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BackgroundBreast cancer imposes a considerable burden on both the health care system and society, and becomes increasingly severe among women in China. To reduce the economic burden of this disease is crucial for patients undergoing the breast cancer surgery, hospital managers, and medical insurance providers. However, few studies have evidenced the prediction of the total hospital expenses (THE) for breast cancer surgery. The aim of the study is to predict THE for breast cancer surgery and identify the main influencing factors.MethodsData were retrieved from the first page of medical records of 3699 patients undergoing breast cancer surgery in one tertiary hospital from 2017 to 2018. Multiple liner regression (MLR), artificial neural networks (ANNs), and classification and regression tree (CART) were constructed and compared.ResultsThe dataset from 3699 patients were randomly divided into training and test sets at a 70:30 ratio (2599 and 1100 records, respectively). The average total hospital expenses were 12520.54 ± 7844.88 ¥ (US$ 1929.20 ± 1208.11). MLR results revealed six factors to be significantly associated with THE: age, LOS, type of disease, having medical insurance, minimally invasive surgery, and receiving general anesthesia. After comparing three models, ANNs was the best model to predict THEs in patients undergoing breast cancer surgery, and its strong predictive performance was also validated.ConclusionsTo reduce the THEs, more attention should be paid to related factors of LOS, major and minimally invasive surgeries, and general anesthesia for these patient groups undergoing breast cancer surgery. This may reduce the information asymmetry between doctors and patients and provide more reliable cost, practical inpatient medical consumption standards and reimbursement standards reference for patients, hospital managers, and medical insurance providers ,respectively.

BackgroundThe distribution of health-care resources is foundational to achieving fairness and having access to health service. China and its local Shanghai’s government have implemented measures to allocate health-care resources with the equity as one of the major goals since 2009-health-care reform. The aim of this study was to analyze differences in regional distribution and inequality in health-resource allocation on institutions, beds, and workforce in Shanghai over 7 years.MethodsThe study was conducted using 2010–2016 data to analyze health-resource allocation on institutions, beds, and workforce in Shanghai, China. The annual growth rate (AGR) was used to evaluate the time trends of health-care resource from 2010 to 2016, and Theil index was calculated to measure inequality of five indicators of health-care resource allocation during this study period.ResultsAll quantities of health-care resources per 1000 people increased across Shanghai districts from 2010 to 2016. Compared with suburban districts, the central districts had higher ratios on five health-care resource indicators, and faster average growth in the bed and nurse indicator. The Theil of the indicators, except for doctors in hospitals, all exhibited downward time trends.ConclusionsRegional difference between urban and rural areas and inequality between institution and workforce, especially for doctors, still existed. Some targeted measures including but not limited to income raising, facilitation of transportation conditions, investment of more fiscal funds, enhancement of health-care service provision for rural residents should be fully considered to narrow resource distribution gap between urban and rural districts and mitigate the inequality of health-care resource allocation.