Background: Re-design of systems or models of care to manage perioperative care has the potential to generate higher value care (improved outcomes; ideally lower cost). However, this needs high quality measurement of cost-effectiveness to allow stakeholders to select those which provide most value, and to plan future interventions. Data on cost-effective analysis in this area are sparse, and trials often apply relatively simple analytic techniques.
Methods: This analysis utilised recently published data from a small feasibility trial of a new model of medium-acuity postoperative care for moderate-risk patients undergoing surgery (Advanced Recovery Room Care; ARRC). The trial 1 data were used to develop a Markov cost-effectiveness model of patient transition (model transition probabilities) between locations of care (model states), each with different effects and cost. Cost was taken from the perspective of hospitals. The effect chosen was patient postoperative days at home after surgery (DAH), an effect reflecting quality of in-hospital care, acknowledged financially by healthcare fundholders, and relevant to consumers. A model cycle time of 4 hours run out to 30 days after surgery reflected clinically relevant timelines and costs.
Results: This model showed the potential differences before and after introduction of ARRC in ICU use, re-admissions, and DAH, in particular, which reflected the findings from the ARRC trial. Incremental cost effectiveness ratio (ICER) of introduction of ARRC was minus $601 for every increased day at home. Sensitivity analysis revealed 805 of 1000 simulations found a negative ICER.
Conclusions: These data suggest that the ARRC model may have positive cost-effectiveness, something to be examined in a future prospective clinical trial. Such cost-effectiveness modelling may have utility in examination of other innovations in perioperative care.
Trial registration: The ARRC trial on which this analysis is based was prospectively registered: ANZCTRN 12617001173381