Why Do Patients Seek Diagnose Dis-accordance With Hierarchical Medical System Related Policies in Tertiary Hospitals? A Qualitative Study in Shanghai From the Perspective of Physicians

Will urban scale affect health services inequity? The empirical evidence from cities in China

Background

The equity of public resources triggered by city shrinkage is a global challenge. Significantly, the impact of city shrinkage on the allocation of health service resources needs to be better understood. This study explores the impact of population change on government investment and health service delivery in shrinking cities.

Data and method

Using data from China's Urban Statistical Yearbook (2010-2020), we employ regression discontinuity (RD) and fixed-effect models to examine the causal relationship between city shrinkage and health service provision.

Result

Shrinking cities show significant disparities in health resources, particularly in bed numbers (-1,167.58, p < 0.05) and doctor availability (-538.54, p < 0.05). Economic development (p < 0.01) and financial autonomy (p < 0.01) influence hospital bed distribution. Investments in public services (primary schools and teachers, p < 0.01) affect health resource delivery. Robustness tests support our results.

Conclusion

This study reveals how city shrinkage disrupts health service provision and equity, establishing a causal relationship between city shrinkage/expansion and health resource allocation, emphasizing the imbalance caused by urban population changes. City expansion intensifies competition for health resources, while shrinking cities struggle to provide adequate resources due to government reluctance. Policymakers should adapt health resource allocation strategies to meet patient demands in changing urban landscapes.

Quality and Dependability of ChatGPT and DingXiangYuan Forums for Remote Orthopedic Consultations: Comparative Analysis

BACKGROUND

The widespread use of artificial intelligence, such as ChatGPT (OpenAI), is transforming sectors, including health care, while separate advancements of the internet have enabled platforms such as China's DingXiangYuan to offer remote medical services.

OBJECTIVE

This study evaluates ChatGPT-4's responses against those of professional health care providers in telemedicine, assessing artificial intelligence's capability to support the surge in remote medical consultations and its impact on health care delivery.

METHODS

We sourced remote orthopedic consultations from "Doctor DingXiang," with responses from its certified physicians as the control and ChatGPT's responses as the experimental group. In all, 3 blindfolded, experienced orthopedic surgeons assessed responses against 7 criteria: "logical reasoning," "internal information," "external information," "guiding function," "therapeutic effect," "medical knowledge popularization education," and "overall satisfaction." We used Fleiss κ to measure agreement among multiple raters.

RESULTS

Initially, consultation records for a cumulative count of 8 maladies (equivalent to 800 cases) were gathered. We ultimately included 73 consultation records by May 2023, following primary and rescreening, in which no communication records containing private information, images, or voice messages were transmitted. After statistical scoring, we discovered that ChatGPT's "internal information" score (mean 4.61, SD 0.52 points vs mean 4.66, SD 0.49 points; P=.43) and "therapeutic effect" score (mean 4.43, SD 0.75 points vs mean 4.55, SD 0.62 points; P=.32) were lower than those of the control group, but the differences were not statistically significant. ChatGPT showed better performance with a higher "logical reasoning" score (mean 4.81, SD 0.36 points vs mean 4.75, SD 0.39 points; P=.38), "external information" score (mean 4.06, SD 0.72 points vs mean 3.92, SD 0.77 points; P=.25), and "guiding function" score (mean 4.73, SD 0.51 points vs mean 4.72, SD 0.54 points; P=.96), although the differences were not statistically significant. Meanwhile, the "medical knowledge popularization education" score of ChatGPT was better than that of the control group (mean 4.49, SD 0.67 points vs mean 3.87, SD 1.01 points; P<.001), and the difference was statistically significant. In terms of "overall satisfaction," the difference was not statistically significant between the groups (mean 8.35, SD 1.38 points vs mean 8.37, SD 1.24 points; P=.92). According to how Fleiss κ values were interpreted, 6 of the control group's score points were classified as displaying "fair agreement" (P<.001), and 1 was classified as showing "substantial agreement" (P<.001). In the experimental group, 3 points were classified as indicating "fair agreement," while 4 suggested "moderate agreement" (P<.001).

CONCLUSIONS

ChatGPT-4 matches the expertise found in DingXiangYuan forums' paid consultations, excelling particularly in scientific education. It presents a promising alternative for remote health advice. For health care professionals, it could act as an aid in patient education, while patients may use it as a convenient tool for health inquiries.

Development and application of a nomogram model for the prediction of carbapenem-resistant Klebsiella pneumoniae infection in neuro-ICU patients

IMPORTANCE

Patients in neuro-ICU are at a high risk of developing nosocomial CRKP infection owing to complex conditions, critical illness, and frequent invasive procedures. However, studies focused on constructing prediction models for assessing the risk of CRKP infection in neurocritically ill patients are lacking at present. Therefore, this study aims to establish a simple-to-use nomogram for predicting the risk of CRKP infection in patients admitted to the neuro-ICU. Three easily accessed variables were included in the model, including the number of antibiotics used, surgery, and the length of neuro-ICU stay. This nomogram might serve as a useful tool to facilitate early detection and reduction of the CRKP infection risk of neurocritically ill patients.

How to promote grass roots medical treatment under China's graded diagnosis and treatment policy? - From the perspective of customer value theory

Objectives

Based on the background of the implementation of graded diagnosis and treatment policy (GDT) in China, this paper studied the service optimization of primary medical institutions from the perspective of the primary medical treatment rate.

Methods

First, the difference-in-difference (DID) empirical strategy is used to analyze the impact of the implementation of the GDT on the improvement of the primary medical attendance rate, and the parallel trend test and the placebo test were used to test the robustness to illustrate the necessity of policy implementation. Second, combined with customer value theory, this paper used a logistic regression model to study the impact of customer value perception on grassroots medical care.

Results

The implementation of the GDT can indeed improve the rate of primary care (p = 0.028), but the impact is relatively small (0.042). From the perspective of value perception hierarchy, emotional value perception (p < 0.05) is the key factor that affects the behavior of grassroots medical treatment at this stage.

Conclusion

This paper fills the gaps in existing research, including empirical research on the grassroots medical treatment behavior of the masses under GDT and the impact of value perception on grassroots medical behavior. Furthermore, the recommendations are made for primary care institutions based on the results of the analysis, which promote the advancement of primary care services in China.