The potential of training to increase acceptance and use of computerized decision support systems for medical diagnosis

Human Factors

Volumn 48, Issue 1, 2006, Pages 95-108

  Lai, Fuji ^a   Macmillan, Jean ^a   Daudelin, Denise H ^b   Kent, David M ^b,c,d  

^a APTIMA INC   (United States)

^b TUFTS MEDICAL CENTER   (United States)

^c TUFTS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d TUFTS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACUTE CARDIAC ISCHEMIA TIME-INSENSITIVE PREDICTIVE INSTRUMENT (ACI-TIPI); COMPUTERIZED DECISION SUPPORT SYSTEMS; PATIENT SAFETY; PROBABILISTIC INFORMATION;

DECISION SUPPORT SYSTEMS; HEALTH CARE; MEDICAL APPLICATIONS; PERSONNEL TRAINING; RISK ASSESSMENT; STATISTICAL METHODS;

DIAGNOSIS;

ARTICLE; AUTOMATION; COMPUTER ASSISTED DIAGNOSIS; MEDICAL DECISION MAKING; MEDICAL EDUCATION; MEDICAL INSTRUMENTATION; PHYSICIAN; QUESTIONNAIRE; SCORING SYSTEM;

ATTITUDE OF HEALTH PERSONNEL; COMPUTER-ASSISTED INSTRUCTION; DECISION SUPPORT SYSTEMS, CLINICAL; HUMANS; INSERVICE TRAINING; MYOCARDIAL ISCHEMIA; UNITED STATES;

EID: 33646381872     PISSN: 00187208     EISSN: None     Source Type: Journal    
DOI: 10.1518/001872006776412306     Document Type: Article

References (24)

1. Baxt, W. G., & Skora, J. (1996). Prospective validation of artificial neural network trained to identify acute myocardial infarction. Lancet, 347(8993), 12-15.
2. Berner, E. S., & Maisiak, R. S. (1999). Influence of case and physician characteristics on perceptions of decision support systems. Journal of the American Medical Informatics Association, 6, 428-434.
3. Christensen, C. R., & Hansen, A. J. (1987). Teaching and the case method. Boston, MA: Harvard Business School.
4. Goldman, L., Cook, F., & Brand, D. A. (1988). A computer protocol to predict myocardial infarction in emergency department patients with chest pain. New England Journal of Medicine, 318, 797-803.
5. Hunt, D. L., Haynes, R. B., Hanna, S. E., & Smith, K. (1998). Effects of computer-based clinical decision support systems on physicians' performance and patient outcomes: A systematic review. Journal of the American Medical Association, 280, 1339-1346.
6. Institute of Medicine, Committee on the Quality of Health Care. (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academies Press. Available from National Academies Press Web site, http://www.nap.edu/catalog/ 10027.html
7. Karcz, A., Korn, R., Burke, M. C., Caggiano, R., Doyle, M. J., Erdos, M. J., et al. (1996). Malpractice claims against emergency physicians in Massachusetts: 1975-1993. American Journal of Emergency Medicine, 14, 341-345.
8. Lee, J. D., & Moray, N. (1994). Trust, self-confidence, and operators' adaptation to automation. International Journal of Human-Computer Studies, 40, 153-184.
9. Masalonis, A. J., & Parasuraman, R. (1999). Trust as a construct for evaluation of automated aids: Past and future theory and research. In Proceedings of the Human Factors and Ergonomics Society 43rd Annual Meeting (pp. 184-188). Santa Monica, CA: Human Factors and Ergonomics Society.
10. McNair, M. P., & Hersum, A. C. (1954). The case method at the Harvard Business School. New York: McGraw-Hill.
11. McNutt, R. A., & Selker, H. P. (1988). How did the acute ischemic heart disease predictive instrument reduce unnecessary coronary care unit admissions? Medical Decision Making, 8, 90-94.
12. Pope, J. H., & Selker, H. P. (1999). Identifying acute cardiac ischemia in the emergency department. In C. P. Cannon (Ed.), Contemporary cardiology: Management of acute coronary syndromes (pp. 111-133). Totowa, NJ: Humana Press.
13. Pozen, M. W., D'Agostino, R. B., & Mitchell, J. B. (1980). The usefulness of a predictive instrument to reduce inappropriate admissions to the coronary care unit. Annals of Internal Medicine, 92, 238-242.
14. Pozen, M. W., D'Agostino, R. B., Selker, H. P., Sytkowski, P. A., & Hood, W. B. Jr. (1984). A predictive instrument to improve coronary care unit admission practices in acute ischemic heart disease: A prospective multicenter clinical trial. New England Journal of Medicine, 310, 1273-1278.
15. Rosenthal, R. R., & Rosnow, R. L. (1984). Essentials of behavioral research. New York: McGraw-Hill.
16. Selker, H. P., Beshansky, J. R., Griffith, J. L., Aufderheide, T. P., Ballin, D. S., Bernard, S. A., et al. (1998). The use of the Acute Cardiac Ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) to assist emergency department triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia: A multicenter controlled clinical trial. Annals of Internal Medicine, 129, 845-855.
17. Selker, H. P., Griffith J. L., Beshansky, J. R., Califf, R. M., D'Agostino, R. B., Laks, M. M., et al. (1992). The Thrombolytic Predictive Instrument (TPI) project: Combining clinical study databases to take medical effectiveness research to the streets. Washington, DC: Department of Health and Human Services, Agency for Health Care Policy Research.
18. Selker, H. P., Griffith, J. L., &, D'Agostino, R. B. (1991). A tool for judging coronary care unit admission appropriateness valid for both real-time and retrospective use: A time-insensitive predictive instrument (TIPI) for acute cardiac ischemia: A multicenter study. Medical Care, 29, 610-627.
19. Selker, H. P., Pozen, M. W., & D'Agostino, R. B. (1985). Optimal identification of the patient with acute myocardial ischemia in the emergency room. In R. M. Calif & G. S. Wagner (Eds.), Acute coronary care: Principles and practice (pp. 289-298). Boston, MA: Martinus Nijhoff.
20. Tierney, W. M., Fitzgerald, I., & McHenry, R. (1986). Physician's estimate of the probability of myocardial infarction in emergency room patients with chest pain. Medical Decision Making, 6, 12-17.
21. Van de Does, E., Lubsen, J., & Pool, J. (1976). Acute coronary events in a general practice: Objectives and design of the Imminent Myocardial Infarction Rotterdam Study. Heart Bulletin, 7, 91-98.
22. Wyatt, J. (1989). Lessons learned from the field trial of ACORN, an expert system to advise on chest pain. In: B. Barber, D. Cao, & D. Qin, (Eds.), Proceedings of the Sixth World Conference on Medical Informatics, (pp. 111-115). Amsterdam: North Holland.
23. Wyatt, J., & Spiegelhalter, D. (1991). Field trials of medical decision-aids: Potential problems and solutions. In P. Clayton (Ed.), Proceedings of the 15th Symposium on Computer Applications in Medical Care (pp. 3-7). New York: McGraw Hill.
24. Zachary, W. W. (1988). Decision support systems: Designing to extend the cognitive limits. In M. Helander (Ed.), Handbook of human-computer interaction (pp. 997-1030). New York: Elsevier Science.