Hospitals participating in accountable care organizations (ACOs) have greater adoption of health information technology, particularly patient-facing technology and health information exchange, than non-ACO hospitals.
Objectives: The aim of this study was to evaluate health information technology (IT) adoption in hospitals participating in accountable care organizations (ACOs) and compare this adoption to non-ACO hospitals.
Study Design: A cross-sectional sample of US nonfederal, acute care hospitals with data from 3 matched sources:
the 2013 American Hospital Association (AHA) Annual Survey, the 2013 AHA Survey of Care Systems and Payments (CSP), and the 2014 AHA Information Technology Supplement.
Methods: To compare health IT adoption in ACO- and non-ACO hospitals, we created measures of Meaningful Use (MU) Stage 1 and Stage 2 core and menu criteria, patient engagement—oriented health IT, and health information exchange (HIE) participation. Adoption was compared using both naïve and multivariate logit models.
Results: Of the 393 ACO hospitals and 810 non-ACO hospitals, a greater percentage of ACO hospitals were capable of meeting MU Stage 1 (50.9% vs 41.6%; P <.01) and Stage 2 (7.6% vs 4.8%; P <.05), having patient engagement health IT (39.8% vs 15.2%; P <.001), and participating in HIE (49.0% vs 30.1%; P <.001). In adjusted models, no difference was found between ACO and non-ACO hospital ability to meet MU Stage 1 or Stage 2, but ACO hospitals were more likely to have patient engagement health IT (odds ratio (OR), 2.20; 95% CI, 1.59-3.04) and be HIE participants (OR, 1.41; 95% CI, 1.03-1.92).
Conclusions: ACO-participating hospitals appear to be focused more on adopting health IT that aligns with broader strategic goals rather than those that achieve MU. Aligning adoption with quality and payment reform may be a productive path forward to encourage hospital health IT adoption behavior.
Am J Manag Care. 2016;22(12):802-807
The passage of the Affordable Care Act in 2010 authorized CMS to disburse incentive payments to accountable care organizations (ACOs) comprised of providers who voluntarily agree to care for Medicare fee-for-service beneficiaries.1 CMS developed 2 ACO program types—the Shared Savings Program and the Pioneer Program—which financially reward ACO-participating providers that surpass quality and cost benchmarks established for predefined populations based on the level of risk assumption.2,3 The ACO model has grown in popularity and has been extended to both private-sector payers and Medicaid.4,5 This expansion has resulted in over 700 ACOs covering nearly 24 million lives, demonstrating modest results in controlling costs and improving quality as shown in early analyses.6-10
Concurrent with the growth of ACOs, the Meaningful Use (MU) policy program that provides financial incentives for eligible hospitals and providers to adopt and use health information technology (IT), and penalizes nonadoption and nonuse, has been credited with the rapid growth in the adoption and use of health IT.11,12 The phased integration of this program has largely dictated the specific health IT components that providers have adopted. In recognition of both success and challenges with the program, CMS recently announced that the MU program for eligible providers will transition to become part of the newly implemented Merit-based Incentive Program System (MIPS) that aims to formally align MU with quality improvement and payment reform initiatives.13 However, the MU program for hospitals will continue, as is, for the foreseeable future, despite changes being anticipated.14
Although ACO incentives are not directly tied to health IT adoption, many experts believe that providers participating in ACOs must possess robust health IT to support care coordination mechanisms necessary to reach cost and quality goals.15-20 To better understand how ACOs are using and developing health IT, Wu et al conducted a mixed-methods study and found wide variation in ACO health IT capabilities.19 Other recent work highlights the role that health IT can play in helping to facilitate care coordination by assisting with decision support.20,21 Moreover, health IT can be linked across organizational boundaries through interoperable platforms, including health information exchanges (HIEs).20 This exchange of information offers providers access to comprehensive health information at the point of care. Importantly, HIEs also can help facilitate population-level health assessments—critical for ACOs that are responsible for the care of a defined patient population.2,22,23 ACOs may also offer patient portals and other patient-facing health IT components to improve patient engagement and better involve patients in the care process, working toward the goal of reducing costs and improving quality.24,25
In the context of these 2 significant policy initiatives—ACOs and MU—the extent to which ACO health IT strategies and MU criteria are aligned in the marketplace remains unknown. Additionally, questions remain regarding the specific health IT elements ACO provider organizations possess that address care coordination challenges relative to non-ACO providers. This study aims to: 1) characterize the health IT capabilities of ACO provider organizations, and 2) compare these organizations with non-ACO participants in an effort to understand how health IT capacity differs between the 2 in regards to MU achievement, patient engagement, and HIE. These findings can contribute to the ongoing discussions around how to align alternative payment models that support quality improvements with MU policy.
