Concordance Between Electronic Health Record-Recorded Race and Ethnicity and Patient Report in Emergency Department Patients.

OBJECTIVE: We assessed the concordance of patient-reported race and ethnicity for emergency department (ED) patients compared with what was recorded in the electronic health record. METHODS: We conducted a single-center, prospective, observational study of 744 ED patients (English- and/or Spanish-speaking), asking them to describe their race and ethnicity. We compared the distributions of ethnicity and race between patient-reported and electronic health record data using McNemar's test. We calculated percent agreement and Cohen's kappa, with 95% confidence intervals (CI), for the concordance of patient-reported race and ethnicity with electronic health record data. RESULTS: Of 744 ED patients, 731 participants who completed the survey reported their ethnicity, resulting in 98.2% of electronic health records obtained ethnicities matched self-reported data (kappa = 0.95; 95% CI: 0.92 to 0.98). For those who self-reported as Hispanic, only 92.3% agreement was observed between the self-reported and electronic health record values. For all patients who had race recorded, 85.4% agreement was observed (kappa = 0.75; 95% CI 0.71 to 0.79). High rates of agreement were observed for Black or African American patients (98.7%) and White patients (96.6%), with low rates for those who identified as "More than one race" (22.9%) or "Other" race (1.8%). In the subset of Hispanic patients, low rates of agreement (25.0%) were observed for race (kappa = 0.10; 95% CI 0.01 to 0.19). CONCLUSIONS: Documentation discordance regarding race and ethnicity exists between electronic health records and self-reported data for our ED patients, particularly for ethnically Hispanic and Latino/a patients. Future efforts should focus on ensuring that demographic information in the electronic health record is accurately collected.

Health-related social needs information in the emergency department: clinician and patient perspectives on availability and use.

BACKGROUND: Patient health-related social needs (HRSN) complicate care and drive poor outcomes in emergency department (ED) settings. This study sought to understand what HRSN information is available to ED physicians and staff, and how HRSN-related clinical actions may or may not align with patient expectations. METHODS: We conducted a qualitative study using in-depth semi-structured interviews guided by HRSN literature, the 5 Rights of Clinical Decision Support (CDS) framework, and the Contextual Information Model. We asked ED providers, ED staff, and ED patients from one health system in the mid-Western United Stated about HRSN information availability during an ED encounter, HRSN data collection, and HRSN data use. Interviews were recorded, transcribed, and analyzed using modified thematic approach. RESULTS: We conducted 24 interviews (8 per group: ED providers, ED staff, and ED patients) from December 2022 to May 2023. We identified three themes: (1) Availability: ED providers and staff reported that HRSNs information is inconsistently available. The availability of HRSN data is influenced by patient willingness to disclose it during an encounter. (2) Collection: ED providers and staff preferred and predominantly utilized direct conversation with patients to collect HRSNs, despite other methods being available to them (e.g., chart review, screening questionnaires). Patients' disclosure preferences were based on modality and team member. (3) Use: Patients wanted to be connected to relevant resources to address their HRSNs. Providers and staff altered clinical care to account for or accommodate HRSNs. System-level challenges (e.g., limited resources) limited provider and staff ability to address patients HRSNs. CONCLUSIONS: In the ED, HRSNs information was inconsistently available, collected, or disclosed. Patients and ED providers and staff differed in their perspectives on how HSRNs should be collected and acted upon. Accounting for such difference in clinical and administrative decisions will be critical for patient acceptance and effective usage of HSRN information.

New insights about community benefit evaluation: Using the Community Health Implementation Evaluation Framework to assess what hospitals are measuring.

BACKGROUND: Nonprofit hospitals are required to conduct community health needs assessments (CHNA) every 3 years and develop corresponding implementation plans. Implemented strategies must address the identified community needs and be evaluated for impact. PURPOSE: Using the Community Health Implementation Evaluation Framework (CHIEF), we assessed whether and how nonprofit hospitals are evaluating the impact of their CHNA-informed community benefit initiatives. METHODOLOGY: We conducted a content analysis of 83 hospital CHNAs that reported evaluation outcomes drawn from a previously identified 20% random sample ( n = 613) of nonprofit hospitals in the United States. Through qualitative review guided by the CHIEF, we identified and categorized the most common evaluation outcomes reported. RESULTS: A total of 485 strategies were identified from the 83 hospitals' CHNAs. Evaluated strategies most frequently targeted behavioral health ( n = 124, 26%), access ( n = 83, 17%), and obesity/nutrition/inactivity ( n = 68, 14%). The most common type of evaluation outcomes reported by CHIEF category included system utilization ( n = 342, 71%), system implementation ( n = 170, 35%), project management ( n = 164, 34%), and social outcomes ( n = 163, 34%). PRACTICE IMPLICATIONS: CHNA evaluation strategies focus on utilization (the number of individuals served), with few focusing on social or health outcomes. This represents a missed opportunity to (a) assess the social and health impacts across individual strategies and (b) provide insight that can be used to inform the allocation of limited resources to maximize the impact of community benefit strategies.

