Objective

One potential benefit of greater electronic health information exchange is a reduction in the effort required for patients to switch between providers. We therefore assessed whether hospital participation in health information organizations (HIOs) led to increased patient sharing.

Data sources

Secondary data from 2010 to 2016.

Study design

Using hospital-pair and year fixed effects regression models, we assessed change in patient sharing volume following HIO participation by hospitals and compared the effect by the level of market competition and by hospital size.

Data extraction methods

We used national data on hospital participation in HIOs from the American Hospital Association Information Technology Supplement and data on the volume of Medicare patients shared between pairs of hospitals from 2010 to 2016.

Principal findings

The volume of patients shared between hospitals increased by 2.6 percent when both hospitals participated in a health information organization (P = .008, 95% CI: 0.7%-4.5%). This increase was greater in competitive markets (3.1 percent increase, P = .03 95% CI: 0.3%-5.9%) and between large hospitals (4.3 percent increase, P = .007 95% CI: 1.2%-7.3%). Participation by only one hospital in a pair had no effect on patient sharing.

Conclusions

Our results suggest that an important policy goal motivating the investment in health information exchange has been at least partially achieved. However, our results also support hospital concerns about the competitive implications of engaging in health information exchange. HIO participation appears to facilitate patient movement between hospitals, likely by lowering switching costs and resulting in greater competition.

Background

Increasing electronic health information exchange (HIE) between provider organizations is a top policy priority that has been pursued by establishing varied types of networks.

Objectives

To measure electronic connectivity enabled by these networks, including community, electronic health record vendor, and national HIE networks, across US hospitals weighted by the volume of shared patients and identify characteristics that predict connectivity.

Research design

Cross-sectional analysis of 1721 hospitals comprising 16,344 hospital pairs and 6,492,232 shared patients from 2018 CareSet Labs HOP data and national hospital surveys.

Subjects

Pairs of US acute care hospitals that delivered care to 11 or more of the same fee-for-service Medicare beneficiaries in 2018.

Measures

Whether a patient was treated by a pair of hospitals connected through participation in the same HIE network ("connected hospitals") or not connected because the hospitals participated in different networks, only 1 participated, or both did not participate.

Results

Sixty-four percent of shared patients were treated by connected hospitals. Of the remaining shared patients, 14% were treated by hospital pairs that participated in different HIE networks, 21% by pairs in which only 1 hospital participated in an HIE network, and 2% by pairs in which neither participated. Patients treated by pairs with at least 1 for-profit hospital, and by pairs located in competitive markets, were less likely to be treated by connected hospitals.

Conclusions

While the majority of shared patients received care from connected hospitals, remaining gaps could be filled by connecting HIE networks to each other and by incentivizing certain types of hospitals that may not participate because of competitive concerns.

OBJECTIVE: To use more precise measures of which hospitals are electronically connected to determine whether health information exchange (HIE) is associated with lower emergency department (ED)-related utilization. MATERIALS AND METHODS: We combined 2018 Medicare fee-for-service claims to identify beneficiaries with 2 ED encounters within 30 days, and Definitive Healthcare and AHA IT Supplement data to identify hospital participation in HIE networks (HIOs and EHR vendor networks). We determined whether the 2 encounters for the same beneficiary occurred at: the same organization, different organizations connected by HIE, or different organizations not connected by HIE. Outcomes were: (1) whether any repeat imaging occurred during the second ED visit; (2) for beneficiaries with a treat-and-release ED visit followed by a second ED visit, whether they were admitted to the hospital after the second visit; (3) for beneficiaries discharged from the hospital followed by an ED visit, whether they were admitted to the hospital. RESULTS: In adjusted mixed effects models, for all outcomes, beneficiaries returning to the same organization had significantly lower utilization compared to those going to different organizations. Comparing only those going to different organizations, HIE was not associated with lower levels of repeat imaging. HIE was associated with lower likelihood of hospital admission following a treat-and-release ED visit (1.83 percentage points [-3.44 to -0.21]) but higher likelihood of admission following hospital discharge (2.78 percentage points [0.48-5.08]). DISCUSSION: Lower utilization for beneficiaries returning to the same organization could reflect better access to information or other factors such as aligned incentives. CONCLUSION: HIE is not consistently associated with utilization outcomes reflecting more coordinated care in the ED setting.

Background

There is growing recognition that health care providers are embedded in networks formed by the movement of patients between providers. However, the structure of such networks and its impact on health care are poorly understood.

Purpose

We examined the level of dispersion of patient-sharing networks across U.S. hospitals and its association with three measures of care delivered by hospitals that were likely to relate to coordination.

Methodology/approach

We used data derived from 2016 Medicare Fee-for-Service claims to measure the volume of patients that hospitals treated in common. We then calculated a measure of dispersion for each hospital based on how those patients were concentrated in outside hospitals. Using this measure, we created multivariate regression models to estimate the relationship between network dispersion, Medicare spending per beneficiary, readmission rates, and emergency department (ED) throughput rates.

Results

In multivariate analysis, we found that hospitals with more dispersed networks (those with many low-volume patient-sharing relationships) had higher spending but not greater readmission rates or slower ED throughput. Among hospitals with fewer resources, greater dispersion related to greater readmission rates and slower ED throughput. Holding an individual hospital's dispersion constant, the level of dispersion of other hospitals in the hospital's network was also related to these outcomes.

Conclusion

Dispersed interhospital networks pose a challenge to coordination for patients who are treated at multiple hospitals. These findings indicate that the patient-sharing network structure may be an overlooked factor that shapes how health care organizations deliver care.

Practice implications

Hospital leaders and hospital-based clinicians should consider how the structure of relationships with other hospitals influences the coordination of patient care. Effective management of this broad network may lead to important strategic partnerships.