What Causes Delays in Admission to Rehabilitation Care? A Structural Estimation Approach

Problem definition: Delays in admission to rehabilitation care can adversely impact patient outcomes. In addition, delayed patients keep occupying their acute care beds, making them unavailable for incoming patients. Admission delays are mainly caused by a lack of rehabilitation bed capacity , the time required to plan for rehabilitation activities, which we refer to as processing times. Because of non-standard bed allocation decisions and data limitations in practice, quantifying the magnitude of the two sources delays can be technically challenging yet critical to the design of evidence-based interven-tions to reduce delays. We propose an empirical approach to understanding the contribu-tions of the two sources of delays when only a single (combined) measure of admission delay is available. Methodology/results: We propose a hidden Markov model (HMM) estimate the unobserved processing times and the status-quo bed allocation policy. Our estimation results quantify the magnitude of processing times versus capacity-driven delays and provide insights into factors impacting the bed allocation decision. We validate our estimated policy using a queueing model of patient flow and find that ignoring proces-sing times or using simple bed allocation policies can lead to highly inaccurate delay esti-mates. In contrast, our estimated policy allows for accurate evaluation of different operational interventions. We find that reducing processing times can be highly effective in reducing admission delays and bed-blocking costs. In addition, allowing early trans-fer-whereby patients can complete some of their processing requirements in the rehabili-tation unit-can significantly reduce admission delays, with only a small increase in rehab LOS. Managerial implications: Our study demonstrates the importance of quantifying dif-ferent sources of delays in the design of effective operational interventions for reducing delays in admission to rehabilitation care. The proposed estimation framework can applied in other transition-of-care settings with personalized capacity allocation decisions and hidden processing delays.