Non-response Bias in Social Risk Factor Screening Among Adult Emergency Department Patients.

Healthcare organizations increasingly use screening questionnaires to assess patients' social factors, but non-response may contribute to selection bias. This study assessed differences between respondents and those refusing participation in a social factor screening. We used a cross-sectional approach with logistic regression models to measure the association between subject characteristics and social factor screening questionnaire participation. The study subjects were patients from a mid-western state safety-net hospital's emergency department. Subjects' inclusion criteria were: (1) ≥ 18 years old, (2) spoke English or Spanish, and (3) able to complete a self-administered questionnaire. We classified subjects that consented and answered the screening questionnaire in full as respondents. All others were non-respondents. Using natural language processing, we linked all subjects' participation status to demographic characteristics, clinical data, an area-level deprivation measure, and social risk factors extracted from clinical notes. We found that nearly 6 out of every 10 subjects approached (59.9%), consented, and completed the questionnaire. Subjects with prior documentation of financial insecurity were 22% less likely to respond to the screening questionnaire (marginal effect = -22.40; 95% confidence interval (CI) = -41.16, -3.63; p = 0.019). No other factors were significantly associated with response. This study uniquely contributes to the growing social determinants of health literature by confirming that selection bias may exist within social factor screening practices and research studies.

Strategic use of health information exchange and market share, payer mix, and operating margins.

BACKGROUND: Health information exchange (HIE) capabilities are tied to health care organizations' strategic and business goals. As a technology that connects information from different organizations, HIE may be a source of competitive advantage and a path to improvements in performance. PURPOSE: The aim of the study was to identify the impact of hospitals' use of HIE capabilities on outcomes that may be sensitive to changes in various contracting arrangements and referral patterns arising from improved connectivity. METHODOLOGY: Using a panel of community hospitals in nine states, we examined the association between the number of different data types the hospital could exchange via HIE and changes in market share, payer mix, and operating margin (2010-2014). Regression models that controlled for the number of different data types shared intraorganizationally and other time-varying factors and included both hospital and time fixed effects were used for adjusted estimates of the relationships between changes in HIE capabilities and outcomes. RESULTS: Increasing HIE capability was associated with a 13 percentage point increase in a hospital's discharges that were covered by commercial insurers or Medicare (i.e., payer mix). Conversely, increasing intraorganizational information sharing was associated with a 9.6 percentage point decrease in the percentage of discharges covered by commercial insurers or Medicare. Increasing HIE capability or intraorganizational information sharing was not associated with increased market share nor with operating margin. CONCLUSIONS: Improving information sharing with external organizations may be an approach to support strategic business goals. PRACTICE IMPLICATIONS: Organizations may be served by identifying ways to leverage HIE instead of focusing on intraorganizational exchange capabilities.

Indoor Public Mask-Wearing Behavior Changes in Response to National, State, and Local COVID-19 Policies.

OBJECTIVE: To estimate changes in public mask-wearing behavior in response to public health policies during COVID-19. DESIGN: Panel of observed public mask-wearing. SETTING: Counts of adult behavior in Marion County, Indiana, between November 15, 2020, and May 31, 2021. DETERMINANTS OF INTEREST: (1) Removal of state masking requirement; (2) introduction of the National Strategy for the COVID-19 Response and Pandemic Preparedness; (3) the Centers for Disease Control and Prevention (CDC) recommendation that vaccinated individuals did not need to wear masks in public; and (4) COVID-19 vaccine availability. OUTCOME: Percent observed with correct mask-wearing. ANALYSES: Fixed-effects models estimated the association between policies and mask-wearing. RESULTS: Ending Indiana's mask requirement was not associated with changes in correct mask-wearing. The CDC's recommendation was associated with a decrease of 12.3 percentage points in correct mask-wearing (95% CI, -23.47 to -1.05; P = .032). CONCLUSIONS: Behavior encouraged by local mask requirements appeared to be resilient to changes in state policy. CDC recommendations appeared influential.

