Jennifer King, PhD

 Although use of the Medicare Annual Wellness Visit (AWV) is increasing nationally, it remains unclear whether it can help contain healthcare costs and improve quality. In the context of 2 primary care physician—led accountable care organizations (ACOs), we tested the hypothesis that AWVs can improve healthcare costs and clinical quality.

A retrospective cohort study using propensity score matching and quasi-experimental difference-in-differences regression models comparing the differential changes in cost, emergency department (ED) visits, and hospitalizations for those who received an AWV versus those who did not from before until after the AWV. Logistic regressions were used for quality measures.

 Between 2014 and 2016, we examined the association of an AWV with healthcare costs, ED visits, hospitalizations, and clinical quality measures. The sample included Medicare beneficiaries attributed to providers across 44 primary care clinics participating in 2 ACOs.

 Among 8917 Medicare beneficiaries, an AWV was associated with significantly reduced spending on hospital acute care and outpatient services. Patients who received an AWV in the index month experienced a 5.7% reduction in adjusted total healthcare costs over the ensuing 11 months, with the greatest effect seen for patients in the highest hierarchical condition category risk quartile. AWVs were not associated with ED visits or hospitalizations. Beneficiaries who had an AWV were also more likely to receive recommended preventive clinical services.

 In a setting that prioritizes care coordination and utilization management, AWVs have the potential to improve healthcare quality and reduce cost.

In the context of 2 primary care physician (PCP)—led accountable care organizations, Medicare Annual Wellness Visits (AWVs) were associated with lower healthcare costs and improved clinical care quality for beneficiaries.

Our findings suggest that an AWV can achieve meaningful improvements in cost and quality, lending support that policy makers and payers should further facilitate the adoption of high-quality AWVs by PCPs.

Because Medicare reimburses $175 for an AWV per member per year and the AWV was associated with a $38 per member per month ($456 per member per year) decrease in costs, these data suggest that the additional expenditure on primary care can be worth the costs, particularly for a higher-risk population.

Future research can help guide policy with respect to whether AWVs should be billable only by the patient’s PCP and whether payment should be higher for higher-risk patients.

The Annual Wellness Visit (AWV) was introduced in 2011 by Medicare and made available to all eligible beneficiaries without deductibles or co-payments. Unlike a traditional periodic health examination or annual physical, which may be performed without a specified protocol,

 the AWV includes a list of required components

 that prioritize preventive care and investing in the relationship with the patient rather than addressing acute complaints or chronic disease (

). It includes assessing risk factors, inquiring about care support, creating a personalized care plan, and educating beneficiaries on how to maintain their health outside of an acute illness episode.

 Notably, the only physical examinations required of the visit are blood pressure measurement and height/weight measurement for body mass index (BMI), reflecting the US Preventive Services Task Force’s recommendation against routine physical examinations for asymptomatic adults 65 years or older.

Previous work has demonstrated growing adoption of AWVs since their introduction, but modest use overall,

 with 7% of Medicare beneficiaries receiving an AWV in 2011, increasing to 16% in 2014.

 More than 90% of AWVs nationally in 2014 were performed by a primary care physician (PCP).

 Although utilization of the service is increasing, the benefits of an AWV for improving patient outcomes and controlling healthcare costs continue to be debated.

 This lack of evidence regarding the possible impact of AWVs on important outcomes restrains providers and policy makers from optimally using the service.

In order to address these gaps, we examined the association of an AWV with healthcare costs, utilization, and measures of clinical quality among beneficiaries cared for by 2 PCP-led accountable care organizations (ACOs). We focused on beneficiaries cared for by PCPs in the ACOs formed in 2015 by Aledade, a national network of independent practices.

 Aledade has prioritized AWVs to improve quality and focus a primary care relationship on preventing adverse health events. It has supported its partners in performing AWVs by identifying high-risk beneficiaries, building user-friendly technology to schedule AWVs with these beneficiaries, providing face-to-face practice transformation support to optimize workflows, implementing templates in the electronic health record (EHR), providing data tools to support performance monitoring, and facilitating best practice sharing across a network of doctors.

 Evaluating the AWV in the context of a highly motivated and supported physician network leads to greater understanding of how AWVs can contribute to improving healthcare quality and reducing costs under optimal conditions.

 We used insurance claims from the CMS Claim and Claim Line Feed

 as our primary data source to assess the association of an AWV with cost and utilization. These data include services provided under Medicare parts A and B for patients assigned to 2 PCP-led ACOs. Data from these specific ACOs were used because they were the first ACOs that Aledade partnered with that had complete follow-up data. The observation period was from January 1, 2014, to December 31, 2016. Permission to use data for the study was granted through the Medicare Shared Savings Program data use agreement, and institutional review board (IRB) approval was granted by Hummingbird IRB (IRB #2017-278).

Intervention and control groups in primary data source

 Among patients attributed to ACOs 1 and 2 at the start of 2015, we identified intervention beneficiaries who had an AWV in 2015 (Current Procedural Terminology codes G0402, G0438, or G0439). We excluded beneficiaries who had missing data, died during the study period, or had received an AWV in 2014 (we wanted to focus on the effects from a first-time AWV, assuming that patients who did not receive an AWV in 2014 did not receive one in 2011-2013). To identify a control group, we matched the intervention beneficiaries to beneficiaries who did not have an AWV in 2015 and who met the same inclusion and exclusion criteria as the intervention group (for additional information about the matching process, see the 

]). Control patients were assigned the AWV month of the intervention patient to whom they matched. We removed the month that the AWV was done (and thus the cost of the AWV itself, because we wanted to focus on the subsequent AWV impact), which allowed us to define the 11 calendar months before the AWV as the pre-AWV period and the 11 calendar months after the AWV as the post-AWV period. Because Medicare only bills AWVs once every 12 months per patient, the intervention group by definition did not have any AWVs in the post-AWV period. After matching, we excluded all control patients who had an AWV in the post-AWV period, as well as all patients with outlier spend (to reduce skewness). For a visual definition of this cohort, see 

Secondary data source for quality of care

 To assess impact of an AWV on clinical quality, we used data on clinical quality measures reported to CMS as part of the ACO program.

 These data are reported to CMS for a different randomly selected sample of beneficiaries for each measure. Beneficiaries met exclusion/inclusion criteria as defined by each measure definition in accordance with Medicare specifications.

