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.
To determine the effect of EHRs on ambulatory quality in a community-based setting.
Cross-sectional study, using data from 2008.
Ambulatory practices in the Hudson Valley of New York, with a median practice size of four physicians.
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.
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.
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).
This is one of the first studies to find a positive association between EHRs and ambulatory quality in a community-based setting.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
American Recovery and Reinvestment Act of 2009, Pub L, No. 111-5, 123 Stat 115.
Steinbrook R. Health care and the American recovery and reinvestment act. N Engl J Med. 2009;360:1057–1060.
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.
Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med. 2010;363:501–504.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
THINC: Taconic Health Information Network and Community. (Accessed August 31, 2012, at www.thincrhio.org.)
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.)
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.)
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.
Health Information Technology Evaluation Collaborative (HITEC). (Accessed August 31, 2012, at www.hitecny.org.)
Taconic IPA. (Accessed August 31, 2012, at www.taconicipa.com.)
MedAllies. (Accessed August 31, 2012, at www.medallies.com.)
DxCG Intelligence. (Accessed August 31, 2012, at www.veriskhealth.com/solutions/enterprise-analytics/dxcg-intelligence.)
Ash AS, Ellis RP, Pope GC, et al. Using diagnoses to describe populations and predict costs. Health Care Financ Rev. 2000;21:7–28.
Medicare Advantage–Rates and Statistics–Risk Adjustment. (Accessed August 31, 2012, at http://www.cms.gov/MedicareAdvtgSpecRateStats/06_Risk_adjustment.asp#TopOfPage.)
National Committee for Quality Assurance. HEDIS & Quality Measurement. (Accessed August 31, 2012, at http://www.ncqa.org/tabid/59/Default.aspx.)
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.
Scholle SH, Roski J, Dunn DL, et al. Availability of data for measuring physician quality performance. Am J Manage Care. 2009;15:67–72.
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.)
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.
Cebul RD, Love TE, Jain AK, Hebert CJ. Electronic health records and quality of diabetes care. N Engl J Med. 2011;365:825–833.
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.
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).
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.
Paulus RA, Davis K, Steele GD. Continuous innovation in health care: implications of the Geisinger experience. Health Aff (Millwood). 2008;27:1235–1245.
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.
American Medical Association. Physician characteristics and distribution in the U.S., 2011 edition, Division of Survey and Data Resources, American Medical Association, 2011.
Bitton A, Martin C, Landon BE. A nationwide survey of patient centered medical home demonstration projects. J Gen Intern Med. 2010;25:584–592.
Institute of Medicine. Pediatric health and health care quality measures. 2010. (Accessed August 31, 2012, at http://www.iom.edu/Activities/Quality/PediatricQualityMeasures.aspx.)
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.
About this article
Cite this article
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
- electronic health records
- primary health care
- quality of health care