Optimisation of medication reconciliation using queueing theory: a computer experiment.

Area of Science:

• Healthcare Operations Research
• Health Services Research

Background:

• Medication reconciliation (MedRec) is crucial for patient care continuity in hospitals but faces completion challenges.
• Optimizing the MedRec process is vital to prevent patient discharge before reconciliation.

Purpose of the Study:

• To evaluate queueing theory's applicability in comparing interventions for optimizing hospital medication reconciliation.
• To identify strategies for reducing the rate of patients discharged prior to completing MedRec.

Main Methods:

• A computer simulation using queueing theory modeled various MedRec interventions.
• Interventions included modifying communication methods, reallocating pharmacy technicians (PTs), and adjusting work schedules.
• A real-world post-hoc intervention (delayed PT start times) was incorporated due to the COVID-19 pandemic.

Main Results:

• The queueing model predicted a significant reduction in incomplete discharge MedRec, from 37.2% to approximately 16%.
• This reduction was achieved by implementing a scenario with earlier PT shift starts and dedicated PTs for discharge MedRec preparation.
• A post-hoc intervention involving later PT start times showed an increase in incomplete MedRec, aligning with simulation predictions.

Conclusions:

• Queueing theory provides a valuable, non-disruptive method for simulating and comparing MedRec process interventions.
• Simulation identified specific interventions, such as optimized scheduling and technician re-allocation, as most effective in reducing incomplete discharge MedRec.