Skip to main content

Advertisement

Log in

Electronic Health Record Impact on Work Burden in Small, Unaffiliated, Community-Based Primary Care Practices

  • Original Research
  • Published:
Journal of General Internal Medicine Aims and scope Submit manuscript

ABSTRACT

BACKGROUND

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.

OBJECTIVE

To study the impact of EHR use on clinician and staff work burden in small, community-based primary care practices.

DESIGN

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.

PARTICIPANTS

All practice members and selected patients in seven community-based primary care practices in the Northeastern US.

KEY RESULTS

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.

CONCLUSIONS

The complex needs of the primary care clinician should be understood and considered as the next iteration of EHR systems are developed and implemented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

REFERENCES

  1. Chaudhry B, Wang J, Wu S, 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.

    PubMed  Google Scholar 

  2. Hillestad R, Bigelow J, Bower A. Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Aff (Millwood). 2005;24:1103–1117.

    Article  Google Scholar 

  3. Apkon M, Singhaviranon P. Impact of an electronic information system on physician workflow and data collection in the intensive care unit. Intensive Care Med. 2001;27(1):122–130.

    Article  PubMed  CAS  Google Scholar 

  4. Roukema J, Los R, Bleeker S, Ginneken A, Lei J, Moll H. Paper versus computer: feasibility of an electronic medical record in general pediatrics. Pediatrics. 2006;117:15–21.

    Article  PubMed  Google Scholar 

  5. Crossing the Quality Chasm: A New Health System for the 21st Century. . In: Medicine Io, ed. Washington, DC: National Academy Press; 2001.

  6. Bates D, Gawande A. Patient safety: improving safety with information technology. N Engl J Med. 2003;348:2526–2534.

    Article  PubMed  Google Scholar 

  7. Rosenbloom ST, Talbert D, Aronsky D. Clinicians’ perceptions of clinical decision support integrated into computerized provider order entry. Int J Med Informat. 2004;73(5):433–441.

    Article  Google Scholar 

  8. Khajouei R, Wiereng P, Hasman A, Jaspers M. Clinicians satisfaction with CPOE ease of use and effect on clinicians’ workflow, efficiency and medication safety. Int J Med Informat. 2011;80:297–309.

    Article  CAS  Google Scholar 

  9. El-Kareh R, Gandhi TK, Poon EG, et al. Trends in primary care clinician perceptions of a new electronic health record. J Gen Intern Med. 2009;24(4):464–468.

    Article  PubMed  Google Scholar 

  10. Poissant L, Pereira J, Tamblyn R, Kawasumi Y. The impact of electronic health records on time efficiency of physicians and nurses: a systematic review. J Am Med Inform Assoc. 2005;12(5):505–516.

    Article  PubMed  Google Scholar 

  11. Koppel R, Metlay J, Cohen A, et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA. 2005;293(10):1197–1203.

    Article  PubMed  CAS  Google Scholar 

  12. Saitwal H, Feng X, Walji M, Patel V, Zhanga J. Assessing performance of an Electronic Health Record (EHR) using Cognitive Task Analysis. Int J Med Informat. 2010;79:501–506.

    Article  Google Scholar 

  13. Campbell E, Sittig D, Ash J, Guappone K, Dykstra R. Types of unintended consequences related to computerized provider order entry. J Am Med Informat Assoc. 2006;13(5):547–556.

    Article  Google Scholar 

  14. Ash J, Sittig D, Poon E, Guappone K, Campbell E, Dykstra R. The extent and importance of unintended consequences related to computerized provider order entry. J Am Med Informat Assoc. 2007;14(4):415–423.

    Article  Google Scholar 

  15. Samaan ZM, Klein MD, Mansour ME, DeWitt TG. The impact of the electronic health record on an academic pediatric primary care center. J Ambul Care Manage. 2009;32(3):180–187.

    PubMed  Google Scholar 

  16. McAlearney A, Robbins J, Hirschd A, Jorina M, Harropa J. Perceived efficiency impacts following electronic health record implementation: an exploratory study of an urban community health center network. Int J Med Informat. 2010;79:807–816.

    Article  Google Scholar 

  17. Pizziferri L, Kittler AF, Volk LA, et al. Primary care physician time utilization before and after implementation of an electronic health record: a time-motion study. J Biomed Inform. 2005;38(3):176–188.

    Article  PubMed  Google Scholar 

  18. Lo H, Newmark L, Yoon C, et al. Electronic health records in specialty care: a time-motion study. J Am Med Informat Assoc. 2007;14(5):609–615.

    Article  Google Scholar 

  19. Daniel G, Ewen E, Willey V, Reese C, Shirazi F, Malone D. Efficiency and economic benefits of a payer-based electronic health record in an emergency department. Acad Emerg Med. 2010;17(8):824–833.

    Article  PubMed  Google Scholar 

  20. Miller R, Sim I, Newman J. Electronic medical records: Lessons from small physician practices. California HealthCare Foundation. 2003:1–27.

  21. Simon SR, Kaushal R, Cleary PD, et al. Physicians and electronic health records: a statewide survey. Arch Intern Med. 2007;167(5):507–512.

    Article  PubMed  Google Scholar 

  22. Torda P, Han ES, Scholle SH. Easing the adoption and use of electronic health records in small practices. Health Aff (Millwood). 2010;29(4):668–675.

    Article  Google Scholar 

  23. Ross S, Schilling L, Fernald D, Davidson A, West D. Health information exchange in small-to-medium sized family medicine practices: motivators, barriers, and potential facilitators of adoption. Int J Med Informat. 2010;79:123–129.

