Electronic Health Records and Ambulatory Quality of Care

ABSTRACT

CONTEXT

The US Federal Government is investing up to $29 billion in incentives for meaningful use of electronic health records (EHRs). However, the effect of EHRs on ambulatory quality is unclear, with several large studies finding no effect.

OBJECTIVE

To determine the effect of EHRs on ambulatory quality in a community-based setting.

DESIGN

Cross-sectional study, using data from 2008.

SETTING

Ambulatory practices in the Hudson Valley of New York, with a median practice size of four physicians.

PARTICIPANTS

We included all general internists, pediatricians and family medicine physicians who: were members of the Taconic Independent Practice Association, had patients in a data set of claims aggregated across five health plans, and had at least 30 patients per measure for at least one of nine quality measures selected by the health plans.

IINTERVENTION

Adoption of an EHR.

MAIN OUTCOME MEASURES

We compared physicians using EHRs to physicians using paper on performance for each of the nine quality measures, using t-tests. We also created a composite quality score by standardizing performance against a national benchmark and averaging standardized performance across measures. We used generalized estimation equations, adjusting for nine physician characteristics.

KEY RESULTS

We included 466 physicians and 74,618 unique patients. Of the physicians, 204 (44 %) had adopted EHRs and 262 (56 %) were using paper. Electronic health record use was associated with significantly higher quality of care for four of the measures: hemoglobin A1c testing in diabetes, breast cancer screening, chlamydia screening, and colorectal cancer screening. Effect sizes ranged from 3 to 13 percentage points per measure. When all nine measures were combined into a composite, EHR use was associated with higher quality of care (sd 0.4, p = 0.008).

CONCLUSIONS

This is one of the first studies to find a positive association between EHRs and ambulatory quality in a community-based setting.

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Figure 1.

REFERENCES

  1. 1.

    American Recovery and Reinvestment Act of 2009, Pub L, No. 111-5, 123 Stat 115.

  2. 2.

    Steinbrook R. Health care and the American recovery and reinvestment act. N Engl J Med. 2009;360:1057–1060.

    PubMed  Article  CAS  Google Scholar 

  3. 3.

    McDonald C, Abhyankar S. Clinical decision support and rich clinical repositories: a symbiotic relationship: comment on “electronic health records and clinical decision support systems”. Arch Intern Med. 2011;171:903–905.

    PubMed  Article  Google Scholar 

  4. 4.

    Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med. 2010;363:501–504.

    PubMed  Article  CAS  Google Scholar 

  5. 5.

    Garg AX, Adhikari NK, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293:1223–1238.

    PubMed  Article  CAS  Google Scholar 

  6. 6.

    Keyhani S, Hebert PL, Ross JS, Federman A, Zhu CW, Siu AL. Electronic health record components and the quality of care. Med Care. 2008;46:1267–1272.

    PubMed  Article  Google Scholar 

  7. 7.

    Linder JA, Ma J, Bates DW, Middleton B, Stafford RS. Electronic health record use and the quality of ambulatory care in the United States. Arch Intern Med. 2007;167:1400–1405.

    PubMed  Article  Google Scholar 

  8. 8.

    Poon EG, Wright A, Simon SR, et al. Relationship between use of electronic health record features and health care quality: results of a statewide survey. Med Care. 2010;48:203–209.

    PubMed  Article  Google Scholar 

  9. 9.

    Romano MJ, Stafford RS. Electronic health records and clinical decision support systems: impact on national ambulatory care quality. Arch Intern Med. 2011;171:897–903.

    PubMed  Article  Google Scholar 

  10. 10.

    Zhou L, Soran CS, Jenter CA, et al. The relationship between electronic health record use and quality of care over time. J Am Med Inform Assoc. 2009;16:457–464.

    PubMed  Article  Google Scholar 

  11. 11.

    Friedberg MW, Coltin KL, Safran DG, Dresser M, Zaslavsky AM, Schneider EC. Associations between structural capabilities of primary care practices and performance on selected quality measures. Ann Intern Med. 2009;151:456–463.

    PubMed  Google Scholar 

  12. 12.

    Garrido T, Jamieson L, Zhou Y, Wiesenthal A, Liang L. Effect of electronic health records in ambulatory care: retrospective, serial, cross sectional study. BMJ. 2005;330:581.

    PubMed  Article  Google Scholar 

  13. 13.

    Walsh MN, Yancy CW, Albert NM, et al. Electronic health records and quality of care for heart failure. Am Heart J. 2010;159:635–42 e1.

    PubMed  Article  Google Scholar 

  14. 14.

    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;144:742–752.

    PubMed  Google Scholar 

  15. 15.

    Buntin MB, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff (Millwood). 2011;30:464–471.

    Article  Google Scholar 

  16. 16.

    THINC: Taconic Health Information Network and Community. (Accessed August 31, 2012, at www.thincrhio.org.)

  17. 17.

    New York State Department of Health. Hudson Valley Region–Health Information Technology (HIT) Grants–HEAL NY Phase 1. 2007. (Accessed August 31, 2012, at www.health.state.ny.us/technology/awards/regions/hudson_valley.)

  18. 18.

    New York Governor’s Office. New York State provides $9.5 million for incentive program to promote high-quality, more affordable health care. 2007. (Accessed August 31, 2012, at www.nyqa.org/NYS-provides.pdf.)

  19. 19.

    Kern LM, Kaushal R. Health information technology and health information exchange in New York State: new initiatives in implementation and evaluation. J Biomed Inform. 2007;40:S17–S20.

    PubMed  Article  Google Scholar 

  20. 20.

