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BACKGROUND: Initiatives in perioperative care warrant robust cost-effectiveness analysis in a cost-constrained era when high-value care is a priority. A model of anesthesia-led early high-acuity postoperative care, advanced recovery room care (ARRC), has shown benefit in terms of hospital and patient outcomes, but its cost-effectiveness has not yet been formally determined. METHODS: Data from a previously published single-center prospective cohort study of ARRC in medium-risk patients were used to generate a Markov model, which described patient transition between care locations, each with different characteristics and costs. The incremental cost-effectiveness ratio (ICER), using days at home (DAH) and hospital costs, was calculated for ARRC compared to usual ward care using deterministic and probabilistic sensitivity analysis. RESULTS: The Markov model accurately described patient disposition after surgery. For each patient, ARRC provided 4.3 more DAH within the first 90 days after surgery and decreased overall hospital costs by $1081 per patient. Probabilistic sensitivity analysis revealed that ARRC had a 99.3% probability of increased DAH and a 77.4% probability that ARRC was dominant from the perspective of the hospital, with improved outcomes and decreased costs. CONCLUSIONS: Early high-acuity care for approximately 24 hours after surgery in medium-risk patients provides highly cost-effective improvements in outcomes when compared to usual ward care.
RESUMO
OBJECTIVES: This study aimed to evaluate the application of cost-effectiveness modeling to redesign of perioperative care pathways, from a hospital perspective. METHODS: A Markov cost-effectiveness model of patient transition between care locations, each with different characteristics and cost, was developed. Inputs were derived from clinical trials piloting a preoperative call center and a postoperative medium-acuity care unit. The effect chosen was days at home (DAH) after surgery, reflecting quality of in-hospital care, acknowledged financially by fundholders, and relevant to consumers. Cost was from the hospital's perspective. A model cycle time of 4 hours for 30 days reflected relevant timelines and costs. RESULTS: A Markov model was successfully created, accounting for the care locations in the 2 pathways as model states and accounting for consequences and costs. Cost-effectiveness analysis allowed the calculation of an incremental cost-effectiveness ratio comparing these pathways, providing a mean incremental cost-effectiveness ratio of -$427 per additional DAH, where incremental costs and DAH were -$644 and +1.51, respectively. Probabilistic sensitivity analysis suggested the new pathway had a 61% probability of reduced costs and a 74% probability of increased DAH and a 58% probability this pathway was dominant. Tornado analysis revealed the major contributor to increased costs as intensive care unit stay and the major contributor to decreased costs as ward stay. For the new pathway, the probability of transfer from ward to home and the probability of staying at home had the greatest impact on DAH. CONCLUSIONS: These data suggest Markov modeling may be a useful tool for the cost-effectiveness analysis of initiatives in perioperative care.