Healthc Inform Res.  2020 Jan;26(1):68-77. 10.4258/hir.2020.26.1.68.

Association between Full Electronic Medical Record System Adoption and Drug Use: Antibiotics and Polypharmacy

Affiliations
  • 1Department of Information and Communication Technology, Health Insurance Review and Assessment Service, Wonju, Korea.
  • 2Research Institute for Health Insurance Review and Assessment, Health Insurance Review and Assessment Service, Wonju, Korea.
  • 3Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.
  • 4Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
  • 5Department of Emergency Medicine, Dong-A University College of Medicine, Busan, Korea.
  • 6Mo-Im Kim Nursing Research Institute, College of Nursing, Yonsei University, Seoul, Korea. monachoi@yuhs.ac

Abstract


OBJECTIVES
We investigated associations between full Electronic Medical Record (EMR) system adoption and drug use in healthcare organizations (HCOs) to explore whether EMR system features such as electronic prescribing, medicines reconciliation, and decision support, might be related to drug use by using the relevant nation-wide data.
METHODS
The study design was cross-sectional. Survey data of the level of adoption of EMR systems were collected for the Organization for Economic Co-operation and Development benchmarking information and communication technologies (ICT) study between November 2013 and January 2014, in Korea. Survey respondents were hospital chief information officers and medical practitioners in primary care clinics. From the national health insurance administrative dataset, two outcomes, the rate of antibiotic prescription and polypharmacy with ≥6 drugs, were extracted.
RESULTS
We found that full EMR adoption showed a 16.1% lower antibiotic drug prescription than partial adoption including paper-based medical charts in the hospital only (p = 0.041). Between EMR adoption status and polypharmacy prescription, only those clinics which fully adopted EMR showed significant associations with higher polypharmacy prescriptions (36.9%, p = 0.001).
CONCLUSIONS
The findings suggested that there might be some confounding effects present and sophisticated ICT may provide some benefits to the quality of care even with some mixed results. Although a negative relationship between full EMR system adoption and antibiotic drug use was only significant in hospitals, EMR system functions searching drugs or listing specific patients might facilitate antibiotic drug use reduction. Positive relationships between full EMR system adoption and polypharmacy rate in general hospitals and clinics, but not hospitals, require further research.

Keyword

Electronic Health Records; Health Care Evaluation Mechanisms; Quality of Health Care; Anti-Bacterial Agents; Polypharmacy

MeSH Terms

Anti-Bacterial Agents*
Benchmarking
Dataset
Delivery of Health Care
Drug Prescriptions
Electronic Health Records*
Electronic Prescribing
Health Care Evaluation Mechanisms
Hospitals, General
Humans
Korea
National Health Programs
Polypharmacy*
Prescriptions
Primary Health Care
Quality of Health Care
Surveys and Questionnaires
Anti-Bacterial Agents

