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Biomedical subjects

E H Shortliffe

Publications and source records attributed to E H Shortliffe.

At least 73 records · Page 4Linked to original sources

Computer-assisted decision making in medicine.

This article reviews the strengths and limitations of five major paradigms of medical computer-assisted decision making (CADM): (1) clinical algorithms, (2) statistical analysis of collections of patient data, (3) mathematical models of physical processes, (4) decision analysis, and (5) symbolic reasoning or artificial intelligence (AI). No one technique is best for all applications, and there is recent promising work which combines two or more established techniques. We emphasize both the inherent power of symbolic reasoning and the promise of artificial intelligence and the other techniques to complement each other.

Computers↗

Expert Systems Research.

Artificial intelligence, long a topic of basic computer science research, is now being applied to problems of scientific, technical, and commercial interest. Some consultation programs, although limited in versatility, have achieved levels of performance rivaling those of human experts. A collateral benefit of this work is the systematization of previously unformalized knowledge in areas such as medical diagnosis and geology.

Anti-Bacterial Agents↗

PUFF: an expert system for interpretation of pulmonary function data.

The application of artificial intelligence techniques to real-world problems has produced promising research results, but seldom has a system become a useful tool in its domain of expertise. Notable exceptions are the DENDRAL (1) and MOLGEN (2) systems. This paper describes PUFF, a program that interprets lung function test data and has become a working tool in the pulmonary physiology lab of a large hospital. Elements of the problem that paved the way for its success are examined, as are significant limitations of the solution that warrant further study.

Diagnosis, Computer-Assisted↗

Evaluating the performance of a computer-based consultant.

The performance of a computer-based clinical consultation system is evaluated. The program, called MYCIN, is designed to function as an aid for infectious disease diagnosis and therapy selection, with an initial emphasis on bacteremias. The evaluation methodology is discussed, as well as the difficulties encountered in attempting to evaluate clinical judgments. Specialists in infectious diseases judged MYCIN's final therapy recommedation, and intermediate conclusions about the significance of the infection and identity of infecting organisms. The evaluation techniques described may be useful in assessing the performance of other clinical decision aids. Results of the evaluation show that the program's therapy recommedations meet Stanford experts' standards of acceptable practice 90.9% of the time (table 2), with some variation noted both among individual experts and between Stanford experts and others (tables 1, 2).

Animals↗

Computerized consultation system for selection of antimicrobial therapy.

Mycin, a computer-based consultation system which provides to physicians antimicrobial therapy recommendations for patients with bacterial infections, is described. The consultation program arrives at therapeutic decisions using a built-in knowledge base as well as patient data entered by the physician. The system is capable of explaining its recommendations and answering questions about its reasoning process. The system's knowledge can be updated and corrected easily by infectious disease experts. At present the system is operational within a research setting; its routine use in a clinical setting will require further evaluation of its reliability and effectiveness.

Anti-Infective Agents↗

Reasoning methods in medical consultation systems: artificial intelligence approaches.

It has been argued that the problem of medical diagnosis is fundamentally ill-structured, particularly during the early stages when the number of possible explanations for presenting complaints can be immense. This paper discusses the process of clinical hypothesis evocation, contrasts it with the structured decision making approaches used in traditional computer-based diagnostic systems, and briefly surveys the more open-ended reasoning methods that have been used in medical artificial intelligence (AI) programs. The additional complexity introduced when an advice system is designed to suggest management instead of (or in addition to) diagnosis is also emphasized. Example systems are discussed to illustrate the key concepts.

Diagnosis, Computer-Assisted↗

The PEN-Ivory project: exploring user-interface design for the selection of items from large controlled vocabularies of medicine.

OBJECTIVE: To explore different user-interface designs for structured progress note entry, with a long-term goal of developing design guidelines for user interfaces where users select items from large medical vocabularies. DESIGN: The authors created eight different prototypes of a pen-based progress-note-writing system called PEN-Ivory. Each prototype allows physicians to write patient progress notes using simple pen-based gestures such as circle, line-out, and scratch-out. The result of an interaction with PEN-Ivory is a progress note in English prose. The eight prototypes were designed in a principled way, so that they differ from one another in just one of three different user-interface characteristics. MEASUREMENTS: Five of the eight prototypes were tested by measuring the time it took 15 users, each using a distinct prototype, to document three patient cases consisting of a total of 63 medical findings. RESULTS: The prototype that allowed the fastest data entry had the following three user-interface characteristics: it used a paging rather than a scrolling form, it used a fixed palette of modifiers rather than a dynamic "pop-up" palette, and it made available all findings from the controlled vocabulary at once rather than displaying only a subset of findings generated by analyzing the patient's problem list. CONCLUSION: Even simple design changes to a user interface can make dramatic differences in user performance. The authors discuss possible influences on performance, such as positional constancy, user uncertainty and system anticipation, that may contribute significantly to the effectiveness of systems that display menus of items from large controlled vocabularies of medicine.

Medical Records↗

The federal role in the health information infrastructure: a debate of the pros and cons of government intervention.

Some observers feel that the federal government should play a more active leadership role in educating the medical community and in coordinating and encouraging a more rapid and effective implementation of clinically relevant applications of wide-area networking. Other people argue that the private sector is recognizing the importance of these issues and will, when the market demands it, adopt and enhance the telecommunications systems that are needed to produce effective uses of the National Information Infrastructure (NII) by the healthcare community. This debate identifies five areas for possible government involvement: convening groups for the development of standards; providing funding for research and development; ensuring the equitable distribution of resources, particularly to places and people considered by private enterprise to provide low opportunities for profit; protecting rights of privacy, intellectual property, and security; and overcoming the jurisdictional barriers to cooperation, particularly when states offer conflicting regulations. Arguments against government involvement include the likely emergence of an adequate infrastructure under free market forces, the often stifling effect of regulation, and the need to avoid a common-and-control mentality in an infrastructure that is best promoted collaboratively.

Delivery of Health Care↗