This study used a cross-sectional design with data from 3 matched sources covering the same sampling frame: the 2013 American Hospital Association (AHA) Annual Survey (organizational and service characteristics), the 2013 AHA Survey of Care Systems and Payment (CSP) (ACO participation), and the 2014 AHA Information Technology (IT) Supplement (health IT adoption and use). The 2014 AHA IT Supplement is administered to respondents of the 2013 AHA Annual Survey, and represents hospital IT capabilities as of fall 2013. The 2013 CSP Survey, fielded in early 2013 to respondents of the 2012 AHA Annual Survey, and is used to determine hospital ACO participation status. Although the CSP Survey precedes the IT Supplement Survey, the lengthy contracts and significant amount of resources required to develop an ACO make it unlikely that these hospitals would change their ACO participation status within the timeframe between the CSP and IT surveys. Only nonfederal, acute care general hospitals within the United States were included in the analytic sample.
ACO participation was determined from the CSP Survey question asking hospitals if their “Hospital established a separate legal entity for an accountable care organization (ACO) with the goal of being able to accept contracts to provide healthcare for a defined population?” Responses of, “Yes, my hospital has established an ACO” and “Yes, my hospital is part of an ACO” were combined into an ACO participant category (“ACO hospital”) and all other hospitals were categorized as nonparticipants (“non-ACO hospital”). The survey did not distinguish among Medicare, private sector, or Medicaid ACOs.
Health IT Capabilities
Following previous work, we mapped survey items from the IT Supplement to each of the 16 MU Stage 1 and 2 core measures (eAppendix [eAppendices available at www.ajmc.com]),11,26 allowing for the assessment of each MU item and aggregate measures of MU Stage 1 and 2. We additionally mapped Survey items to the 6 menu items in MU Stage 2 (eAppendix Table A). We also evaluated hospital health IT activity in 2 different domains particularly relevant to ACO development: patient engagement and health information exchange (HIE).
Two MU Stage 2 core criteria are relevant to patient engagement: the ability for patients to view, download, and transmit their health record; and electronic access to patient-specific education. However, the AHA IT supplement contains 5 additional items concerning health IT and patient interaction: request change to health record online, schedule an appointment online, submit patient-generated data, request a prescription refill, and secure messaging. These patient-facing components are particularly relevant for ACOs that rely on patient retention.24 The responses for the 7 components (2 MU Stage 2 criteria and 5 patient interaction questions) were summed to calculate an aggregate measure of patient engagement health IT capability. Hospitals utilizing greater than half of patient engagement components (≥4 components) were considered to possess patient engagement—oriented health IT. We conducted sensitivity analysis around this cut-off to ensure that this number of patient engagement health IT components was not too restrictive. We found a sharp drop in the number of hospitals with at least 2 or more capabilities, but found relative stability between 2 and 6 capabilities; only a small percentage of hospitals had all 7 capabilities (see eAppendix Table B). Thus, we decided that the threshold (≥4 components) was appropriate.
Given that the ACO model requires hospitals to exchange health information with providers potentially outside of their organization, it remains relevant to assess exchange capacity beyond the ability to transmit clinical care documents, required as one of the MU Stage 2 core criteria. Therefore, hospitals that noted they are “participating and actively exchanging data in at least 1 HIE/RHIO [regional health information organization]” in response to the AHA IT Survey question asking hospitals to “Please indicate your level of participation in a regional health information exchange (HIE) or regional health information organization (RHIO,)” were included.
