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The American Journal of Managed Care Special Issue: Health Information Technology
Improving Adherence to Cardiovascular Disease Medications With Information Technology
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Eta S. Berner, EdD; Jeffrey H. Burkhardt, PhD; Anantachai Panjamapirom, PhD; and Midge N. Ray, MSN, RN
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Primary Care Capacity as Insurance Coverage Expands: Examining the Role of Health Information Technology
Renuka Tipirneni, MD, MSc; Ezinne G. Ndukwe, MPH; Melissa Riba, MS; HwaJung Choi, PhD; Regina Royan, MPH; Danielle Young, MPH; Marianne Udow-Phillips, MHSA; and Matthew M. Davis, MD, MAPP
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Primary Care Capacity as Insurance Coverage Expands: Examining the Role of Health Information Technology

Renuka Tipirneni, MD, MSc; Ezinne G. Ndukwe, MPH; Melissa Riba, MS; HwaJung Choi, PhD; Regina Royan, MPH; Danielle Young, MPH; Marianne Udow-Phillips, MHSA; and Matthew M. Davis, MD, MAPP
Primary care physicians using more health information technology were less likely to accept new patients.
Overall, 83% of PCPs reported they would have capacity to accept new patients in the future. In bivariate analyses, with each incremental increase in the number of HITs in use, there were significantly lower odds of self-reported primary care capacity (OR = 0.87; 95% CI, 0.78-0.97).

In multivariable analysis adjusting for physician-, practice- and community-level covariates, the odds of self-reported primary care capacity decreased by 14% with each additional HIT in use (adjusted OR = 0.86; 95% CI, 0.76- 0.97) (ORs reported in Table 2 and predicted probabilities of capacity presented in Figure 2). Among other covariates, greater number of years in practice was significantly associated with lower odds of capacity. Physicians who specialized in pediatrics, had a high visit volume, or had a predominantly Medicare payer mix had significantly greater odds of capacity.

For the secondary outcome of capacity by payer type among respondents who reported they had capacity to accept new patients, we found that greater use of HITs was associated with significantly lower odds of anticipated capacity to accept privately insured patients (adjusted OR 0.87; 95% CI, 0.77-0.97) (Table 2). However, use of HIT was not significantly associated with lower odds of anticipated capacity to accept patients with either Medicaid (adjusted OR 0.94; 95% CI, 0.84-1.05) or Medicare (adjusted OR 0.91; 95% CI, 0.83-1.01).

Among individual HIT types examined, electronic health records (adjusted OR 0.54; 95% CI, 0.30-0.96) and electronic access to admitting hospital records (adjusted OR 0.46; 95% CI, 0.22-0.96) were each significantly associated with lower odds of self-reported capacity (Table 3). All other HIT types examined (electronic prescribing, state immunization registry, patient registry, reminder/ recall system, and Web portals to either request refills or schedule appointments) were not significantly associated with anticipated capacity.

We also examined possible variation in the association between HIT use and capacity by practice size. We found that in smaller practices (1 to 5 physicians), the inverse relationship remained significant (adjusted OR 0.83; 95% CI, 0.71-0.97), while in larger practices (>5 physicians) the association was not present (adjusted OR 0.95; 95% CI, 0.79-1.15).


In this cross-sectional study of a representative sample of Michigan primary care physicians, we found that physicians using a greater number of health information technologies were significantly less likely to anticipate capacity to accept new patients. PCPs with higher HIT use were also specifically less likely to accept patients with private insurance, but not with Medicaid or Medicare. Among individual HITs examined, electronic health records and electronic access to admitting hospital records were significantly associated with lower anticipated primary care capacity. Additionally, we found that the inverse association of HIT use and capacity may be more apparent in small versus large practices.

Our findings have a few possible interpretations. First, it is plausible that use of technologies may lead to decreased physician efficiency, and that decreased efficiency leads to decreased capacity to see additional patients. Several prior studies have found either increased time per patient encounter or decreased physician or practice productivity associated with HIT use.10,12,18 One small study also showed downstream negative impact on patients’ ability to access care.12 However, other studies,4-8 including one review of the literature,6 have instead found improved physician productivity with HIT use. In one study of 42 primary care practices across the United States between 2006 and 2009, while there was improved physician productivity associated with EHR use in large practices, there was decreased productivity in small practices, suggesting this association may depend on practice size or other characteristics.7 Our results suggest a similar practice-size-related phenomenon in this statewide sample of primary care physicians. Furthermore, previous studies have defined “efficiency” or “productivity” in myriad ways—including as time spent per patient encounter, work relative value units, overall “work burden” of physicians, care utilization, practice revenue, and patient visit volume—which may partly explain the mixed results in the literature.

