Forecasting surgical costs: Towards informed financial consent and financial risk reduction

Background: Health care expenditure is increasing around the world and surgery is a major cause of financial hardship to patients and their families. Using pancreatoduodenectomy (PD), one of the most complex, morbid and costly operation as an example, this study aimed to identify the cost drivers of surgery, estimate relative contribution of these drivers, and derive and validate a cohort-specific cost forecasting tool. Methods: Data on the costs of 1406 patients undergoing PD in three tertiary hospitals in India, Italy and the United States were analysed. Cost drivers were identified and cost models developed using a 4-stage process. Results: There was a significant difference in overall cost of PD between the 3 cohorts. The cost drivers common to the 3 cohorts included duration of hospital stay and the outcome of death (Clavien-Dindo 5). Significant cohort-specific cost drivers included co-morbidities, operating theatre utilisation times and operative blood loss, development of pancreatectomy-specific complications (POPF, DGE, PPH), and need for interventional radiology to manage complications. Based on this, a cost forecasting tool was developed. Conclusions: Drivers of costs for a surgical procedure (e.g. PD) are different between hospitals. Developing cost models/nomograms to predict the expected cost of surgery and perioperative care will not be applicable between hospitals. However, the approach could be used to develop context-specific data that will provide patients (at the time of the informed financial consent) and funding agencies with a more realistic cost estimate for a given operation. The developed cost forecasting tool warrants future validation.