BackgroundManaging inpatient demand is challenging, especially for high-volume emergency surgical casemixes such as acute appendicitis and cholecystitis. These conditions drive unplanned admissions and consume significant bed capacity, despite their suitability for accelerated pathways. We evaluated a bundled care model for this casemix, incorporating same-day discharge protocols and remote patient monitoring to reduce unnecessary inpatient stays and optimize hospital resources. MethodsA decision tree model compared the proposed same-day discharge surgical care model supported by remote patient monitoring with conventional inpatient care for suitable emergency department presentations requiring appendicectomy or cholecystectomy. The primary outcome was the number of inpatient bed days saved. Costs were estimated from the healthcare payer perspective in 2023 Australian dollars over a 12-month horizon. National datasets and published literature informed model inputs. Robustness was assessed using deterministic and probabilistic sensitivity analyses. ResultsThe same-day discharge surgical care model saved 0. 57 bed days per patient (2. 07 days for conventional inpatient care vs. 1. 50 days for the same-day model) and reduced healthcare costs by AU772 per patient (AU9436 vs. AU8664). Findings remained consistent across wide parameter variations in both sensitivity analyses. ConclusionThe proposed same-day discharge surgical care model supported by remote patient monitoring was a dominant strategy, achieving cost savings and reducing bed-day utilization compared with conventional inpatient care. These results highlight the potential of telehealth-enabled surgical pathways to improve hospital efficiency, optimize resource allocation, and advance digitally integrated models of perioperative care.
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John Paul Kuwornu
David Brain
Kheng-Seong Ng
Journal of Telemedicine and Telecare
The University of Sydney
Queensland University of Technology
Royal Prince Alfred Hospital
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Kuwornu et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69fd7e23bfa21ec5bbf06604 — DOI: https://doi.org/10.1177/1357633x261442274