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Time dependent patient no-show predictive modelling development.

Yu-Li Huang1, David A Hanauer2

  • 1Mayo Clinic Minnesota, Rochester, Minnesota, USA.

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Summary
This summary is machine-generated.

Predictive no-show models using past appointment data significantly improve clinic scheduling efficiency. Incorporating patient history enhances predictability, reducing costs and optimizing patient flow.

Keywords:
Continuous quality improvementEfficiencyEvidence-based practiceImprovement modelsModellingPerformance measurement

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Area of Science:

  • Healthcare Management
  • Operations Research
  • Data Science

Background:

  • Patient no-shows disrupt clinic operations, leading to increased costs and reduced access to care.
  • Existing predictive models often lack the granularity to capture the dynamic nature of patient behavior.

Purpose of the Study:

  • To develop evidence-based predictive no-show models.
  • To incorporate past appointment status as a time-dependent predictor.
  • To enhance the predictability of patient no-shows in a clinical setting.

Main Methods:

  • A retrospective analysis of a 10-year dataset from a pediatric clinic (7,291 patients).
  • Logistic regression was used to build 26 predictive models based on historical appointment data.
  • Simulation was employed to evaluate model effectiveness on operational costs.

Main Results:

  • Model predictability (misclassification rate and AUC) improved with increased historical appointment data.
  • The optimal overbooking strategy involved up to the 16th predictive model.
  • This approach reduced cost per patient by 9.4% and allowed for two additional patients per day.

Conclusions:

  • Predictive no-show models incorporating time-dependent patient history offer significant improvements in clinic scheduling.
  • Systematic implementation is needed to demonstrate robustness across various scheduling systems.
  • Accurate no-show prediction enables proactive scheduling, mitigating the negative impacts of missed appointments.