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The American Journal of Managed Care Special Issue: Health Information Technology - Guest Editor: Farzad Mostashari, MD, ScM
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Farzad Mostashari, MD, ScM, Visiting Fellow, Brookings Institute, Former National Coordinator for Health IT, US Department of Health and Human Services
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The Impact of Electronic Health Record Use on Physician Productivity
Julia Adler-Milstein, PhD; and Robert S. Huckman, PhD
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Marsha Gold, ScD; Mynti Hossain, MPP; Dustin R. Charles, MPH; and Michael F. Furukawa, PhD
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Penny Gilbert, MBA, BSM, BSN, RN, CPHQ; Michael D. Rutland, MBA, FHFMA, FACHE, FABC; and Dorothy Brockopp, PhD, RN
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Rohima Begum, MPH; Mandy Smith Ryan, PhD; Chloe H. Winther, BA; Jason J. Wang, PhD; Naomi S. Bardach, MD; Amanda H. Parsons, MD; Sarah C. Shih, MPH; and R. Adams Dudley, MD, MBA
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Richard Lam, MD, MBA; Victor S. Lin BS; Wendy S. Senelick, MPH; Hong-Phuc Tran, MD; Alison A. Moore, MD, MPH; and Brandon Koretz, MD, MBA
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William E. Encinosa, PhD; and Jaeyong Bae, MA

The Impact of Electronic Health Record Use on Physician Productivity

Julia Adler-Milstein, PhD; and Robert S. Huckman, PhD
Although increasing electronic health record use and delegating the related work improve physician productivity, these 2 strategies interact differently based on practice size.
We used the subset of tasks for which there is a “collect” (ie, newly entered) and a “review” (ie, viewed but not changed) option (eg, allergies; Table 1) to control for the monthly ratio of new to existing data in the EHR. This adjusted for the extent to which data need to be newly entered in the month, which could result from a changing patient mix or the approach of transitioning from paper to electronic records (eg, up-front conversion of all records, gradual conversion upon first patient visit), both of which may influence the intensity of EHR use and productivity. We included a full set of calendar month indicators to account for the possibility that productivity might be affected by seasonal changes in patient volume and illness severity (eg, flu season, holidays).


Summary Statistics

Average productivity in our sample was 17.5 RVUs (2.86 log RVUs) per clinician workday (Table 2). That is equivalent to approximately six 1-hour visits with a complex new patient, which are assigned 3.0 work RVUs each by Medicare. The average degree of EHR use in our sample was 370 actions per appointment. On average, clinicians delegated EHR tasks to clinical support staff 16% of the time (Table 2), although that varied widely across tasks (Table 1; results stratified by practice size are reported in eAppendix Table 2, available at

Model Results. More intensive EHR use and greater delegation of EHR tasks were independently associated with higher productivity (coefficient of 0.029 for EHR use and 0.502 for delegation; P <.05 for both, Table 3, column 1). That is, practices that increased the number of tasks performed in the EHR per visit realized increased clinician productivity and practices that increased the extent to which EHR tasks were performed by clinical support staff as opposed to clinicians saw an independent increase in productivity. To understand the magnitude of the effects, an increase in EHR use of 1 standard deviation (from the mean level of 370 tasks per appointment to 548 tasks per appointment) was associated with a 5.3% increase in RVUs per clinician workday. That represents an increase of 0.9 RVUs above the sample average of 17.5 RVUs per workday, which is approximately equivalent to an additional 20-minute visit with a patient new to the practice. An increase in delegation of 1 standard deviation (from the mean level of 16% to 37%) resulted in an 11.0% increase in RVUs per clinician workday. That represents an increase of 1.9 RVUs above the sample average and is approximately equivalent to a 40-minute visit with an established patient. (We confirmed constant variance by splitting our sample in half by practice size and then calculating the coefficient of variation on our untransformed dependent variable [RVUs per provider workday]. For the 21 small practices, the coefficient of variation was 0.4846; and for the 21 large practices, it was 0.4765.  That reflected less than a 2% difference, so we did not apply a smearing method upon retransformation.)

On average across the entire sample, we did not find evidence that EHR use and delegation operate as either complements or substitutes (Table 3, column 2). The continuous interaction term was not statistically significant at conventional levels (coefficient, –0.057; P = .23). We did, however, find that the interaction varied by practice size, with evidence of a complementary relationship for large practices and a substitution relationship for small practices. Specifically, when we split the sample in half by size, the continuous interaction term was negative and significant for small practices (coefficient, –0.142; P <.01; Table 3, column 3). For large practices, the interaction term was positive and significant (coefficient, 0.200; P <.01), as was the total interaction effect (0.058 from the sum of –0.142 and 0.200; P <.05 from a postestimation test that the sum of the 2 coefficients was not equal to zero). We found similar results when we split our sample into thirds, with a negative, significant interaction effect for the smallest practice size group (coefficient, –0.133; P <.01; Table 3, column 4) and a positive, significant interaction effect for the largest group of practices (coefficient, 0.224; P <.01). The total interaction effect for the largest group of practices was also positive, though not statistically significant (.091 from the sum of –0.133 and 0.224; P = .16 from a postestimation test of the sum of the 2 coefficients).