 Each sample was then divided into 2 groups: The control group included beneficiaries who did not receive a first-time AWV in 2015 (but could have received one in 2014 or 2016) and the intervention group included beneficiaries who did receive a first-time AWV in 2015 (including the “Welcome to Medicare” visit). We included all beneficiaries who were reported on by CMS regardless of whether they were in our final primary analytic sample.

The primary outcome was the differential change in healthcare cost (in the post-AWV period compared with the pre-AWV period) between those who did and did not receive an AWV. Costs in the month that the AWV was performed, including the approximately $175 cost of the AWV itself, were excluded. Cost was evaluated in separate analyses as total Medicare cost (all parts A and B Medicare spending) and category-specific costs within Part A (hospital acute care; hospital outpatient; hospital outpatient non—emergency department [ED]; skilled nursing facility, home health, other outpatient facility spending) and Part B (provider/supplier, durable medical equipment). The secondary outcomes included counts of ED visits and hospitalizations in the pre- and post-AWV periods. The outcomes for the quality measures analysis were 16 clinical quality measures with definitions specified by Medicare in 3 domains: preventive health, clinical care for at-risk populations, and care coordination.

Similar to other observational studies using real-world evidence, this study’s approach accounted for the likelihood that individuals who received AWVs differed from other patients in substantial ways that might affect the outcomes measured. In particular, issues may include that practices did not reach out to all patients with equal likelihood, practices succeeded in reaching patients at different rates, and patients who were willing and able to come in for a primary care visit differed from patients who were not. We used several analytic methods to explore the impact of such selection bias and account for it. We employed propensity score matching to identify control subjects who were similar to patients who received AWVs in key respects, including level of engagement with primary care in the pre-AWV period. We estimated the impact of AWVs by specifying a series of difference-in-differences (DID) regression models.

Specifically, for the primary data source, we checked the parallel trends assumptions and used a DID study design to assess changes in subsequent 11-month healthcare costs, ED visits, and hospitalizations between beneficiaries who did and did not receive an AWV in the index month. We used a mixed-effects negative binomial model for total cost and mixed-effects zero-inflated negative binomial (ZINB) models for category-specific costs and utilization. For the secondary data source, we used mixed-effects logistic regression models to assess the association between receiving an AWV and quality measures. In order to account for multiple comparisons, the Hochberg sequential procedure was used.

 For additional details on the modeling approach, see the eAppendix.

We also conducted a series of sensitivity analyses to evaluate the robustness of our results. First, we evaluated whether the intervention effect differed among those patients who received outreach using the Aledade app in 2015 versus those patients who did not receive outreach in 2015. Second, we used coarsened exact matching instead of propensity score matching to identify the comparison group. Third, we repeated our primary analysis (including the matching) only among beneficiaries who were continuously attributed throughout the entire pre- and post-AWV period. Fourth, we excluded intervention patients who matched to controls who had an AWV in the post-AWV period. Finally, we evaluated whether the AWV associations were different for “early” (January 2015—July 2015) versus “late” AWVs (August 2015–December 2015). (See eAppendix for additional details.)

All statistical analyses were conducted using R version 3.3.3 (R Foundation for Statistical Computing; Vienna, Austria). Propensity score and coarsened exact matching were conducted using the 

package, mixed-effects logistic models were fit using the 

 package, and the mixed-effects negative binomial and ZINB models were fit using the 

 Graphics and plots were generated using the 

The primary analysis sample of matched intervention and control beneficiaries included 8917 beneficiaries, of whom 4789 (54%) received a first-time AWV in 2015 (

). Differences in clinical and sociodemographic characteristics between the matched intervention and control groups were small. (For characteristics of the population of beneficiaries who entered the matching process, see the eAppendix.) The only covariate that was significantly different between those who did and did not receive an AWV in the final analytic sample was the specific ACO of the patient, and thus it was adjusted for in final regression models.

Association of AWV With Healthcare Costs and Utilization

In the pre-AWV period, average trends in healthcare cost were similar for the intervention and control groups (

). In 2015, a first-time AWV was associated with a 5.7% (95% CI, 0.3%-11.4%) reduction in total healthcare costs in the post-AWV period (excluding the cost of the AWV itself). This association translated to a $38 (95% CI, $9-$67) per-member-per-month (PMPM) reduction over 11 months of follow-up, or approximately $418 per beneficiary (

). The association between a first-time AWV and reduced costs was stronger among beneficiaries in the top hierarchical condition category (HCC) risk quartile. In this population, the adjusted differential change in total healthcare cost between the intervention and control groups was 6.3%, a PMPM decrease of $81 (95% CI, $12-$150) over 11 months of follow-up (Figure 1

Analysis of category-specific costs suggested that the primary drivers of this impact were reductions in hospital acute care costs (incidence rate ratio [IRR], 0.88; 95% CI, 0.80-0.97) and hospital outpatient non-ED costs (IRR, 0.93; 95% CI, 0.89-0.97). 

 reports the differential change in each category-specific cost between the intervention and control groups. With respect to healthcare utilization, first-time AWVs were not associated with a statistically significant change in the total number of ED visits (IRR, 0.97; 95% CI, 0.83-1.15) or hospitalizations (IRR, 0.95; 95% CI, 0.78-1.11).

Results of the sensitivity analyses were consistent with the main findings. Of note, the AWV association from the main analysis was not systematically different among beneficiaries who were invited by phone or email to schedule an AWV by the PCP practice (defined as receiving outreach) and beneficiaries who were not invited. In addition, using an alternative matching method to identify the comparison group and limiting the sample to those who were continuously attributed were consistent with a robust association between receipt of an AWV and reduced healthcare costs. Additionally, results were consistent with the main analysis when intervention patients who matched to controls who had an AWV in the post-AWV period were excluded. Finally, the effect of the AWV was not different between AWVs conducted in the early versus late part of the calendar year (See eAppendix for additional details.)

), a first-time AWV in 2015 was significantly associated with greater performance on 7 measures in adjusted analyses (all 

 <.01): fall risk screening (94% vs 15%), pneumococcal vaccination (86% vs 69%), tobacco screening and cessation (91% vs 77%), depression screening and follow-up planning (87% vs 18%), colorectal cancer screening (69% vs 60%), breast cancer screening (81% vs 66%), and controlled glycated hemoglobin (A1C) (77% vs 65%). AWVs were not statistically significantly associated with diabetes eye exams, use of aspirin, controlled hypertension, BMI screening, medication documentation, blood pressure control, influenza vaccination, diabetes therapy, or heart failure therapy. We speculate that AWVs had a lesser impact on these quality measures because they may already be prioritized in general primary care settings.

