Journal of Medical Systems

, 38:138 | Cite as

Opioid Prescribing by Physicians With and Without Electronic Health Records

  • Christopher A. Harle
  • Robert L. Cook
  • Heidi S. Kinsell
  • Jeffrey S. Harman
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement

Abstract

Background

Physicians in the U.S. are adopting electronic health records (EHRs) at an unprecedented rate. However, little is known about how EHR use relates to physicians’ care decisions. Using nationally representative data, we estimated how using practice-based EHRs relates to opioid prescribing in primary care.

Methods

This study analyzed 33,090 visits to primary care physicians (PCPs) in the 2007–2010 National Ambulatory Medical Care Survey. We used logistic regression to compare opioid prescribing by PCPs with and without EHRs.

Results

Thirteen percent of all visits and 33 % of visits for chronic noncancer pain resulted in an opioid prescription. Compared to visits without EHRs, visits to physicians with EHRs had 1.38 times the odds of an opioid prescription (95 % CI, 1.22–1.56). Among visits for chronic noncancer pain, physicians with EHRs had significantly higher odds of an opioid prescription (adj. OR = 1.39; 95 % CI, 1.03–1.88). Chronic pain visits involving electronic clinical notes were also more likely to result in an opioid prescription compared to chronic pain visits without (adj. OR = 1.51; 95 % CI, 1.10–2.05). Chronic pain visits involving electronic test ordering were also more likely to result in an opioid prescription compared to chronic pain visits without (adj. OR = 1.31; 95 % CI, 1.01–1.71).

Conclusions

We found higher levels of opioid prescribing among physicians with EHRs compared to those without. These results highlight the need to better understand how using EHR systems may influence physician prescribing behavior so that EHRs can be designed to reliably guide physicians toward high quality care.

Keywords

Electronic health records Opioid prescribing Primary care Electronic test orders Electronic clinical notes 

