Electronic Health Record Impact on Work Burden in Small, Unaffiliated, Community-Based Primary Care Practices
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The use of electronic health records (EHR) is widely recommended as a means to improve the quality, safety and efficiency of US healthcare. Relatively little is known, however, about how implementation and use of this technology affects the work of clinicians and support staff who provide primary health care in small, independent practices.
To study the impact of EHR use on clinician and staff work burden in small, community-based primary care practices.
We conducted in-depth field research in seven community-based primary care practices. A team of field researchers spent 9–14 days over a 4–8 week period observing work in each practice, following patients through the practices, conducting interviews with key informants, and collecting documents and photographs. Field research data were coded and analyzed by a multidisciplinary research team, using a grounded theory approach.
All practice members and selected patients in seven community-based primary care practices in the Northeastern US.
The impact of EHR use on work burden differed for clinicians compared to support staff. EHR use reduced both clerical and clinical staff work burden by improving how they check in and room patients, how they chart their work, and how they communicate with both patients and providers. In contrast, EHR use reduced some clinician work (i.e., prescribing, some lab-related tasks, and communication within the office), while increasing other work (i.e., charting, chronic disease and preventive care tasks, and some lab-related tasks). Thoughtful implementation and strategic workflow redesign can mitigate the disproportionate EHR-related work burden for clinicians, as well as facilitate population-based care.
The complex needs of the primary care clinician should be understood and considered as the next iteration of EHR systems are developed and implemented.
KEY WORDSelectronic health records primary care work burden qualitative research
This work was supported by a grant from the National Heart, Lung and Blood Institute (R21HL092046).
Conflict of Interest
Elizabeth C. Clark: Demissie K, PI; Clark EC, co-investigator. “Long term medication adherence in renal transplant patients.” Novartis Pharmaceuticals. 2011–2012; annual direct costs: $72,000. Benjamin F. Crabtree: royalties from Sage Publications, Inc. for edited book, Doing Qualitative Research.
- 5.Crossing the Quality Chasm: A New Health System for the 21st Century. . In: Medicine Io, ed. Washington, DC: National Academy Press; 2001.Google Scholar
- 20.Miller R, Sim I, Newman J. Electronic medical records: Lessons from small physician practices. California HealthCare Foundation. 2003:1–27.Google Scholar
- 33.Venkash V, Morris M, Davis G, Davis F, Venkatesh VMM, Davis G, Davis F. User acceptance of information technology: toward a unified view. MIS Q. 2003;27(3):425–478.Google Scholar
- 34.Holden R, Karsh B. The technology acceptance model: its past and its future in health care. 2010. 2010;43(1):159-172.Google Scholar
- 36.Strauss A, Corbin J. Basics of qualitative research: grounded theory procedures and techniques. Newbury Park: Sage; 1990.Google Scholar
- 37.Borkan J. Immersion/Crystallization. In: Crabtree, B and Miller, W, eds. Doing Qualitative Research 2nd ed; 1999.Google Scholar
- 38.Amatayakul M. Why workflow redesign alone is not enough for EHR success Healthcare Financial Management 2011(March):130–132.Google Scholar
- 45.Karsh B-T. Clinical practice improvement and redesign: how change in workflow can be supported by clinical decision support. AHRQ Publication 2009;No. 09-0054-EF.Google Scholar
- 52.Grumbach K, Bodenheimer T. Can health care teams improve primary care practice? J Am Med Informat Assoc. 2004;291(10):1246–1251.Google Scholar
- 54.Lapin J, Beasley J, Smith P, al. e. Proactive risk assessment of primary care of the elderly. ARHQ Conference. Bethesda, MD2008.Google Scholar