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As telehealth utilization for ambulatory and home-based care skyrockets, there has been a paradigm shift to a decentralized and hybrid care delivery modality integrating both in-person and telehealth services provided at different layers of the care delivery network, i.e., central hospitals, satellite clinics, and patient homes. The operations of such care delivery systems need to take into consideration patients’ mobility and care needs, and rely on multiple types of nurses who can support and facilitate telehealth (with hospital physicians) in clinics and patient homes. We formulate an optimization problem, aiming at operationalizing the proposed care delivery network. Decisions regarding the type of care delivered, the location of care delivered, and the scheduling of all kinds of nurses are determined jointly to minimize operating costs while simultaneously satisfying patients’ care needs. We propose a bi-level approximation that exploits the structure of the hybrid telehealth system, and develop column generation-based heuristic algorithms to identify the joint decision rules for clinic selection, patient assignment, and visiting nurse routing problems. Numerical experiment results demonstrate our algorithm’s capability to achieve high-quality solutions in reasonable computation time, and is capable of solving instances with large patient sizes and time windows. Our work supports the efficient and effective operation of the proposed hybrid telehealth systems to improve patient access to care.
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This content will become publicly available on October 18, 2026
'Shopping Around': An Experiment in Preferences and Incentives for Placing Long-term Patients
Hospitals and care homes devote significant resources to placing post-acute patients from hospitals into long-term care. This paper describes a two-phase experiment over SMS, conducted with a hospital in Hawai'i, in which care homes express preferences, indicate availability to accept patients, and express interest in patients. In the first phase, the treatment asks care homes to reconsider their stated preferences to better support matching. The second phase measures whether resulting changes in preferences increased how often homes express interest in patients that match newly stated preferences. First, to motivate and inform experiment design, we explore factors contributing to extended hospital stays for patients, uncovering how care homes' preferences play a major role in what patients they consider accepting. Second, we conduct a 16-week randomized controlled trial with 960 homes, where we experimentally probed, via SMS messages, homes' willingness to change their preferences to improve potential patient match recommendations. We show that inducing homes to reflect on their preferences increased the number of homes who changed their preferences by over 50%: 9.8% of homes who received our treatment changed their preference compared to 6.0% of homes in the control group (p-value = 0.0421).Third, followup interviews with 22 home operators highlight how preference malleability is shaped by a combination of design constraints and on-the-ground realities, such as load-balancing existing patient rosters. Finally, we discuss implications for real-world systems like ours that must balance constrained communication with situational complexity towards improving outcomes.
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- Award ID(s):
- 2339427
- PAR ID:
- 10686391
- Publisher / Repository:
- ACM Digital Library
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 9
- Issue:
- 7
- ISSN:
- 2573-0142
- Page Range / eLocation ID:
- 1 to 31
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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