In this cohort of Medicare beneficiaries, first-time AWVs were associated with a significant improvement in use of preventive care and a reduction in total healthcare costs compared with matched controls. Rates of screening for fall risk and for clinical depression with follow-up plan, which are not typical components of a traditional evaluation and management visit but are components of an AWV and included in visit templates, were more than 70 percentage points higher among beneficiaries who received an AWV. These beneficiaries were also more likely to experience improved A1C control and to receive other key preventive services, including breast and colorectal cancer screening and tobacco use screening with cessation intervention. Changes in total healthcare costs were greatest among beneficiaries in the highest quartile of HCC and were driven by reductions in hospital acute care and hospital outpatient non-ED spending. Although there was a trend toward reduced hospital utilization, it was not as pronounced as that for hospital costs and did not achieve statistical significance. This would imply that the hospitalizations that did occur tended to have lower severity. These findings suggest that an AWV can be a helpful tool for improving care quality and containing costs within a primary care setting that prioritizes patient engagement, utilization management, and care coordination.

To our knowledge, this study is the first in the peer-reviewed literature to estimate the association of an AWV with measures of healthcare quality, costs, and utilization within the same study. Use of the AWV has been rising slowly since its introduction as a Medicare-reimbursable service in 2011, but there has been a paucity of evidence to guide AWV implementation into routine clinical practice in the primary care setting and to establish the potential value of this type of visit for ACOs and the Medicare program. Our findings add to a growing body of literature suggesting that the AWV can substantially improve rates of preventive services

 (which may be directly related to the administration of screening tools) while providing new evidence of substantial near-term effects on total cost of care. By further delineating that the association of the AWV with healthcare costs may be most pronounced among highest-risk patients, our findings lend support to a strategy of population risk segmentation for prioritization of AWV outreach efforts to maximize savings benefits. Furthermore, although there is general consensus that strong primary care is essential to containing healthcare costs,

 recent payment and delivery system innovations that intend to enhance primary care services beyond usual care have shown mixed results.

 Our study results suggest that a primary care service under a system that provides the right incentives for all may contribute to cost reductions.

The mechanisms explaining the cost reductions of AWVs are not well known, but we speculate that numerous aspects of the AWV might explain its benefits. A successful AWV means that practices are not merely “checking the box.” These screenings can be used to provide updates on medical history and self-reported data as an opportunity to step back from typical acute complaints and meaningfully engage in personalized conversations about risk factors, preventive needs, and a patient’s long-term health goals. This attention on wellness may improve clinical quality, including the delivery of general clinical preventive services and secondary prevention among patients with chronic conditions. An optimal AWV can include medication review and regimen optimization, identification of uncoordinated use of specialty care, and discussion of social or environmental barriers to self-care that may benefit from enhanced care coordination. This up-front investment in preventive care and care coordination may avert subsequent spending. By devoting time to explore the patient’s overall health status, risks, and values, the AWV may enrich the patient—provider relationship, improve patient engagement, and reinforce the core primary care tenets.

Several factors should be considered in the interpretation of this study. First, the results should be interpreted in the context of the specific setting studied. AWVs were a key strategy of the ACOs, and rates of AWVs (54%) were substantially higher than national averages. It is possible that the results of this study would be replicable only in a setting willing to undergo workflow optimization to accommodate a high-value visit. In addition, the cohort studied did not include beneficiaries at the end of life and those who may be less able to receive an AWV due to being homebound, institutionalized, hospitalized, or enrolled in hospice. Thus, the findings cannot be generalized to all Medicare beneficiaries. Second, as with all nonrandomized study designs, a possibility exists for selection bias and residual confounding due to unmeasured differences. However, we used propensity score matching to account for observable differences and a DID design to account for unobservable time-dependent changes in spend, as was done recently in a CMS evaluation of chronic care management.

 A key matching variable for cases and controls was the number of baseline primary care visits, to account for the level of engagement with a PCP and the ability to come in for a visit. Furthermore, the association between receipt of AWV and healthcare cost reductions did not vary by whether or not patients received outreach, providing little evidence to suggest that the intervention effect was driven by differential outreach to patients who were predisposed to favorable cost trajectories. Finally, we were not able to ascertain whether patients had an AWV prior to 2014, although data suggest that fewer than 16% of Medicare patients were receiving them in 2013.

These findings point to several priorities for payers and providers to consider. Because Medicare reimburses $175 for an AWV per member per year (PMPY), it is important to note that the true cost reduction may be smaller than the $456 PMPY effect estimate that we reported. However, given that the effect estimate is more than 2-fold the cost of the AWV itself, these data suggest that the additional expenditure on primary care can be worth the costs, particularly for a higher-risk population. It is also worth acknowledging that AWVs are one way to improve coding accuracy, and “upcoding” can be balanced by the legislatively afforded renormalization factor to account for risk inflation.

Future research can help guide policy with respect to whether AWVs should be billable only by the patient’s PCP, who may be in the best position to comprehensively assess patient risk factors and preventive care needs. Our findings show that an AWV may achieve meaningful improvements in cost and quality, lending support that policy makers should further facilitate the adoption of high-quality AWVs by PCPs. Given that underserved populations are less likely to adopt AWVs,

 policies should be explored to expand access to AWVs for this important subgroup of patients.

Additional research is still needed to further understand answers to several key questions. Given the recent introduction of the AWV, it will be important to understand whether the impact of an AWV changes over a longer time horizon. Future research should also attempt to differentiate effects of specific AWV components on outcomes. Additional outcomes of interest beyond the scope of this initial study include effects on patient satisfaction, health behaviors, self-management of chronic conditions, and care continuity.

Among beneficiaries cared for by PCPs affiliated with 2 ACOs, the AWV was associated with delivery of greater preventive care and lower total healthcare costs, particularly for those among the highest quartile of HCC risk. The AWV may therefore be an important service for achieving the triple aim of “improving the experience of care, improving the health of populations, and reducing per capita costs of health care.”

 Future research should focus on replicating these results among other populations, given that the current study used data from 2 specific ACOs. It will be important for future studies to test the efficacy of AWVs in different geographical areas and in ACOs with different characteristics in order to provide robust evidence of AWV impact. Furthermore, identifying tactics to further facilitate adoption and optimize the effectiveness of the AWV in primary care practice will be important avenues for future research.

Adam L. Beckman, BS, and Adan Z. Becerra, PhD, contributed equally to this work and are listed as co—first authors.

The authors first acknowledge the 114 independent primary care providers from Delaware ACO and Primary Care ACO who led the ACO efforts through 2016. They also appreciate editorial inputs from Travis Broome, MPH, vice president of policy at Aledade.

Aledade, Inc (ALB, AZB, AM, CAD, KL, EM, FM, JK), Bethesda, MD; Harvard Medical School (ALB), Boston, MA; Social & Scientific Systems (AZB), Silver Spring, MD.

 All authors were employed by Aledade when this study was conducted; as detailed in the manuscript, this is a peer-reviewed evaluation of our own program. Dr Becerra is now employed at Social & Scientific Systems, which was not involved in the study.

 Concept and design (ALB, AZB, KL, EM, FM, JK); acquisition of data (JK); analysis and interpretation of data (ALB, AZB, CAD, KL, EM, FM, JK); drafting of the manuscript (ALB, AZB, AM, CAD, EM, FM); critical revision of the manuscript for important intellectual content (ALB, AZB, CAD, KL, EM, FM, JK); statistical analysis (AZB, FM, JK); administrative, technical, or logistic support (AM, KL); supervision (EM, JK); and direct work with clinics (AM).

 Adam L. Beckman, BS, Aledade Inc, 4550 Montgomery Ave, Ste 950, Bethesda, MD 20814. Email: abeckman@aledade.com.

Articles

In the context of 2 primary care physician–led accountable care organizations, Medicare Annual Wellness Visits were associated with lower healthcare costs and improved clinical care quality for beneficiaries.

Medicare Annual Wellness Visit Association With Healthcare Quality and Costs

To examine whether physicians using health information technology and participating in new models of payment and delivery were more likely to perform care processes associated with improved care delivery.

Nationally representative, cross-sectional data on US office-based physicians from the 2012 National Ambulatory Medical Care Survey Physician Workflow Survey.

Multivariate regression analysis of whether physicians routinely performed 14 specific care processes in 4 categories: population management, quality measurement, patient communication, and care coordination. Key independent measures were electronic health record (EHR) use and accountable care organization (ACO) or patient-centered medical home (PCMH) participation.

A majority of physicians reported routinely conduct at least 1 care process related to care coordination (89%), patient communication (69%), and population management (67%); less than half reported performing at least 1 quality measurement process routinely (44%). EHR use and ACO or PCMH participation were independently associated with a higher likelihood of performing care processes. Physicians who were using EHRs in combination with participation in ACO or PCMH initiatives had the highest likelihood of routinely performing the care processes: physicians who used an EHR and participated in ACO or PCMH initiatives were between 6 and 22 percentage points more likely to routinely perform the care processes than physicians with EHRs alone.

 In 2012, physicians using EHRs and participating in ACO or PCMH initiatives were more likely than other physicians to be routinely engaging in care processes expected to improve healthcare outcomes. Yet, many US physicians were not performing these processes routinely. This analysis highlights several specific areas where more work is necessary to facilitate wider adoption of these activities.

National policies are supporting the use of health information technology (IT) and testing new models of payment and delivery, such as accountable care organizations (ACO) and patient-centered medical homes (PCMH). This study examined whether use of health IT and participation in new models of care are associated with improved care delivery.

		In 2012, physicians using electronic health records (EHRs) and participating in ACO or PCMH initiatives were more likely to routinely perform population management, quality measurement, patient communication, and care coordination processes; yet, many physicians were not performing these processes routinely.

		There are several areas where EHRs may be important to facilitate wider adoption of these activities.

There is wide consensus that changes are necessary in the way healthcare is delivered in the United States in order to realize reductions in healthcare spending and improvements in population health outcomes.

 National policies currently support 2 programs that may substantially change the way care is delivered; one related to health information technology (IT) adoption and the other related to payment reform and the use of new care delivery models.

The Health Information Technology Act (HITECH) Act of 2009 was designed to provide the infrastructure necessary for improvements in care delivery by supporting greater adoption of IT tools, including electronic health records (EHRs), which can facilitate high-quality care.

 The HITECH Act authorized the Medicare and Medicaid EHR Incentive Programs, which provide financial assistance to eligible professionals for the “meaningful use” (MU) of certified EHRs with functionalities associated with enhanced quality of care, such as electronic prescribing and medication alerts.

Concurrently, the 2010 Affordable Care Act (ACA) is supporting the development and implementation of new delivery and payment models, such as patient-centered medical homes (PCMHs) and shared savings arrangements like accountable care organizations (ACOs). Although EHRs are not required for participation in all types of PCMH or ACO programs, it is widely believed that providers need a robust health IT infrastructure to be fully successful in these payment and delivery arrangements.

Early indicators suggest strong physician participation in initiatives to support health IT adoption and to reform healthcare payment and delivery.

 However, evidence on whether provider participation in these initiatives has translated to better care delivery is just beginning to emerge.

 Areas identified as priorities for improvement include care processes, such as population management and prevention, quality measurement and reporting, care coordination, and patient engagement.2 Although studies prior to HITECH and the ACA found health IT and external reporting or payment incentives to be associated with a higher likelihood of performing these care processes, they are performed at low rates even when these factors are in place.

 At least 1 study of primary care physicians found evidence that those in PCMH practices using EHRs had greater quality improvements and changes in utilization over time on some measures than those in non-PCMH practices with or without EHRs.

We examined 4 main questions about the relationship between EHR use, participation in new payment and delivery models, and the extent to which physicians perform care processes that may lead to improved outcomes. First, how routinely did US physicians perform population management, quality measurement, patient communication, and care coordination processes? Second, were physicians using EHRs and participating in ACO or PCMH initiatives more likely than other physicians to perform these 4 types of care processes? Third, given that health IT and new payment models often are envisioned to work symbiotically to improve care,

 were physicians using EHRs and participating in ACO or PCMH initiatives more likely to routinely perform these types of care processes than those using EHRs alone? Finally, since previous studies have shown room for improvement in the extent to which EHR adopters use their EHRs to engage in these care processes,

 were certain EHR-related factors associated with use of computerized tools to routinely perform these processes? The results of this study may be useful to policy makers and researchers concerned with the level of adoption of these advanced care processes and the potential policies and tools to support their greater diffusion.

We used data from the 2012 National Ambulatory Medical Care Survey (NAMCS) Physician Workflow Survey, the second wave of a panel survey conducted by the National Center for Health Statistics to collect nationally representative information on physicians’ attitudes toward and experiences with EHRs.

 The first wave of the Physician Workflow Survey was conducted in 2011 as a follow-up to the 2011 NAMCS Electronic Health Records Survey.

The sample for the 2012 NAMCS Physician Workflow Survey came from the 2011 NAMCS Electronic Health Records Survey (ie, nonfederal office-based physicians, excluding radiologists, anesthesiologists, and pathologists). Eligibility status was determined for 8198 of the 10,302 physicians sampled, and those deemed eligible were mailed the Physician Workflow questionnaire. In 2012, 2567 physicians responded for an unweighted response rate of 45%. There were no significant differences in characteristics between responders and nonresponders.

The 2012 NAMCS survey included 14 separate items that queried physicians on 14 specific care processes in 4 categories: population management, quality measurement, patient communication, and care coordination. For each care process, the survey asked “Is this done routinely?” (yes/no) and then “Is this process computerized?” (yes, usually/yes, sometimes/no).

Use of care processes. In the multivariate analyses described below, we used 2 sets of dependent variables. First, to examine factors associated with routinely performing the care processes overall, we assigned the dependent variables a value of 1 if the physician reported routinely performing each process, and 0 otherwise. Second, to examine EHR-related factors associated with routinely performing the care processes using computerized tools, we assigned the dependent variables a value of 1 if the physician reported the process was performed routinely and the process was computerized (either usually or sometimes), and 0 otherwise. Missing responses, which ranged from 2% to 11% across the items, were assigned the value of 0.

For ease of communicating results across the 14 processes queried in the survey, we also created 1 measure for each of the 4 categories indicating whether at least 1 of the processes in the category was performed routinely. We reported multivariate results for these category-level measures in the main text and reported multivariate results for all 14 individual processes in the 

]). The results for the category-level measures were substantively similar to the results for the individual process measures.

EHR use and characteristics. We measured EHR use based on the question, “Which of the following best describes [your primary practice] location’s current EHR adoption status?” with response options: 1) “We do not have an EHR system,” 2) “We are not actively using an EHR system but have one installed,” and 3) “We are actively using an EHR system.” Our main measure of EHR use assessed whether or not the physician reported their primary practice location was actively using an EHR. We also reported the percent of physicians who responded, “We are not actively using an EHR system but have one installed” or answered yes to another question, “At the reporting location, are there plans for installing a new EHR system within the next 12 months?”

We examined 3 EHR-related characteristics among physicians using EHRs: whether the EHR met MU criteria, the length of EHR experience, and the receipt of technical assistance for initial EHR implementation. Physicians who answered “yes” to the question, “Does your current system meet meaningful use criteria as defined by the Centers for Medicare & Medicaid Services (CMS)?” were considered to have EHRs that met MU criteria. Length of EHR experience was calculated based on the difference between year of the survey (ie, 2012) and year reported in response to the item: “In which year did you install your EHR system?” Receipt of technical assistance was based on questions about whether the physician’s primary practice location received assistance “in implementing an EHR system” (yes/no/uncertain) and “with training staff in using your EHR system” (yes/no/uncertain).

ACO or PCMH participation. We measured participation in ACO or PCMH arrangements based on responses to 2 questions: 1) “Does [your primary practice location] participate in an 

 or similar arrangement by which you may share savings with insurers (including private insurance, Medicare, Medicaid, and other public options)?” and 2) “Does [your primary practice location] receive any additional compensation beyond routine visit fees for offering 

 (PCMH)-type services or does [the location] participate in a certified PCMH arrangement?”

In multivariate analyses (described below), we used a measure of whether or not the physician reported participating in either an ACO or PCMH arrangement. Results of sensitivity analyses examining ACO and PCMH participation as separate variables were similar to results from the combined variable (results not shown but are available on request). 

We first conducted univariate and bivariate descriptive statistics of the key independent and dependent variables. We then performed multivariate logistic regression to examine the relationship between EHR use, ACO or PCMH participation, and routinely performing the care processes while controlling for other physician and office characteristics. We estimated 18 logistic regression models (1 for each of the 14 individual process measures and the 4 category-level measures), regressing the dependent variable (whether or not the physician routinely performed the process of interest) on whether the physician used any EHR, whether the physician participated in an ACO or PCMH, and an interaction between the EHR use and ACO/PCMH participation variables. The models also included physician and practice characteristics as control variables: physician age, physician specialty, practice size, practice ownership, practice specialty, county metropolitan status, and region (eAppendix Table 1).

To assess whether EHR use and ACO or PCMH participation were independently associated with routinely performing the care processes, we used the logistic regression results to calculate the average incremental effects of EHR use and ACO or PCMH participation. To assess whether physicians using EHRs and participating in ACO or PCMH initiatives are more likely to routinely perform these care processes than those using EHRs alone, we used the regression results to calculate predicted probabilities for combinations of the interaction. 

Finally, we conducted multivariate logistic regression to assess whether, among EHR adopters, certain EHR-related factors were associated with a higher likelihood that EHR users routinely perform the care processes using computerized tools. The analysis sample for these regressions was the subset of office-based physicians who were using an EHR. We estimated 18 logistic regression models (1 for each of the 14 individual process measures and the 4 summary measures), regressing the dependent variable (whether or not the physician routinely performed the process of interest using computerized tools) on the 3 EHR-related characteristics: met MU criteria, EHR installation year, and staff received EHR training. The models also included the same control variables as above and the ACO or PCMH participation variable. We used the regression results to calculate the average incremental effects of the EHR related factors.

All analyses were conducted using Stata 11.2 (StataCorp LP, College Station Texas), using weights to account for nonresponse and adjusting standard errors for the complex survey design. We used a regression model that predicts the likelihood of responding to the survey, to develop weights to ensure that our survey was nationally representative.

EHR Use and Participation in ACO or PCMH Initiatives

In 2012, a majority of office-based physicians were using an EHR (62%), with about half of all physicians using an EHR that was certified to meet MU criteria (48%) (

). Over 1 in 5 physicians (22%) were participating in either an ACO (16%) or PCMH (9%) (Figure 1)—4% reported participating in both models, 13% in ACO only, and 6% in PCMH only (data not shown). A majority of physicians participating in these models used an EHR certified for MU (Figure 1). Overall, 18% of physicians reported using an EHR and participating in an ACO or PCMH. Additionally, 44% of physicians reported using an EHR but not participating in one of these care delivery models, and the remaining 38% of physicians did not use an EHR.

The majority of physicians reported performing at least 1 care process routinely in the categories of care coordination (89%), patient communication (69%), and population management (67%) (

). Less than half (44%) reported performing quality measurement processes routinely. There was variation in the extent to which physicians routinely performed specific processes within each category (eg, the population management processes ranged from 13% for creating lists of patients by lab results to 58% for providing patients with reminders for follow-up or preventive care). Among physicians who routinely performed a given process, the majority reported that the process was computerized.

Relationship Between EHR Use, Participation in ACO or PCMH Initiatives, and Performance of Care Processes

Physicians using EHRs were significantly more likely to perform care processes across all 4 categories (

). For example, physicians using EHRs were 15 percentage points more likely than physicians without EHRs to routinely perform at least 1 population management process. Similarly, physicians participating in new delivery models were significantly more likely to routinely perform these processes. The magnitude of the incremental effects for EHR use and ACO or PCMH participation were similar (ie, there were no statistically significant differences between the incremental effects [test not shown]).

Physicians who were using EHRs in combination with participation in ACO or PCMH initiatives had the highest likelihood of routinely performing the care processes (

). Among physicians not participating in either type of initiative, using an EHR was associated with a 14 to 27 percentage point greater likelihood of routinely performing at least 1 care processes in 3 of the 4 categories (not care coordination). However, across all categories, physicians who had an EHR and participated in ACO or PCMH initiatives were between 6 and 22 percentage points more likely to routinely perform the care processes than physicians with EHRs alone. The same results were found in analyses of the individual care processes in each category (eAppendix Table 3).

Relationship Between EHR-Related Factors and Performance of Care Processes Among Physicians Using EHRs

Among the 62% of physicians using an EHR in 2012, those with EHRs certified to meet MU criteria were significantly more likely to routinely perform at least 1 care process in a computerized manner across all 4 categories (

). For example, physicians using EHRs certified to meet MU criteria were 15 percentage points more likely than physicians using other EHRs to routinely perform at least 1 population management process in a computerized manner. The same results were found in analyses of the individual care processes in each category

Physicians at practices with longer experience with their current EHR were more likely to routinely perform 1 or more processes in a computerized manner across all categories. Assistance with initial EHR implementation and assistance with training staff to use the EHR were not significantly associated with the likelihood of routinely performing any care process in a computerized manner in the categories.

The National Quality Strategy has prioritized several areas for improvement, including population management and prevention, quality measurement and reporting, care coordination, and patient engagement.

 This study finds that US physicians’ rates of performing these care processes varied; although a majority reported routinely conducting at least 1 care process related to care coordination, patient communication, and population management, less than half reported performing at least 1 quality measurement process routinely.

In 2012, electronic health record use and participation in accountable care organization or patient-centered medical home initiatives were associated with performing care processes expected to improve healthcare outcomes.

The Role of Health IT and Delivery System Reform in Facilitating Advanced Care Delivery

To assess physician attitudes on ease of use of electronic health record (EHR) functionalities related to “Meaningful Use” (MU) and whether perceived ease of use was associated with EHR characteristics, including meeting MU criteria, technical assistance from EHR vendors or regional extension centers, and the amount of clinical staff training. 

A cross sectional analysis of the 2011 Physician Workflow study, nationally representative of US office-based physicians. 

Cross-sectional data were used to examine physician attitudes on ease of use of 14 EHR functionalities related to MU, among physicians with any EHR system. 

For 11 of the 14 EHR functions examined, physicians with EHRs that met MU criteria were significantly more likely than physicians that also utilized EHR systems to report that EHR functions were easy to use. For 8 of the functions examined, physicians receiving technical assistance from a vendor or regional extension center were significantly more likely to report that the EHR function was easy to use. 

Our study of a nationally representative survey of office-based physicians found that physicians’ adoption and perceived ease of use of EHR functionalities related to MU was generally high.

	Physician attitudes on ease of use of electronic health record (EHR) functionalities were generally positive. 

		Among a nationally representative sample of office-based physicians, EHR adoption and perceived ease of use of EHR functionalities related to Meaningful Use (MU) were relatively high. 

		Physicians using an EHR that met MU criteria were much more likely to report that EHR functionalities were easy to use. 

		Technical assistance and training were associated with ease of use for some but not all EHR functionalities.

The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 created incentives and provided technical assistance to spur widespread adoption of electronic health records (EHRs) in the United States. HITECH was designed to support greater use of health information technology in order to improve the efficiency and quality of patient care. Among its many provisions, HITECH authorized the Medicare and Medicaid EHR Incentive Programs, which began providing incentive payments to physicians and hospitals that demonstrated “Meaningful Use” (MU) of EHRs in 2011. HITECH also established a national EHR Certification Program to ensure that certified EHR technology included specific functionalities that meet MU criteria. In 2011, more than half of office-based physicians had adopted some type of EHR system, and about three-fourths of these physicians reported that their EHR was certified to meet MU criteria.

To achieve the aims of HITECH, it is important to understand physician attitudes toward the use of EHRs after adoption. A key factor in physician acceptance of EHRs is the extent to which users perceive the technology to be easy to use and useful in enhancing patient care.

 Since certification requires that functionalities meet technical standards, physician attitudes toward newer, more robust EHRs that meet MU criteria may differ from older systems. Attitudes may also depend on EHR implementation, such as receipt of technical assistance and amount of training.

 Anecdotal reports suggest growing physician dissatisfaction with the usability of EHRs,

 and physician difficulties in using EHR systems may lead to unintended consequences such as new work and safety issues.

 Thus, understanding the factors related to physician attitudes on ease of use of specific EHR functions has important clinical and policy implications.

Prior research on physician attitudes regarding EHRs has focused on the barriers and benefits of EHR adoption,

 and influences of EHR use on professional and workplace satisfaction.

 Fewer studies have focused on the association of EHR characteristics with ease of use related to specific EHR functions.

 Physician surveys from single states have found relationships between EHR robustness and physician satisfaction with EHRs overall,

 and a recent study using national data found that physicians using certified EHRs were more likely to report clinical benefits.

 However, these studies were limited to EHR robustness in relation to perceptions of satisfaction and usefulness, but not ease of use. Important gaps remain in our understanding of the associations between EHR characteristics with perceived ease of use, particularly for newer functionalities, such as secure messaging and public health reporting.

This study used nationally representative survey data from 2011 to examine physician attitudes on the ease of use of EHR functionalities related to MU. We also assessed whether perceived ease of use varied by EHR characteristics, including EHR certification, receipt of technical assistance from vendors or regional extension centers, and the amount of clinical staff training. Findings have important policy implications for the potential role of robustness and implementation support to influence physician attitudes on usability of some EHR functions.

The data source was the 2011 National Ambulatory Medical Care Survey (NAMCS) Physician Workflow study, the first wave of a longitudinal panel survey of US office-based physicians.

 The Physician Workflow study was conducted by the National Center for Health Statistics and collected information on physicians’ attitudes toward and experiences with EHRs across many domains. Survey content was developed with the guidance of an expert advisory panel, and separate questionnaires were developed for physicians using an EHR and physicians who had not yet adopted an EHR. Questions about ease of use of specific EHR functionalities were included on the EHR adopter questionnaire only.

The sample for the Physician Workflow study was a subset of physicians who were contacted to participate in the 2011 NAMCS Electronic Health Records Survey (NEHRS). The target universe for the NAMCS was nonfederal, office-based physicians in the United States, excluding radiologists, anesthesiologists, and pathologists. A total of 5232 physicians were sampled for the Physician Workflow study; the response rate for the 2011 survey was 61%, yielding a final sample size of 3180. This analysis was limited to respondents who used an EHR at their primary practice location in 2011 (n = 1793). Information on adoption of specific EHR functions was obtained from corresponding respondents of the 2011 NEHRS. Additional information on the survey methods is available elsewhere.

Rates of Adoption and Physician Attitudes on Ease of Use of Specific EHR Functions

We examined rates of adoption and physician attitudes on the ease of use of EHR functionalities related to MU. Adoption of specific EHR functions was measured using a NEHRS question asking whether the physician’s reporting location had computerized capabilities for each function. Fourteen of the 16 EHR functions listed in the Physician Workflow study mapped to MU Stage 1 or Stage 2 core or menu requirements; we focused our analyses on these 14 functions.

Perceived ease of use was measured from physician responses to the question: “Please indicate your level of ease or difficulty for each EHR function.” Response categories included: “very easy,” “easy,” “difficult,” “very difficult,” and “not applicable.” Physicians were instructed to select “not applicable” if they did not have or did not use a particular EHR function. Physicians were considered as having adopted and been using a specific function if they had a response other than “not applicable” for the question about ease of use of the function. We reported on physicians’ perceived ease of use of EHR functionalities, conditional on their adoption and use of that specific function. To report on ease of use, we used dichotomous variables that combined the “very easy” and “easy” responses into 1 group and combined “difficult,” “very difficult,” and missing responses in the comparison group. Missing responses across items ranged between 1% and 13% of physicians. Because we coded missing responses as “difficult” or “very difficult” in the analysis, rather than excluding them from the denominator, the estimates of ease of use may be conservative. Results were not sensitive to the exclusion of missing responses.

EHR Characteristics Associated With Perceived Ease of Use

The study hypothesized that EHR characteristics, including certification and implementation support, would be positively associated with perceived ease of use.

To measure EHR certification, we created a dichotomous variable indicating whether or not the respondent’s EHR was certified to meet the Stage 1 MU criteria. This variable was created based on responses to the question: “Does your current system meet Meaningful Use criteria as defined by the Centers for Medicare & Medicaid Services (CMS)?” Response categories were “yes,” “no,” and “uncertain.” Physicians who answered “yes” were considered to have EHRs that met MU criteria. Of the entire sample, 8.4% answered “no,” 14.4% answered “uncertain,” and 1.4% did not answer the question. Sensitivity analyses using an alternative measure of whether physicians had 9 of the 15 computerized capabilities that compose the core MU requirements yielded similar results to the main analysis.

To measure EHR implementation support, we created variables for technical assistance from EHR vendors or regional extension centers and the amount of clinical staff training. Receipt of technical assistance was measured using the questions: “Did you receive help from EHR vendors in analyzing your practice work flow?” and “Did you receive help from regional extension centers (RECs) in analyzing your practice work flow?” Since respondents may have received assistance from both vendors and RECs, we combined “yes” responses to either question as having received technical assistance. The amount of clinical staff training was determined using the question: “How many hours, on average, did clinical staff spend in training to implement your practice’s EHR system?” To balance cell sizes, we created 3 categories for the amount of training as 0 to 8 hours (combining responses “1 to 8 hours” and “did not receive training”), 9 to 40 hours, and 41 or more hours (combining “41 to 80 hours” and “over 80 hours”).

In multivariate analyses, we included additional controls for physician (age, specialty) and office characteristics (size [number of physicians], ownership, practice type, metropolitan status, region) that have been associated with EHR adoption and physician attitudes toward EHRs in previous research (listed in 

Univariate descriptive statistics were calculated to describe the percent of physicians that adopted specific EHR functions, and among EHR adopters, the percent of physicians who reported the EHR function was easy to use (either “very easy” or “easy”). Multivariate logistic regression analyses were used to examine whether perceived ease of use varied by EHR characteristics while controlling for other physician and office characteristics. All analyses were conducted using Stata version 11.2 software (StataCorp LP, College Station, Texas) using weights to account for nonresponse and adjusting standard errors for the complex survey design of the data.

EHR Characteristics Among Physicians With Any EHR

Using a nationally representative sample of EHR adopters, more than three-fourths (76%) reported that their EHR met MU criteria (

). Fewer than half (45%) of EHR adopters reported the receipt of technical assistance with analyzing practice work flow from EHR vendors or RECs. About 1 in 5 (22%) physicians with any EHR received 41 or more hours in clinical staff training to implement their EHR system.

Adoption and Perceived Ease of Use of EHR Functionalities Related to Meaningful Use

Among physicians that had adopted an EHR, physicians’ rate of adopting and using 14 EHR functions related to MU ranged from 98% (recording a comprehensive list of medications and allergies) to 40% (public health reporting) (

). Overall, at least 75% of EHR adopters reported adopting and using 9 of the 14 EHR functions we examined. Functions related to documentation had the highest rates of adoption and use; exchanging secure messages with patients and public health reporting had the lowest adoption rates.

Among physicians who reported adopting and using a given EHR function, the percent who reported the function was easy to use ranged from 91% (viewing laboratory results) to 49% (public health reporting). At least 75% of EHR adopters considered 10 of the 14 functions we examined as easy to use; this included functions related to documentation, ordering, viewing results, decision support, patient engagement, and clinical data exchange.

In general, the percent of physicians who considered a particular EHR function to be easy to use was higher for more commonly adopted EHR functions and lower for less commonly adopted EHR functions. For example, the percent of physicians who considered the least commonly adopted functions as easy to use, were relatively low; these included public health reporting (49%), viewing data on quality measures (63%), secure messaging with patients (68%), and reminders based on guidelines (69%).

EHR Characteristics and Perceived Ease of Use of Specific EHR Functions

Physician attitudes on ease of use of specific EHR functions varied according to some EHR characteristics (

]). For 12 of the 14 functions examined, physicians with EHRs that met MU criteria had 56% to 196% higher odds of reporting the EHR function was easy to use. EHR certification had the strongest association with perceived ease of use in providing reminders for guideline-based interventions or screening tests (+196% difference in the odds of ease of use) and providing patients with a clinical summary for each visit (+176%). EHR functions related to documentation and viewing clinical data showed smaller differences based on whether physicians’ EHRs met MU criteria. For example, EHR certification had no significant effect on ease of use for recording a comprehensive list of medication and allergies or viewing imaging reports.

Receipt of technical assistance from EHR vendors or RECs had some association with perceived ease of use (Table 4). For 8 of the 14 functions examined, physicians receiving technical assistance had 55% to 174% higher odds of reporting that the EHR function was easy to use. Technical assistance had the strongest relationships with ease of use of EHR functions related to public health reporting (+174%), viewing data on quality of care (+101%), and exchanging secure messages with patients (+98%).

The amount of clinical staff training had little association with perceived ease of use (Table 4). Relative to 0 to 8 hours, clinical staff training of 9 to 40 hours had no effect on perceived ease of use for any EHR functions. Training of 41 or more hours was associated with 51% to 60% lower odds of reporting that the EHR function was easy to use for 4 functions: viewing imaging reports, recording clinical notes, providing patients with clinical summaries, and recording problem lists.

Physician and Office Characteristics Associated With Perceived Ease of Use of Specific EHR Functions

With the exception of practice ownership, we found little association of physician and office characteristics with perceived ease of use that was consistent across the 14 EHR functions (see eAppendix Table). For 9 of the 14 functions examined, physicians in practices owned by a health maintenance organization (HMO) or other healthcare corporation had 125% to 494% higher odds of reporting the EHR function was easy to use compared with physicians working in physician-owned practices.

Using a nationally representative survey of office-based physicians conducted in 2011,

 in this study, we provide one of the first studies on the association of EHR characteristics with physician attitudes on ease of use of EHR functionalities related to MU.

 We found that both adoption of specific EHR functions and perceived ease of use were relatively high. In 2011, at least three-fourths of EHR adopters reported adopting and using 9 of 14 EHR functions related to MU; furthermore, at least 3 of 4 EHR adopters considered 10 of the 14 EHR functions as easy to use. However, we found that physicians considered less commonly available EHR functions as less easy to use.

We also found that some EHR characteristics were associated with physicians’ ease of using EHR functions. For 12 of the 14 EHR functions we examined, physicians with EHRs that met MU criteria were significantly more likely than physicians with other EHRs to report that EHR functions were easy to use. Technical assistance during EHR implementation played a significant, though less important, role in perceived ease of use compared with EHR certification. More clinical staff training was associated with lower ease of use for a few EHR functions. This finding might be due to the complexity of implementation, concomitant changes to practice work flow, or the possibility that physicians with less experience with EHRs required more initial training.

Our findings suggest that EHR certification and implementation support might play a role in physician attitudes toward EHRs. Although concern has been expressed that EHR vendors have developed poorly designed systems,

 our findings offer preliminary evidence that EHR certification is associated with perceived ease of use. The MU criteria were selected with the goal of enabling EHRs to support improved safety, quality, and efficiency of patient care.

 MU criteria require that EHRs have the capabilities to enable the collection of important patient data, in addition to computerized ordering and clinical decision support capabilities considered critical to improving quality of care.

 Our findings are consistent with prior studies that found higher EHR satisfaction and greater reporting of EHR benefits among physicians using EHRs that were relatively robust or met MU criteria.

 Since the advent of HITECH, the number of certified EHR vendor products has increased dramatically.

 The role of certification in influencing physician attitudes toward EHRs warrants further examination.

We did find variation in perceived ease of use across EHR functions, with more physicians reporting that less common EHR functions were more difficult to use. Prior research has found that perceptions of EHR usefulness improved as users moved beyond the implementation stage and acquired more EHR experience.

 Physicians may have less experience with more novel and advanced EHR functions that involve more complex work flow such as decision support, engagement with patients, or clinical data exchange with outside providers. Perceived ease of use was lowest for EHR functions requiring external coordination, including public health reporting, secure messaging with patients, and viewing data on quality measures. Improvements in the adoption and ease of use of public health reporting functions of EHRs may hinge on improvements in state and local public health systems’ capacity to exchange data with physicians.

 Our findings suggest that technical assistance from EHR vendors or RECs may play a role in improving ease of use of some EHR functions.

We assessed physician attitudes on ease of use of electronic health record (EHR) functionalities related to “Meaningful Use” and whether ease of use was associated with EHR characteristics.

Physician Attitudes on Ease of Use of EHR Functionalities Related to Meaningful Use

 To provide national estimates of physician capability to electronically share clinical information with other providers and to describe variation in exchange capability across states and electronic health record (EHR) vendors using the 2011 National Ambulatory Medical Care Survey Electronic Medical Record Supplement.

Survey of a nationally representative sample of nonfederal office—based physicians who provide direct patient care.

The survey was administered by mail with telephone follow-up and had a 61% weighted response rate. The overall sample consisted of 4326 respondents. We calculated estimates of electronic exchange capability at the national and state levels, and applied multivariate analyses to examine the association between the capability  to exchange different types of clinical information and physician and practice characteristics.

 In 2011, 55% of physicians had computerized capability to send prescriptions electronically; 67% had the capability to view lab results electronically; 42% were able to incorporate lab results into their EHR; 35% were able to send lab orders electronically; and, 31% exchanged patient clinical summaries with other providers. The strongest predictor of exchange capability is adoption of an  EHR. However, substantial variation exists across geography and EHR vendors in exchange capability, especially electronic exchange of clinical summaries.

In 2011, a majority of office-based physicians could exchange lab and medication data, and approximately one-third could exchange clinical summaries with patients or other providers. EHRs serve as a key mechanism  by which physicians can exchange clinical data, though physicians’ capability to exchange varies by vendor and by state.

Physicians’ ability to access key clinical information electronically in a timely manner can potentially improve the safety, quality, and efficiency of patient care.

		Our findings indicate that the majority of US physicians have the capability to exchange laboratory and pharmacy data electronically.

We assessed physicians' capability to electronically share clinical information with other providers and describe variation in exchange capability across states and electronic health record vendors.

Jennifer King, PhD

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