References

  1. 1.
    Blumenthal, D., and Tavenner, M., The meaningful use regulation for electronic health records. N. Engl. J. Med. 363(6):501–504, 2010. doi:10.1056/NEJMp1006114.CrossRefGoogle Scholar
  2. 2.
    Centers for Medicare and Medicaid Services (CMS), EHR Incentive Program - Data and Program Reports. http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/DataAndReports.html. Accessed 8 Aug 2014, 2014.
  3. 3.
    Wright, A., Henkin, S., Feblowitz, J., McCoy, A. B., Bates, D. W., and Sittig, D. F., Early results of the meaningful use program for electronic health records. N. Engl. J. Med. 368(8):779–780, 2013. doi:10.1056/NEJMc1213481.CrossRefGoogle Scholar
  4. 4.
    Beeuwkes Buntin, M., Burke, M. F., Hoaglin, M. C., and Blumenthal, D., The benefits of health information technology: A review of the recent literature shows predominantly positive results. Health Aff. 30(3):464–471, 2011.CrossRefGoogle Scholar
  5. 5.
    Cortelyou-Ward, K., Swain, A., and Yeung, T., Mitigating error vulnerability at the transition of care through the use of health IT applications. J. Med. Syst. 36(6):3825–3831, 2012. doi:10.1007/s10916-012-9855-x.CrossRefGoogle Scholar
  6. 6.
    Stusser, R. J., and Dickey, R. A., Quality and cost improvement of healthcare via complementary measurement and diagnosis of patient general health outcome using electronic health record data: Research rationale and design. J. Med. Syst. 37(6):1–13, 2013. doi:10.1007/s10916-013-9977-9.CrossRefGoogle Scholar
  7. 7.
    Harman, J. S., Rost, K. M., Harle, C. A., and Cook, R. L., Electronic medical record availability and primary care depression treatment. J. Gen. Intern. Med. 27(8):962–967, 2012. doi:10.1007/s11606-012-2001-0.CrossRefGoogle Scholar
  8. 8.
    Linder, J. A., Ma, J., Bates, D. W., Middleton, B., and Stafford, R. S., Electronic health record use and the quality of ambulatory care in the United States. Arch. Intern. Med. 167(13):1400–1405, 2007. doi:10.1001/archinte.167.13.1400.CrossRefGoogle Scholar
  9. 9.
    Romano, M. J., and Stafford, R. S., Electronic health records and clinical decision support systems: Impact on national ambulatory care quality. Arch. Intern. Med. 171(10):897–903, 2011.Google Scholar
  10. 10.
    Leverence, R. R., Williams, R. L., Potter, M., Fernald, D., Unverzagt, M., Pace, W., Parnes, B., Daniels, E., Skipper, B., Volk, R. J., Brown, A. E., Rhyne, R. L., and on behalf of PNc, Chronic non-cancer pain: A siren for primary care—A Report From the PRImary care MultiEthnic Network (PRIME Net). J. Am. Board. Fam. Med. 24(5):551–561, 2011.CrossRefGoogle Scholar
  11. 11.
    O’Rorke, J. E., Chen, I., Genao, I., Panda, M., and Cykert, S., Physicians’ comfort in caring for patients with chronic nonmalignant pain. Am. J. Med. Sci. 333(2):93–100, 2007.CrossRefGoogle Scholar
  12. 12.
    Upshur, C. C., Bacigalupe, G., and Luckmann, R., “They don’t want anything to do with you”: Patient views of primary care management of chronic pain. Pain Med. 11(12):1791–1798, 2010.CrossRefGoogle Scholar
  13. 13.
    Committee on Advancing Pain Research Care Education, Institute of Medicine, Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education, and Research. The National Academies Press, Washington, DC, 2011.Google Scholar
  14. 14.
    Gaskin, D. J., and Richard, P., The economic costs of pain in the United States. J. Pain 13(8):715–724, 2012. doi:10.1016/j.jpain.2012.03.009.CrossRefGoogle Scholar
  15. 15.
    Mafi, J. N., McCarthy, E. P., Davis, R. B., and Landon, B. E., Worsening trends in the management and treatment of back pain. JAMA Intern. Med. 173(17):1573–1581, 2013. doi:10.1001/jamainternmed.2013.8992.CrossRefGoogle Scholar
  16. 16.
    Kunhimangalam, R., Ovallath, S., and Joseph, P., A clinical decision support system with an integrated EMR for diagnosis of peripheral neuropathy. J. Med. Syst. 38(4):1–14, 2014. doi:10.1007/s10916-014-0038-9.CrossRefGoogle Scholar
  17. 17.
    Sari, M., Gulbandilar, E., and Cimbiz, A., Prediction of low back pain with two expert systems. J. Med. Syst. 36(3):1523–1527, 2012. doi:10.1007/s10916-010-9613-x.CrossRefGoogle Scholar
  18. 18.
    Singh, S., Kumar, A., Panneerselvam, K., and Vennila, J. J., Diagnosis of Arthritis through fuzzy inference system. J. Med. Syst. 36(3):1459–1468, 2012. doi:10.1007/s10916-010-9606-9.CrossRefGoogle Scholar
  19. 19.
