A Robust Predictive Resource Planning under Demand Uncertainty to Improve Waiting Times in Outpatient Clinics.

Background and context:

Resource planning is performed ahead of time within outpatient clinics (OPC). Due to local control of operations (department-centric decision-making) and limited resources, OPCs cannot handle high variability and uncertainty in demand. There is always a difference between planning and reality, and this leads to operational problems such as excessive waiting times. The OPCs often react to the situation when problems are encountered and reaction times play an important role in determining patient waiting times.

Objectives:

To propose a predictive resource planning that incorporates variability in the short term with the OPC-wide perspective, not department-centric.

Methodology:

The process and patient data were collected from the OPC under study by observation, interviews and from the records of the hospital management information system. A resource planning model (RPM) was developed that matched resources according to demand in short term. A mathematical model with outputs resource plan for a day was formulated utilizing Takt time (the average time a patient needs to move out of the OPC system) management that is used in Toyota Production System (TPS), to allocate resources to all the departments. Using a Discrete Event Simulation Model, the effects of predictive resource planning with different reaction times on waiting times and cycle times were analyzed. The resource plans were implemented in the OPC of Aravind Eye Hospital, Madurai, Tamil Nadu, India, that has high patient volumes and random patient arrivals.

Results and discussion:

The simulation and implementation results indicate that predictive resource planning is robust and improves waiting times, and cycle times in OPCs. Study findings confirm that the predictive planning model reduces the average waiting time by 43.4 per cent during simulation and by 41.1 per cent during its implementation. The reduction in standard deviations in waiting times indicate reduction of unregulated waiting times. The OPC scheduled 28 resources throughout the day, whereas with predictive resource planning, the number of resources varied between a minimum of 12 to a maximum around 30–34 resources.

Conclusions:

The OPCs currently match demands to their supply, while matching resources to varying demand in short term; throughout the OPC (all departments) improves patient flow, and minimizes waiting time and cycle time. Previously, Takt time management (TTM) has applied to systems with even and stable demand; in this study, it has been applied to stochastic demand.

Implications:

This planning model helps the management to identify resource requirements: types of resources and number of resources, for the future demand growth and expansion. It can probably be extended to general hospitals by considering their demand forecast, precedence constraints and workflow complexities.