Evaluation of a Korean version of a tool for assessing the incorporation of human factors into a medication-related decision support system: the I-MeDeSA
13 January 2014
Accepted: 07 May 2014
21 December 2017 (online)
Objective: The Instrument for Evaluating Human-Factor Principles in Medication-Related Decision Support Alerts (I-MeDeSA) was developed recently in the US with a view towards improving considerations of human-factor principles when designing alerts for clinical decision support (CDS) systems. This study evaluated the generalizability of this tool, in cooperation with its authors, across cultures by applying it to a Korean system. We also examined opportunities to promote user acceptance of the system.
Methods: We developed a Korean version of the I-MeDeSA (K-I-MeDeSA) and used it to evaluate drug-drug interaction alerts in a large academic tertiary hospital in Seoul. We involved four reviewers (A, B, C, and D). Two (A and B) conducted the initial independent scoring, while the other two (C and D) performed a final review and assessed feedback from the initial reviewers. The obtained scores were compared with those from 13 previously reported CDS systems. The feedback was summarized qualitatively.
Results: The translation of the I-MeDeSA had excellent interrater agreement in terms of face validity (scale-level content validity index = 0.95). The system’s K-I-MeDeSA score was 10 out of 26, with a good agreement between reviewers (κ = 0.77), which showed a lack of human-factor considerations. The reviewers readily identified two of the nine principles that needed primary improvement: prioritization and text-based information. The reviewers also expressed difficulty judging the following four principles: alarm philosophy, visibility, color, and learnability and confusability. Conclusion: The K-I-MeDeSA was semantically and operationally equivalent to the original tool. Only minor cultural problems were identified, leading the reviewers to suggest the need for clarification of certain words plus a more detailed description of the tool’s rationale and exemplars. Further evaluation is needed to empirically assess whether the implementation of changes in an electronic health record system could improve the adoption of CDS alerts.
Citation: Cho I, Lee J, Han H, Phansalkar S, Bates DW. Evaluation of a Korean version of a tool for assessing the incorporation of human factors into a medicationrelated decision support system: the I-MeDeSA. Appl Clin Inf 2014; 5: 571–588 http://dx.doi.org/10.4338/ACI-01-RA-0005
- 1 Linder JA, Ma J, Bates DW, Middleton B, Stafford RS. Electronic health record use and the quality of ambulatory care in the United States. Arch Intern Med 2007; 167 (13) 1400-1405.
- 2 Bates DW, Gawande AA. Improving safety with information technology. N Engl J Med 2003; 348 (25) 2526-24534.
- 3 Fischer MA, Solomon DH, Teich JM, Avorn J. Conversion from intravenous to oral medications: Assessment of a computerized intervention for hospitalized patients. Arch Intern Med 2003; 163 (21) 2585-259.
- 4 Wang SJ, Middleton B, Prosser LA, Bardon CG, Spurr CD, Carchidi PJ, Kittler AF, Goldszer RC, Fairchild DG, Sussman AJ, Kuperman GJ, Bates DW. A cost-benefit analysis of electronic medical records in primary care. Am J Med 2003; 114 (Suppl. 05) 397-403.
- 5 Tierney WM, Overhage JM, Murray MD, Harris LE, Zhou XH, Eckert GJ, Smith FE, Nienaber N, McDonald CJ, Wolinsky FD. Effects of computerized guidelines for managing heart disease in primary care. J Gen Intern Med 2003; 18 (12) 967-976.
- 6 Khajouei R, Jaspers MWM. The impact of CPOE medication systems’ design aspects on usability, work-flow and medication orders: a systematic review. Methods Inf Med 2010; 49 (Suppl. 01) 3-19.
- 7 Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, Morton SC, Shekelle PG. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006; 144 (10) 742-752.
- 8 Johnston D, Pan E, Walker J. The value of CPOE in ambulatory settings. J Healthc Inf Manag 2004; 18 (Suppl. 01) 5-8.
- 9 Van Der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc 2006; 13 (Suppl. 02) 138-147.
- 10 Nightingale PG, Adu D, Richards NT, Peters M. Implementation of rules based computerised bedside prescribing and administration: intervention study. BMJ 2000; 320 7237 750-753.
- 11 Abookire SA, Teich JM, Sandige H, Paterno MD, Martin MT, Kuperman GJ, Bates DW. Improving allergy alerting in a computerized physician order entry system. Proc AMIA Symp 2000: 2-6.
- 12 Weingart SN, Toth M, Sands DZ, Aronson MD, Davis RB, Phillips RS. Physicians’ decisions to override computerized drug alerts in primary care. Arch Intern Med 2003; 163 (21) 2625-2631.
