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Biomedical subjects

C P Friedman

Publications and source records attributed to C P Friedman.

At least 37 records · Page 2Linked to original sources
PubMed1997

Acceptability and usage patterns of an image analysis workstation.

Critical to the successful deployment and use of new computer systems is the acceptance of the system by the users, i.e., the clinicians. We describe a study which evaluated, in an experimental setting, the potential acceptability of an image analysis workstation for radiation therapy. The acceptability and usage patterns were measured using semi-structured questionnaires and maintaining logs of user interactions. The results of the study showed that the radiation oncologists, who were the subjects for the study, perceived the workstation as acceptable. The results also suggested several areas for improvement of workstation that could increase its acceptance in the clinical setting.

Attitude of Health Personnel↗
PubMed1997

A decision analytic method for scoring performance on computer-based patient simulations.

As computer based clinical case simulations become increasingly popular for training and evaluating clinicians, approaches are needed to evaluate a trainee's or examinee's solution of the simulated cases. We developed a decision analytic approach to scoring performance on computerized patient case simulations. We developed decision models for computerized patient case simulations in four specific domains in the field of infectious disease. The decision models were represented as influence diagrams. A single decision node represents the possible diagnoses the user may make. One chance node represents a probability distribution over the set of competing diagnoses in the simulations. The value node contains the utilities associated with all possible combinations of diagnosis and disease. All relevant data that the user may request from the simulation are represented as chance nodes with arcs to or from the diagnosis node and/or each other. Probabilities in the decision model were derived from the literature, where available, or expert opinion. Utilities were assessed by standard gamble from clinical experts. The process of solving computer based patient simulations involves repeated cycles of requesting data (history, physical examination or laboratory) and receiving these data from the simulations. Each time the user requests clinical data from the simulation, the influence diagram is evaluated with and without an arc from the corresponding chance node to the decision node. The difference in expected utility between the two solutions of the influence diagram represents the expected value of information (VOI) from the requested clinical datum. The ratio of the expected VOI from the data requested and the expected value of perfect information about the diagnosis is a normative measure of the quality of each of the user's data requests. This approach provides a continuous measure of the quality of the user's data requests in a way that is sensitive to the previous data collected. The score distinguishes serious from minor misdiagnoses. And the same influence diagram can be used to evaluate performance on multiple simulations in the same clinical domain.

Computer Simulation↗
PubMed1996

Preclinical course-evaluation methods at U.S. and Canadian medical schools.

BACKGROUND: Despite the apparent prevalence of the use of course evaluations by medical schools, course and curriculum evaluation have gone relatively unnoticed in the medical education literature. METHOD: In the fall of 1993, a 17-item questionnaire was mailed to all 141 U.S. and Canadian medical schools to elicit information concerning any course-evaluation systems in place in the schools' preclinical curricula. RESULTS: A total of 101 schools (72%) returned usable questionnaires. Of these, 79 reported having a centralized course-evaluation system and 56 used oversight committees consisting of administrators, faculty, and students. Beyond the use of written questionnaires (reported by 100 schools), course-evaluation practices varied widely. Eighteen schools reported that questionnaire content was the same across courses, while 56 used a common core of items with modifications for specific courses. The frequency and timing of questionnaire distribution varied from once at the final examination to weekly during the course. Summarized course-evaluation results were made available to departments (91 schools), the oversight committee (50 schools), the administration (47 schools), and students (13 schools). The feature most frequently cited as being a positive aspect of the course-evaluation process was student involvement (23 schools). Most frequently cited as areas of concern were low response rates to questionnaires (20 schools) and the need for simpler, more reliable methods of data collection (13 schools). Seventy schools reported specific types of changes that had resulted from course evaluation. CONCLUSION: The results confirm that course evaluation via student questionnaires is ubiquitous in North American medical schools. Most schools used centralized systems, but individual schools had developed their own combinations of technique and organization. This lack of uniformity may be due to the sensitivity of evaluation processes to local contexts, but it may also be due to the lack of literature on the subject.