Student perceptions of a teaching electronic medical record in Health Administration education.

Given the ubiquity of electronic health records (EHR), health administrators should be formally trained on the use and evaluation of EHR data for common operational tasks and managerial decision-making. A teaching electronic medical record (tEMR) is a fully operational electronic medical record that uses de-identified electronic patient data and provides a framework for students to familiarize themselves with the data, features, and functionality of an EHR. Although purported to be of value in health administration programs, specific benefits of using a tEMR in health administration education is unknown. We sought to examine Master of Health Administration (MHA) students' perceptions of the use, challenges, and benefits of a tEMR. We analyzed qualitative data collected from a focus group session with students who were exposed to the tEMR during a semester MHA course. We also administered pre- and post-survey questions on students' self-efficacy and perceptions of the ease of use, usefulness, and intention to use health care data analysis in their future jobs. We found several MHA students valued their exposure to the tEMR, as this provided them a realistic environment to explore de-identified patient data. Scores for students' perceived ease of using healthcare data analysis in their future job significantly increased following use of the tEMR (pre-test score M=3.31, SD=0.21; post-test score M=3.71, SD=0.18; p=0.01). Student exposure and use of a tEMR may positively affect perceptions of using EHR data for strategic and managerial tasks typical of health administrators.

Consumer Perspectives on Maternal and Infant Health Apps: Qualitative Content Analysis.

BACKGROUND: Despite the popularity of maternal and infant health mobile apps, ongoing consumer engagement and sustained app use remain barriers. Few studies have examined user experiences or perceived benefits of maternal and infant health app use from consumer perspectives. OBJECTIVE: This study aims to assess users' self-reported experiences with maternal and infant health apps, perceived benefits, and general feedback by analyzing publicly available user reviews on two popular app stores-Apple App Store and Google Play Store. METHODS: We conducted a qualitative assessment of publicly available user reviews (N=2422) sampled from 75 maternal and infant health apps designed to provide health education or decision-making support to pregnant women or parents and caregivers of infants. The reviews were coded and analyzed using a general inductive qualitative content analysis approach. RESULTS: The three major themes included the following: app functionality, where users discussed app features and functions; technical aspects, where users talked about technology-based aspects of an app; and app content, where users specifically focused on the app content and the information it provides. The six minor themes included the following: patterns of use, where users highlighted the frequency and type of use; social support, where users talked about receiving social support from friends, family and community of other users; app cost, where users talked about the cost of an app within the context of being cost-effective or a potential waste of money; app comparisons, where users compared one app with others available in app stores; assistance in health care, where users specifically highlighted the role of an app in offering clinical assistance; and customer care support, where users specifically talked about their interaction with the app customer care support team. CONCLUSIONS: Users generally tend to value apps that are of low cost and preferably free, with high-quality content, superior features, enhanced technical aspects, and user-friendly interfaces. Users also find app developer responsiveness to be integral, as it offers them an opportunity to engage in the app development and delivery process. These findings may be beneficial for app developers in designing better apps, as no best practice guidelines currently exist for the app environment.

Sensitivity and Specificity of Real-World Social Factor Screening Approaches.

Health care organizations are increasingly documenting patients for social risk factors in structured data. Two main approaches to documentation, ICD-10 Z codes and screening questions, face limited adoption and conceptual challenges. This study compared estimates of social risk factors obtained via screening questions and ICD-10 Z diagnoses coding, as used in clinical practice, to estiamtes from validated survey instruments in a sample of adult primary care and emergency department patients at an urban safety-net health system. Financial strain, transportation barriers, food insecurity, and housing instability were independently assessed using instruments with published reliability and validity. These four social factors were also being collected by the health system in screening questions or could be mapped to ICD-10 Z code diagnosis code concepts. Neither the screening questions nor ICD-10 Z codes performed particularly well in terms of accuracy. For the screening questions, the Area Under the Curve (AUC) scores were 0.609 for financial strain, 0.703 for transportation, 0.698 for food insecurity, and 0.714 for housing instability. For the ICD-10 Z codes, AUC scores tended to be lower in the range of 0.523 to 0.535. For both screening questions and ICD-10 Z codes, the measures were much more specific than sensitive. Under real world conditions, ICD-10 Z codes and screening questions are at the minimal, or below, threshold for being diagnostically useful approaches to identifying patients' social risk factors. Data collection support through information technology or novel approaches combining data sources may be necessary to improve the usefulness of these data.