- 13 Ash JS, Sittig DF, Dykstra RH, Guappone K, Carpenter JD, Seshadri V. Categorizing the unintended socio-technical consequences of computerized provider order entry. Int J Med Inform 2007; 76 (Suppl. 01) S21-S27.
- 14 Jung M, Hoerbst A, Hackl WO, Kirrane F, Borbolla D, Jaspers MW, Oertle M, Koutkias V, Ferret L, Mass-ari P, Lawton K, Riedmann D, Darmoni S, Maglaveras N, Lovis C, Ammenwerth E. Attitude of physicians towards automatic alerting in computerized physician order entry systems. A comparative international survey. Methods Inf Med 2013; 52 (Suppl. 02) 99-108.
- 15 Payne TH, Nichol WP, Hoey P. Characteristics and override rates of order checks in a practitioner order entry system. Proc AMIA Symp 2002: 602-606.
- 16 Cho I, Kim JA, Kho YT, Song SH, Park RW. Clinical decision support system. Seoul: Elsevier Korea; 2010
- 17 Paterno MD, Maviglia SM, Gorman PN, Seger DL, Yoshida E, Seger AC, Bates DW, Gandhi TK. Tiering drug-drug interaction alerts by severity increases compliance rates. J Am Med Inform Assoc 2009; 16 (Suppl. 01) 40-46.
- 18 President’s Council of Advisors on Science and Technology.. Report to the president realizing the full potential of helath information technology to improve healthcare for Americans: The path forward. Washington, DC. 2010 pp. 1-108
- 19 Zhang J, Walji MF. TURF: Toward a unified framework of EHR usability. J Biomed Inform 2011; 44 (Suppl. 06) 1056-1067.
- 20 Bertman J, Skolnik N, Frieden J. Poor usability keeps EHR adoption rates low. Family Practice News 2010; 40 (Suppl. 08) 54.
- 21 Saitwal H, Feng X, Walji M, Patel V, Zhang J. Assessing performance of an electronic health record (EHR) using cognitive task analysis. Int J Med Inform 2010; 79 (Suppl. 07) 501-506.
- 22 Phansalkar S, Edworthy J, Hellier E, Seger DL, Schedlbauer A, Avery AJ, Bates DW. A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems. J Am Med Inform Assoc 2010; 17 (Suppl. 05) 493-501.
- 23 Zachariah M, Phansalkar S, Seidling HM, Neri PM, Cresswell KM, Duke J, Bloomrosen M, Volk LA, Bates DW. Development and preliminary evidence for the validity of an instrument assessing implementation of human-factors principles in medication-related decision-support systems - I-MeDeSA. J Am Med Inform Assoc 2011; 18 (Suppl. 01) i62-i72.
- 24 Sittig DF, Singh H. A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Qual Saf Health Care 2010; 19 (Suppl. 03) i68-i74.
- 25 Herdman M, Fox-Rushby J, Badia X. A model of equivalence in the cultural adaptation of HRQoL instruments: the universalist approach. Quality of life Research 1998; 7 (Suppl. 04) 323-335.
- 26 Polit DF, Beck CT, Owen SV. Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Res Nurs Health 2007; 30 (Suppl. 04) 459-467.
- 27 Zachariah M, Phansalkar S, Seidling HM, Volk LA, Bloomrosen M, Bates DW. Evaluation of medication alerts for compliance with human factors principles: a multi-center study. Proc AMIA Symp 2011: 2021
- 28 Lee Y, Lee J, Lee S. Analysis of drug interaction information. Kor J Clin Pharm 2009; 19 (Suppl. 01) 1-17.
- 29 Park J-Y, Park K-W. The contraindication of comedication drugs and drug utilization review. J Korean Med Assoc 2012; 55 (Suppl. 05) 484-490.
- 30 Shah NR, Seger AC, Seger DL, Fiskio JM, Kuperman GJ, Blumenfeld B, Recklet EG, Bates DW, Gandhi YK. Improving acceptance of computerized prescribing alerts in ambulatory care. J Am Med Inform Assoc 2006; 13 (Suppl. 01) 5-11.
- 31 Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc 2004; 11 (Suppl. 02) 104-112.
- 32 Choi N-K, Park B-J. Strategy for establishing an effective Korean drug utilization review system. J Korean Med Assoc 2010; 53 (12) 1130-1138.
- 33 Phansalkar S, Desai AA, Bell D, Yoshida E, Doole J, Czochanski M, Middleton B, Bates DW. High-priority drug-drug interactions for use in electronic health records. J Am Med Inform Assoc 2012; 19 (Suppl. 05) 735-743.