Previous work has identified differences between respondents and nonrespondents of both the AHA IT Supplement and the CSP Survey compared with the AHA Annual Survey.17 To reduce this response bias, we applied an inverse probability weighting approach that regresses hospital characteristics (bed size, ownership type, system status, teaching status, region, urban/rural location) on whether the hospital responded to all 3 surveys.27,28 Predicted probabilities yielded from this model are applied as weights in all analyses.
Using the weighting approach, we compared the aforementioned hospital characteristics of ACO hospitals and non-ACO hospitals using 2 tests. Next, we used 2 tests to compare naïve adoption rates between ACO and non-ACO hospitals of the aggregate MU Stage 1 and Stage 2 measures, each core component, the menu items, the additional patient engagement and exchange indicators, and the aggregate patient engagement measure. We additionally evaluated separate multivariate logistic regression models to assess the effect of ACO participation on 3 indicators of health IT adoption: the ability to meet MU Stage 2 (core measures only), patient engagement health IT, and HIE participation. We chose to evaluate the stricter MU Stage 2 set of criteria rather than Stage 1 due to its focus on use of health IT rather than adoption (eAppendix Table C). These models included the set of hospital-level control variables listed above.
Inverse probability weighted hospital characteristics are shown by hospital ACO participation status in . Overall, the analytic sample included 1203 US nonfederal acute care hospitals that responded to all 3 surveys. Of these hospitals, weighted results revealed that 32% (393) of these hospitals were ACO participants. ACO participants were more likely to be large, nonprofit, teaching, and system members, located in urban areas and in the northeast.
Weighted unadjusted comparisons of aggregate measures of health IT adoption and adoption of specific health IT components between ACO and non-ACO hospital participants are presented in . A greater proportion of ACO hospitals are capable of meeting MU Stage 1 and Stage 2, although only 7.6% of ACO hospitals and 4.8% of non-ACO hospitals could meet Stage 2. A greater proportion of ACO hospitals adopted 11 of the 16 core criteria, while no difference was found in the remaining 5 criteria. Similarly, a greater proportion of ACO hospitals had adopted 5 of the 6 menu items, and no difference was found with the sixth item. For the additional patient engagement activities, a greater proportion of ACO hospitals had adopted each of the 5 capabilities. Lastly, a greater proportion of ACO hospitals were participating in an HIE (49.0 vs 30.1; P <.001).
For the MU Stage 2 aggregate measures, the aggregate measure of patient engagement capability and HIE participation, we conducted multivariate analyses to determine the adjusted effect of ACO participation on health IT adoption (). Adjusted analyses revealed no difference between ACO and non-ACO hospital ability to meet MU Stage 2. Alternatively, ACO hospitals were more likely to be participating in an HIE than non-ACO hospitals (odds ratio [OR], 1.41; 95% CI, 1.03-1.92). Our aggregate measure of patient engagement capability included hospitals that had at least 4 of the 7 functions that involved patient activity. ACO hospitals were significantly more likely than non-ACO hospitals to achieve this level of patient engagement health IT adoption and possess robust patient-oriented health IT (OR, 2.20; 95% CI, 1.59-3.04).
In reporting 2014 results for the Shared Savings and Pioneer ACO programs, CMS stated that the programs had generated greater than $411 million in total savings, while ACO provider organizations outpaced quality benchmarks since program inception.29 Among the measures demonstrating significant year-to-year improvement in that report was the percentage of ACO participating providers that qualified for an electronic health record (EHR) incentive payment (77% in 2013 increased to 81% in 2014). Despite this evidence that ACO providers continue to invest in health IT, little is known about the specific health IT capabilities of ACO providers and how they differ from non-ACO providers.