Second, it is also possible that any potential decline in efficiency may be only temporarily associated with the HIT implementation period, as physicians take time to get accustomed to a new technology. A few studies have found that this implementation-related reduction in efficiency may last up to 12 months.13,20 Because the timing of our survey in late 2012 corresponded to the end of the 2011-2012 rollout of meaningful use stage 1 incentives for EHR implementation, it is conceivable that our findings represent only a temporary implementation period effect. However, this timing of EHR incentive rollout and possible implementation period drop in productivity may not necessarily explain the additional inverse association we found between anticipated primary care capacity and other types of HIT, such as having access to admitting hospital records.

Third, it could be that practices with higher HIT use are generally well resourced and that these types of practices typically see more affluent patients. Such practices may have reservations about accepting newly insured patients, who are often of low socioeconomic status and may represent a different demographic than that typically seen in those practices.25 In this way, HIT use may be a marker of well-resourced practices. However, we controlled for predominant payer mix, a variable related to overall practice resources, and continued to find a significant inverse association between HIT use and anticipated capacity.

In addition, it was surprising to find that greater HIT use was associated specifically with lower anticipated capacity for privately insured patients, but not with Medicaid or Medicare patients, particularly since private insurance frequently provides higher reimbursements than Medicaid or Medicare. We speculate about 2 possible explanations for this finding. First, with the increase of Medicaid reimbursement rates for primary care physicians to the level of Medicare reimbursement rates during this period, new patients with Medicaid or Medicare may have appeared more attractive due to a steadier and predictably high level of reimbursement. Second, as noted above, it is possible that practices with the resources to adopt HIT and with a typically higher proportion of privately insured patients may be less likely to accept new patients generally.

This study should be interpreted in the context of several potential limitations. First, our cross-sectional data limits inferences regarding causal relationships. We did not characterize the timeline of HIT implementation in our study, and it is possible practice efficiency and capacity could improve after HIT implementation. Nevertheless, given the national timeline of EHR meaningful use incentives noted above, it is likely that our findings represent the association of capacity with the first 1 to 2 years following HIT implementation. Second, we did not distinguish which HIT vendors were used by physicians, and given differences in user interface, different brands may have varying impacts on practice capacity. Third, it is possible that the variables of physician specialty and predominant payer mix were collinear (eg, internists are more likely to accept Medicare patients, and pediatricians are more likely to accept Medicaid patients), but there was no evidence of multicollinearity to suggest that our estimates of the HIT/capacity association were biased. Fourth, we relied on respondents’ self-reported HIT use and likelihood of accepting new patients. While there is a potential for social desirability bias in both types of measures, surveys are the commonly used method for assessing these aspects of practice.26 Fifth, we did have missing data in our multivariable model, but this was unlikely to have impacted the findings since our sensitivity analyses using multiple imputations demonstrated equivalent results. Furthermore, we considered conducting the analysis with hierarchical regression, but had insufficient objective data at the practice and community levels to inform the model. Therefore, we fit the model assigning practice-level and community-level characteristics as reported by each physician. Lastly, while our results are representative of primary care physicians in a large Midwestern state, they may not be generalizable to all US states.

The notion that HIT expansion necessarily translates into improved efficiency and capacity in primary care practices has been widely disseminated.21 Our findings call into question whether this is occurring, at least during this early implementation time period. In an era of concurrent expansion of health information technology through the HITECH Act and expansion of insurance coverage through the ACA, policy makers must weigh the unintended consequences of each in order to maximize improvements in both healthcare quality and access. This is a challenging undertaking, and one best informed by data-driven approaches. Further research, such as additional physician surveys or simulated patient studies examining actual acceptance of new patients, is needed to better understand the impact of HIT implementation on access to primary care for newly insured individuals over time. 


The authors thank Lauren S. Hughes, MD, MPH, and Michelle H. Moniz, MD, both of the Robert Wood Johnson Foundation Clinical Scholars Program at the University of Michigan, for thoughtful comments on drafts of this article. The authors also thank Elyse N. Reamer, BS, of the University of Michigan Medical School, for assistance with literature review, as well as Jessica Landgraf, MA, of the of the Robert Wood Johnson Foundation Clinical Scholars Program at the University of Michigan, and Laura Spera, MS, MCS, of the University of Michigan Child Health Evaluation and Research Unit, Division of General Pediatrics, for assistance with data management.

Author Affiliations: Robert Wood Johnson Foundation Clinical Scholars Program, University of Michigan, Ann Arbor (RT, HJC, MMD); Division of General Medicine, Department of Internal Medi- cine, University of Michigan, Ann Arbor (RT, HJC, MMD); Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor (RT, MMD); Center for Healthcare Research & Transformation, Ann Arbor, MI (EN, MR, DY, MU-P); City of Detroit Department of Health and Wellness Promotion, Detroit, MI (RR); Child Health Evaluation and Research Unit, Department of Pediatrics, University of Michigan, Ann Arbor (RR, MMD).

Source of Funding: Center for Healthcare Research & Transforma- tion; Robert Wood Johnson Foundation Clinical Scholars Program.

Author Disclosures: Dr Davis serves as chief medical executive for the State of Michigan. The findings described herein are those of the authors and do not necessarily reflect the views of the State of Michigan.

