| Sumario: | With China introducing diagnosis-related groups to insurance payment policies as a component of health care reform, the emphasis on operational management has become increasingly important and specific. Under the current payment and evaluation system, tertiary-level public hospitals are facing escalating pressures to reconcile competing priorities such as case mix index, bed utilization, and value-based revenue growth. In collaboration with a 1500-bed tertiary public hospital, the authors developed a data-driven decision support mechanism, designed to be used from strategic planning through operations, while pursuing performance improvements under resource constraints. With the use of this mechanism, a system was developed to transform hospital performance metrics into actionable plans. This system was designed to be capable of adjusting to unmet goals and aligning with seasonal demand fluctuations. Mixed-integer programming models were employed to enable the balancing of competing goals to achieve operational excellence, and bidirectional feedback was used to resolve top-down and bottom-up mismatches, ensuring alignment between hospital-level goals and department-level execution. Utilizing deidentified data from the hospital, the authors demonstrated that the system, in scenario-based case mix planning iterations, is potentially capable of improving performance metrics with reasonable trade-offs. Future research aims at extending the framework to provide a holistic solution to support hospitals in achieving operational excellence by enhancing medium- to long-term resource planning, dynamically aligning supply with demand and, ultimately, leading to a new operational pattern for hospitals. A tertiary hospital in Beijing developed a data-driven analytical framework to bridge the gap between strategic objectives and operational practices.
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