Assessment of hospital performance with a case-mix standardized mortality model using an existing administrative database in Japan
1 Department of Healthcare Quality Assessment, Graduate School of Medicine, University of Tokyo, Japan
2 Department of Health Economics and Epidemiology Research, School of Public Health, University of Tokyo, Japan
3 Department of Health Management and Policy, Graduate School of Medicine, University of Tokyo, Japan
4 Division of Healthcare Informatics, Department of Health Policy, Graduate School of Medicine and Dentistry, Tokyo Medical and Dental University, Tokyo, Japan
5 Department of Preventive Medicine and Community Health, University of Occupational and Environmental Health, Japan
BMC Health Services Research 2010, 10:130 doi:10.1186/1472-6963-10-130Published: 19 May 2010
Few studies have examined whether risk adjustment is evenly applicable to hospitals with various characteristics and case-mix. In this study, we applied a generic prediction model to nationwide discharge data from hospitals with various characteristics.
We used standardized data of 1,878,767 discharged patients provided by 469 hospitals from July 1 to October 31, 2006. We generated and validated a case-mix in-hospital mortality prediction model using 50/50 split sample validation. We classified hospitals into two groups based on c-index value (hospitals with c-index ≥ 0.8; hospitals with c-index < 0.8) and examined differences in their characteristics.
The model demonstrated excellent discrimination as indicated by the high average c-index and small standard deviation (c-index = 0.88 ± 0.04). Expected mortality rate of each hospital was highly correlated with observed mortality rate (r = 0.693, p < 0.001). Among the studied hospitals, 446 (95%) had a c-index of ≥0.8 and were classified as the higher c-index group. A significantly higher proportion of hospitals in the lower c-index group were specialized hospitals and hospitals with convalescent wards.
The model fits well to a group of hospitals with a wide variety of acute care events, though model fit is less satisfactory for specialized hospitals and those with convalescent wards. Further sophistication of the generic prediction model would be recommended to obtain optimal indices to region specific conditions.