Reference

1. Ventola CL. The antibiotic resistance crisis: part 1: causes and threats. P T. 2015; 40(4):277–283.
2. Mair A, Fernandez-Llimos F. SIMPATHY Consortium. Polypharmacy management programmes: the SIMPATHY Project. Eur J Hosp Pharm. 2017; 24(1):5–6.
Article
3. Fleming-Dutra KE, Hersh AL, Shapiro DJ, Bartoces M, Enns EA, File TM Jr, et al. Prevalence of inappropriate antibiotic prescriptions among US Ambulatory Care Visits, 2010–2011. JAMA. 2016; 315(17):1864–1873.
Article
4. Safdar N, Tape TG, Fox BC, Svenson JE, Wigton RS. Factors affecting antibiotic prescribing for acute respiratory infection by emergency physicians. Health. 2014; 6:774–780.
Article
5. Brauer R, Ruigomez A, Downey G, Bate A, Garcia Rodriguez LA, Huerta C, et al. Prevalence of antibiotic use: a comparison across various European health care data sources. Pharmacoepidemiol Drug Saf. 2016; 25 Suppl 1:11–20.
Article
6. World Health Organization. A glossary of terms for community health care and services for older persons [Internet]. Geneva, Switzerland: World Health Organization;2004. cited at 2020 Jan 30. Available from: https://apps.who.int/iris/bitstream/handle/10665/68896/WHO_WKC_Tech.Ser._04.2.pdf?sequence=1&isAllowed=y.
7. Haider SI, Johnell K, Weitoft GR, Thorslund M, Fastbom J. The influence of educational level on polypharmacy and inappropriate drug use: a register-based study of more than 600,000 older people. J Am Geriatr Soc. 2009; 57(1):62–69.
Article
8. Bushardt RL, Massey EB, Simpson TW, Ariail JC, Simpson KN. Polypharmacy: misleading, but manageable. Clin Interv Aging. 2008; 3(2):383–389.
Article
9. Hovstadius B, Astrand B, Petersson G. Assessment of regional variation in polypharmacy. Pharmacoepidemiol Drug Saf. 2010; 19(4):375–383.
Article
10. Kantor ED, Rehm CD, Haas JS, Chan AT, Giovannucci EL. Trends in prescription drug use among adults in the United States From 1999-2012. JAMA. 2015; 314(17):1818–1831.
Article
11. McKay R, Mah A, Law MR, McGrail K, Patrick DM. Systematic review of factors associated with antibiotic prescribing for respiratory tract infections. Antimicrob Agents Chemother. 2016; 60(7):4106–4118.
Article
12. Choi KH, Park SM, Lee JH, Kwon S. Factors affecting the prescribing patterns of antibiotics and injections. J Korean Med Sci. 2012; 27(2):120–127.
Article
13. Li P, Metlay JP, Marcus SC, Doshi JA. Factors associated with antimicrobial drug use in medicaid programs. Emerg Infect Dis. 2014; 20(5):829–832.
Article
14. Henry J, Pylypchuk Y, Searcy T, Patel V. Adoption of electronic health record systems among US non-federal acute care hospitals: 2008–2015 [Internet]. Washington (DC): The Office of the National Coordinator for Health Information Technology (ONC);2016. cited at 2020 Jan 30. Available from: https://www.healthit.gov/sites/default/files/briefs/2015_hospital_adoption_db_v17.pdf.
15. Sabes-Figuera R, Maghiros I. European hospital survey: benchmarking deployment of e-Health services (2012–2013) [Internet]. Seville, Spain: European Commission;2013. cited at 2020 Jan 30. Available from: https://www.key4biz.it/files/000261/00026119.pdf.
16. Codagnone C, Lupianez-Villanueva F. Benchmarking deployment of eHealth among general practitioners (2013) [Internet]. Luxembourg: European Commission;2013. cited at 2020 Jan 30. Available from: http://ec.europa.eu/newsroom/dae/document.cfm?doc_id=4897.
17. Park YT, Han D. Current status of electronic medical record systems in hospitals and clinics in Korea. Healthc Inform Res. 2017; 23(3):189–198.
Article
18. Nimbal V, Segal JB, Romanelli RJ. Estimating generic drug use with electronic health records data from a health care delivery system: implications for quality improvement and research. J Manag Care Spec Pharm. 2016; 22(10):1143–1147.
Article
19. Phansalkar S, Desai AA, Bell D, Yoshida E, Doole J, Czochanski M, et al. High-priority drug-drug interactions for use in electronic health records. J Am Med Inform Assoc. 2012; 19(5):735–743.
Article
20. Shemeikka T, Bastholm-Rahmner P, Elinder CG, Veg A, Tornqvist E, Cornelius B, et al. A health record integrated clinical decision support system to support prescriptions of pharmaceutical drugs in patients with reduced renal function: design, development and proof of concept. Int J Med Inform. 2015; 84(6):387–395.
Article
