摘要:

AbstractThe American Nurses Association (ANA) Cabinet on Nursing Practice mandated the formation of the Steering Committee on Databases to Support Clinical Nursing Practice. The Committee has established the process and the criteria by which to review and recommend nursing classification schemes based on the ANA Nursing Process Standards and elements contained in the Nursing Minimum Data Set (NMDS) for inclusion of nursing data elements in national databases. Four classification schemes have been recognized by the Committee for use in national databases. These classification schemes have been forwarded to the National Library of Medicine (NLM) for inclusion in the Unified Medical Language System (UMLS) and to the International Council of Nurses for the development of a proposed International Classification of Nursing Practice.

ISSN:1527-974X    

DOI:10.1136/jamia.1994.95153431

出版商:BMJ Group    

年代:1994

数据来源: BMJ

摘要:

AbstractObjective: To measure the attitudes of physicians and nurses who use the Health Evaluation through Logical Processing (HELP) clinical information system.Design: Questionnaire survey of 360 attending physicians and 960 staff nurses practicing at the LDS Hospital. The physicians' responses were signed, permitting follow-up for nonresponse and use of demographic data from staff files. The nurses' responses were anonymous and their demographic data were obtained from the questionnaires.Measurements: Fixed-choice questions with a Likert-type scale, supplemented by free-text comments. Question categories included: computer experience; general attitudes about impact of the system on practice; ranking of available functions; and desired future capabilities.Results: The response rate was 68% for the physicians and 39% for the nurses. Age, specialty, and general computer experience did not correlate with attitudes. Access to patient data and clinical alerts were rated highly. Respondents did not feel that expert computer systems would lead to external monitoring, or that these systems might compromise patient privacy. The physicians and nurses did not feel that computerized decision support decreased their decision-making power.Conclusion: The responses to the questionnaire and “free-text comments” provided encouragement for future development and deployment of medical expert systems at LDS Hospital and sister hospitals. Although there has been some fear on the part of medical expert system developers that physicians would not adapt to or appreciate recommendations given by these systems, the results presented here are promising and may be of help to other system developers and evaluators.

ISSN:1527-974X    

DOI:10.1136/jamia.1994.95153432

出版商:BMJ Group    

年代:1994

数据来源: BMJ

摘要:

AbstractObjective: Develop a prototype expert system for preterm birth risk assessment of pregnant women. Normal gestation involves a term of 40 weeks, but because 8–12% of the newborns in the United States are delivered prior to 37 weeks' gestation, problems associated with prematurity continue to plague individuals, families, and the health care system.Design: A knowledge-base development methodology used machine learning, statistical analysis, and validation techniques to analyze three large datasets (18,890 subjects and 214 variables). The dependent (i.e., decision) variable studied was weeks of gestation at delivery, with dichotomous coding of preterm delivery (prior to 37 weeks) and full-term delivery (37 + weeks).Results: Machine learning with a program named Learning from Examples using Rough Sets (LERS) induced 520 usable rules that were entered into a prototype expert system. The prototype expert system was 53–88% accurate in predicting preterm delivery for 9,419 patients.Conclusion: The prototype expert system was more accurate than traditional manual techniques in predicting preterm birth.

ISSN:1527-974X    

DOI:10.1136/jamia.1994.95153433

出版商:BMJ Group    

年代:1994

数据来源: BMJ

摘要:

AbstractObjective: To develop optimal MEDLINE search strategies for retrieving sound clinical studies of the etiology, prognosis, diagnosis, prevention, or treatment of disorders in adult general medicine.Design: Analytic survey of operating characteristics of search strategies developed by computerized combinations of terms selected to detect studies meeting basic methodologic criteria for direct clinical use in adult general medicine.Measures: The sensitivities, specificities, precision, and accuracy of 134,264 unique combinations of search terms were determined by comparison with a manual review of all articles (the “gold standard”) in ten internal medicine and general medicine journals for 1986 and 1991.Results: Less than half of the studies of the topics of interest met basic criteria for scientific merit for testing clinical applications. Combinations of search terms reached peak sensitivities of 82% for sound studies of etiology, 92% for prognosis, 92% for diagnosis, and 99% for therapy in 1991. Compared with the best single terms, multiple terms increased sensitivity for sound studies by over 30% (absolute increase), but with some loss of specificity when sensitivity was maximized. For 1986, combinations reached peak sensitivities of 72% for etiology, 95% for prognosis, 86% for diagnosis, and 98% for therapy. When search terms were combined to maximize specificity, over 93% specificity was achieved for all purpose categories in both years. Compared with individual terms, combined terms achieved near-perfect specificity that was maintained with modest increases insensitivity in all purpose categories except therapy. Increases in accuracy were achieved by combining terms for all purpose categories, with peak accuracies reaching over 90% for therapy in 1986 and 1991.Conclusions: The retrieval of studies of important clinical topics cited in MEDLINE can be substantially enhanced by selected combinations of indexing terms and textwords.

ISSN:1527-974X    

DOI:10.1136/jamia.1994.95153434

出版商:BMJ Group    

年代:1994

数据来源: BMJ

摘要:

AbstractObjective: To test the effect of diagnosis on training an artificial neural network (ANN) to predict length of stay (LOS) for psychiatric patients involuntarily admitted to a state hospital.Design: A series of ANNs were trained representing schizophrenia, affective disorders, and diagnosis-related group (DRG) 430. In addition to diagnosis, variables used in training included demographics, severity of illness, and others identified to be significant in predicting LOS.Results: Depending on diagnosis, ANN predictions compared with actual LOS indicated accuracy rates ranging from 35% to 70%. The validity of ANN predictions was determined by comparing LOS estimates with the treatment team's predictions at 72 hours following admission, with the ANN predicting as well as or better than did the treatment team in all cases.Conclusions: One problem in traditional approaches to predicting LOS is the inability of a derived predictive model to maintain accuracy in other independently derived samples. The ANN reported here was capable of maintaining the same predictive efficiency in an independently derived cross-validation sample. The results of ANNs in a cross-validation sample are discussed and the application of this tool in augmenting clinical decision is presented.

ISSN:1527-974X    

DOI:10.1136/jamia.1994.95153435

出版商:BMJ Group    

年代:1994

数据来源: BMJ

ISSN:1527-974X    

DOI:10.1136/jamia.1994.95153437

出版商:BMJ Group    

年代:1994

数据来源: BMJ