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The American Journal of Managed Care January 2020
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
Eliminating Barriers to Virtual Care: Implementing Portable Medical Licensure
Pooja Chandrashekar, AB; and Sachin H. Jain, MD, MBA
Trust in Provider Care Teams and Health Information Technology–Mediated Communication
Minakshi Raj, MPH; Jodyn E. Platt, PhD, MPH; and Adam S. Wilk, PhD
The Health IT Special Issue: Enduring Barriers to Adoption and Innovative Predictive Methods
Ilana Graetz, PhD
What Accounts for the High Cost of Care? It’s the People: A Q&A With Eric Topol, MD
Interview by Allison Inserro
Does Machine Learning Improve Prediction of VA Primary Care Reliance?
Edwin S. Wong, PhD; Linnaea Schuttner, MD, MS; and Ashok Reddy, MD, MSc
Health Information Technology for Ambulatory Care in Health Systems
Yunfeng Shi, PhD; Alejandro Amill-Rosario, MPH; Robert S. Rudin, PhD; Shira H. Fischer, MD, PhD; Paul Shekelle, MD; Dennis Scanlon, PhD; and Cheryl L. Damberg, PhD
The Challenges of Consumerism for Primary Care Physicians
Timothy Hoff, PhD
Advancing the Learning Health System by Incorporating Social Determinants
Deepak Palakshappa, MD, MSHP; David P. Miller Jr, MD, MS; and Gary E. Rosenthal, MD
Predicting Hospitalizations From Electronic Health Record Data
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.

Objectives: To (1) assess whether hospitals in states requiring explicit patient consent (“opt-in”) for health information exchange (HIE) are more likely to report regulatory barriers to HIE and (2) analyze whether these policies correlate with hospital volume of HIE.

Study Design: Cross-sectional analysis of US nonfederal acute care hospitals in 2016.

Methods: We combined legal scholarship surveying HIE-relevant state laws with the American Hospital Association Annual Information Technology Supplement for regulatory barriers and hospital characteristics. Data from CMS reports for hospitals attesting to Meaningful Use stage 2 (MU2; renamed “Promoting Interoperability” in 2018) in 2016 captured hospital HIE volume. We used multivariate logistic regression and linear regression to estimate the association of opt-in state consent policies with reported regulatory barriers and HIE volume, respectively.

Results: Hospitals in states with opt-in consent policies were 7.8 percentage points more likely than hospitals in opt-out states to report regulatory barriers to HIE (P = .03). In subgroup analyses, this finding held among hospitals that did not attest to MU2 (7.7 percentage points; P = .02). Among hospitals attesting, we did not find a relationship between opt-in policies and regulatory barriers (8.0 percentage points; P = .13) or evidence of a relationship between opt-in policies and HIE volume (β = 0.56; P = .76).

Conclusions: Our findings suggest that opt-in consent laws may carry greater administrative burdens compared with opt-out policies. However, less technologically advanced hospitals may bear more of this burden. Furthermore, opt-in policies may not affect HIE volume for hospitals that have already achieved a degree of technological sophistication. Policy makers should carefully consider the incidence of administrative burdens when crafting laws pertaining to HIE.

Am J Manag Care. 2020;26(1):e14-e20.
Takeaway Points

Variation in consent policies for health information exchange (HIE) poses a challenge to realizing the goal of interoperable HIE. In particular, opt-in policies require explicit patient consent and may impose administrative burdens on hospitals sharing data.
  • Hospitals in states with opt-in patient consent requirements for HIE are more likely to report regulatory barriers to exchange compared with those in states with opt-out policies that assume consent.
  • This finding holds only for less technologically advanced hospitals, suggesting that these hospitals may bear more of this burden.
  • Among more technologically advanced hospitals, opt-in consent policy was not related to level of HIE.
Policy makers continue to advance interoperable health information exchange (HIE) as an important goal for the US healthcare system.1,2 One of the anticipated benefits of HIE is reduced administrative burden of data sharing, which in turn can give providers more complete information at the point of care.3-5 Digitization of the US healthcare system has brought us closer to realizing this goal through the Health Information Technology for Economic and Clinical Health Act’s incentives for hospitals and physician practices to adopt increasingly sophisticated electronic health records (EHRs).5,6 These incentives included supplementary Medicare reimbursements for hospitals that successfully demonstrated “meaningful use” of EHR functionality.5 In the case of HIE, this consisted of sending electronic summaries of care during care transitions.4 Despite these incentives, HIE capabilities among hospitals have continued to lag behind EHR adoption.7,8 As patients struggle with a lack of infrastructure for exchange between providers,9 hospitals have their own administrative burdens to enabling HIE, including the complex regulatory environment governing HIE across states,10 which can create significant barriers for hospitals that already face uncertain incentives to share patient data.

