A predictive decision analytics approach for primary care operations management: A case study of double-booking strategy design and evaluation. (March 2023)

• Record Type: Journal Article
• Authors: Zhou, Yuan; Viswanatha, Amith; Motaleb, Ammar Abdul; Lamichhane, Prabin; Chen, Kay-Yut; Young, Richard; Gurses, Ayse P.; Xiao, Yan
• Is Part Of: Computers & industrial engineering. Volume 177(2023)
• Publication Date: 2023-03
• Subjects: Primary care -- Decision-making -- Prediction -- Simulation -- Patient no-show -- Double-booking; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2023.109069
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Prediction is linked to simulation for predicted decision-making in primary care. The predictive decision analytics approach enables more effective decision-making. Trade-offs of primary care measures provide more insights for decision evaluations. Prediction-based double-booking strategy helps achieve better no-show management. Abstract: Primary care plays a vital role for individuals and families in accessing care, staying 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) 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 simulation modeling 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 to manage patient no-shows. 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.