Capacity planning for cardiac catheterization: a case study.

Area of Science:

• Health Services Research
• Operations Research
• Cardiology

Background:

• Excessive waiting times for procedures like cardiac catheterization are a significant healthcare system challenge.
• Delays stem from a mismatch between patient demand and available resources, further complicated by dynamic referral rates, procedure durations, and patient urgency.
• Accurate prediction of capacity needs has been difficult due to these dynamic factors.

Purpose of the Study:

• To demonstrate a method for accurately calculating healthcare capacity needs.
• To utilize computer simulation to model patient flow and predict waiting times for cardiac procedures.
• To identify strategies for minimizing patient waiting times in cardiac catheterization labs.

Main Methods:

• A patient flow model was developed and populated with 16 months of operational data from a regional cardiac center (n=6215 referrals).
• Computer simulation was employed to analyze various "what-if" scenarios for catheterization laboratory operations.
• Patients were categorized into three urgency levels: hospitalized (U1), urgent outpatients (U2), and elective outpatients (U3). Model accuracy was validated against actual data, showing a significant correlation (0.94).

Main Results:

• Simulation revealed that simply increasing capacity to clear backlogs did not effectively reduce waiting times.
• Targeting additional capacity to higher urgency categories (U1, U2) reduced overall waiting times and also benefited lower urgency patients (U3).
• Improving lab efficiency can be achieved by reducing changeover times, standardizing pre- and post-procedural management, and optimizing booking schedules to minimize slack and overtime.

Conclusions:

• Capacity determination is a complex, dynamic process requiring a blend of clinical and administrative data.
• Computer simulation models are essential tools for predicting capacity needs and effectively managing waiting lists.
• This simulation approach is generalizable and can optimize waiting list management for various medical procedures.