We developed the methodology of welfare-based healthcare planning. For proof of concept, we empirically identified welfare-optimal hospital locations for thoracic surgical treatment of lung cancer (TSTLC) in Germany.
We used statutory health insurance data to estimate a volume-outcome model capturing the case-volume elasticity of the 1-year survival odds in patients with TSTLC. We conducted a discrete choice experiment to estimate the willingness to travel of representative (potential) patients for increases in the 1-year survival probability after TSTLC. Combining these results with a gravity model fitted to observed locations of patients and hospitals, we simulated different health planning scenarios (HPS) in 2035. For each HPS, we applied a Nash social welfare function to derive social welfare.
Using data on 1449 patients with TSTLC treated in 189 hospitals, we estimated a case-volume elasticity of 0.27 (95% confidence interval [CI] = 0.07;0.46). The discrete choice experiment revealed that, for an increase in the 1-year survival probability from 90% to 91%, representative individuals would be willing to travel additional 66 minutes (95% CI = 45;93 minutes) when traveling 60 minutes and additional 23 minutes (95% CI = 18;33 minutes) when traveling 240 minutes. The top 1000 HPS according to welfare included between 15 and 22 hospitals. The welfare-optimal HPS included 19 hospitals with an average travel time of 54 minutes (status-quo HPS: 40 minutes) and a 1-year survival probability between 90.5% and 93.6% (status-quo HPS: 89.1%).
Our findings highlight the potential of welfare-based healthcare planning to increase the welfare of patients in Germany due to centralization of TSTLC.