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

Abstract

Problem definition: Delays in admission to rehabilitation care can adversely impact patient outcomes and are costly for the healthcare system as delayed patients keep occupying their acute care beds, making them unavailable for incoming patients. Existing evidence suggests that admission delays are mainly caused by two sources: lack of rehabilitation bed capacity and the time required to plan for rehabilitation activities, which we refer to as processing times. However, due to the complex care transition process, non-standard bed allocation decisions, and data limitations in practice, quantifying the magnitude of the two sources of delays can be technically challenging yet critical to the design of evidence-based interventions to reduce delays. In this paper, we propose an empirical approach to understanding the contributions 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) to estimate the unobserved processing requirements and the status-quo bed allocation policy, where the utility of allocating a bed to a patient depends on the patient's characteristics, the system's state, and various other factors. We validate our estimated policy using a queueing model of patient flow, and find that ignoring processing delays or using simple bed allocation policies such as First-Come First-Served or strict priority can lead to highly inaccurate estimates. In contrast, our estimated policy matches the empirical delay distributions well and allows for accurate evaluation of different operational interventions. Through counterfactual experiments, we examine interventions targeted at addressing different sources of delays. We find that reducing processing times can be highly effective in reducing admission delays and bed-blocking costs. In addition, allowing early transfer – whereby patients can complete some of their processing requirements in the rehabilitation unit – can significantly reduce admission delays, with only a small increase in rehab LOS.

Managerial implications: Our study demonstrates the importance of quantifying different sources of delays in design of effective operational interventions for reducing delays in admission to rehabilitation care. The proposed estimation framework can be applied in other transition-of-care settings with personalized capacity allocation decisions and hidden processing delays.

Note:
Funding Information: The work of Vahid Sarhangian was partially supported by a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) and a Connaught New Researcher award.

Conflict of Interests: The authors did not have any competing interests.

Ethical Approval: Our study was exempted from review for human participant research by the Research Ethics Board of the University of Toronto.