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Using Applied Machine Learning to Predict Healthcare Utilization Based on Socioeconomic Determinants of Care
Soy Chen, MS; Danielle Bergman, BSN, RN; Kelly Miller, DNP, MPH, APRN, FNP-BC; Allison Kavanagh, MS; John Frownfelter, MD, MSIS; and John Showalter, MD
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Trust in Provider Care Teams and Health Information Technology–Mediated Communication
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Kyle Morawski, MD, MPH; Yoni Dvorkis, MPH; and Craig B. Monsen, MD, MS
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Opt-In Consent Policies: Potential Barriers to Hospital Health Information Exchange
Nate C. Apathy, BS; and A. Jay Holmgren, MHI

Opt-In Consent Policies: Potential Barriers to Hospital Health Information Exchange

Nate C. Apathy, BS; and A. Jay Holmgren, MHI
Opt-in patient consent requirements for health information exchange correlate with more reported regulatory barriers, especially among less technologically advanced hospitals.
Measures

Regulatory barriers to HIE. For our first aim, examining the relationship between consent policy and hospitals experiencing regulatory barriers to HIE, our dependent variable was hospital-reported regulatory barriers to HIE. In the AHA survey, hospitals describe “barriers when trying to electronically (not eFax) send, receive, or find (query) patient health information to/from other care settings or organizations.” We classified hospitals as experiencing regulatory barriers to HIE if they responded “yes” to the following barrier: “The complexity of state and federal privacy and security regulations makes it difficult for us to determine whether it is permissible to electronically exchange patient health information.”

Hospital HIE volume. For our second aim, evaluating the relationship between state laws and the amount of HIE in which a hospital engaged, our primary dependent variable was hospital level of HIE usage, measured via MU2 public use files for eligible hospitals in 2016. As a requirement of attestation to MU2, hospitals report the percentage of patient transfers sent with an electronic summary of care record (eSCR). To successfully attest, the hospital must send at least 10% of transfers with eSCRs. This measure is calculated by dividing the number of patient transitions or referrals in which an eSCR was transmitted using certified EHR technology or an HIE or health information organization (HIO) by the total number of transfers of care a hospital completed during the 90-day MU2 reporting period.26 This measure has been used in previous work investigating how hospital factors and payment reform efforts relate to the level of HIE among hospitals.7,25 It is worth noting that the nature of this measure introduces possible confounding via selection bias for this aim. We discuss the implications and empirical evaluations of this selection bias in the Limitations section. Additionally, it may be that hospitals already using HIE and attesting to MU2 may be insensitive to regulatory barriers, having already achieved HIE in their state’s regulatory environment. We investigate this specifically in aim 1 of our analysis.

State HIE consent laws. The independent variables of interest capture state laws governing patient consent policies for HIE that were in effect as of June 2016 and do not include the effective dates of each policy, thus precluding a longitudinal analysis. Schmit et al surveyed the HIE legal landscape as of 2016 and classified state laws that affected HIE organizations and participants.11 Our independent variable captures the state law regarding patient consent for HIE. These laws are classified as opt-in, opt-out, or other (Figure 1). The “other” group consists of state laws that are ambiguous or describe patient HIE participation as “voluntary” without specifying a consent approach. For example, in Texas, the relevant legislation stipulates that HIE organizations may “implement, promote, and facilitate the voluntary exchange of secure electronic health information.”27 Ambiguous state policies were those that could not be interpreted as strictly opt-in or opt-out. For example, Nevada stipulates that patient consent must be obtained upon retrieval of information from an HIE but does not explicitly dictate the nature of that consent, nor does it clarify rules for consent to patient participation in the HIE prior to information retrieval.28 We operationalized this variable with opt-out policies as the reference group to estimate the effect of opt-in consent policies compared with opt-out policies. Our hypothesis was that opt-in consent policies would be associated with greater likelihood of reporting regulatory barriers to exchange. We matched policies via the state that each hospital reported in the 2016 AHA survey.

Hospital Characteristics

Control variables were constructed from the AHA IT Supplement to control for a variety of organizational, technological, and market factors that have been shown to be related to the adoption of IT and HIE in existing literature.8,29 We included the following control variables for organizational IT: hospital EHR adoption level (less than basic EHR, basic EHR, or comprehensive EHR)30 and primary EHR vendor (individual vendors for the top 90% of hospitals listed individually, with all others classified as “other”). We also included a measure of whether or not the hospital used the dominant EHR in the hospital referral region (HRR), which has been found to be related to HIE capabilities.31 For our second aim, we included hospital program year in the MU program as a proxy for experience with MU. We controlled for the following hospital factors: participation in a regional HIO (RHIO), ownership (for-profit, local government, or nonprofit), size (<100 beds, 100-400 beds, or >400 beds), health system membership, teaching hospital status, critical access hospital status, medical home status, participation in CMS’ bundled payments program, participation in an accountable care organization (ACO), percentage of total inpatient days for Medicare patients, and percentage of total inpatient days for Medicaid patients. Regional control variables included US Census region where the hospital is located and urbanicity, defined by core-based statistical area codes of rural, micro, and metro. Finally, we included a measure of HRR market concentration using the Herfindahl-Hirschman Index. We used the total number of hospital beds per hospital in the HRR as the measure of hospital market share.32 This was classified into Federal Trade Commission categories of unconcentrated, moderately concentrated, and highly concentrated.33

Analytic Approach: Design and Statistical Analyses

We examined differences in hospitals under different HIE consent policy regimes, using χ2 analyses to test for bivariate relationships. We used logistic regression adjusting for hospital technology adoption levels and HIE participation, as well as hospital and regional characteristics, with standard errors clustered at the state level to correct for correlation across hospitals in the same state. From these models, we computed marginal effects estimates and 95% CIs for ease of interpretation. We also conducted subgroup analyses to disaggregate the findings from aim 1 across hospitals differing in their levels of technological sophistication. We divided our sample into hospitals attesting to MU2 (ie, those with HIE volume performance data) and those not attesting to MU2, and we used the same logistic regression model. We compared these results with our main model including all hospitals to identify potential differences in the relationship between opt-in consent policies and reported regulatory barriers across these 2 groups of hospitals. This allowed us to investigate the hypothesis that less technologically advanced hospitals were more likely to report regulatory barriers than relatively more technologically advanced hospitals.

For our second aim, we used multiple linear regression, controlling for hospital technology adoption and experience in the MU program, organizational factors, and regional characteristics. We clustered standard errors at the state level. As a robustness check for both aims, we added controls for potential collider variables capturing hospital HIE capabilities that we excluded from our main regression models in separate regression models to validate that our primary estimates did not differ from those adjusting for these capabilities. All data preparation and analyses were performed in R (R Project; Vienna, Austria) using the RStudio development environment (RStudio; Boston, Massachusetts).


 
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