Opioid-Related Emergency Department Encounters: Patient, Encounter, and Community Characteristics Associated With Repeated Encounters.

STUDY OBJECTIVE: We describe the prevalence, trends, and factors associated with repeated emergency department (ED) encounters for opioid usage across multiple, independent hospital systems. METHODS: A statewide regional health information exchange system provided ED encounters from 4 Indiana hospital systems for 2012 to 2017. In accordance with a series of International Classification of Diseases, Ninth Revision (ICD-9) and ICD-10 diagnosis codes for opioid abuse, adverse effects of opioids, opioid dependence and unspecified use, and opioid poisoning, we identified patients with an ED encounter associated with opioid usage (9,295 individuals; 12,642 encounters). Multivariate logistic regression models then described the patient, encounter, prescription history, and community characteristics associated with the odds of a patient's incurring a subsequent opioid-related ED encounter. RESULTS: The prevalence of repeated nonfirst opioid-related ED encounters increased from 9.0% of all opioid encounters in 2012 to 34.3% in 2017. The number of previous opioid-related ED encounters, unique institutions at which the individual had had encounters, the encounter's being heroin-related, the individual's having a benzodiazepine prescription filled within 30 days before the encounter, and being either Medicaid insured or uninsured compared with private insurance were associated with significantly greater odds of having a subsequent encounter. CONCLUSION: The ED is increasingly a site utilized as the setting for repeated opioid-related care. Characteristics of the individual, encounter, and community associated with repeated opioid-related encounters may inform real-time risk-prediction tools in the ED setting. Additionally, the number of institutions to which the individual has presented may suggest the utility of health information exchange data and usage in the ED setting.

Data mining techniques utilizing latent class models to evaluate emergency department revisits.

BACKGROUND: The use of machine learning techniques is especially pertinent to the composite and challenging conditions of emergency departments (EDs). Repeat ED visits (i.e. revisits) are an example of potentially inappropriate utilization of resources that can be forecasted by these techniques. OBJECTIVE: To track the ED revisit risk over time using the hidden Markov model (HMM) as a major latent class model. Given the HMM states, we carried out forecasting of future ED revisits with various data mining models. METHODS: Information integrated from four distributed sources (e.g. electronic health records and health information exchange) was integrated into four HMMs which capture the relationships between an observed and a hidden progression that shift over time through a series of hidden states in an adult patient population. RESULTS: Assimilating a pre-analysis of the various patients by applying latent class models and directing them to well-known classifiers functioned well. The performance was significantly better than without utilizing pre-analysis of HMM for all prediction models (classifiers(. CONCLUSIONS: These findings suggest that one prospective approach to advanced risk prediction is to leverage the longitudinal nature of health care data by exploiting patients' between state variation.

Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach.

BACKGROUND: As the most commonly occurring form of mental illness worldwide, depression poses significant health and economic burdens to both the individual and community. Different types of depression pose different levels of risk. Individuals who suffer from mild forms of depression may recover without any assistance or be effectively managed by primary care or family practitioners. However, other forms of depression are far more severe and require advanced care by certified mental health providers. However, identifying cases of depression that require advanced care may be challenging to primary care providers and health care team members whose skill sets run broad rather than deep. OBJECTIVE: This study aimed to leverage a comprehensive range of patient-level diagnostic, behavioral, and demographic data, as well as past visit history data from a statewide health information exchange to build decision models capable of predicting the need of advanced care for depression across patients presenting at Eskenazi Health, the public safety net health system for Marion County, Indianapolis, Indiana. METHODS: Patient-level diagnostic, behavioral, demographic, and past visit history data extracted from structured datasets were merged with outcome variables extracted from unstructured free-text datasets and were used to train random forest decision models that predicted the need of advanced care for depression across (1) the overall patient population and (2) various subsets of patients at higher risk for depression-related adverse events; patients with a past diagnosis of depression; patients with a Charlson comorbidity index of ≥1; patients with a Charlson comorbidity index of ≥2; and all unique patients identified across the 3 above-mentioned high-risk groups. RESULTS: The overall patient population consisted of 84,317 adult (aged ≥18 years) patients. A total of 6992 (8.29%) of these patients were in need of advanced care for depression. Decision models for high-risk patient groups yielded area under the curve (AUC) scores between 86.31% and 94.43%. The decision model for the overall patient population yielded a comparatively lower AUC score of 78.87%. The variance of optimal sensitivity and specificity for all decision models, as identified using Youden J Index, is as follows: sensitivity=68.79% to 83.91% and specificity=76.03% to 92.18%. CONCLUSIONS: This study demonstrates the ability to automate screening for patients in need of advanced care for depression across (1) an overall patient population or (2) various high-risk patient groups using structured datasets covering acute and chronic conditions, patient demographics, behaviors, and past visit history. Furthermore, these results show considerable potential to enable preventative care and can be easily integrated into existing clinical workflows to improve access to wraparound health care services.

