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:
Robust and adaptive optimal healthcare staff scheduling
About
Traditional deterministic scheduling methods can cause inefficient assignments and costly staff turnover due to poor schedules that are not robust to unpredictable demand. Given significant advances in robust optimization methods over the past several years, there is an opportunity to contribute to more effective uses of operations research in healthcare.
The project’s objective was to develop and test several different robust optimization approaches to staff-scheduling under uncertain demand. This was done by using an integer programming model as a test-bed to maximize employee satisfaction with scheduling assignments.
Results
Project deliverables completed to-date include robust optimization model development and analysis under a variety of conditions, solution comparison to deterministic results, and a journal-ready paper on results and implications.
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Our findings indicate that the robust optimization approach performs better than traditional scheduling processes when faced with unpredictable demand. Future work will focus on fuller implementation of the mathematical model and impact evaluation.