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Objective: To determine whether there are significant differences in costs of treatment and readmission rates for hospital consultations undertaken by female versus male physicians in China. Methods: Using data from the Urban Employee Basic Medical Insurance program from January 2018 through October 2019, we investigated spending patterns and clinical outcomes for patients at tertiary hospitals in one of the largest cities in China by the gender of the attending physician. Our sample included 79,085 hospitalizations treated by 3993 physicians in internal medicine departments. We examined the association between physician gender and visit cost using a multivariable linear model. We examined the association between physician gender and 30-days readmission rates using a multivariable probability model. We adjusted for a rich set of patient characteristics, primary diagnosis fixed effects, and hospital fixed effects. In addition, we used patient fixed effects in a robustness analysis. Results: Adjusting for primary diagnosis fixed effects, spending per visit was 4.1% higher for patients treated by male physicians than for those treated by female physicians, a statistically significant difference (95% CI [1.5%, 6.7%]). This pattern persisted after further adjusting for hospital fixed effects (3.2% [1.2%, 5.2%]), patient characteristics (3.2% [1.2%, 5.1%]), and patient fixed effects (4.2% [1.8%, 6.7%]). The difference is mainly driven by higher spending on drugs (8.7% [3.9%, 13.6%]) and out-of-pocket costs (3.9% [1.7%, 6.0%]). No statistically significant differences were observed in the readmission rates of patients treated by male and female physicians in any of our three model specifications. Conclusions: Spending per visit was significantly higher among patients treated by male physicians than among those treated by female physicians, with the difference mainly driven by spending on drugs and out-of-pocket costs. No significant difference was observed in the hospital readmission rates of patients treated by male and female physicians. These findings have important implications for gender equality in medicine and health care quality and efficiency in developing countries. [ABSTRACT FROM AUTHOR]

Background: The Risk Stratification Index and the Hierarchical Condition Categories model baseline risk using comorbidities and procedures. The Hierarchical Condition categories are rederived yearly, whereas the Risk Stratification Index has not been rederived since 2010. The two models have yet to be directly compared. The authors thus rederived the Risk Stratification Index using recent data and compared their results to contemporaneous Hierarchical Condition Categories.Methods: The authors reimplemented procedures used to derive the original Risk Stratification Index derivation using the 2007 to 2011 Medicare Analysis and Provider review file. The Hierarchical Condition Categories were constructed on the entire data set using software provided by the Center for Medicare and Medicaid Services. C-Statistics were used to compare discrimination between the models. After calibration, accuracy for each model was evaluated by plotting observed against predicted event rates.Results: Discrimination of the Risk Stratification Index improved after rederivation. The Risk Stratification Index discriminated considerably better than the Hierarchical Condition Categories for in-hospital, 30-day, and 1-yr mortality and for hospital length-of-stay. Calibration plots for both models demonstrated linear predictive accuracy, but the Risk Stratification Index predictions had less variance.Conclusions: Risk Stratification discrimination and minimum-variance predictions make it superior to Hierarchical Condition Categories. The Risk Stratification Index provides a solid basis for care-quality metrics and for provider comparisons. [ABSTRACT FROM AUTHOR]

Background: The Risk Stratification Index was developed from 35 million Medicare hospitalizations from 2001 to 2006 but has yet to be externally validated on an independent large national data set, nor has it been calibrated. Finally, the Medicare Analysis and Provider Review file now allows 25 rather than 9 diagnostic codes and 25 rather than 6 procedure codes and includes present-on-admission flags. The authors sought to validate the index on new data, test the impact of present-on-admission codes, test the impact of the expansion to 25 diagnostic and procedure codes, and calibrate the model.Methods: The authors applied the original index coefficients to 39,753,036 records from the 2007-2012 Medicare Analysis data set and calibrated the model. The authors compared their results with 25 diagnostic and 25 procedure codes, with results after restricting the model to the first 9 diagnostic and 6 procedure codes and to codes present on admission.Results: The original coefficients applied to the 2007-2012 data set yielded C statistics of 0.83 for 1-yr mortality, 0.84 for 30-day mortality, 0.94 for in-hospital mortality, and 0.86 for median length of stay-values nearly identical to those originally reported. Calibration equations performed well against observed outcomes. The 2007-2012 model discriminated similarly when codes were restricted to nine diagnostic and six procedure codes. Present-on-admission models were about 10% less predictive for in-hospital mortality and hospital length of stay but were comparably predictive for 30-day and 1-yr mortality.Conclusions: Risk stratification performance was largely unchanged by additional diagnostic and procedure codes and only slightly worsened by restricting analysis to codes present on admission. The Risk Stratification Index, after calibration, thus provides excellent discrimination and calibration for important health services outcomes and thus appears to be a good basis for making hospital comparisons. [ABSTRACT FROM AUTHOR]