Perhaps the biggest legal challenge to a robust national HIE infrastructure is the varied state policies regarding patient consent for data exchange.10 State variation in consent policies comes from different approaches to default patient consent assumptions. States generally have either “opt-in” or “opt-out” patient consent requirements, although some have ambiguous or undefined patient consent policies.11 Opt-in states like New York assume no patient consent and therefore require explicit consent from each patient to allow providers to share and access that patient’s information via HIE.12 Opt-out states like Kansas assume patient consent for exchange but allow patients the opportunity to decline exchange.13 Rates of participation in HIE have been found to vary by consent policy, with more than 95% of patients participating under opt-out scenarios and only 19% participating in opt-in settings,10 consistent with the behavioral economics literature.14-17 In the case that a patient chooses to not participate in HIE, that patient’s information is not permitted to be shared or sent outside of the organization collecting the information. Disallowing this sharing would in turn depress the overall volume of HIE observed at the organization. Therefore, one would expect organizations under opt-in policies to engage in less exchange because of lower patient participation rates.

Despite this potential impact of opt-in consent policies on HIE, we are aware of no national studies of the impact of these laws on hospitals’ barriers to and volume of HIE. Previous studies have examined patient decisions in the context of a single HIE and individual patient-level factors associated with opt-in to HIE data sharing.18,19 These studies’ findings provide important insight into the factors related to patients’ HIE consent decisions and imply that there is significant administrative cost to obtaining patient consent. Research at the organizational level has found that policies requiring explicit (ie, opt-in) consent correlate with lower volume of clinical document exchange.20 Finally, 2 nationwide studies found the presence of consent laws to be associated with increased participation in HIE efforts.21,22 Importantly, these studies compared the presence of explicit consent laws with the lack of consent laws, as unclear legal environments (ie, when no consent laws exist) often require organizations to behave as conservatively as possible. To advance this literature, it is important to also study the nature of these laws. However, we are aware of no studies directly comparing opt-in and opt-out consent policies at the state level to determine if opt-in approaches lead to perceived regulatory barriers to HIE for hospitals and, in turn, reduced HIE volume.

The purpose of our study was 2-fold. First, we aimed to estimate the relationship between state laws governing patient consent requirements and hospitals’ probability of reporting regulatory and compliance barriers to HIE. Our second aim was to examine the extent to which these laws correlated with the level of hospital engagement in HIE. We anticipate our findings to be of interest to both state and federal policy makers and regulators currently crafting regulations for the future of HIE in the 21st Century Cures Act.23 Understanding the impact of different regulatory approaches to HIE on creating barriers to hospital participation and volume of data sharing is critical to informing the construction of future HIE infrastructure.


Setting and Data Sources

We used the American Hospital Association (AHA) Annual Survey and AHA Annual Survey Information Technology (IT) Supplement for 2016, as well as hospital-level Meaningful Use stage 2 (MU2; renamed “Promoting Interoperability” in 2018) performance measures. The AHA Annual Survey is sent annually to the chief executive officer of every hospital in the United States, with a request to complete it or designate completion to the person most knowledgeable in the organization. Respondents receive multiple mailings and follow-up phone calls. The 2016 survey was fielded from November 2016 to April 2017, and the IT Supplement received a response rate of 58%. We limited our sample to nonfederal acute care hospitals to construct the largest sample of comparable hospitals in the data, especially those subject to similar market forces. This sample construction allows for comparison with the existing literature discussing hospital IT adoption and use.6,24,25 Our analytic sample included 2613 hospitals.

We merged these data with 2016 publicly reported MU2 HIE performance measures using each hospital’s National Provider Identifier to obtain the level of HIE usage for that particular hospital. MU2 performance measures are derived from the Medicare incentive program for hospitals and providers to adopt and use increasingly sophisticated health IT. Hospitals report performance measures annually, with successful attestation translating into increased reimbursements from Medicare. These data were available only for hospitals that had attested to MU2 in 2016, limiting our subsample for this analysis to 1135 hospitals.

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