A population ecology perspective on the functioning and future of health information organizations.

BACKGROUND: Increasingly, health care providers need to exchange information to meet policy expectations and business needs. A variety of health information organizations (HIOs) provide services to facilitate health information exchange (HIE). However, the future of these organizations is unclear. PURPOSE: The aim of this study was to explore the environmental context, potential futures, and survivability of community HIOs, enterprise HIEs, and electronic health record vendor-mediated exchange using the population ecology theory. APPROACH: Qualitative interviews with 33 key informants representing each type of HIE organization were analyzed using template analysis. RESULTS: Community HIOs, enterprise HIEs, and electronic health record vendors exhibited a high degree of competition for resources, especially in the area of exchange infrastructure services. Competition resulted in closures in some areas. In response to environmental pressures, each organizational type was endeavoring to differentiate its services and unique use case, as well as pursing symbiotic relationships or attempting resource partitioning. CONCLUSION: HIOs compete for similar resources and are reacting to environmental pressures to better position themselves for continued survival and success. Our ecological research perspective helps move the discourse away from situation of a single exchange organization type toward a view of the broader dynamics and relationships of all organizations involved in facilitating HIE activities. PRACTICE IMPLICATIONS: HIOs are attempting to partition the environment and differentiate services. HIE options should not be construed as an "either/or" decision, but one where multiple and complementary participation may be required.

Fostering Local Health Department and Health System Collaboration Through Case Conferences for At-Risk and Vulnerable Populations.

In case conferences, health care providers work together to identify and address patients' complex social and medical needs. Public health nurses from the local health department joined case conference teams at federally qualified health center primary care sites to foster cross-sector collaboration, integration, and mutual learning. Public health nurse participation resulted in frequent referrals to local health department services, greater awareness of public health capabilities, and potential policy interventions to address social determinants of health.

Accuracy of Electronic Health Record Food Insecurity, Housing Instability, and Financial Strain Screening in Adult Primary Care.

This study assesses the accuracy of electronic health record­based screening questionnaires about social risk factors using external single-domain questionnaires as a comparator.

Meaningful Use of Electronic Health Records by Outpatient Physicians and Readmissions of Medicare Fee-for-Service Beneficiaries.

BACKGROUND: Nearly one-fifth of hospitalized Medicare fee-for-service beneficiaries are readmitted within 30 days. Participation in the Meaningful Use initiative among outpatient physicians may reduce readmissions. OBJECTIVE: To evaluate the impact of outpatient physicians' participation in Meaningful Use on readmissions. SUBJECTS AND RESEARCH DESIGN: The study population included 90,774 Medicare fee-for-service beneficiaries from New York State (2010-2012). We compared changes in the adjusted odds of readmission for patients of physicians who participated in Meaningful Use-stage 1, before and after attestation as meaningful users, with concurrent patients of matched control physicians who used paper records or electronic health records without Meaningful Use participation. Three secondary analyses were conducted: (1) limited to patients with 3+ Elixhauser comorbidities; (2) limited to patients with conditions used by Medicare to penalize hospitals with high readmission rates (acute myocardial infarction, congestive heart failure, and pneumonia); and (3) using only patients of physicians with electronic health records who were not meaningful users as the controls. MAIN OUTCOME: Thirty-day readmission. RESULTS: Patients of Meaningful Use physicians had 6% lower odds of readmission compared with patients of physicians who were not meaningful users, but the estimate was not statistically significant (odds ratio: 0.94, 95% confidence interval, 0.88-1.01). Estimated odds ratios from secondary analyses were broadly consistent with our primary analysis. CONCLUSIONS: Physician participation in Meaningful Use was not associated with reduced readmissions. Additional studies are warranted to see if readmissions decline in future stages of Meaningful Use where more emphasis is placed on health information exchange and outcomes.

