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Research on outpatient capacity planning combining lean thinking and integer linear programming.

Li Hua1,2, Mu Dongmei3,4, Yang Xinyu2

  • 1Abdominal Ultrasound Department, Diagnostic Ultrasound Center, First Hospital of Jilin University, Changchun, Jilin, China.

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|February 14, 2023
PubMed
Summary
This summary is machine-generated.

Optimizing outpatient capacity planning using integer linear programming significantly boosts doctor efficiency and patient flow. This approach enhances medical services and addresses healthcare resource shortages.

Keywords:
Clinical pathwayCplexInteger programmingLean thinkingOutpatient capacity planning

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Area of Science:

  • Healthcare Operations Management
  • Operations Research
  • Lean Healthcare

Background:

  • Hospital operational efficiency is critically linked to outpatient capacity size and cost.
  • Outpatient capacity planning is a key challenge studied from an operations management perspective.

Purpose of the Study:

  • To model outpatient service quantity allocation as an integer linear programming problem.
  • To improve doctors' work efficiency, reduce patient waiting times, and enhance patient satisfaction.

Main Methods:

  • Refining outpatient services into examination and diagnosis categories based on lean thinking.
  • Employing CPLEX to solve the integer linear programming problem for service allocation.
  • Minimizing maximum working time through constraint solutions.

Main Results:

  • Senior doctors' work efficiency increased by an average of 25% compared to non-stratified methods.
  • Associate senior doctors experienced a 50% increase in patient flow.
  • Demonstrated optimal working time ranges using CPLEX simulations.

Conclusions:

  • The proposed outpatient capacity planning method enhances senior doctor efficiency and patient throughput.
  • Reduced patient waiting times and improved associate senior doctor performance contribute to better medical resource utilization.
  • This strategy aids in elevating doctors' technical skills and mitigating medical resource scarcity.