An application of computable biomedical knowledge to transform patient centered scheduling.

Area of Science:

• Healthcare Operations Research
• Health Informatics
• Clinical Workflow Optimization

Background:

• Outpatient scheduling faces challenges from variability, uncertainty, no-shows, and walk-ins, leading to patient wait times and resource under-utilization.
• Existing scheduling methods are often limited in outpatient settings, unlike inpatient environments.

Purpose of the Study:

• To develop and pilot an optimized appointment scheduling solution for outpatient settings.
• To minimize time between associated procedures and reduce overall lead time for patients.

Main Methods:

• A linear integer programming model was developed to select optimal appointment groupings.
• The model incorporates appointment requests, resource availability, order constraints, and patient preferences.
• The solution includes technical infrastructure for integration within electronic medical record systems.

Main Results:

• A pilot study is planned to evaluate the algorithm in a single department.
• Key performance indicators include schedule utilization, resource idle time, patient satisfaction, and appointment lead/wait times.
• Qualitative data will be gathered through informal staff interviews.

Conclusions:

• The developed algorithm aims to improve outpatient scheduling efficiency.
• Future enhancements include incorporating machine learning for visit type variability and expanding the system to other departments.
• The goal is to enhance accuracy, flexibility, and ultimately patient care.