With respect to effect magnitudes in model 3 (see Table 3), for large practices at the mean level of delegation (13%), increasing EHR use from the mean to 1 standard deviation above the mean (367 to 529 actions) resulted in a small predicted increase in RVUs per workday of 0.69. At a higher  level of delegation (26%; 1 standard deviation above the mean), the equivalent increase in EHR use resulted in a somewhat larger predicted increase in RVUs per clinician workday of 0.94. Compared with the mean RVUs per  clinician workday in large practices of 18.73, these results reflected a 4% and a 5% increase, respectively. For small practices, at the mean level of delegation (18%), increasing EHR use 1 standard deviation above the mean (372 to 559 actions) increased predicted RVUs per workday by 0.81. At a higher level of delegation (27%; 1 standard deviation above the mean), the equivalent increase in EHR use resulted in a small predicted decrease in RVUs per clinician workday of –0.45. Compared with the mean RVUs per clinician workday in small practices of 20.07, these results reflected a 4% increase and a 2% decrease, respectively.


Our study is among the first to present empirical evidence on the productivity implications of practice choices about EHR use and the delegation of EHR-related work. Using a novel data set, we found that more intensive EHR use and greater delegation are independently associated with higher clinician productivity in ambulatory settings. We also found that EHR use and delegation operate as complements in large practices and as substitutes in small practices, suggesting that organizational size affects the marginal impact on productivity of EHR use and delegation. More broadly, our study addressed physician concerns about productivity losses from EHR adoption by presenting empirical data that such losses are not inevitable and that choices about how EHRs are used influence productivity.

Given the current increase in EHR adoption spurred by the Medicare and Medicaid EHR Incentive Programs, our findings have important implications for how practices approach EHR use. Although our results suggest that clinicians are more productive when they find ways to delegate tasks to support staff, practices should be aware of potentially unintended consequences when greater delegation is accompanied by more intensive EHR use. Specifically, in small practices—typically composed of a consistent, limited group of clinicians and support staff—the coordination challenges created by delegation may be relatively small, as individuals have more direct interaction with one another and are accustomed to coordinating and adjusting in an ad hoc manner. However, in small practices, these same factors may serve to interfere with, rather than improve, coordination in the face of reduced interpersonal contact resulting from EHR use. In larger practices, cohesive, stable teams are less common, resulting in increased reliance on a depersonalized set of roles that define the tasks of each type of staff member and minimize the amount of coordination required to work together efficiently.20 Having these explicit task expectations in place may be what enables larger practices to gain greater

productivity benefits from increased EHR use in the presence of high levels of delegation. There are other potential explanations for the size-based difference that we observed. For example, large practices may spend more time on training and work flow redesign, thereby achieving greater productivity benefits.

For policy makers designing future stages of meaningful use criteria, the contingent relationship between approaches to EHR use and clinician productivity based on practice size could suggest a differential impact of meaningful use on the quality and efficiency of care. Because physicians will  be expected to demonstrate that meaningful use results in improved care, understanding the key contextual factors that shape these gains will help inform the establishment of targets.

Much of the literature to date examined the impact of EHRs on physician productivity by treating adoption dichotomously and comparing performance before and after EHR adoption.21-23 These studies speak to the average impact of EHR adoption on productivity, not to what explains variation in clinician performance after adopting an EHR, a key contribution of our study. Prior studies also focused largely on the use of specific functionalities within the EHR,24,25 as opposed to more comprehensive measures. Our study extends the current literature by directly measuring EHR use—not simply EHR adoption—across a wide range of EHR-related tasks, revealing that increased use leads to meaningful productivity gains. More broadly, our study offers an example of the new type of research that is made possible by the window that EHRs provide into how practices deliver care.

Our study has several potential limitations. First, though we attempted to include a comprehensive set of covariates in our models, omitted variables might have biased our results. In particular, we were not able to account for the length of the clinician workday. Second, our results might also reflect bias introduced by several types of potential measurement error. Tasks recorded in the EHR could be attributed to the incorrect staff type if staff members either logged in to the system using the credentials of an employee of another staff type or if the most recent user failed to log off properly, introducing error into our delegation measure. Anecdotal data collected from athenahealth staff suggest that this behavior is uncommon, and when it does occur, it typically involves support staff logging in as clinicians (to access functionality not available to lower-level staff). That would have resulted in our delegation measure underestimating the true level of delegation, therefore making it harder to find support for our hypotheses.

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