Authorship Information: Concept and design (RT, EGN, MR, DY, MUP, MMD); acquisition of data (MR, RR, MU-P, MMD); analysis and interpretation of data (RT, EGN, MR, HC); drafting of the manuscript (RT, EGN); critical revision of the manuscript for important intellectual content (RT, EGN, MR, HC, RR, DY, MU-P, MMD); statistical analysis (RT, HC); obtaining funding (MU-P); administrative, technical, or logistic support (EGN, MR, RR, DY); and supervision (RT, MMD).

Address correspondence to: Renuka Tipirneni, MD, MSc, Robert Wood Johnson Foundation Clinical Scholar, Clinical Lecturer in Inter- nal Medicine, University of Michigan, 2800 Plymouth Rd, Bldg 10, Rm G016, Ann Arbor, MI 48109. E-mail:

1. Colwill JM, Cultice JM, Kruse RL. Will generalist physician supply meet demands of an increasing and aging population? Health Aff (Millwood). 2008;27(3):w232-w241.

2. BodenheimerTS, Smith MD. Primary care: proposed solutions to the physician shortage without training more physicians.
Health Aff (Millwood). 2013;32(11):1881-1886.

3. Shipman SA, Sinsky CA. Expanding primary care capacity by reducing waste and improving the efficiency of care. Health Aff (Millwood). 2013;32(11):1990-1997.

4. Joos D, Chen Q, Jirjis J, Johnson KB. An electronic medical record in primary care: impact on satisfaction, work efficiency and clinic processes.
AMIA Annu Symp Proc. 2006:394-398.

5. Cheriff AD, Kapur AG, Qiu M, Cole CL. Physician productivity and the ambulatory EHR in a large academic multi-specialty physician group. Int J Med Inform. 2010;79(7):492-500.

6. Fontaine P, Ross SE, ZinkT, Schilling LM. Systematic review of health information exchange in primary care practices.
J Am Board Fam Med. 2010;23(5):655-670.

7. Adler-Milstein J, Huckman RS.The impact of electronic health record use on physician productivity. Am J Manag Care. 2013;19(10 Spec No 1):SP345-SP352.

8. Weiner JP, Yeh S, Blumenthal D. The impact of health information technology and e-health on the future demand for physician services.
Health Aff (Millwood). 2013;32(11):1998-2004.

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

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

11. 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.

12. Samaan ZM, Klein MD, Mansour ME, DeWittTG.The impact of the electronic health record on an academic pediatric primary care center.
J Ambul Care Manage. 2009;32(3):180-187. 13. De Leon S, Connelly-Flores A, Mostashari F, Shih SC. The business end of health information technology. can a fully integrated electronic health record increase provider productivity in a large community practice?
J Med Pract Manage. 2010;25(6):342-349.

14. Furukawa MF. Electronic medical records and efficiency and pro- ductivity during office visits. Am J Manag Care. 2011;17(4):296-303.

15. Goetz Goldberg D, Kuzel AJ, Feng LB, DeShazo JP, Love LE. EHRs in primary care practices: benefits, challenges, and successful strategies. Am J Manag Care. 2012;18(2):e48-e54.

16. Howard J, Clark EC, Friedman A, et al. Electronic health record im- pact on work burden in small, unaffiliated, community-based primary care practices. J Gen Intern Med. 2013;28(1):107-113.

17. BishopTF, Press MJ, Mendelsohn JL, Casalino LP. Electronic com- munication improves access, but barriers to its widespread adoption remain. Health Aff (Millwood). 2013;32(8):1361-1367.

18. ZhouY, Ancker JS, Upadhye M, et al.The impact of interoperabil- ity of electronic health records on ambulatory physician practices: a discrete-event simulation study. Inform Prim Care. 2013;21(1):21-29.

19. Goldzweig CL, Orshansky G, Paige NM, et al. Electronic patient portals: evidence on health outcomes, satisfaction, efficiency, and attitudes: a systematic review. Ann Intern Med. 2013;159(10):677-687.

20. Fleming NS, Becker ER, Culler SD, et al.The impact of electronic health records on workflow and financial measures in primary care practices. Health Serv Res. 2014;49(1, pt 2):405-420.

21. Buntin MB, Jain SH, Blumenthal D. Health information technol- ogy: laying the infrastructure for national health reform. Health Aff (Millwood). 2010;29(6):1214-1219.

22. United States Department of Agriculture (USDA), Economic Re- search Service. Urban Influence Codes. USDA website. http://www Published 2013. Accessed May 15, 2014.

23. Royston P. Multiple imputation of missing values: further up- date of with an emphasis on categorical variables. Stata Journal. 2009;9(3):466-477.

24. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377-399.

25. Chirayath HT. Who serves the underserved? predictors of physician care to medically indigent patients. Health (London). 2006;10(3):259-282.

26. Jha AK, Ferris TG, Donelan K, et al. How common are electronic health records in the United States? a summary of the evidence. Health Aff (Millwood). 2006;25(6):w496-w507.

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