A Predictive Decision Analytics Approach for Primary Care Operations Management: A Case Study of Double-Booking Strategy Design and Evaluation

Abstract

Primary care plays a vital role for individuals and families in accessing care, keeping well, and improving quality of life. However, the complexities and uncertainties in the primary care delivery system (e.g., patient no-shows/walk-ins, staffing shortage, COVID-19 pandemic) have brought significant challenges in its operations management, which can potentially lead to poor patient outcomes and negative primary care operations (e.g., loss of productivity, inefficiency). This paper presents a decision analytics approach developed based on predictive analytics and hybrid simulation to better facilitate management of the underlying complexities and uncertainties in primary care operations. A case study was conducted in a local family medicine clinic to demonstrate the use of this approach for patient no-show management. In this case study, a patient no-show prediction model was used in conjunction with an integrated agent-based and discrete-event simulation model to design and evaluate double-booking strategies. Using the predicted patient no-show information, a prediction-based double-booking strategy was created and compared against two other strategies, namely random and designated time. Scenario-based experiments were then conducted to examine the impacts of different double-booking strategies on clinic's operational outcomes, focusing on the trade-offs between the clinic productivity (measured by daily patient throughput) and efficiency (measured by visit cycle and patient wait time for doctor). The results showed that the best productivity-efficiency balance was derived under the prediction-based double-booking strategy. The proposed hybrid decision analytics approach has the potential to better support decision-making in primary care operations management and improve the system's performance. Further, it can be generalized in the context of various healthcare settings for broader applications.

Keywords: decision-making; double-booking; patient no-show; prediction; primary care; simulation.

Figure 1.

A conceptual framework of the hybrid decision analytics approach

Figure 2.

A high-level DES-ABS simulation design

Figure 3.

A general flow chart of clinic personnel working behavior logic

Figure 4(a)

Patient wait time for doctor of selected experimental scenarios under different double-booking strategies (prediction-based vs. random/designated time)

Figure 4(b)

Visit cycle time of selected experimental scenarios under different double-booking strategies (prediction-based vs. random/designated time)

Figure 5.

Trade-offs between visit cycle time and clinic throughput under different double-booking strategies