Canada↗
PubMed1996

The virtual clinical campus.

The increased use of community sites for the clinical training of medical students creates many challenges for educators. Among them is the need to provide students in community settings with access to the same range of educational resources-the medical literature, student colleagues, feedback, and faculty-that are customarily available at academic medical centers. One way to make this access possible is to use information technology to create a "virtual clinical campus," which would allow students to enjoy the best of both worlds: the immersion in primary care offered by the community-based setting and the knowledge-rich resources of the academic medical center, including the all-important library. With a virtual campus in place, students would be able to access most library resources, interact with their peers, ensure that they were meeting the goals of their community rotations, and participate with their colleagues in didactic sessions without having to travel. The virtual campus is technologically feasible and economically within reach. It is possible that the movement of clinical training into the community will make it imperative for all medical students to own their own computers and for medical centers to provide the infrastructure that would enable community sites to have access to a range of educational resources.

Clinical Clerkship↗
PubMed1996

Preparing tomorrow's health sciences librarians: feasibility and marketing studies.

The University of North Carolina at Chapel Hill is devising and evaluating five curricular models designed to improve education for health sciences librarianship. These models fit into a continual learning process from the initial professional preparation to lifelong learning opportunities. Three of them enhance existing degree and certificate programs in the School of Information and Library Science (SILS) with a health sciences specialization, and two are new programs for working information professionals. The approaches involve partnerships among SILS, the Health Sciences Library, and the program in Medical Informatics. The planning process will study the feasibility of the proposed programs, test the marketability of the models to potential students and employers, and make recommendations about implementation.

Certification↗
PubMed1996

A comparison of hypertext and Boolean access to biomedical information.

This study explored which of two modes of access to a biomedical database better supported problem solving in bacteriology. Boolean access, which allowed subjects to frame their queries as combinations of keywords, was compared to hypertext access, which allowed subjects to navigate from one database node to another. The accessible biomedical data were identical across systems. Data were collected from 42 first year medical students, each randomized to the Boolean or hypertext system, before and after their bacteriology course. Subjects worked eight clinical case problems, first using only their personal knowledge and, subsequently, with aid from the database. Database retrievals enabled students to answer questions they could not answer based on personal knowledge only. This effect was greater when personal knowledge of bacteriology was lower. The results also suggest that hypertext was superior to Boolean access in helping subjects identify possible infectious agents in these clinical case problems.

Bacteriology↗
PubMed1996

The influence of a decision support system on the differential diagnosis of medical practitioners at three levels of training.

As computer-based diagnostic consultation systems become, available, their influence and usefulness need to be evaluated. This report, based on partial data from a larger study, examines the influence of Iliad, a diagnostic consultation system, on the differential diagnosis of fourth year medical students, residents in medicine, and attendings in general internal medicine. Our results show that when faced with difficult diagnostic cases, medical students add significantly more diagnoses from Iliad's differential than do residents or attendings. However, the quality of Iliad's diagnostic advice in terms of the presence of the correct diagnosis, is no better for consultations done by students or residents compared to attendings.

Clinical Competence↗
PubMed1996

Verifying radiotherapy treatment setup by interactive image registration.

Digital image analysis techniques can be used to assist the physician in diagnostic or therapeutic decision making. In radiation oncology, portal image registration can improve the accuracy of detection of errors during radiation treatment. Following a discussion of the general paradigm of interactive image registration, we describe PortFolio, a workstation for portal image analysis.

Humans↗
PubMed1995

Anatomy of the clinical simulation.

Computer-based clinical simulations have been used in medical education for the past 25 years. During this period, the technology has evolved from mainframe computers to microcomputers to multimedia. All designers of simulations must decide which elements of reality to include explicitly in a simulated case, which to leave to the user's imagination, and when to intervene for educational purposes. Once these decisions are made, developers of simulations have many options for structuring the simulation itself. They can develop simulations with single or multiple patient encounters, with menu or natural-language requests for data, with varying levels of volunteered information about the simulated patient, with interpreted or uninterpreted clinical findings, with deterministic or probablistic evolution of the case, with various ways to give users feedback about their progress through the case, and with manual or automated creation of specific cases. Simulations derive their specific character from how these options are implemented.

