Closing the Loop by Operationalizing Systems Engineering and Design (CLOSED)
Motivation:
Specific Aims :
Aim 1:​Use systems engineering and patient engagement to design, develop, and refine a highly reliable “closed loop” system for diagnostic tests and referrals that ensures diagnostic orders and follow-up occur reliably within clinically- and patient-important time-frames.
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Aim 2: Use systems engineering and patient engagement to design, develop, and refine a highly reliable “closed loop” system for symptoms that ensures clinicians receive and act on feedback about evolving symptoms and physical findings of concern to patients or clinicians.
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Aim 3: Design for generalizability across health systems more broadly so that the processes created in Aims 1 and 2 are effective in (1) a practice in an underserved community, (2) a large tele-medicine system, and (3) a representative range of simulated other health system settings and populations.
Partners:
Sunday, June 2, 2019
Sunday, June 2, 2019
Approach:
Sunday, June 2, 2019
Results to Date:
Location Allocation of State-Wide Treatment Capacity
About
This project aims to develop models to both optimize the location of new treatment facilities and to simulate the impact of additional treatment locations on outcomes such as: wait times, number of people receiving treatment, and overdoses.
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It is estimated that 80% of people with opioid addiction disorders are not getting treatment (Saloner, 2015). National and state treatment capacity is well-below demand and drug users often face long waits to receive treatment. In Massachusetts, wait times to receive medication-assisted treatment range from 2 days to 24 weeks (Record, 2016) despite the many treatment centers that exist throughout the state. Given the growing demand for opioid treatment and lack of adequate funding, optimal location of treatment facilities will be essential to meet demand within constrained budgets.This project is developing a modeling approach that both determines the optimal location for new facilities and simulates the expected future patient and operational impact for the entire state. This combination of information is important for policy makers and public health officials as they work to improve access to treatment and expand treatment capacity.
Results
Results will be announced as research progresses.