Evaluation of a Course on Patient Records

A. Hasman; J. M. A. Roos; A. M. M. Muijtjens; E. H. B. M. Gronenschild; P. P. M Ahles

doi:10.1055/s-0038-1628136

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2007; 46(01): 70-73
DOI: 10.1055/s-0038-1628136

Original Articles

Schattauer GmbH

Evaluation of a Course on Patient Records

Authors

A. Hasman

¹Dpt of Medical Informatics, Academic Medical Centre – University of Amsterdam, The Netherlands
J. M. A. Roos

²Dpt of Medical Informatics, Medical Faculty, University of Maastricht, The Netherlands
A. M. M. Muijtjens

³Dpt of Educational Development and Research, Medical Faculty, University of Maastricht, The Netherlands
E. H. B. M. Gronenschild

²Dpt of Medical Informatics, Medical Faculty, University of Maastricht, The Netherlands
P. P. M Ahles

²Dpt of Medical Informatics, Medical Faculty, University of Maastricht, The Netherlands

Abstract

Summary

Objectives: To determine whether self-directed learning about (electronic) patient records during a PBL (problem-based learning) block, dealing with the content of disciplines concerned with the diagnosis and therapy of diseases of the abdomen, increased the knowledge of the students with respect to the patient records.

Methods: At the beginning and at the end of the tenweek block the same questionnaire was offered to the students (180). Cohen’s d for effect size was used to determine the increase in knowledge.

Results: For those students that answered the questionnaire twice (53), a Cohen’s d of 0.94 was obtained.

Conclusions: The knowledge of the students concerning the advantages and limitations of (electronic) patient records increased significantly. The corresponding effect size was large.

Keywords

Electronic patient records - paper patient records - education - evaluation - effect size

Full Text

References

References
1 Hasman A, Boshuizen HP. Medical informatics and problem-based learning. Methods Inf Med 2001; 40: 78-82.
2 Tufo HM, Speidel JJ. Problems with medical records. Medical Care 1971; 9: 509-17.
3 Fries JF. Alternatives in medical record formats. Medical Care 1974; 12: 871-81.
4 Tang PC, Fafschamps D, Shortliffe EH. Traditional medical records as a source of clinical data in the outpatient setting. Proc Annu Symp Comput Appl Med Care 1994; 575-9.
5 Tange HJ. The paper-based medical record: is it really so bad?. Comput Methods Programs Biomed 1995; 48: 127-31.
6 Wyatt JC, Wright P. Design should help us use of patients’ data. Lancet 1998; 352: 1375-8.
7 Nygren E, Wyatt JC, Wright P. Helping clinicians to find data and avoid delays. Lancet 1998; 352: 1462-6.
8 Wright P, Jansen C, Wyatt JC. How to limit clinical errors in interpretation of data. Lancet 1998; 352: 1539-43.
9 Powsner SM, Wyatt JC, Wright P. Opportunities for and challenges of computerisation. Lancet 1998; 352: 1617-22.
10 Stausberg J, Koch D, Ingenerf J, Betzler M. Comparing paper-based with electronic patient records: lessons learned during a study on diagnosis and procedure codes. JAMIA 2003; 10: 470-7.
11 Mitchell E, Sullivan F. A descriptive feast but an evaluative famine: systematic review of published articles on primary care computing during 1980-97. BMJ 2001; 322: 279-82.
12 Laerum H, Karlsen TJH, Faxvaag A. Effects of scanning and eliminating paper-based medical records on hospital physicians’clinical work practice. JAMIA 2003; 10: 588-95.
13 Van Bemmel JH, Musen MA. Handbook of medical informatics, chapters 6 and 7. Bohn Stafleu Van Loghum; Houten: 1997
14 Hunt DL, Haynes RB, Hanna SE, Smith K. Effects of computer-based clinical decision support systems on physician performance and patient outcomes. JAMA 1998; 280: 1339-46.
15 Wyatt JC. Decision support systems. J R Soc Med 2000; 93: 629-33.
16 Van Wijk MAM, Van der Lei J, Mosseveld M, Bohnen AM, Van Bemmel JH. Assessment of decision support for blood test ordering in primary care. Ann Intern Med 2001; 134: 274-81.
17 Hasman A, Safran C, Takeda H. Quality of health care: Informatics foundations. Methods Inf Med 2003; 42: 509-18.
18 Bates DW, Kuperman GJ, Wang S. et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. JAMIA 2003; 10: 523-30.
19 Berner ES, Jackson JR, Algina J. Relationships among performance scores of four diagnostic decision support systems. JAMIA 1996; 3: 208-15.
20 Berner ES, Maisiak RS, Cobbs CG, Taunton OD. Effects of a decision support system on physicians’ diagnostic performance. JAMIA 1999; 6: 420-7.
21 Lemaire JB, Schaefer JP, Martin LA, Faris P, Ainslie MD, Hull RD. Effectiveness of the Quick Medical Reference as a diagnostic tool. CMAJ 1999; 161: 725-8.
22 Friedman CP, Elstein AS, Wolf FM. et al. Enhancement of clinicians’ diagnostic reasoning by computer-based consultation. JAMA 1999; 282: 1851-6.
23 Balas EA, Boren SA. Managing clinical knowledge for healthcare improvement. Yearbook of Medical Informatics. 2000 pp 65-70.
24 Hersh WR, Crabtree MK, Hickam DH. et al. Factors associated with success in searching MEDLINE and applying evidence to answer clinical questions. JAMIA 2002; 9: 283-93.
25 Delvenne C, Pasleau F. Organising access to evidence- based medicine resources on the web. Comput Methods Programs Biomed 2003; 71: 1-10.
26 Bates DW, Cohen M, Leape LL, Overhage JM, Shabot MM, Sheridan T. Reducing the frequency of errors in medicine using information technology. JAMIA 2001; 8: 299-308.
27 Bates DW, Gawande AA. Improving safety with information technology. N Engl J Med 2003; 348: 2526-34.
28 Kohn KT, Corrigan JM, Donaldson MS. To Err Is Human: Building a Safer Health System. Washington, DC: National Academy Press;; 1999
29 Hojat X, Xu G. A visitor’s guide to effect sizes. Advances in Health Sciences Education 2004; 9: 241-9.
30 Cohen J. Statistical power analysis for behavioral sciences. Hillsdale, NJ: Erlbaum;; 1987