Computer Simulation↗
PubMed1995

Issues and challenges in the design of curriculum information systems.

Medical schools are increasingly using curriculum information systems to better manage their curricula, to incorporate alternative learning environments, to expand subject-specific instruction, or to adapt the curricula to the changing health care environment. A curriculum information system contains key features, selected by the system's designers, that describe the objectives, the specific content, and/or the educational activities that compose the curriculum. The underlying purpose of such a system is to document and describe the knowledge, behaviors, skills, attitudes, or activities students will be expected to develop or learn. While it might be ideal to have one system that would meet the needs of all possible users, the programming and maintenance requirements of such a single system would exceed most medical schools' resources. Thus, designers of curriculum information systems must first identify the primary intended-user group: students, faculty, or administrators. The system designed for one group will typically differ from the systems for other groups in the emphasis on and level of content detail and curriculum structure information. The general structure and purposes of curriculum information systems are expressed in three distinct system designs: a curriculum database (most helpful for administrators), a curriculum textbase (for faculty), and an electronic syllabus (for students). The authors describe these different designs, and they discuss challenges faced by system designers.

Curriculum↗
PubMed1994

Information-seeking behaviors of medical students: a classification of questions asked of librarians and physicians.

To solve a problem, a person often asks questions of someone with more expertise. This paper reports on a study of the types of questions asked and how the experts are chosen. In the study, sixty-three first-year medical students responded to clinical scenarios, each describing a patient affected by a toxin and asking questions concerning the identity of the toxin and its characteristics. After answering those questions, the students were asked to imagine that they had access to a medical reference librarian and an internist specializing in toxicology. The students then generated two questions for each expert about each clinical scenario. Each question was categorized according to the type of information requested, and the frequency of each type of question was calculated. The study found that students most often asked for the identification of the toxin(s), references about the scenario, or the effects of the toxin; an explanation of the patient's symptoms; or a description of the appropriate treatment. Students were more likely to address questions on the identity of the toxin and references to the hypothetical librarian; they were more likely to ask the internist for explanations of the symptoms and descriptions of the treatment. The implications of these results for the design of information and educational systems are discussed.

Humans↗
PubMed1994

Academic physicians' assessment of the effects of computers on health care.

We assessed the attitudes of academic physicians towards computers in health care at two academic medical centers that are in the early stages of clinical information-system deployment. We distributed a 4-page questionnaire to 470 subjects, and a total of 272 physicians (58%) responded. Our results show that respondents use computers frequently, primarily to perform academic-oriented tasks as opposed to clinical tasks. Overall, respondents viewed computers as being slightly beneficial to health care. They perceive self-education and access to up-to-date information as the most beneficial aspects of computers and are most concerned about privacy issues and the effect of computers on the doctor-patient relationship. Physicians with prior computer training and greater knowledge of informatics concepts had more favorable attitudes towards computers in health care. We suggest that negative attitudes towards computers can be addressed by careful system design as well as targeted educational activities.

Academic Medical Centers↗
PubMed1994

Automated medical knowledge acquisition: a study of consistency.

Knowledge bases are more representative of the population of medical experts if they are constructed by a group of individuals, rather than one practitioner. However, one runs into problems with consistency when information is elicited from a group without a consistent format and terminology. This study examines the consistency of relatively unconstrained computer-elicited medical knowledge using the computer program, KSSO. The results of this study show that the group of ten general internists were somewhat consistent in the diagnoses they listed for a patient presenting with chest pain. They were much less consistent in the findings they listed to differentiate between the diagnoses they had listed. The mean number of subjects listing each diagnosis was 3.3 +/- 2.7 while the mean for findings was 2.0 +/- 1.5. The implications of these data are discussed.

Artificial Intelligence↗