    Health Information Technology Evaluation Collaborative (HITEC). (Accessed August 31, 2012, at www.hitecny.org.)

  21. 21.

    Taconic IPA. (Accessed August 31, 2012, at www.taconicipa.com.)

  22. 22.

    MedAllies. (Accessed August 31, 2012, at www.medallies.com.)

  23. 23.

    DxCG Intelligence. (Accessed August 31, 2012, at www.veriskhealth.com/solutions/enterprise-analytics/dxcg-intelligence.)

  24. 24.

    Ash AS, Ellis RP, Pope GC, et al. Using diagnoses to describe populations and predict costs. Health Care Financ Rev. 2000;21:7–28.

    PubMed  CAS  Google Scholar 

  25. 25.

    Medicare Advantage–Rates and Statistics–Risk Adjustment. (Accessed August 31, 2012, at http://www.cms.gov/MedicareAdvtgSpecRateStats/06_Risk_adjustment.asp#TopOfPage.)

  26. 26.

    National Committee for Quality Assurance. HEDIS & Quality Measurement. (Accessed August 31, 2012, at http://www.ncqa.org/tabid/59/Default.aspx.)

  27. 27.

    Scholle SH, Roski J, Adams JL, et al. Benchmarking physician performance: reliability of individual and composite measures. Am J Manage Care. 2008;14:833–838.

    Google Scholar 

  28. 28.

    Scholle SH, Roski J, Dunn DL, et al. Availability of data for measuring physician quality performance. Am J Manage Care. 2009;15:67–72.

    Google Scholar 

  29. 29.

    National Committee for Quality Assurance. The state of health care quality: Reform, the quality agenda and resource use. 2010. (Accessed August 31, 2012, at http://www.ncqa.org/tabid/836/Default.aspx.)

  30. 30.

    Pawlson LG, Scholle SH, Powers A. Comparison of administrative-only versus administrative plus chart review data for reporting HEDIS hybrid measures. Am J Manage Care. 2007;13:553–558.

    Google Scholar 

  31. 31.

    Cebul RD, Love TE, Jain AK, Hebert CJ. Electronic health records and quality of diabetes care. N Engl J Med. 2011;365:825–833.

    PubMed  Article  CAS  Google Scholar 

  32. 32.

    Dexheimer JW, Talbot TR, Sanders DL, Rosenbloom ST, Aronsky D. Prompting clinicians about preventive care measures: a systematic review of randomized controlled trials. J Am Med Inform Assoc. 2008;15:311–320.

    PubMed  Article  Google Scholar 

  33. 33.

    U.S. Department of Health and Human Services. Medicare and Medicaid Programs; Electronic Health Record Incentive Program; Final Rule. 75 Federal Register 44314 (2010) (42 CFR Parts 412, 413, 422 and 495).

  34. 34.

    Chen C, Garrido T, Chock D, Okawa G, Liang L. The Kaiser Permanente Electronic Health Record: transforming and streamlining modalities of care. Health Aff (Millwood). 2009;28:323–333.

    Article  Google Scholar 

  35. 35.

    Paulus RA, Davis K, Steele GD. Continuous innovation in health care: implications of the Geisinger experience. Health Aff (Millwood). 2008;27:1235–1245.

    Article  Google Scholar 

  36. 36.

    Perlin JB, Kolodner RM, Roswell RH. The Veterans Health Administration: quality, value, accountability, and information as transforming strategies for patient-centered care. Am J Manage Care. 2004;10:828–836.

    Google Scholar 

  37. 37.

    American Medical Association. Physician characteristics and distribution in the U.S., 2011 edition, Division of Survey and Data Resources, American Medical Association, 2011.

  38. 38.

    Bitton A, Martin C, Landon BE. A nationwide survey of patient centered medical home demonstration projects. J Gen Intern Med. 2010;25:584–592.

    PubMed  Article  Google Scholar 

  39. 39.

    Institute of Medicine. Pediatric health and health care quality measures. 2010. (Accessed August 31, 2012, at http://www.iom.edu/Activities/Quality/PediatricQualityMeasures.aspx.)

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Acknowledgements

This work was supported by the Commonwealth Fund, the Taconic Independent Practice Association, and the New York State Department of Health (contract #C023699). The authors specifically thank A. John Blair III, MD, President of the Taconic IPA and CEO of MedAllies, and Susan Stuard, MBA, Executive Director of THINC. All authors have contributed sufficiently to be authors and have approved the final manuscript. The authors take full responsibility for the design and conduct of the study and controlled the decision to publish. The authors had full access to the data, and take responsibility for the integrity of the data and the accuracy of the data analysis. A full list of HITEC Investigators can be found at: www.hitecny.org/about-us/our-team/. This work was previously presented as a poster at the Annual Symposium of the American Medical Informatics Association on October 25, 2011.

Conflict of Interest

The authors declare that they do not have conflicts of interest.

Role of the Funding Agencies

The funding sources had no role in the study’s design, conduct or reporting.

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Corresponding author

Correspondence to Lisa M. Kern MD, MPH.

APPENDIX

APPENDIX

Figure 2.
figure2

Logic for attributing patients to primary care physicians. *See Figure 1 for a derivation of the study sample.

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Kern, L.M., Barrón, Y., Dhopeshwarkar, R.V. et al. Electronic Health Records and Ambulatory Quality of Care. J GEN INTERN MED 28, 496–503 (2013). https://doi.org/10.1007/s11606-012-2237-8

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KEY WORDS

  • electronic health records
  • primary health care
  • quality of health care