    Peccora, C. D., Gimlich, R., Cornell, R. P., Vacanti, C. A., Ehrenfeld, J. M., and Urman, R. D., Anesthesia report card—A customizable tool for performance improvement. J. Med. Syst. 38(9):1–10, 2014. doi:10.1007/s10916-014-0105-2.CrossRefGoogle Scholar
  20. 20.
    National Center for Health Statistics, National Ambulatory Medical Care Survey (NAMCS). http://www.cdc.gov/nchs/ahcd.htm, 2007–2010.
  21. 21.
    Kale, M. S., Bishop, T. F., Federman, A. D., and Keyhani, S., Trends in the overuse of ambulatory health care services in the united states. JAMA Intern. Med. 173(2):142–148, 2013. doi:10.1001/2013.jamainternmed.1022.CrossRefGoogle Scholar
  22. 22.
    Bryant, E. E., and Shimizu, I., Sample Design, Sampling Variance, and Estimation Procedures for the National Ambulatory Medical Care Survey. National Center for Health Statistics, Hyattsville, 1988.Google Scholar
  23. 23.
    National Center for Health Statistics, Ambulatory Health Care Data - Questionnaires, Datasets, and Related Documentation. http://www.cdc.gov/nchs/ahcd/ahcd_questionnaires.htm, 2013.
  24. 24.
    Cerner Multum, Lexicon. http://www.multum.com/lexicon.htm, 2013.
  25. 25.
    Caudill-Slosberg, M. A., Schwartz, L. M., and Woloshin, S., Office visits and analgesic prescriptions for musculoskeletal pain in US: 1980 vs. 2000. Pain 109(3):514–519, 2004. doi:10.1016/j.pain.2004.03.006.CrossRefGoogle Scholar
  26. 26.
    Olsen, Y., Daumit, G. L., and Ford, D. E., Opioid prescriptions by U.S. primary care physicians from 1992 to 2001. J. Pain 7(4):225–235, 2006. doi:10.1016/j.jpain.2005.11.006.CrossRefGoogle Scholar
  27. 27.
    DuGoff, E. H., Schuler, M., and Stuart, E. A., Generalizing observational study results: Applying propensity score methods to complex surveys. Health Serv. Res. 49(1):284–303, 2014. doi:10.1111/1475-6773.12090.CrossRefGoogle Scholar
  28. 28.
    StataCorp, Stata Statistical Software: Release 10. StataCorp LP, College Station, 2007.Google Scholar
  29. 29.
    Greene, W. H., Econometric Analysis, 5th edition. Prentice Hall, Upper Saddle River, 2003.Google Scholar
  30. 30.
    Kupersmith, J., Francis, J., Kerr, E., Krein, S., Pogach, L., Kolodner, R. M., and Perlin, J. B., Advancing evidence-based care for diabetes: Lessons from the veterans health administration. Health Aff. 26(2):w156–w168, 2007.CrossRefGoogle Scholar
  31. 31.
    O’Connor, P. J., Sperl-Hillen, J. M., Rush, W. A., Johnson, P. E., Amundson, G. H., Asche, S. E., Ekstrom, H. L., and Gilmer, T. P., Impact of electronic health record clinical decision support on diabetes care: A randomized trial. Ann. Fam. Med. 9(1):12–21, 2011.CrossRefGoogle Scholar
  32. 32.
    Liebschutz, J., and Alford, D., Safe opioid prescribing: A long way to go. J. Gen. Intern. Med. 26(9):951–952, 2011. doi:10.1007/s11606-011-1797-3.CrossRefGoogle Scholar
  33. 33.
    Beuscart-Zéphir, M. C., Elkin, P., Pelayo, S., Beuscart, R., The human factors engineering approach to biomedical informatics projects: State of the art, results, benefits and challenges. IMIA Yearbook 2007: Biomed. Inform. Sustain. Health Syst. 2(1):109–127, 2007.Google Scholar
  34. 34.
    Johnson, C. M., Johnson, T. R., and Zhang, J., A user-centered framework for redesigning health care interfaces. J. Biomed. Informa. 38(1):75–87, 2005. doi:10.1016/j.jbi.2004.11.005.CrossRefGoogle Scholar
  35. 35.
    Campbell, E. M., Sittig, D. F., Ash, J. S., Guappone, K. P., and Dykstra, R. H., Types of unintended consequences related to computerized provider order entry. J. Am. Med. Inform. Assoc. 13(5):547–556, 2006.CrossRefGoogle Scholar
  36. 36.
    Ash, J. S., Berg, M., and 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. 11(2):104–112, 2004.CrossRefGoogle Scholar
  37. 37.
    Harrison, M. I., Koppel, R., and Bar-Lev, S., Unintended consequences of information technologies in health care–An interactive sociotechnical analysis. J. Am. Med. Inform. Assoc. 14(5):542–549, 2007.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Christopher A. Harle
    • 1
  • Robert L. Cook
    • 2
  • Heidi S. Kinsell
    • 1
  • Jeffrey S. Harman
    • 1
  1. 1.Department of Health Services Research, Management and Policy, College of Public Health and Health ProfessionsUniversity of FloridaGainesvilleUSA
  2. 2.Department of Epidemiology, College of Public Health and Health Professions & College of MedicineUniversity of FloridaGainesvilleUSA

Personalised recommendations