference systems implementing the same guideline but taking different approaches. The execution flexibility and generality provided by GLEE, and its integration with a local environment, need to be further evaluated in clinical settings. Integration of GLEE with a specific event-monitoring and order-entry environment is the next step of our work to demonstrate its use for clinical decision support. Potential uses of GLEE also include quality assurance, guideline development, and medical education.
Database Management Systems↗
The InterMed approach to sharable computer-interpretable guidelines: a review.
InterMed is a collaboration among research groups from Stanford, Harvard, and Columbia Universities. The primary goal of InterMed has been to develop a sharable language that could serve as a standard for modeling computer-interpretable guidelines (CIGs). This language, called GuideLine Interchange Format (GLIF), has been developed in a collaborative manner and in an open process that has welcomed input from the larger community. The goals and experiences of the InterMed project and lessons that the authors have learned may contribute to the work of other researchers who are developing medical knowledge-based tools. The lessons described include (1) a work process for multi-institutional research and development that considers different viewpoints, (2) an evolutionary lifecycle process for developing medical knowledge representation formats, (3) the role of cognitive methodology to evaluate and assist in the evolutionary development process, (4) development of an architecture and (5) design principles for sharable medical knowledge representation formats, and (6) a process for standardization of a CIG modeling language.
Computer Systems↗
GESDOR - a generic execution model for sharing of computer-interpretable clinical practice guidelines.
We developed the Guideline Execution by Semantic Decomposition of Representation (GESDOR) model to share guidelines encoded in different formats at the execution level. For this purpose, we extracted a set of generalized guideline execution tasks from the existing guideline representation models. We then created the mappings between specific guideline representation models and the set of the common guideline execution tasks. Finally, we developed a generic task-scheduling model to harmonize the existing approaches to guideline task scheduling. The evaluation has shown that the GESDOR model can be used for the effective execution of guidelines encoded in different formats, and thus realizes guideline sharing at the execution level.
Decision Making, Computer-Assisted↗
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↗
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↗
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↗
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↗
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↗
Doctors, patients, and computers: will information technology dehumanize health-care delivery?
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Attitude↗
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↗
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↗