The traditional model of assessing and treating behavioral health (BH) and physical health (PH) in silos is inadequate for supporting whole-person health and wellness. The integration of BH and PH may result in better care quality, patient-provider experiences, outcomes, and reduced costs. Cross-organizational health data sharing between BH and PH providers is critical to patients with BH conditions (BHCs). In the last few decades, many initiatives -including health information exchange organizations- have facilitated cross-organizational health data sharing. The current challenge is affording meaningful consent and ensuring patient privacy, two of the core requirements for advancing the adoption and use of health information technology (HIT) in the US. The Office of the National Coordinator for HIT (ONC) recommends that patients should be given granular control beyond the “share all” or “share none” approach widely used currently in consent practices. But there is no consensus on the variables relevant to promote granularity in data sharing to honor privacy satisfaction for patients. As a result, existing granular data sharing (GDS) studies use ad-hoc and non-standardized approaches to implement or investigate patient data sharing preferences. Novel informatics methods were proposed and piloted to support patient-driven GDS and to validate the suitability and applicability of such methods in clinical environments. The hypotheses were: H1) the variables recommended by the ONC are relevant to support GDS; H2) there is diversity in medical record sharing preferences of individuals with BHCs; and H3) the most frequently used sensitive data taxonomy captures sensitive data sharing preferences of patients with BHCs. Findings validated the study hypotheses by proposing an innovative standards-based GDS framework, validating the framework with the design and pilot testing of a clinical decision support system with 209 patients with BHCs, validating with patients the adequacy of the most frequently used sensitive data taxonomy, and systematically exploring data privacy views and data sharing perceptions of patients with BHCs. This research built the foundations for a new generation of future data segmentation methods and tools that advances the vision of the ONC of creating standards-based, interoperable models to share sensitive health information in compliance with patients’ data privacy preferences.

Sensitive data sharing presents many challenges in case of unauthorized disclosures, including stigma and discrimination for patients with behavioral health conditions (BHCs). Sensitive information (e.g. mental health) warrants consent-based sharing to achieve integrated care. As many patients with BHCs receive cross-organizational behavioral and physical health care, data sharing can improve care quality, patient-provider experiences, outcomes, and reduce costs. Granularity in data sharing further allows for privacy satisfaction. Though the subjectivity in information patients consider sensitive and related sharing preferences are rarely investigated. Research, federal policies, and recommendations demand a better understanding of patient perspectives of data sensitivity and sharing.

The goal of this research is to enhance the understanding of data sensitivity and related sharing preferences of patients with BHCs. The hypotheses are that 1) there is a diversity in medical record sensitivity and sharing preferences of patients with BHCs concerning the type of information, information recipients, and purpose of sharing; and 2) there is a mismatch between the existing sensitive data categories and the desires of patients with BHCs.

A systematic literature review on methods assessing sensitivity perspectives showed a lack of methodologies for characterizing patient perceptions of sensitivity and assessing the variations in perceptions from clinical interpretations. Novel informatics approaches were proposed and applied using patients’ medical records to assess data sensitivity, sharing perspectives and comparing those with healthcare providers’ views. Findings showed variations in perceived sensitivity and sharing preferences. Patients’ sensitivity perspectives often varied from standard clinical interpretations. Comparison of patients’ and providers’ views on data sensitivity found differences in sensitivity perceptions of patients. Patients’ experiences (family history as genetic data), stigma towards category definitions or labels (drug “abuse”), and self-perceptions of information applicability (alcohol dependency) were influential factors in patients’ sensitivity determination.

This clinical informatics research innovation introduces new methods using medical records to study data sensitivity and sharing. The outcomes of this research can guide the development of effective data sharing consent processes, education materials to inform patients and providers, granular technologies segmenting electronic health data, and policies and recommendations on sensitive data sharing.