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A Predictive Decision Analytics Approach for Primary Care Operations Management: A Case Study of Double-Booking

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Summary

A new decision analytics approach using predictive analytics and hybrid simulation optimizes primary care operations by managing patient no-shows. This strategy balances clinic productivity and efficiency, improving patient care delivery.

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decision-makingdouble-bookingpatient no-showpredictionprimary caresimulation

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Area of Science:

  • Operations Research
  • Health Services Research
  • Decision Analytics

Background:

  • Primary care is essential but faces operational challenges like patient no-shows and staffing shortages.
  • These complexities can negatively impact patient outcomes and clinic efficiency.
  • Effective operations management is crucial for optimizing primary care delivery.

Purpose of the Study:

  • To present a decision analytics approach using predictive analytics and hybrid simulation for primary care operations management.
  • To specifically address patient no-show management in a family medicine clinic.
  • To evaluate the effectiveness of a prediction-based double-booking strategy.

Main Methods:

  • Developed a decision analytics approach combining predictive analytics and hybrid simulation (agent-based and discrete-event).
  • Implemented a patient no-show prediction model within the simulation framework.
  • Conducted a case study in a family medicine clinic to test double-booking strategies.

Main Results:

  • The prediction-based double-booking strategy achieved the best balance between clinic productivity (patient throughput) and efficiency (visit cycle, wait time).
  • Scenario-based experiments demonstrated the impact of different double-booking strategies on operational outcomes.
  • The approach effectively managed uncertainties associated with patient no-shows.

Conclusions:

  • The proposed hybrid decision analytics approach can significantly improve primary care operations management.
  • This method offers a potential solution for enhancing decision-making and system performance in healthcare settings.
  • The approach is generalizable to various healthcare contexts for broader application.