21. Holroyd-Leduc JM, Lorenzetti D, Straus SE, Sykes L, Quan H. The impact of the electronic medical record on structure, process, and outcomes within primary care: a systematic review of the evidence. J Am Med Inform Assoc. 2011; 18(6):732–737.
Article
22. Organization for Economic Co-operation and Development. Draft OECD guide to measuring ICTs in the health sector [Internet]. Paris, France: Organization for Economic Co-operation and Development;2015. cited at 2020 Jan 30. Available from: https://www.oecd.org/health/health-systems/Draft-oecd-guide-to-measuringicts-in-the-health-sector.pdf.
23. Meyer JW, Rowan B. Institutionalized organizations: formal structure as myth and ceremony. Am J Sociol. 1977; 83(2):340–363.
Article
24. DiMaggio PJ, Powell WW. The iron cage revisited: institutional isomorphism and collective rationality in organizational fields. Am Sociol Rev. 1983; 48(2):147–160.
Article
25. Richard Scott W. Organizations: rational, natural, and open systems. 5th ed. Englewood Cliffs (NJ): PrenticeHall;2003.
26. Burnett S, Mendel P, Nunes F, Wiig S, van den Bovenkamp H, Karltun A, et al. Using institutional theory to analyse hospital responses to external demands for finance and quality in five European countries. J Health Serv Res Policy. 2016; 21(2):109–117.
Article
27. Ji H, Yoo S, Heo EY, Hwang H, Kim JW. Technology and policy challenges in the adoption and operation of health information exchange systems. Healthc Inform Res. 2017; 23(4):314–321.
Article
28. Campanella P, Lovato E, Marone C, Fallacara L, Mancuso A, Ricciardi W, et al. The impact of electronic health records on healthcare quality: a systematic review and meta-analysis. Eur J Public Health. 2016; 26(1):60–64.
Article
29. Cebul RD, Love TE, Jain AK, Hebert CJ. Electronic health records and quality of diabetes care. N Engl J Med. 2011; 365(9):825–833.
Article
30. Patterson ME, Marken P, Zhong Y, Simon SD, Ketcherside W. Comprehensive electronic medical record implementation levels not associated with 30-day all-cause readmissions within Medicare beneficiaries with heart failure. Appl Clin Inform. 2014; 5(3):670–684.
Article
31. McCullough JS, Christianson J, Leerapan B. Do electronic medical records improve diabetes quality in physician practices? Am J Manag Care. 2013; 19(2):144–149.
32. Park YT, Yoon JS, Speedie SM, Yoon H, Lee J. Health insurance claim review using information technologies. Healthc Inform Res. 2012; 18(3):215–224.
Article
33. Zelmer J, Ronchi E, Hypponen H, Lupianez-Villanueva F, Codagnone C, Nohr C, et al. International health IT benchmarking: learning from cross-country comparisons. J Am Med Inform Assoc. 2017; 24(2):371–379.
Article
34. Medical Service Act, Article 3: Medical Institutions (Jan 28, 2015).
35. Walsh MN, Yancy CW, Albert NM, Curtis AB, Stough WG, Gheorghiade M, et al. Electronic health records and quality of care for heart failure. Am Heart J. 2010; 159(4):635–642.e1.
Article
36. Kaushal R, Kern LM, Barrón Y, Quaresimo J, Abramson EL. Electronic prescribing improves medication safety in community-based office practices. J Gen Intern Med. 2010; 25(6):530–536.
Article
37. Galbraith JR. Organization design: an information processing view. Interfaces. 1974; 4(3):28–36.
Article
38. Wells BJ, Lobel KD, Dickerson LM. Using the electronic medical record to enhance the use of combination drugs. Am J Med Qual. 2003; 18(4):147–149.
Article
39. Tundia NL, Kelton CM, Cavanaugh TM, Guo JJ, Hanseman DJ, Heaton PC. The effect of electronic medical record system sophistication on preventive healthcare for women. J Am Med Inform Assoc. 2013; 20(2):268–276.
Article
40. Furukawa MF. Electronic medical records and the efficiency of hospital emergency departments. Med Care Res Rev. 2011; 68(1):75–95.
Article
41. Makoul G, Curry RH, Tang PC. The use of electronic medical records: communication patterns in outpatient encounters. J Am Med Inform Assoc. 2001; 8(6):610–615.
Article
42. Health Insurance Review & Assessment Service. Improving utilization of relative resource use measures and low-value care measures for preventing wastes (Report No. G000F81-2017-26) [Internet]. Sejong, Korea: Ministry of Economy and Finance;2017. cited at 2020 Jan 30. Available from: http://alio.go.kr/download.dn?fileNo=2267937.
Full Text Links
  • HIR
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2022 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr