Telehealth platforms, which include both competitors and complements to traditional care delivery, will offer many benefits for both consumers and clinicians, and may promote increased specialization and competition in service delivery. Traditional medical services providers face a challenge similar to that faced by traditional taxicabs after Uber entered the marketplace: how to compete with a connection services platform that threatens to disrupt existing, regulated, and licensed service providers.

Through telehealth platforms, the sharing economy has entered the market for medical care delivery and begun to both challenge and complement traditional modes of delivery. Akin to Uber, telehealth platforms are connection services platforms that have the potential to disrupt existing, regulated, licensed service providers. As they develop, telehealth platforms will offer many benefits to both consumers and clinicians, and may promote increased specialization and competition in service delivery.The rise of telehealth represents a new frontier of the sharing economy, both complementing and challenging traditional players in the market for medical care delivery. Telehealth platforms have been forced to navigate local regulatory and competitive barriers to entry, in addition to attracting both patients and clinicians. Their development path has mirrored the recent experience of on-demand driver-for-hire services like Uber, Lyft, and other companies, which serve as electronic platforms connecting drivers and passengers more efficiently than traditional taxis. Although there are differences in the 2 marketplaces, telehealth companies may be able to take advantage of modern technology in a similar fashion.

Uber is a software-based technology platform that uses GPS-enabled smartphone technology to connect drivers and riders in real time. Drivers can be professionals driving for hire full time or nonprofessionals who meet basic requirements (eg, background check, vehicle quality) and seeking to monetize their free time. Ride-sharing companies compete against traditional taxis, which match drivers with riders through street hails or via telephone dispatch. Telehealth platforms are following a similar path: initially serving as a platform to facilitate physician utilization of their medical licenses when not seeing patients in person, while concurrently migrating into the market for traditional care delivery.

The Rise of the Sharing Economy: Ride-Sharing Platforms

Historically, the taxi marketplace has been characterized by significant economic regulation, such as restrictions on entry (eg, a fixed number of taxi medallions), limitations on site-specific customer access (eg, a prohibition on airport pickups), special license requirements for drivers, and other regulatory barriers to entry. Therefore, when ride-sharing platforms entered the marketplace, many local jurisdictions sought to apply regulations designed to govern the traditional taxi industry.

Even prior to entry by ride-sharing platforms, many traditional taxi regulations traditionally had “no persuasive economic rationale,” while imposing a “disproportionate burden on low-income people,”1 as Federal Trade Commission staff found in 1984 when examining these restrictions. Entry restriction and the concordant negative effects on output were not counterbalanced by consumer benefits. Resistance to entry by new ride-sharing platforms came primarily from those who benefited from regulatory barriers to competition: existing taxi companies and dispatch services.

Despite navigating significant regulatory entry barriers, ride-sharing platforms achieved market penetration by reducing connection costs for drivers and riders, providing innovative pricing services, such as real-time, consumer demand—driven pricing; and convincing regulators and lawmakers to adopt entry-friendly policies. Consumers benefited from decreases in price, search time, and wait time, coupled with increased convenience. Generally, platforms themselves take steps to ensure quality, safety, and price by making information more transparent to consumers through reputation review systems. Subsequently, consumers are satisfied, with over 140 million trips on ride-sharing services in 2014.2 Economists’ estimates of the consumer benefits caused by Uber’s entry into the taxi marketplace range as high as $6.8 billion in 2015 alone.

 In addition to consumer benefits, ride-sharing platforms benefit drivers, as well: drivers select their working hours and there is evidence that ride-sharing platforms are more efficient, allowing drivers to spend more time transporting passengers instead of searching or waiting for new fares.

Expansion of the Sharing Economy: The Rise of Telehealth Platforms

Modern telehealth platforms present a variety of unique benefits to both consumers and physicians. Patients may seek telemedicine services for episodic care, specialty management of chronic illnesses, and primary care. Consumers stand to experience increased convenience in accessing medical services and encountering a variety of connection services platforms, choosing the modality they prefer based on their own unique characteristics as consumers. In contrast to traditional, office-based services, patients may access medical services in an “on-demand” fashion, engaging in instant message exchanges, video chats, and remote exams. Lastly, clinicians will also benefit from improved work-life balance due to flexible practice hours and location, in addition to potential increased earnings.

Both consumers and physicians will also benefit from the systems innovations that will accompany increased use of telehealth platforms. For example, in order to mitigate malpractice risk, telehealth platforms will need to differentiate between, and then route, patients requiring higher-acuity or more specialized care to the right point and service provider. Telehealth platforms may also innovate in how they regulate quality and safety standards and screen and select providers, and in how consumers rate service providers—a common feature of many platforms in the sharing economy. By facilitating information collection and making it transparent, telehealth platforms have the potential to protect and empower consumers.

Telehealth platforms can further expand access to general medical services by reaching out to consumers in underserved areas and providing access to highly specialized consult services. Currently, medical care delivery is highly local, with physicians practicing in their towns or cities and patient interaction occurring primarily in person in an office or hospital setting. Highly localized delivery, coupled with state-based licensure systems, potentially restricts the supply of physicians and limits consumer access to, and price competition for, telemedicine services. Telehealth platforms offer an opportunity to expand access without major financial investment, in addition to increased price competition for some medical services.

Empirical Evidence of Benefits With Limited Harms

Early market evidence supports the conclusion that telehealth platforms yield substantial consumer and clinician benefits. Teladoc, a publicly traded telehealth platform company, had 576,000 visits in 2015 with 95% consumer satisfaction,5 while physician hourly income increased 50%. These and other benefits will power market growth, with estimates of the current telemedicine market ranging from $1.9 billion to $30 billion. Despite this rapid growth, some have raised concerns regarding the safety of telehealth platforms as a rationale for restricting the market entry and growth of telehealth platforms.

In addressing this concern, one must note that physicians have successfully practiced telemedicine for over 100 years by using the telephone to conduct physician-to-physician consults, diagnose and treat patients, prescribe medications, and order diagnostic tests. Like the taxi industry’s move from telephone dispatch to matching through GPS-enabled smartphones, modern telehealth platforms represent a natural outgrowth of the practice of medicine, using modern technology to grow a systems approach to safety and quality.

Competition between competing connections services platforms—telehealth and physical plant health systems (ie, hospitals)&mdash;will serve to accelerate the development of these safety and quality systems, in addition to spurring competition along the dimensions of cost, access, and convenience. Traditional physical plant health systems have existed since the 1400s and have been continuously improving: the American College of Surgeons published the first manual of hospital standardization in 1920 and established the Joint Commission in 1950. Due to increased competition, we expect that safety systems for telehealth platforms will rapidly evolve.

Telehealth platforms have the potential to disrupt the market for supplying medical services. Compared with ride-sharing, medical care delivery is complex, varying in acuity, intensity, and degree of specialization. Some medical services, due to training, complexity, or infrastructure requirements, cannot be substituted, whereas other services are substitutable and are not provider-specific. Due to the well-documented shortage of primary care and other physician specialties,6 this potential substitutability is currently not transparent to consumers. Telehealth platforms will improve the transparency of the potential substitutability of certain service providers. For example, for common, episodic needs, patients might not require an ongoing, personal relationship with a physician in order to receive safe and effective care.7 Thus, telehealth platforms may connect patients to a capable and appropriate provider for on-demand services, as opposed to an already known physician.

Likewise, for a variety of low-acuity and general medical services, telehealth platforms may offer more convenient or lower-priced care, supplanting traditional physical plant—focused delivery platforms. Telehealth platforms can be complements or competitors. At this stage in the growth of telehealth opportunities, health systems and plans are in a position to think strategically about telehealth and be proactive in defining their relationship to this platform. They can develop, buy, or collaborate with telehealth platforms. Thus far, their response is varied: from partnering, as demonstrated by the Highmark BCBS’s Teladoc partnership, to development, as exemplified by Kaiser Permanente’s new video chat service. Practicing clinicians face similar choices and can adopt and steer the coming wave of transparency of safety, quality, and consumer ratings or face increased competition.

Telehealth platforms will promote increased competition in medical care delivery. As competing platforms offer on-demand connection services to patients and physicians, consumers will benefit from having access to another mode of medical care—one that does not require traveling to a special location or advance scheduling&mdash;while clinicians will benefit from increased flexibility in how, when, and where they work. Telehealth platforms have the potential to provide significant consumer benefits and promote increased competition in the healthcare system.

 Kenan-Flagler Business School, University of North Carolina (BJM), Chapel Hill, NC; Federal Trade Commission (DWM), Washington, DC; The Johns Hopkins University School of Medicine (CWS), Baltimore, MD.

 This paper does not reflect the views of the Federal Trade Commission or any of its Commissioners. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (BJM, DWM, CWS); drafting of the manuscript (BJM, DWM, CWS); critical revision of the manuscript for important intellectual content (BJM, DWM, CWS); and supervision (BJM).

 Brian J. Miller, MD, MBA, MPH, Adjunct Assistant Professor, UNC Kenan-Flagler Business School, 300 Kenan Center Dr, Chapel Hill, NC 27599. E-mail: millerbj@unc.edu.

1. Frankena MW, Pautler PA. An economic analysis of taxi regulation. Federal Trade Commission website. https://www.ftc.gov/sites/default/files/documents/reports/economic-analysis-taxicab-regulation/233832.pdf. Published May 1984. Accessed November 5, 2016.

2. Huet E. Uber says it’s doing 1 million rides per day, 140 million in last year. Forbes website. http://www.forbes.com/sites/ellenhuet/2014/12/17/uber-says-its-doing-1-million-rides-per-day-140-million-in-last-year/. Published December 17, 2014. Accessed November 5, 2016.

3. Cohen P, Hahn R, Hall J, Levitt S, Metcalfe R. Using big data to estimate consumer surplus: the case of Uber [NBER working paper No. 22627]. National Bureau of Economic Research website. http://www.nber.org/papers/w22627. Published September 2016. Accessed November 5, 2016.

4. Cramer J, Krueger AB. Disruptive change in the taxi business: the case of Uber. 

5. Teladoc, Inc: form 10-K [2015]. US Securities and Exchange Commission website. https://www.sec.gov/Archives/edgar/data/1477449/000155837016003803/tdoc-20151231x10k.htm. Published March 3, 2016. Accessed November 5, 2016.

6. Dall T, West T, Chakrabarti R, Iacobucci W; IHC Inc. The complexities of physician supply and demand projections: from 2013 to 2025: final report. Association of American Medical Colleges website. https://www.aamc.org/download/426242/data/ihsreportdownload.pdf. Published March 2015. Accessed November 5, 2016.

7. Uscher-Pines L, Mehrota A. Analysis of Teladoc use seems to indicate expanded access to care for patients without prior connection to a provider. 

. 2014;33(2):258-264. doi: 10.1377/hlthaff.2013.0989.&ensp;

Articles

WEB EXCLUSIVE

Telehealth platforms will promote increased competition in the marketplace for medical care delivery, benefiting both consumers and clinicians.

Telemedicine and the Sharing Economy:  The "Uber" for Healthcare

 To assess the importance of commonly identified issues in electronic health record (EHR) implementation using item response theory (IRT).

 Secondary data from the 2012 American Hospital Association’s Annual Survey Information Technology Supplement were used in the analyses. Results were compared and contrasted with the standard descriptive statistic frequencies that have been used to guide most recommendations made using the same data.

 IRT was used to measure the magnitude of difficulty that particular challenges pose in implementing EHRs that meet federal guidelines for Meaningful Use.

 The IRT analyses yielded significantly different results from descriptive statistics in estimating the magnitude of specific EHR implementation challenges. In particular, IRT revealed that “obtaining physician cooperation” and “ongoing costs of maintaining and upgrading systems” were the most challenging implementation features. However, the frequency counts identified “upfront capital costs” and “complexity of meeting Meaningful Use criteria within implementation timeline” as the most challenging implementation features.

 For managers and policy makers, having an accurate assessment of EHR implementation challenges is essential to designing effective programs. IRT provides a statistical approach that allows prior studies to be assessed more accurately and future studies to retain the easier-to-use, check-all-that-apply survey structure while gaining valuable information.

	Many surveys of hospital administrators have sought to identify barriers to the Meaningful Use of electronic health record (EHR) technology. However, the surveys used often lack the precision to provide a list of barriers ordered from most difficult to least difficult. Instead, surveys tend to report the most common or well-known barriers to adoption. 

		The analysis identifies obtaining physician cooperation as the most challenging barrier to EHR adoption. 

		Some frequently cited barriers, such as dealing with the complexity of the Meaningful Use requirements are shown to be less challenging.

Implementing an electronic health record (EHR) in a hospital brings many challenges—some bigger than others.

 Managers often recognize common but readily addressed EHR implementation issues, while more daunting challenges are identified less frequently or later in the process. Prior research has shown that hospitals are slow to implement key EHR features that enhance patient safety and care quality.

 In particular, professional buy-in and implementation costs have been identified as major impediments to EHR use.

 The latter issue—implementation cost barriers&mdash;is further exacerbated because demonstrating an EHR’s return on investment is difficult.

 Determining which EHR implementation issues hinder the widespread “Meaningful Use” of the technology is important.

The problem in identifying and prioritizing EHR implementation issues stems from managerial knowledge gaps arising from a lack of experience with the process. To identify knowledge gaps, professional organizations survey health system leaders about their EHR implementation challenges.

 The American Hospital Association (AHA)’s Annual Survey Information Technology (IT) Supplement (referred to hereafter as “AHA Survey”) asks managers to pick among a list of options for all EHR implementation challenges that their hospital currently faces or anticipates. Although this approach is likely to capture how common an issue is among hospitals, it does not explicitly address how difficult a specific challenge is. Moreover, using response frequencies as a proxy measure for EHR implementation difficulty may lead to inaccurate inferences about the areas where managers should focus their efforts.

The purpose of this paper is 2-fold: first, the challenges hospital administrators face in implementing an EHR were examined using a holistic approach to item difficulty; second, an established psychometric methodology was employed to quantify the relative difficulty levels of various EHR implementation challenges, based on managers’ responses to a “pick all that apply” questionnaire. Item response theory (IRT)

 was used to analyze the AHA Survey’s questions related to EHR implementation challenges in meeting federal requirements for Meaningful Use.

 Results were compared and contrasted with the standard, descriptive statistics that have been widely reported.

A 2014 literature review of hospital EHR implementation studies identified 6 main categories of barriers

: 1) financial capabilities, related to the total cost of EHR system ownership (acquisition and operation); 2) vendor selection, related to integrating ITs into the workplace; 3) IT staffing, related to having employees with prior implementation experience; 4) health system culture, related to organizational collaboration challenges; 5) organizational complexity, related to the bureaucracy and change management capacity of the hospital; and 6) clinical professional barriers, related to the ITs’ impact on providers’ abilities to deliver care. Many dimensions play roles in the EHR implementation process, but 2 themes emerge consistently across the 6 domains identified in the prior research: experience with EHR implementation and the complexity of managing the particular barrier.

The common factors that affect a barrier’s potential importance can be put into a 2-dimensional framework of barrier complexity and experience. The experience dimension can be translated into where a health system is in its tacit knowledge about EHR implementation barriers. IRT is specifically designed to order respondents’ relative knowledge about specific content. The model is presented in the 

The most complex managerial challenge when implementing an EHR is gaining clinician buy-in.

 By both social and legal conventions, physicians have the professional autonomy to make clinical decisions.

 The introduction of an EHR with predefined order sets, automated reminders, and decision support is perceived by many physicians as a direct attempt to diminish their professional autonomy. In addition, many physicians are not directly employed by health systems, further reducing their organizational commitment. Taken together, the resistance to managerial oversight and lack of organizational identification make gaining physician buy-in a major issue. Nurses have a lesser degree of professional autonomy and are typically employed by the hospital; hence, they tend to be less of a management challenge than physicians when it comes to EHR implementation and use. Nevertheless, the physician—nurse relationship is critical to EHR implementation success.

In the Figure, the clinical staff response (items 3 and 4) begins during the EHR adoption phase. Normatively, doctors and nurses should be brought in at the earliest stage. In practice, they tend to be included after the decision to adopt the EHR has been made during vendor selection.

 The importance of clinical staff engagement is an ongoing management challenge, as the degree of clinical functionality in the EHR and required care process documentation is ever-increasing. Therefore, management experience with implementing more sophisticated functionalities is a learned capability that continues ad infinitum.

The financial capacity (items 1 and 2) of the health system to support the EHR is also an ongoing challenge. The initial cost of purchasing an EHR is considerable, but knowable; however, it is the ongoing cost of operating the systems that many managers underestimate.

 Losses in productivity have a concomitant impact on revenues,

 and the double impact of unexpectedly high ownership costs and reduced revenue cause managers significant challenges. Managers typically become fully cognizant of these total cost of ownership (TCO) issues only after they begin the implementation process; the start date in the Figure reflects this fact.

Similar to the experience-informed learning that occurs with the recognition of TCO issues, the additional levels of organizational complexity (item 10) that are part-and-parcel of an EHR implementation become clearer over time.

 Post-EHR implementation, health systems have to roll out additional functionalities to meet organizational needs and comply with ever-increasing regulations. Hence, this construct also encompasses the entire EHR lifecycle.

Organizations’ cultures (item 9) are also impacted by the introduction of new technologies and the workflow changes that come with them.

 Depending on when organizational members are engaged in the EHR adoption and implementation processes, the impact on organizational culture can start very early on and continue indefinitely. One means of making the cultural shift a positive one is to engage staff members during the vendor selection phase to promote buy-in.

Vendor selection and operations are closely related to issues of EHR product certification (item 6), ensuring system security against privacy breaches (item 5), and ongoing system support (item 7). A major criterion in vendor selection is to reduce the management burden related to these items.

 Therefore, they appear lower on the management complexity axis. Despite being lower on the complexity dimension, ongoing vendor relations mean that it is an enduring management activity.

The lowest EHR implementation challenge construct, in terms of management complexity, is information technology staffing (item 8).

 The employees dedicated to supporting the technology are easier to manage because, unlike that of clinical staff, their job description explicitly includes following the direction of leadership. Nevertheless, managers often underestimate the number of IT staff needed to support an EHR.

 Therefore, the management challenge continues well into the implementation phase until the organization reaches some level of homeostasis with respect to the EHR.

The EHR adoption/implementation challenges used in the AHA Survey are not a perfect fit to the constructs listed in the literature review of Boonstra and colleagues.

 Nevertheless, the AHA Survey does touch on each of the major domains in some fashion and serves as a good starting point from which to explore EHR implementation challenges from a health system perspective.

 was funded by the Office of the National Coordinator for Health Information Technology (ONC) and sent to 5756 US hospitals (including those not members of the AHA). The survey had a 59% response rate for a sample of 3396. The survey question, “What is (are)/would be the primary challenge(s) in implementing an EHR system that meets the federal requirements for meaningful use? (Please check all that apply)” was analyzed. The item offered 10 structured responses, including: 1) upfront capital costs/lack of access to capital to install systems, 2) ongoing cost of maintaining and upgrading systems, 3) obtaining physician cooperation, 4) obtaining other staff cooperation, 5) concerns about security or liability for privacy breaches, 6) uncertainty about certification requirements, 7) limited vendor capacity, 8) lack of adequate IT personnel in hospital to support implementation/maintenance, 9) challenge/complexity of meeting all Meaningful Use criteria within implementation timeframe, and 10) complexity associated with coordinating decisions with system-level leadership. These responses were used in the IRT phase.

IRT is a collection of modeling techniques for analyzing item-level data obtained to measure variation between respondents. Unlike regression methods, IRT does not require a dependent variable against which to parse variance; instead, it compares the items against one another to establish the pattern of responses from a sample of survey or test takers. In addition, by evaluating the confidence intervals (CIs) of the item difficulty measurements, it is possible to assess if there are potentially missing items in the scale. When sorted by item difficulty, each CI should overlap with the next item in the list. CIs that do not overlap indicate that there may be another item that would improve the classification of survey respondents. The CI comparison can be used to detect potential gaps in an assessment scale’s items.

One important assumption of IRT is that the scale being assessed is unidimensional. To test this assumption, a factor analysis is performed in the items to ensure they are measuring a common construct (see 

] for a description of the factor analysis). Following that procedure, the IRT itself can be undertaken to assess both item and respondent characteristics (see 

 display the characteristics of the hospitals in the sample and the correlations among the responses to the 10-item questionnaire, asking: “What is (are, or would be) the primary challenge(s) in implementing an EHR/EHR system that meets the federal requirements for Meaningful Use? (Please check all that apply).” These items were factor-analyzed using the principal factors and unrotated modeling specifications. The amount of variance explained by the first factor was 73.9%, and the factor loading for each item was ≥0.460. The ratio of the Eigen value for the first factor (3.184) to the second (0.839) was 3.8. Overall, these results indicated that the responses form a single construct, with varying levels of organizational challenge being the distinguishing feature.

The results of the IRT analyses are presented in 

, ranked by item difficulty. The “obtaining physician cooperation is a primary challenge” response had the highest item difficulty rating (

 = 1.039), but was the fourth most frequently identified challenge (43.12% of responses). The 2 challenges related to EHR financing— ongoing costs of maintaining and upgrading systems” (

 = 0.286) and “upfront capital costs/lack of access to capital to install systems” (

 = 0.267)—were the second and third most difficult items, respectively, based on the IRT analysis. However, these 2 items were the first (upfront capital cost) and fifth (ongoing cost) most common responses in the descriptive statistics using frequency counts. In addition to being a major challenge, the “ongoing cost” response also had the highest discrimination of any item (

 = 1.696). IRT analysis re-ordered the challenges relative to the response frequency approach for assessing item importance.

The fourth and fifth items based on the difficulty measure related to technical aspects of implementing an EHR. “Challenge/complexity of meeting all Meaningful Use criteria within implementation timeline” was the fourth most difficult challenge (

 = —0.198). This item also had lower discrimination (

 = 0.993). The “lack of adequate IT personnel in the hospital to support implementation/maintenance” item was the next most difficult (

 = —0.303) and had a slightly higher discrimination index (

 = 1.041) than the “…meeting all Meaningful Use criteria…” item. “Obtaining other staff cooperation” item (

 = —1.150) did not group with the other personnel challenge related to gaining physician buy-in. However, it did have relatively high discrimination (

 = 1.331) compared with the physician item (

 = 1.039). The last item with good discrimination was “concerns about security or liability for privacy breaches” (

 = 1.303). The remaining items had both low difficulty and discrimination measures, indicating that these issues are not impacting EHR deployment.

Examining the item difficulty CIs, there are 4 potential gaps in the scale used to measure EHR implementation challenges. The first gap appears between the items “upfront capital costs/lack of access to capital to install systems” and “challenge/complexity of meeting all Meaningful Use criteria within implementation timeline.” There are 4 more potential gaps noted in Table 3. Potential missing items are discussed in the next section.

The findings have both policy and methodological implications. “Obtaining physician cooperation” is the biggest challenge to implementing an EHR based on the IRT analysis. However, the Meaningful Use program provides no explicit rewards to doctors in the hospital environment. In the ambulatory setting, individual physicians are targeted for rewards if they are early adopters, and penalties if they procrastinate about EHR implementation. The rationale for the rewards in the ambulatory environment was to compensate doctors for their productivity levels likely suffering in the near term after implementation. The effect of an EHR implementation in the hospital is similar for doctors, but no offsetting compensation is made available. Hence, increased resistance from physicians is likely to impact hospital managers charged with EHR implementation.

The IRT results also offer methodological insights. “Obtaining physician cooperation” was the most challenging EHR implementation barrier in the IRT results. These findings are similar to those of other studies determining that many physicians do not routinely use EHRs to improve care quality.

 The “upfront capital cost” response was the most frequently identified EHR implementation challenge, but it was only the third most difficult measure based on an IRT analysis. The switching of positions from the counts listing to the IRT is an indication that descriptive statistics alone do not provide a complete or accurate assessment. The “ongoing…” and “upfront…” cost measures were also very challenging for hospitals.

 The Item Discrimination Index is highest for the challenge related to “ongoing costs.” Therefore, this concern is the item most likely to differentiate respondents.

Although inferential statistics in general and logistic regression, in particular, have the potential to highlight differences in hospitals’ EHR implementation challenges, they also have limitations. Logistic regression models require a dependent variable to assess an item’s relative importance. For example, the study by Adler-Milstein and colleagues,

 which analyzed the same battery of EHR implementation challenges using Meaningful Use attestation as the dependent variable, found that cost measures, rather than physician cooperation, were the biggest challenge. The difference arises because EHR implementation challenge items are not being compared with one another; instead, variance is being allocated across them in a zero-sum fashion.

In contrast, IRT identifies that the “challenge/complexity of meeting Meaningful Use within specified timelines” was a major challenge regardless of other organizational characteristics. In the regression analysis, both groups believed the issue to be important, but no statistical difference was detected, so the item was not considered to be significant in the discussion. Moreover, the relatively low Item Discrimination Index for the “complexity of meeting Meaningful Use within specified timelines” measure indicates that the variable will not differentiate hospitals on other characteristics. Therefore, this is a relatively difficult challenge common to most facilities regardless of other characteristics.

IRT identifies the relative placement of EHR implementation barriers by utilizing response patterns within individual hospitals to inform item hierarchical ordering. Although the Meaningful Use attainment measure can be used as proxy for EHR implementation difficulty, it did not assess the underlying organizational capabilities and resources that make some barriers more difficult than others for a particular hospital. In that respect, the regression analysis approach is underspecified, given that omitted variables are likely. Therefore, having a means of assessing organizational barriers/challenges that does not require other constructs to be fully identified allows for alternative explanations as to why some EHR implementation challenges are greater than others.

There are 3 interpretations for why the IRT produced different results than either the frequency count or logistic regression approaches used in the other studies. From a learning perspective, those facilities with more experience will have a better understanding of different challenges’ operational impact. Therefore, those that have achieved Meaningful Use Stage 2 are more likely to prioritize challenges relying on the benefit of hindsight. A second interpretation relates to the innovation adoption-implementation lifecycle. Hospitals further into the process will be facing different challenges than those just beginning. Specifically, “obtain physician buy-in” is likely to be easier when the EHR is an abstract idea and more difficult when the reality of using the systems is encountered. Organizational capacity provides a third view for considering EHR adoption-implementation challenges. Under this interpretation, hospitals with better management will identify the more intractable EHR adoption-implementation challenges with greater frequency.

 Through any of these perspectives, the “obtaining physician cooperation” challenge would be the furthest along on the learning curve.

Another important feature of the IRT analysis was the identification of potential gaps in the EHR implementation challenge scale as it was fielded. The potential gap between “upfront capital costs/lack of access to capital to install systems” and “challenge/complexity of meeting all Meaningful Use criteria within implementation timeline” is at the highest item difficulty level. The “upfront capital…” item is clearly written, but the “challenge/complexity of meeting all Meaningful Use criteria within implementation timeline” response has 2 potential issues.

First, the “challenge/complexity of meeting all Meaningful Use criteria within implementation timeline” item is poorly phrased. The part of the question using the phrase “challenge/complexity” lacks specificity. What aspect of the implementation is “complex”? EHR implementations require health systems to “build” many of the user interfaces, create order sets, and link to other information systems. Respondents may be responding to this aspect of EHR implementation complexity. Alternatively, EHR implementations require significant staff training evolutions that are different than the concepts captured in the “obtaining staff/physician cooperation” items. There is no mention of staff training in the scale as it was put to the respondents.

A second issue is that the “challenge/complexity of meeting all Meaningful Use criteria within implementation timeline” item has 2 questions embedded within it. The “challenge/complexity” phrase is one element and the EHR “…implementation timeline” is a second distinct concept. Creating 1 or 2 time-related items would be an improvement. One item would address meeting the timelines set by the organization for implementing the EHR. The second would assess the health system’s ability to meet the Meaningful Use target timelines. Given health systems could join the Meaningful Use program at different points without penalty; the 2 timelines are not necessarily the same.

Disaggregating and more accurately specifying the concepts in the “challenge/complexity of meeting all Meaningful Use criteria within implementation timeline” question could also fill in the gaps that appear in the scale. Per the earlier discussion, breaking out the EHR “build” features could help fill the gap above the “lack of adequate IT personnel in the hospital to support implementation/maintenance” and “obtaining other staff cooperation” items. One possible solution is to look to the survey itself. The survey asks respondents to list “other barriers to EHR implementation.” Exploring those might yield potential items to fill in the gaps identified in the scale as it is currently employed. The potential “staff training” and “EHR development” items discussed above were drawn from that list.

Having EHR implementation challenge scales that are lacking potential items is an important issue for policy makers. The ONC funds research into areas that have been identified as impediments to effective EHR use. Lacking accurate and complete feedback from health system administrators diminishes the ONC’s ability to target EHR implementation challenges.

Similar to having incomplete information, having the EHR implementation challenges incorrectly sorted may lead to misguided investment strategies. As the IRT analysis demonstrates, “obtaining physician cooperation” is the most difficult item. Changing the culture of the medical community is a far more difficult undertaking than funding EHR purchases and implementations. To alter the physician community’s beliefs, it may be necessary to start while physicians are in medical school. Such a transformation would take a generation to implement.

 Ongoing organizational training may help to improve EHR acceptance.

Both EHR implementation and methodological lessons can be drawn. With respect to EHR implementation, among the challenges evaluated, “obtaining physician cooperation” is the most difficult part of the process. For those hospitals in the initial stages of EHR implementation, the importance of gaining physician buy-in may not be apparent. Similarly, the importance of managing ongoing ownership and operating costs for EHR systems may not be fully understood by early adopters compared with late adopters. Public policies that provide rewards for implementing more sophisticated EHR functionalities can be tailored to ameliorate these challenges. The IRT analysis also helps quantify the relative difficulty of each challenge that health systems face.

Many surveys of healthcare providers and managers employ multiple selection items (eg, “pick all responses that apply”) to reduce respondent burden. To interpret which responses are most significant, researchers typically employ either simple statistics, such as frequency counts to order items, or use inferential approaches, like linear regression, to measure importance in relationship to some other factor. Neither approach is ideal. The frequency counts do not provide an accurate picture of how difficult a particular response is vis-à-vis other options. Frequency is about how often something is encountered, not necessarily how important a particular challenge is to the implementation success. The regression approach is limited to addressing the relationship of survey responses to a particular variable of interest rather than allowing them to be context-free and self-referent.

The IRT approach addresses the 2 aforementioned shortcomings and provides an effective complement to those analytic strategies. For those designing surveys, including the IRT approach as part of the analytic strategy should be established a priori. For those analyzing secondary data with "pick all that apply" options, using IRT in conjunction with other techniques could yield a more complete picture of the phenomena.

 Health Policy and Management, Johns Hopkins Bloomberg School of Public Health (KSC, HK, MP, EWF), Baltimore, MD.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

It is unclear which barriers cause the greatest threats to the successful implementation of an electronic health record (EHR). This paper prioritizes the potential threats to EHR adoption using a novel analytic strategy: item response theory.

Assessing Electronic Health Record Implementation Challenges Using Item Response Theory

 As defined by the Affordable Care Act, health homes seek to improve healthcare coordination through data exchange and health information technologies; however, few examples of how to use such technology are available. The present effort describes a payer—provider patient registry for behavioral health home service.

 An observational study design was used to describe characteristics of individuals identified by the payer—provider patient registry.

 In Pennsylvania, behavioral health agencies serve as health homes, with support by a behavioral health managed care organization (BHMCO) in the absence of a state waiver for health homes. The BHMCO initiates a priority patient registry monthly based on diagnoses for serious mental illness (SMI) and at least 1 chronic physical health condition. Providers contribute health data through a secure Web-based portal that become part of the registry and identify new participants.

 We identified 3759 individuals in the priority patient registry; 91% were identified by the payer. Most commonly, individuals with SMI were identified with hypertension (39%), asthma/chronic obstructive pulmonary disease (27%), hyperlipidemia (20%), and diabetes (18%). Annual behavioral health Medicaid expenditures for individuals in the 12 months prior to appearing on the registry averaged $14,685 per individual. Twelve percent of registry participants had annual behavioral health care expenditures over $25,000.

 The use of claims data and health assessment information can identify individuals presenting with complex healthcare needs that may benefit from behavioral health home service.

	Behavioral health homes seek to improve healthcare coordination for individuals with serious mental illness and chronic physical health conditions by using data exchange and health information technologies. 

		Monthly registries initiated by a behavioral health managed care organization utilizing behavioral health and pharmacy claims identify individuals that may benefit most from health homes. 

		Behavioral health providers contribute health data securely that become part of the registry in an ongoing manner.

		The behavioral health organization identified patients at a rate 10 times higher than providers. 

		Health data revealed high rates of hypertension medication in individuals with serious mental illness.

 provides an opportunity for states to support healthcare coordination within health homes. The core components of a health home are patient-centered whole-person care, integrated physical and behavioral health care, inclusion of family support, and a connection to community support services. Health homes are targeted to individuals with 2 or more chronic health conditions, 1 chronic health condition with an increased health risk for a second chronic condition, or 1 chronic health condition if it is a serious mental illness (SMI). The goal of the health home is to improve the quality of care for publically funded individuals with chronic conditions while reducing unnecessary hospitalizations, readmissions, and emergency department utilization. To accomplish these goals, healthcare providers must develop methods to: 1) identify individuals with chronic conditions, 2) facilitate comprehensive care, and 3) monitor and track health outcomes over time. Utilization of health information technology (IT) to aid in the coordination of services and ongoing monitoring of outcomes can improve health homes and is strongly encouraged by the ACA.

Behavioral health homes, in which behavioral health providers seek to coordinate care, are critically needed to address the premature mortality in individuals with SMI associated with chronic disease and lifestyle factors.

 Individuals with SMI have higher rates of factors associated with mortality, such as poverty, social isolation, side effects from antipsychotic medication, and poor access to quality healthcare.

 These individuals also have high rates of lifestyle factors that may contribute to disease, such as smoking, sedentary lifestyle, and a diet low in fruits and vegetables.

 Thus, with the potential to reach those with mental health disorders that may not be connected to physical health, behavioral health care facilities are important settings for health homes. Under the ACA, behavioral health home providers are encouraged to use patient and/or disease registries; however, little guidance is available to define how best to develop and maintain a patient registry and use health IT, especially in smaller community-based health centers with few electronic health resources.

In Pennsylvania, even though the state has not applied for the Medicaid waiver associated with this model, a behavioral health managed care organization (BHMCO), Community Care Behavioral Health of the UPMC Insurance Services Division, supports behavioral health providers—including community mental health and substance use disorder (SUD) treatment facilities&mdash;in the implementation of a behavioral health home model, utilizing electronic patient registries and training in wellness. This BHMCO manages mental health and SUD services for over 950,000 Medicaid-eligible adults and children across the state.

To support behavioral health home services, monthly patient registries are shared between payer and providers. The purpose of the registry is to aid provider staff in identifying and targeting health home services to individuals with SMI and chronic physical health conditions who may benefit most from integrated care. The objective of the present quality improvement effort is to describe a payer—provider process that utilizes behavioral health and pharmacy claims data, as well as health assessment information, to identify individuals for health home services.

Identifying Individuals for Health Home Service

Health homes are embedded within case management, psychiatric rehabilitation, and certified peer specialist services. Although all individuals seen at the participating providers may utilize health home services, individuals with SMI and at least 1 known chronic physical health condition are targeted and prioritized for inclusion. Prioritized individuals are Medicaid-funded adults, aged 18 to 64 years, who reside in a county within the BHMCO network in Pennsylvania. Individuals must also have an SMI diagnosis, which includes schizophrenic disorders, episodic mood disorders, or nonorganic psychoses. Individuals must have current service utilization at the agency, with behavioral health home services defined as having at least 2 claims within 6 months for case management, psychiatric rehabilitation, or a certified peer specialist service. Finally, prioritized individuals must have at least 1 chronic physical health condition. The national drug codes from pharmacy claims were used to identify individuals receiving medications within the prior 180 days as proxy for hypertension, diabetes, hyperlipidemia, chronic obstructive pulmonary disease (COPD), or asthma. Psychotropic medication utilization is also included on the registry with indication of antipsychotics, antidepressants, mood stabilizers, opiates, and benzodiazepines.

Each month, the BHMCO identifies priority individuals for behavioral health home service using the above criteria and a 6-month look-back period. All individuals who meet the criteria each month are added to the registry, which is sent to the provider using a secure file transfer protocol that encrypts data. Additionally, the file is password protected and the provider is alerted to the password by a separate mechanism. The registry is produced in Microsoft Excel for ease of manipulation by the receiving agency staff to sort the registry by individual and/or date, as needed.

Data on the registry include name, date of birth, date/month appearing on the registry, behavioral health medication, and physical health medication category. Providers can add information to the registry using a shared health online tool (SHOT) via an authenticated session on a secure Web portal that utilizes 128-bit encryption. Information that can be added to the registry on behalf of a provider includes number of wellness contacts, height, weight and body mass index (BMI), smoking status, existence of an SUD, and pregnancy. Information added to the registry by a provider through the secure Web portal is merged with data from the BHMCO and appears on the registry for that individual the following month. Behavioral health home providers are also trained in wellness coaching based on the 8 domains of wellness

 and may engage individuals in wellness planning. In addition, a provider can add and remove individuals from the behavioral health home registry based on eligibility. Providers are expected to include priority individuals in health home service and improve healthcare coordination, but individuals ultimately chose the level of engagement for wellness activities.

Activities conducted to provide identified registry information to providers for the purpose of targeting appropriate services were approved as quality improvement by the University of Pittsburgh’s Total Quality Council. The BHMCO has other case management and service initiatives to address Medicaid-funded children and adults that may be at higher risk for inpatient hospitalization and readmission where behavioral health homes are not yet available.

Information from calendar years 2014 and 2015 were summarized to define the population on the priority patient registry and process described above. Demographic and pharmacy data were derived from administrative data from the Pennsylvania Department of Human Services. Behavioral health diagnoses, service utilization, and expenditures were derived from the BHMCO’s administrative claims data. Health assessment information (BMI, smoking status) was derived from information that providers added to the registry from the SHOT.

Twenty-nine providers offered behavioral health home services. The majority of providers (90%) offered behavioral health home services in case management, in certified peer specialist service (52%), and in psychiatric rehabilitation (24%). We identified 3759 unique individuals on the priority patient registry; the majority of the individuals on the registry were female (64%). Almost all (99%) were non-Hispanic; 79% were European American, 17% were African American, and 4% were other or more than 1 race. The average age was 41.9 years (standard deviation, 12.9). The majority of the individuals on the registry were identified by the BHMCO (91%; n = 3430) versus the provider (9%; n = 329).

Characteristics of individuals identified on the priority patient registry are presented in the Table. The most common SMI diagnoses were major depressive disorder, bipolar disorder, schizoaffective disorder, and schizophrenia. The most common behavioral health medications for individuals with SMI were antidepressants, antipsychotics, mood stabilizers, opiates, and benzodiazepines. The medications most commonly utilized for physical health conditions indicated hypertension, asthma/COPD, hyperlipidemia, and diabetes. Characteristics were similar for individuals identified as priority by providers.

There was variation in behavioral health care expenditures: on average, annual Medicaid behavioral health expenditures were $14,685 per individual in the year prior to appearing on the registry. Twelve percent of registry participants had annual behavioral health care expenditures over $25,000, and 4% had over $50,000. Two years prior to appearing on the registry (13-24 months prior), annual expenditures averaged $10,513 per individual; 7% of individuals on the registry had annual behavioral health expenditures over $25,000; and 2% had over $50,000. In the year prior, the highest behavioral health care costs were spent, on average, for inpatient mental health ($14,705), with $11,267 for nonhospital SUD; $8036 for assertive community treatment; and $5743 for  peer specialist service. In the 2 years prior to appearing on the registry, the highest average costs were paid for assertive community treatment ($18,045), inpatient mental health ($12,762), nonhospital SUD ($7598), and  peer specialist services ($6978).

Providers reported health data for 1331 individuals receiving health home services. Sixty-five percent were indicated as overweight (BMI >25), and 54% used tobacco. Of tobacco users, 70% engaged in or discussed a tobacco cessation plan. Sixty-nine percent of individuals engaged with staff for a wellness assessment including wellness planning.

Through administrative claims and health assessment data, a resulting priority patient registry identified 3759 individuals for targeted intervention. A benefit of the registry is the ability to quickly and systematically identify individuals with SMI and chronic physical health conditions; however, more information is needed to determine if access to data by the payer across care settings, and pharmacy data not otherwise available to the provider, resulted in a larger number of priority individuals being identified for intervention by the BHMCO than could have been determined by providers otherwise. Also, the average annual behavioral health expenditures for individuals on the priority patient registry were moderate, but a substantial proportion of individuals on the registry had expenditures in the highest tier. The use of claims data and health assessment information can identify individuals presenting with complex healthcare needs that may benefit from behavioral health home services.

The ACA encourages utilization of health IT as “feasible and appropriate”; however, few examples are available. In the Missouri model for health homes within community mental health centers for Medicaid-funded individuals, similar, timely reports of psychotropic medications and medication adherence are shared with provider staff.

 Health information from providers through metabolic monitoring and health assessments is exchanged, which enhances the ability to monitor health outcomes. In the current process, claims-based reporting from the BHMCO serves a dual purpose to provide health information to providers, and to help staff identify and target individuals for health home service. It is expected that health data will be added to the registry in an ongoing manner in order to track and monitor health outcomes for these individuals; however, this process varies across providers and is assessed separately by the BHMCO through fidelity monitoring of the behavioral health home model. The current registry could be enhanced through incorporation of additional health outcomes, such as glycated hemoglobin and blood pressure.

Integration of physical and behavioral health data is difficult, especially by smaller, community-based providers with limited technological resources. Common barriers to maintaining disease registries include the need for double documentation and freestanding tracking systems.

 Until electronic health records are updated with ways to monitor healthcare integration processes implemented by providers, interim steps can be helpful, including electronic worksheets.

 Resources shared with providers, such as secure Web-based portals and exchange protocols for the purpose of data sharing, are also valuable.

The current priority patient registry identifies individuals that have both SMI and chronic physical health conditions, and this population would be considered a subset of those eligible for home health service, as defined by the ACA. These criteria were adopted to prioritize service to those with the highest need. Individuals meeting health home criteria without SMI were still identified and included in health home service by provider staff, albeit at a much lower rate. In the current registry, the resulting population was found to have large variability in behavioral health expenditures. Identifying and including individuals based on healthcare expenditure would likely result in different individuals targeted for health home services. Physical health expenditures and outcomes for this population will be addressed in an investigation of behavioral health homes, made possible through additional funding from the Patient-Centered Outcomes Research Institute.

The BHMCO has developed a payer—provider process utilizing service claims and health assessment information to identify individuals through a patient registry that may benefit from behavioral health home service. This registry can inform processes to improve engagement and support for individuals eligible for health home services.

The authors wish to thank Ms Amanda Allen, Community Care Behavioral Health Organization, UPMC Insurance Services Division, for assistance with data analyses.

 Community Care Behavioral Health Organization, UPMC Insurance Services Division (MM, MP, SLH, DS, SD, MD, JMS), Pittsburgh, PA.

 Dr Schuster, Ms Daub, Ms Doyle, Ms Hutchison, Ms Mesiano, Ms Parthasarathy, and Mr Salai are employees of Community Care/UPMC. Dr Schuster is also an ex-officio board member of Community Care.

 Concept and design (SD, SLH, MM, MP, JMS); acquisition of data (SD, MM, MP, DS, JMS); analysis and interpretation of data (SD, SLH, MM, MP, DS, JMS); drafting of the manuscript (SD, SLH, MM, MP, JMS); critical revision of the manuscript for important intellectual content (MD, SD, MM, MP, JMS); statistical analysis (MP, DS); administrative, technical, or logistic support (MD, SD, SLH, MM); and supervision (MD, SLH, MM).

 Michele Mesiano, MSW, Director, Decision Support and Data Analytics, Community Care Behavioral Health Organization, UPMC Insurance Services Division, 339 Sixth Ave, Ste 1300, Pittsburgh, PA 15222. E-mail: mesianomm@ccbh.com.

1. Affordable Care Act, 42 U.S.C. § 18001. Patient Protection and Affordable Care Act, 42 U.S.C. § 18001 et seq. HHS website. http://www.hhs.gov/sites/default/files/ii-role-of-public-programs.pdf. Accessed July 18, 2016.

2. Colton CW, Manderscheid, RW. Congruencies in increased mortality rates, years of potential life lost, and causes of death among public mental health clients in eight states. Prev Chronic Dis. 2006;3(2):A42.

3. Walker ER, McGee RE, Druss BG. Mortality in mental disorders and global disease burden implications: a systematic review and meta-analysis. JAMA Psychiatry. 2015;72(4):334-341. doi: 10.1001/jamapsychiatry.2014.2502.

4. Druss BG, Walker ER. Mental disorders and medical comorbidity. Synth Proj Res Synth Rep. 2011;(21):1-26.

5. Compton MT, Daumit GL, Druss BG. Cigarette smoking and overwight/obesity among individuals with serious mental illnesses: a preventive perspective. Harv Rev Psychiatry. 2006;14(4):212-222.

6. Kilbourne AM, Cornelius JR, Han X, et al. Burden of general medical conditions among individuals with bipolar disorder. Bipolar Disord. 2004;6(5):368-373.

7. Scott D, Happell B. The high prevalence of poor physical health and unhealthy lifestyle behaviours in individuals with severe mental illness. Issues Ment Health Nurs. 2011;32(9):589-597. doi: 10.3109/01612840.2011.569846.

8. Osborn DP, Nazareth I, King MB. Physical activity, dietary habits, and coronary heart disease risk factor knowledge amongst people with severe mental illness: a cross-sectional comparative study in primary care. Soc Psychiatry Psychiatr Epidemiol. 2007;42(10):787-793.

9. Bartlem K, Bowman J, Freund M, et al. Evaluating the effectiveness of a clinical practice change intervention in increasing clinician provision of preventive care in a network of community-based mental health services: a study protocol of a non-randomized, multiple baseline trial. Implement Sci. 2013;8:85. doi: 10.1186/1748-5908-8-85.

10. Gill KJ, Zechner M, Zambo Anderson E, Swarbrick M, Murphy A. Wellness for life: a pilot of an interprofessional intervention to address metabolic syndrome in adults with serious mental illnesses. Psychiatr Rehabil J. 2016;39(2):147-153. doi: 10.1037/prj0000172.

11. Taylor C, Holsinger B, Flanagan JV, Ayers AM, Hutchison SL, Terhorst L. Effectiveness of a brief care management intervention for reducing psychiatric hospitalization readmissions. J Behav Health Serv Res. 2016;43(2):262-271. doi: 10.1007/s11414-014-9400-4.

A payer—provider, patient registry to identify individuals with serious mental illness and chronic physical health conditions for utilization in behavioral health homes is described.

Payer—Provider Patient Registry Utilized in a Behavioral Health Home

 To assess US hospital engagement in the 4 core domains of interoperability (find, send, receive, integrate) and whether engaging in these domains is associated with electronic availability of clinical data from outside providers.

 Analysis of the American Hospital Association (AHA) Annual Survey of Hospitals and the American Hospital Association (AHA) Annual Survey of Hospitals - IT Supplement datasets for 2014. Respondents included 3307 US hospitals to the AHA Annual Survey — IT Supplement. We created measures of hospital engagement in 4 core domains of interoperability, as well as access to electronic clinical data from outside providers. Regression analysis was to identify hospital characteristics associated with each measure.

 Twenty-one percent of US hospitals engaged in all 4 interoperability domains, and 25% engaged in none. Hospitals engaged in all 4 domains were more likely to have a “basic” (odds ratio [OR], 3.53; 

 <.01) or “comprehensive” (OR, 5.04; 

<.01) electronic health record (EHR) in comparison to a less than “basic” EHR, participate in a Regional Health Information Organization (OR, 4.29; 

<.01), use a single EHR vendor (OR, 2.15; 

<.01), and have a third-party health information exchange vendor (OR, 2.32; 

 <.01). They also differed by non-IT characteristics, such as medical home participation (OR, 1.77; 

<.01) information were more likely to report routine clinical information availability from outside providers.

 The one-fifth of US hospitals engaged in key domains of interoperability were more likely to have certain information technology infrastructure and participate in delivery reform. Encouragingly, interoperability engagement was associated with routine clinical information availability. Our results point to the need for ongoing efforts to expand interoperability, with the potential benefit of better information availability for clinicians and better care.

	The Office of the National Coordinator for Health Information Technology (IT) has defined 4 domains of interoperability: find, send, receive, and integrate patient health information from outside sources. We evaluated the current state of US hospitals’ engagement in interoperability:

		Only 21% of all US hospitals engage in all 4 domains of interoperability.

		Health IT infrastructure, such as robust electronic health record systems and health information organization participation, and delivery reform engagement are positively associated with engagement in all 4 domains.

		Three of the 4 domains—finding, receiving, and integrating&mdash;are significantly associated with provider access to patient information from outside providers.

		Hospitals that have invested in interoperability capabilities appear to have better access to patient information. However, given that less than 1 in 4 hospitals have these capabilities, additional policy interventions to encourage interoperability are needed.

Following the large increase in electronic health record (EHR) adoption in US hospitals over the past 6 years,

 attention is increasingly focused on ensuring that EHRs can exchange and integrate patient data. Interoperability is expected to generate substantial gains in quality and efficiency by reducing redundant care

 and improving care coordination through better frontline clinician access to information.

 It is also expected to be a key enabler of population-based payment and care delivery reform models through data aggregation

 and information technology (IT)—enabled performance measurement.

 are devoting substantial resources to increase interoperability by enhancing technical capabilities, as well as by addressing governance, financial, and policy issues.

In order to track progress and inform policy efforts, it is critical to assess where we stand today.

 Prior research has tracked EHR adoption over time and found that hospital characteristics, such as size, location, and ownership, are associated with adoption.

 A related set of studies have examined hospital characteristics associated with engaging in specific forms of electronic health information sharing; for example, private nonprofit hospitals and hospitals that were part of a healthcare system were more likely to participate in a community or Regional Health Information Organization (RHIO).

 A key difference between the 2 literatures is that the former uses a functionality-based, technology-agnostic definition of EHRs to assess adoption patterns,

 whereas the latter has lacked such a definition. We therefore took advantage of the recent effort by the Office of the National Coordinator for Health IT (ONC) to create a similar functionality-based, technology-agnostic definition of interoperability

 in order to begin to measure and track interoperability in a consistent and comparable way over time.

A national assessment of hospital engagement in key domains of interoperability, characteristics associated with engagement in interoperability, and the relationship between interoperability and provider access to clinical data.

December 2016

Telemedicine and the Sharing Economy: The "Uber" for Healthcare

Assessing Electronic Health Record Implementation Challenges Using Item Response Theory

Payer—Provider Patient Registry Utilized in a Behavioral Health Home

US Hospital Engagement in Core Domains of Interoperability

The Health Information Technology Special Issue: Current Trends and Future Directions

Accountable Care Organization Hospitals Differ in Health IT Capabilities

How Health Plans Promote Health IT to Improve Behavioral Health Care

Data-Driven Clinical and Cost Pathways for Chronic Care Delivery

Building Health IT Capacity to Improve HIV Infection Health Outcomes

Getting From Here to There: Health IT Needs for Population Health