Impact of Medical-Pharmaceutical Separation Reform on Hospitalization Expenditure in Tertiary Public Hospitals: Difference-in-Difference Analysis Based on Panel Data from Beijing.

Purpose: The medical-pharmaceutical separation (MPS) reform is a healthcare reform that focuses on reducing the proportion of drug expenditure. This study aims to analyze the impact of the MPS reform on hospitalization expenditure and its structure in tertiary public hospitals. Methods: Using propensity score matching and multi-period difference-in-difference methods to analyze the impact of the MPS reform on hospitalization expenditure and its structure, a difference-in-difference-in-difference model was established to analyze the heterogeneity of whether the tertiary public hospital was a diagnosis-related-group (DRG) payment hospital. Of 22 municipal public hospitals offering tertiary care in Beijing, monthly panel data of 18 hospitals from July 2011 to March 2017, totaling 1242 items, were included in this study. Results: After the MPS reform, the average drug expenditure, average Western drug expenditure, and average Chinese drug expenditures per hospitalization decreased by 24.5%, 24.6%, and 24.1%, respectively (P < 0.001). The proportions of drug expenditure decreased by 4.5% (P < 0.001), and the proportion of medical consumables expenditure increased significantly by 2.7% (P < 0.001). Conclusion: The MPS reform may significantly optimize the hospitalization expenditure structure and control irrational increases in expenditure. DRG payment can control the tendency to increase the proportions of medical consumables expenditure after the reform and optimize the effect of the reform. There is a need to strengthen the management of medical consumables in the future, promote the MPS reform and DRG payment linkage, and improve supporting measures to ensure the long-term effect of the reform.

Chamber Attention Network (CAN): Towards interpretable diagnosis of pulmonary artery hypertension using echocardiography.

INTRODUCTION: Accurate identification of pulmonary arterial hypertension (PAH) in primary care and rural areas can be a challenging task. However, recent advancements in computer vision offer the potential for automated systems to detect PAH from echocardiography. OBJECTIVES: Our aim was to develop a precise and efficient diagnostic model for PAH tailored to the unique requirements of intelligent diagnosis, especially in challenging locales like high-altitude regions. METHODS: We proposed the Chamber Attention Network (CAN) for PAH identification from echocardiographic images, trained on a dataset comprising 13,912 individual subjects. A convolutional neural network (CNN) for view classification was used to select the clinically relevant apical four chamber (A4C) and parasternal long axis (PLAX) views for PAH diagnosis. To assess the importance of different heart chambers in PAH diagnosis, we developed a novel Chamber Attention Module. RESULTS: The experimental results demonstrated that: 1) The substantial correspondence between our obtained chamber attention vector and clinical expertise suggested that our model was highly interpretable, potentially uncovering diagnostic insights overlooked by the clinical community. 2) The proposed CAN model exhibited superior image-level accuracy and faster convergence on the internal validation dataset compared to the other four models. Furthermore, our CAN model outperformed the others on the external test dataset, with image-level accuracies of 82.53% and 83.32% for A4C and PLAX, respectively. 3) Implementation of the voting strategy notably enhanced the positive predictive value (PPV) and negative predictive value (NPV) of individual-level classification results, enhancing the reliability of our classification outcomes. CONCLUSIONS: These findings indicate that CAN is a feasible technique for AI-assisted PAH diagnosis, providing new insights into cardiac structural changes observed in echocardiography.

A Scd1-mediated metabolic alteration participates in liver responses to low-dose bavachin.

Hepatotoxicity induced by bioactive constituents in traditional Chinese medicines or herbs, such as bavachin (BV) in Fructus Psoraleae, has a prolonged latency to overt drug-induced liver injury in the clinic. Several studies have described BV-induced liver damage and underlying toxicity mechanisms, but little attention has been paid to the deciphering of organisms or cellular responses to BV at no-observed-adverse-effect level, and the underlying molecular mechanisms and specific indicators are also lacking during the asymptomatic phase, making it much harder for early recognition of hepatotoxicity. Here, we treated mice with BV for 7 days and did not detect any abnormalities in biochemical tests, but found subtle steatosis in BV-treated hepatocytes. We then profiled the gene expression of hepatocytes and non-parenchymal cells at single-cell resolution and discovered three types of hepatocyte subsets in the BV-treated liver. Among these, the hepa3 subtype suffered from a vast alteration in lipid metabolism, which was characterized by enhanced expression of apolipoproteins, carboxylesterases, and stearoyl-CoA desaturase 1 (Scd1). In particular, increased Scd1 promoted monounsaturated fatty acids (MUFAs) synthesis and was considered to be related to BV-induced steatosis and polyunsaturated fatty acids (PUFAs) generation, which participates in the initiation of ferroptosis. Additionally, we demonstrated that multiple intrinsic transcription factors, including Srebf1 and Hnf4a, and extrinsic signals from niche cells may regulate the above-mentioned molecular events in BV-treated hepatocytes. Collectively, our study deciphered the features of hepatocytes in response to BV insult, decoded the underlying molecular mechanisms, and suggested that Scd1 could be a hub molecule for the prediction of hepatotoxicity at an early stage.