SHARES: Substance use HeAlth REcord Sharing

Document text

Principal Investigator: MARIA ADELA GRANDO
Organization: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
Fiscal Year: 2024
Award: $632,566
Funding agency: National Institute on Drug Abuse

PROJECT SUMMARY
Integrated care is critical and becoming more common, but balancing the privacy needs of
patients with substance use disorders (SUD) and increasing patient safety through data access
is a challenge to realizing its full potential. Our previous award (R01MH108992) found: 1) a
general dearth of research on SUD data privacy and confidentiality; 2) few publications on how
SUD data sharing protections impact care; 3) inadequate interdisciplinary research on SUD data
protections; 4) minimal inclusion of patients and advocates in SUD data privacy literature; 5)
sparse quantitative and qualitative research on SUD data privacy (opinions and legal reviews
are predominant); and 6) low accuracy for electronic health record sensitive data segmentation
software sponsored by the Substance Abuse and Mental Health Services Administration
(SAMHSA).
The focus is to amplify patients’ and providers' voices on
Substance use HeAlth REcord
Sharing (SHARES).
Research question: Elucidate SUD data sharing challenges and the
feasibility of automated data segmentation to improve data interoperability and health care for
those with SUD. Study sites: three integrated clinics, a health system, a SUD treatment clinic, a
health plan and a state’s health information exchange. Specific aims:
Aim 1: Explore patient
and provider views on SUD data sharing and health care to inform policies on SUD data
privacy and confidentiality; Aim 2: Demonstrate the feasibility and accuracy of automated
SUD data segmentation by proposing novel informatics methods and tools that advance
automated SUD data segmentation; Aim 3: Evaluate the clinical accuracy and impact of
automated SUD data segmentation demonstrating that patient-controlled data sharing
improves service delivery and SUD patient outcomes. Impact: SHARES introduces novel,
standard-based, institution-independent, EHR-agnostic and scalable sensitive health data
segmentation methods and technology to improve data sharing and interoperability between
healthcare institutions and service delivery and patient outcomes for those with SUD.

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