Our results suggest that, in a simple comparison, ACO hospitals are significantly more likely than non-ACO hospitals to be able to attest to MU Stage 1 and Stage 2. Interestingly, despite the percentage differences, we found no difference in ability to meet MU Stage 1 or 2 when accounting for hospital characteristics. Our findings of significant differences between ACO and non-ACO hospitals in HIE and patient engagement health IT in adjusted models suggest that ACO hospitals may be looking beyond incentives for health IT adoption to guide their investment choices, and these hospitals may be circumventing MU Stage 2 requirements to pursue a strategy of aligning health IT investments with ACO goals.
As an example of this alignment, consider the need for ACOs to conduct population health management. Health IT is a critical asset toward this strategy; it can enable tracking of patients across healthcare settings, as well as population-level analytics. The greater participation in an HIE by ACO hospitals, as seen in our study, may demonstrate ACO reliance on greater shared information for care management processes,20 including limiting duplication of services, predicting inefficient utilization, and monitoring patient cost. However, these choices may result in intriguing dynamics in the HIE landscape. ACOs may be preferential to closed HIE networks, often called Enterprise HIEs, that limit participation and sharing of clinical data to providers within a network or health system, such as an ACO.22 The alternative HIE model, the community HIE, may offer greater potential in terms of public health benefits, yet the business case may be less clear for ACOs. Future research may focus on the competitive dynamics playing out as a result of the relationship between ACO networks and interoperability initiatives.
Similarly, the growth of ACOs may be driving the push toward technology that empowers patients. Consumers demand the ease with which technology can facilitate access to their records and healthcare-related information. As a result, patient-facing technologies may be requisite for ACOs that need to retain patients that may not know they are part of an ACO.24 Leadership at ACO hospitals may see investment in tools that facilitate better communication with patients as a key tactic to enhance quality, increase patient retention, reduce costs, and improve outcomes.25 This indication of stronger patient orientation suggests that ACO hospitals have chosen to pursue a technology strategy that closely aligns with ACO metrics. Although investing in patient-facing health IT and population health tools may be a logical investment for ACO hospitals, non-ACO hospitals may only have the bandwidth to focus on incentives for MU adoption, as evidenced by similar results between ACO and non-ACO hospitals in our regression models.
This study faces important limitations. First, the combination of the AHA IT Supplement and the CSP Survey limits our sample, and these hospitals differ from the population of acute care hospitals in the United States. Although we do apply inverse probability weighting to address this bias, the bias cannot be eliminated. Second, our study is limited to a cross-sectional view of ACO and non-ACO hospital health IT status. The ACO model is rapidly spreading across the healthcare landscape, as is health IT adoption and use. Our use of cross-sectional data allows us to determine a baseline of health IT adoption and to better glimpse the way that ACO hospitals may be orienting their technological capability compared with non-ACO hospitals. However, the causal direction of this relationship remains unknown, and is a ripe area for future research.
Policy makers have recently taken steps to more tightly align policy aims and health IT adoption through programs such as the Alternative Payment Models and MIPS for eligible providers. Although this alignment does not yet exist for hospitals, our findings that ACO hospitals are already adopting and exchange-oriented health IT suggests that ACO incentives may be a better driver of care change than MU incentives. As a result, alignment of MU goals with the quality and cost goals of ACOs may be a productive policy path forward pertaining to hospitals.
Author Affiliations: Department of Family Medicine, College of Medicine, Ohio State University (DMW, ASM), Columbus, OH; Global Health Management and Policy, Tulane University School of Public Health and Tropical Medicine (AMM), New Orleans, LA.
Source of Funding: None.
Author Disclosures: 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.
Authorship Information: Concept and design (AMM, DMW); acquisition of data (DMW); analysis and interpretation of data (AMM, ASM, DMW); drafting of the manuscript (AMM, ASM, DMW); critical revision of the manuscript for important intellectual content (AMM, ASM, DMW); statistical analysis (DMW).
Address Correspondence to: Daniel M. Walker, PhD, MPH, Department of Family Medicine College of Medicine, Ohio State University, 273 Northwood-High Building, 2231 North High St, Columbus, OH 43201. E-mail: Daniel.Walker@osumc.edu.
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