A study of the types and manifestations of physicians' unintended behaviors in the DRG payment system.

Introduction: In recent years, China has implemented the Diagnosis Related Groups (DRG) payment system as part of its healthcare insurance reimbursement policy. Numerous studies have focused on the effectiveness of DRG payment system in controlling unreasonable growth in medical expenses. However, there has been no systematic report on the types of unintended behaviors exhibited by doctors under the DRG payment system. Methods: The study first utilized interrupted time series analysis to analyze medical records and insurance data from eight hospitals. It investigated the data changes in MDC and ADRG groups before and after the implementation of the DRG payment system. Subsequently, a semi-structured interview method was employed to conduct qualitative research on the unintended behaviors of physicians, aiming to gain a more accurate understanding of specific changes in physician behavior after the implementation of the DRG payment system. Results: This study discovered that doctors engage in unintended behaviors within the framework of the DRG payment system. Discussion: In the early implementation of the DRG payment system in China, the contradictions between the flawed DRG payment methods and supporting systems and the actual diagnostic and treatment work manifested in the form of unintended doctor behaviors. Most of these unintended behaviors can be considered reasonable feedback from doctors to cope with the existing system flaws. They are conducive to identifying the deficiencies in China's DRG payment system and suggesting directions for improvement.

Retrospective Study on the Influencing Factors and Prediction of Hospitalization Expenses for Chronic Renal Failure in China Based on Random Forest and LASSO Regression.

Aim: With the improvement in people's living standards, the incidence of chronic renal failure (CRF) is increasing annually. The increase in the number of patients with CRF has significantly increased pressure on China's medical budget. Predicting hospitalization expenses for CRF can provide guidance for effective allocation and control of medical costs. The purpose of this study was to use the random forest (RF) method and least absolute shrinkage and selection operator (LASSO) regression to predict personal hospitalization expenses of hospitalized patients with CRF and to evaluate related influencing factors. Methods: The data set was collected from the first page of data of the medical records of three tertiary first-class hospitals for the whole year of 2016. Factors influencing hospitalization expenses for CRF were analyzed. Random forest and least absolute shrinkage and selection operator regression models were used to establish a prediction model for the hospitalization expenses of patients with CRF, and comparisons and evaluations were carried out. Results: For CRF inpatients, statistically significant differences in hospitalization expenses were found for major procedures, medical payment method, hospitalization frequency, length of stay, number of other diagnoses, and number of procedures. The R2 of LASSO regression model and RF regression model are 0.6992 and 0.7946, respectively. The mean absolute error (MAE) and root mean square error (RMSE) of the LASSO regression model were 0.0268 and 0.043, respectively, and the MAE and RMSE of the RF prediction model were 0.0171 and 0.0355, respectively. In the RF model, and the weight of length of stay was the highest (0.730). Conclusions: The hospitalization expenses of patients with CRF are most affected by length of stay. The RF prediction model is superior to the LASSO regression model and can be used to predict the hospitalization expenses of patients with CRF. Health administration departments may consider formulating accurate individualized hospitalization expense reimbursement mechanisms accordingly.

Perioperative Complications and Safety Evaluation of Robot-Assisted Radical Hysterectomy of Cervical Cancer After Neoadjuvant Chemotherapy.

PURPOSE: To evaluate the perioperative complications of patients with cervical cancer who are treated with robot-assisted radical hysterectomy (RRH) and to further evaluate the safety of patients undergoing NACT. METHODS: A total of 805 consecutive cervical cancer patients undergoing RRH were involved in this report. Their clinical characteristics were retrieved from hospital medical records. Perioperative complications were subdivided into intraoperative and postoperative complications, which were graded according to the Clavien-Dindo classification (CDC), and the complications of grade III and above were defined as severe complications. Furthermore, the two-level logistic regression model was used to estimate the risk factors of perioperative and severe complications and to further confirm the relationship between NACT and perioperative and severe complications. RESULTS: The perioperative complication rate and severe complications were 45.09% and 7.83%, respectively. Poorly differentiated tumor and NACT were identified as independent risk factors for perioperative complications by multifactor analysis. Furthermore, we concentrated on the relations between NACT and complications. The risk of perioperative complications of the group with NACT (OR = 11.08, 95% CI: 5.70-21.54) was significantly higher than the group without NACT, especially in postoperative complications (OR=17.65, 95% CI: 8.63-36.08), even after adjusting confounding factors. However, there was no statistically significant difference in terms of severe complications (OR=1.68, 95% CI: 0.64-4.41) and intraoperative complications (OR=0.51, 95% CI: 0.18-1.41). Moreover, as the times of NACT increase, the impact on perioperative complications is more pronounced. A similar trend was observed in postoperative complications, while this statistical difference was still not observed in intraoperative and severe complications. CONCLUSION: This result demonstrates the feasibility and safety of RRH of cervical carcinoma after NACT in generally, since it only causes mild complications, not severe complications.

Diagnosis related group grouping study of senile cataract patients based on E-CHAID algorithm.

AIM: To figure out the contributed factors of the hospitalization expenses of senile cataract patients (HECP) and build up an area-specified senile cataract diagnosis related group (DRG) of Shanghai thereby formulating the reference range of HECP and providing scientific basis for the fair use and supervision of the health care insurance fund. METHODS: The data was collected from the first page of the medical records of 22 097 hospitalized patients from tertiary hospitals in Shanghai from 2010 to 2012 whose major diagnosis were senile cataract. Firstly, we analyzed the influence factors of HECP using univariate and multivariate analysis. DRG grouping was conducted according to the exhaustive Chi-squared automatic interaction detector (E-CHAID) model, using HECP as target variable. Finally we evaluated the grouping results using non-parametric test such as Kruskal-Wallis H test, RIV, CV, etc. RESULTS: The 6 DRGs were established as well as criterion of HECP, using age, sex, type of surgery and whether complications/comorbidities occurred as the key variables of classification node of senile cataract cases. CONCLUSION: The grouping of senile cataract cases based on E-CHAID algorithm is reasonable. And the criterion of HECP based on DRG can provide a feasible way of management in the fair use and supervision of medical insurance fund.