Optimizing Provider Schedules for the Real World
Leveraging linear optimization and capacity analysis to inform decisions on physician scheduling and space allocations when opening a new hospital
Industrial and Systems Engineering Conference, January 2017
Translating Capacity and Provider Constraints into a Mathematical Model for Linear Optimization of Clinic Schedules
Background
Background
A new comprehensive hospital was set to open with over 25 subspecialties, 25 emergency center rooms, 4 operating rooms, and 60 inpatient beds but outpatient clinic locations and schedules were critical pathway for successful opening.
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Historically, developing operating room and clinic schedules has been incredibly manual and time consuming. This becomes even more daunting when adding in the complexity of opening a new hospital: space allocations, co-location of services, and provider recruitment timing.
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Taking a systems engineering approach was needed to be nimble to changing information while still accounting for constraints and optimizing clinic schedules.
Analysis Framework
A design for six sigma framework for new process development was utilized: DMADV (define, measure, analyze, design, validate). This approach leverages data for more thorough analysis of new processes, services, or products to more accurately predict success.
Define
Defining Goals & Design Outcomes
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Established inclusion criteria and hierarchy of goals for optimization work needed prior to outpatient activation.
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Maximizing provider efficiency while taking into account pre-defined facility and care model
Measure
Stakeholder Interviews
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Compiled considerations across administration, staff, medical and surgical providers to establish the Voice of the Customer.
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These Critical to Quality (CTQ) requirements were translated into constraints for the linear optimization model.
Analyze
Demand Capacity Calculations
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Demand and capacity analysis utilized provider recruitment and patient volume projections
Demand per Floor:
Capacity per Floor:
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Extent of capacity overload drove mitigation scenario options
Design
Mathematical Model Generation
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Structured model based on facility, floor, and provider information with outputs designed to align with operational tools.
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Matlab was utilized to code the optimization objectives and constraints with output to excel to store the results
Validate
Model Refinement
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Iterated based on mitigation scenarios and updated projected provider and patient volumes to establish an optimal schedule across outpatient clinics and operating rooms
Appling Insights for Impact
The resulting schedules based on capacity overload mitigation scenarios were summarized and reviewed with hospital leadership to determine the best option.
Using this information, a schedule was selected that met all requirements (provider coverage, capacity constraints, OR schedule, etc.) for 21 specialties and 75 providers. This schedule enabled 99% exam room utilization within 1 year of the outpatient building opening.
The Bottom Line
Continuous improvement methodologies are no longer sufficient to address the performance improvement challenges of today's hospitals.
Design For Six Sigma (DFSS) creates new and reimagines existing processes based on detailed customer data. The end-to-end perspective reduces needed resources and efficiency is maintained or increased while quality is embedded in the design.
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