About this trial
This study evaluates the effectiveness of a health systems strategy (the Systems Analysis and Improvement Approach - SAIA) that packages systems engineering tools (including cascade analysis, flow mapping, and continuous quality improvement) to optimize the management of opioid use disorder (MOUD) care cascade and improve linkages between jails and clinical referral sites.
The investigators will
1. study the effectiveness of SAIA on MOUD care cascade quality and continuity for patients receiving care in jail and exiting to referral clinics 2. explore determinants of adoption, implementation, and sustainment of SAIA-MOUD across implementation clinics, and 3. estimate the cost and cost-effectiveness of SAIA-MOUD
Eligibility criteria
This trial accepts healthy volunteersQualifiers
clinic staff/providers at study clinics (JHS, OBOT, Pathways, Sound)
age 18+
current patients at SAIA clinic in the community (OBOT, Pathways, Sound) with jail involvement in the last 12 months
age 18+
Disqualifiers
do not consent
not on Medicaid
Trial design
Single group
Treatments tested in this trial
Systems Analysis and Improvement Approach (SAIA)
Other interventionSAIA an evidence-based, multi-prong, implementation strategy to improve systems thinking amongst frontline care teams to optimize care quality and continuity. The intervention includes monthly meetings where patient care cascades, process mapping and continuous quality improvement are used to identify bottlenecks and prioritize clinic based solutions.
Treatment groups
Trial outcomes
Primary outcomes
Linkage to MOUD Care Upon Release from Jail
the proportion of individuals exiting jails in King County on MOUD who link to any MOUD clinical provider in the community within 30 days
Secondary outcomes
Retention in MOUD Care after Initial Linkage
the proportion of individuals who return for a second MOUD clinical visit within 30 days of initial linkage post jail release
Sponsors and contacts
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University of Washington
Lead sponsor
National Institute of Nursing Research (NINR)
Collaborator