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Description
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.
ContributorsKarway, George K (Author) / Grando, Adela Maria (Thesis advisor) / Murcko, Anita C (Committee member) / Franczak, Michael (Committee member) / Arizona State University (Publisher)
Created2022