Geography of community health information organization activity in the United States: Implications for the effectiveness of health information exchange.

BACKGROUND: The United States has invested nearly a billion dollars in creating community health information organizations (HIOs) to foster health information exchange. Community HIOs provide exchange services to health care organizations within a distinct geographic area. While geography is a key organizing principle for community HIOs, it is unclear if geography is an effective method for organization or what challenges are created by a geography-based approach to health information exchange. PURPOSE: This study describes the extent of reported community HIO coverage in the United States and explores the practical and policy implications of overlaps and gaps in HIO service areas. Furthermore, because self-reported service areas may not accurately reflect the true extent of HIOs activities, this study maps the actual markets for health services included in each HIO. METHODOLOGY: An inventory of operational community HIOs that included self-reported geographic markets and participating organizations was face-validated using a crowd-sourcing approach. Aggregation of the participating hospitals' individual health care markets provided the total geographic market served by each community HIO. Mapping and overlay analyses using geographic information system methods described the extent of community HIO activity in the United States. FINDINGS: Evidence suggests that community HIOs may be inefficiently distributed. Parts of the United States have multiple, overlapping HIOs, while others do not have any providing health information exchange services. In markets served by multiple community HIOs, 45% of hospitals were participants of only one HIO. PRACTICE IMPLICATIONS: The current geography of community HIO activity does not provide comprehensive patient information to providers, nor community-wide information for public health agencies. The discord between the self-reported and market geography of community HIOs raises concerns about the potential effectiveness of health information exchange, illustrates the limitations of geography as an organizing principle, and indicates operational challenges facing those leading and working with community HIOs.

Differing Strategies to Meet Information-Sharing Needs: Publicly Supported Community Health Information Exchanges Versus Health Systems' Enterprise Health Information Exchanges.

POLICY POINTS: Community health information exchanges have the characteristics of a public good, and they support population health initiatives at the state and national levels. However, current policy equally incentivizes health systems to create their own information exchanges covering more narrowly defined populations. Noninteroperable electronic health records and vendors' expensive custom interfaces are hindering health information exchanges. Moreover, vendors are imposing the costs of interoperability on health systems and community health information exchanges. Health systems are creating networks of targeted physicians and facilities by funding connections to their own enterprise health information exchanges. These private networks may change referral patterns and foster more integration with outpatient providers. CONTEXT: The United States has invested billions of dollars to encourage the adoption of and implement the information technologies necessary for health information exchange (HIE), enabling providers to efficiently and effectively share patient information with other providers. Health care providers now have multiple options for obtaining and sharing patient information. Community HIEs facilitate information sharing for a broad group of providers within a region. Enterprise HIEs are operated by health systems and share information among affiliated hospitals and providers. We sought to identify why hospitals and health systems choose either to participate in community HIEs or to establish enterprise HIEs. METHODS: We conducted semistructured interviews with 40 policymakers, community and enterprise HIE leaders, and health care executives from 19 different organizations. Our qualitative analysis used a general inductive and comparative approach to identify factors influencing participation in, and the success of, each approach to HIE. FINDINGS: Enterprise HIEs support health systems' strategic goals through the control of an information technology network consisting of desired trading partners. Community HIEs support obtaining patient information from the broadest set of providers, but with more dispersed benefits to all participants, the community, and patients. Although not an either/or decision, community and enterprise HIEs compete for finite organizational resources like time, skilled staff, and money. Both approaches face challenges due to vendor costs and less-than-interoperable technology. CONCLUSIONS: Both community and enterprise HIEs support aggregating clinical data and following patients across settings. Although they can be complementary, community and enterprise HIEs nonetheless compete for providers' attention and organizational resources. Health policymakers might try to encourage the type of widespread information exchange pursued by community HIEs, but the business case for enterprise HIEs clearly is stronger. The sustainability of a community HIE, potentially a public good, may necessitate ongoing public funding and supportive regulation.