    Article  Google Scholar 

  24. Overhage J, Perkins S, Tierney W, McDonald C. Controlled trial of direct physician order entry: effects on physicians’ time utilization in ambulatory primary care internal medicine practices. J Am Med Informat Assoc. 2001;14(4):361–371.

    Article  Google Scholar 

  25. DesRoches C, Donelan K, Buerhaus P, Zhonghe L. Registered nurses’ use of electronic health records: findings from a national survey. Medscape J Med. 2008;10(7):164.

    PubMed  Google Scholar 

  26. LaDuke S. Online nursing documentation: finding a middle ground. JONA. 2001;31(6):283–286.

    Article  CAS  Google Scholar 

  27. Cohen D, McDaniel R, Crabtree B. A practice change model for quality improvement in primary care practice. J Healthc Manag. 2004;49(3):155–168.

    PubMed  Google Scholar 

  28. Davis F. Perceived usefulness, perceived ease of use, and user acceptance of information technologies. MIS Q. 1989;13(3):319–340.

    Article  Google Scholar 

  29. Davis F, Bagozzi P, Warshaw P. User acceptance of computer technology: a comparison of two theoretical models. Manag Sci. 1989;35(8):982–1003.

    Article  Google Scholar 

  30. Venkatesh V, Davis F. A model of the antecedents of perceived ease of use: development and test. Decis Sci. 1996;27(3):451–481.

    Article  Google Scholar 

  31. Venkatesh V, Davis F. A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci. 2000;46(2):186–204.

    Article  Google Scholar 

  32. Venkatesh V, Morris M. Why do not men ever stop to ask for directions? General, social influence and their role in technology acceptance and usage behavior. MIS Q. 2000;24(1):115–139.

    Article  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.

  35. Pommerenke F, Dietrich A. Improving and maintaining preventive services. Part I: applying the patient path model. J Fam Pract. 1992;34(1):86–91.

    PubMed  CAS  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.

  38. Amatayakul M. Why workflow redesign alone is not enough for EHR success Healthcare Financial Management 2011(March):130–132.

  39. Beasley J, Wetterneck T, Temte J, et al. Information chaos in primary care: implications for physician performance and patient safety. J Am Board Fam Pract. 2011;24:745–751.

    Article  Google Scholar 

  40. Chesluk BJ, Holmboe ES. How teams work—or don’t–in primary care: a field study on internal medicine practices. Health Aff (Millwood). 2010;29(5):874–879.

    Article  Google Scholar 

  41. Nelson BA, Massey R. Implementing an electronic change-of-shift report using transforming care at the bedside processes and methods. J Nurs Adm. 2010;40(4):162–168.

    Article  PubMed  Google Scholar 

  42. Sinsky CA, Sinsky TA, Althaus D, Tranel J, Thiltgen M. Practice profile. ‘Core teams’: nurse-physician partnerships provide patient-centered care at an Iowa practice. Health Aff (Millwood). 2010;29(5):966–968.

    Article  Google Scholar 

  43. Nutting P, Crabtree B, Miller W, Stewart E, Stange K, Jaen C. Journey to the patient-centered medical home: a qualitative analysis of the experiences of practices in the national demonstration project. Ann Fam Med. 2010;8(supp 1):S45–S56.

    Article  PubMed  Google Scholar 

  44. Crosson J, Etz R, Wu S, Straus S, Eisenman D, Bell D. Meaningful use of electronic prescribing in 5 exemplar primary care practices. Ann Fam Med. 2011;9(5):392–397.

    Article  PubMed  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.

  46. Campbell E, Guappone K, Sittig D, Dykstra R, Ash J. Computerized provider order entry adoption: implications for clinical workflow. J Gen Intern Med. 2008;24(1):21–26.

    Article  PubMed  Google Scholar 

  47. Aarts J, Ash J, Berg M. Extending the understanding of computerized physician order entry: implications for professional collaboration, workflow and quality of care. Int J Med Informat. 2007;76S:S4–S13.

    Article  Google Scholar 

  48. Bodenheimer T, Laing BY. The teamlet model of primary care. Ann Fam Med. 2007;5(5):457–461.

    Article  PubMed  Google Scholar 

  49. Ferrer RL, Mody-Bailey P, Jaen CR, Gott S, Araujo S. A medical assistant-based program to promote healthy behaviors in primary care. Ann Fam Med. 2009;7(6):504–512.

    Article  PubMed  Google Scholar 

  50. Yarnall KS, Pollak KI, Ostbye T, Krause KM, Michener JL. Primary care: is there enough time for prevention? Am J Public Health. 2003;93(4):635–641.

    Article  PubMed  Google Scholar 

  51. Morrison I, Smith R. Hamster health care: time to stop running faster and redesign health care. BMJ. 2000;321:1541–1542.

    Article  PubMed  CAS  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.

    CAS  Google Scholar 

  53. Carayon P, Hundt A, Karsh B, Gurses A, Alvarado C, Smith M. Work system design for patient safety: the SEIPS model. Qual Saf Health Care. 2006;15(Supp I):i50–i58.

    Article  PubMed  Google Scholar 

  54. Lapin J, Beasley J, Smith P, al. e. Proactive risk assessment of primary care of the elderly. ARHQ Conference. Bethesda, MD2008.

Download references

Funding

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jenna Howard PhD.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Howard, J., Clark, E.C., Friedman, A. et al. Electronic Health Record Impact on Work Burden in Small, Unaffiliated, Community-Based Primary Care Practices. J GEN INTERN MED 28, 107–113 (2013). https://doi.org/10.1007/s11606-012-2192-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11606-012-2192-4

KEY WORDS

Navigation