Healthc Inform Res.  2015 Apr;21(2):118-124. 10.4258/hir.2015.21.2.118.

Effects of Health Information Technology on Malpractice Insurance Premiums

• Kim HY
• Lee J

• KMID: 2284615
• DOI: http://doi.org/10.4258/hir.2015.21.2.118

Abstract

OBJECTIVES
The widespread adoption of health information technology (IT) will help contain health care costs by decreasing inefficiencies in healthcare delivery. Theoretically, health IT could lower hospitals' malpractice insurance premiums (MIPs) and improve the quality of care by reducing the number and size of malpractice. This study examines the relationship between health IT investment and MIP using California hospital data from 2006 to 2007.
METHODS
To examine the effect of hospital IT on malpractice insurance expense, a generalized estimating equation (GEE) was employed.
RESULTS
It was found that health IT investment was not negatively associated with MIP. Health IT was reported to reduce medical error and improve efficiency. Thus, it may reduce malpractice claims from patients, which will reduce malpractice insurance expenses for hospitals. However, health IT adoption could lead to increases in MIPs. For example, we expect increases in MIPs of about 1.2% and 1.5%, respectively, when health IT and labor increase by 10%.
CONCLUSIONS
This study examined the effect of health IT investment on MIPs controlling other hospital and market, and volume characteristics. Against our expectation, we found that health IT investment was not negatively associated with MIP. There may be some possible reasons that the real effect of health IT on MIPs was not observed; barriers including communication problems among health ITs, shorter sample period, lower IT investment, and lack of a quality of care measure as a moderating variable.

Keyword

Health Information Systems; Malpractice; Electronic Health Record; Investments; Insurance Premium

MeSH Terms

California
Delivery of Health Care
Electronic Health Records
Health Care Costs
Health Information Systems
Humans
Insurance*
Investments
Malpractice*
Medical Errors
Medical Informatics*

Reference

1. Parente ST, McCullough JS. Health information technology and patient safety: evidence from panel data. Health Aff (Millwood). 2009; 28(2):357–360.

2. Lee J, McCullough JS, Town RJ. The impact of health information technology on hospital productivity. Rand J Econ. 2013; 44(3):545–568.

3. Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. Washington (DC): National Academies Press;2001.

4. Kuperman GJ, Gibson RF. Computer physician order entry: benefits, costs, and issues. Ann Intern Med. 2003; 139(1):31–39.

5. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005; 293(10):1223–1238.

6. Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006; 144(10):742–752.

7. Parente ST, Van Horn RL. Valuing hospital investment in information technology: does governance make a difference? Health Care Financ Rev. 2006; 28(2):31–43.

8. Borzekowski R. Measuring the cost impact of hospital information systems: 1987-1994. J Health Econ. 2009; 28(5):938–949.

9. Yu FB, Menachemi N, Berner ES, Allison JJ, Weissman NW, Houston TK. Full implementation of computerized physician order entry and medication-related quality outcomes: a study of 3364 hospitals. Am J Med Qual. 2009; 24(4):278–286.

10. Himmelstein DU, Wright A, Woolhandler S. Hospital computing and the costs and quality of care: a national study. Am J Med. 2010; 123(1):40–46.

11. Certification Commission for Healthcare Information Technology. CCHIT certified electronic health records reduce malpractice risk. [place unknown]: Certification Commission for Healthcare Information Technology;2007.

12. Sloan FA, Shadle JH. Is there empirical evidence for "Defensive Medicine"? A reassessment. J Health Econ. 2009; 28(2):481–491.

13. Texas Medical Association. EMR implementation guide - online course instruction [Internet]. Austin (TX): Texas Medical Association;c2013. cited at 2015 Apr 15. Available from: http://www.texmed.org/Template.aspx?id=6188.

14. MMIC Group. The Certified Electronic Medical Record Risk Management Premium Credit [Internet]. Minneapolis (MN): MMIC Group;2014. cited at 2015 Apr 15. Available from: http://mmicgroup.com/pdf/EHR_Application.pdf.

15. King P. Can electronic medical records help to decrease malpractice insurance costs? [Internet]. [place unknown]: netdoc.com;2010. cited at 2015 Apr 15. Available from: http://www.netdoc.com/Physician-Practice-Articles/General-Medical-Practice/Can-Electronic-Medical-Records-Help-to-Decrease-Malpractice-Insurance-Costs?/.

16. Lee J, Dowd B. Effect of health information technology expenditure on patient level cost. Healthc Inform Res. 2013; 19(3):215–221.

17. Reiter KL, Song PH. The role of financial market performance in hospital capital investment. J Health Care Finance. 2011; 37(3):38–50.

18. McCullough JS. The adoption of hospital information systems. Health Econ. 2008; 17(5):649–664.

19. Gowrisankaran G, Town RJ. Estimating the quality of care in hospitals using instrumental variables. J Health Econ. 1999; 18(6):747–767.

20. Lave JR, Pashos CL, Anderson GF, Brailer D, Bubolz T, Conrad D, et al. Costing medical care: using Medicare administrative data. Med Care. 1994; 32:7 Suppl. JS77–JS89.

21. Hayes KJ, Pettengill J, Stensland J. Getting the price right: Medicare payment rates for cardiovascular services. Health Aff (Millwood). 2007; 26(1):124–136.

22. Gapenski LC. Healthcare finance: an introduction to accounting and financial management. 5th ed. Chicago (IL): Health Administration Press;2012.

23. Office of Statewide Health Planning & Development. Case Mix Index [Internet]. Sacramento (CA): Office of Statewide Health Planning & Development, State of California;2014. cited at 2015 Apr 15. Available from: http://www.oshpd.ca.gov/HID/Products/PatDischarge-Data/CaseMixIndex/.

24. Cui J. QIC program and model selection in GEE analyses. Stata J. 2007; 7(2):209–220.

25. Boonstra A, Broekhuis M. Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions. BMC Health Serv Res. 2010; 10:231.

26. DesRoches CM, Campbell EG, Rao SR, Donelan K, Ferris TG, Jha A, et al. Electronic health records in ambulatory care--a national survey of physicians. N Engl J Med. 2008; 359(1):50–60.

27. Vishwanath A, Scamurra SD. Barriers to the adoption of electronic health records: using concept mapping to develop a comprehensive empirical model. Health Informatics J. 2007; 13(2):119–134.

28. KevinMD. Do electronic medical records decrease liability risk? [Internet]. [place unknown]: KevinMD.com;2009. cited at 2015 Apr 15. Available from: http://www.kevinmd.com/blog/2009/12/electronic-medicalrecords-decrease-liability-risk.html.

29. Victoroff MS, Drury BM, Campagna EJ, Morrato EH. Impact of electronic health records on malpractice claims in a sample of physician offices in Colorado: a retrospective cohort study. J Gen Intern Med. 2013; 28(5):637–644.