Zeitschrift fur Evidenz Fortbildung und Qualitaet im Gesundheitswesen最新文献

Background: During the COVID-19 pandemic many people supported compulsory vaccination and other far-reaching restrictions for unvaccinated individuals. This indicates that the COVID-19 pandemic was accompanied by a social delegitimization of individualism, which undermined personal autonomy and informed consent.

Purpose & method: Against this background, I intend to identify conceptual notions that can help to explain the drivers of the social delegitimization of individualism during the COVID-19 pandemic. For this purpose, I conceptually analyzed theories from social science research on vaccination behavior by applying the technique of concept mapping.

Results: The theoretical analysis reveals that (1) the essential need for health protection, (2) the demand for complexity reduction, (3) the optimization of social identity, (4) the reassurance of previous decisions, as well as (5) the feeling of being socially underprivileged may have contributed to the social delegitimization of individualism during the COVID-19 pandemic.

Conclusions: The five identified conceptual notions can explain the drivers behind the collective support for compulsory vaccination and other far-reaching restrictions against unvaccinated individuals. From a conceptual perspective, dynamic interactions between these drivers are likely. In the future, the identified notions may become relevant beyond COVID-19 if the social delegitimization of individualism continues to evolve into a ubiquitous phenomenon.

Background: Out-of-hospital intensive care (AKI) is a growing sector and is gaining increasing attention in research. In addition, the GKV-Intensive Care and Rehabilitation Strengthening Act (GKV-IPReG) influences the organization of care for people with AKI as well as the implementation and obligations of service providers. The aim is to promote decannulation and weaning from mechanical ventilation through surveys of potential. However, there is still a lack of scientifically sound evidence on care structures and care processes in AKI and the effects of the GKV-IPReG. These care structures were analyzed in the innovation fund project ATME ("Needs, requirements and cross-sectoral care pathways of out-of-hospital ventilated intensive care patients").

Methods: As part of a mixed-methods approach, a quantitative online survey was conducted as part of the ATME project using a literature-based and interview-based questionnaire. Nurses in the AKI were surveyed from August to November 2023 on the care and care processes of patients in the AKI. Data was analyzed descriptively using SPSS. Critical care points and challenges of AKI were derived.

Results: There is a lack of both supply and personal capacities in the out-of-hospital intensive care setting. Hospital care is seen as critical and can lead to discontinuity of care. 308 nurses perceive themselves to be playing a central role in the coordination of care and everyday life, but at the same time they deplore the lack of recognition and want more room for autonomous care decisions. The primary therapeutic goal is not to wean patients from ventilation or decannulation, but to maintain their current health status. There is concern that the new legislation (GKV-IPReG) will lead to increased disruptions in care and additional bureaucracy.

Conclusion: The findings serve as a basis for further research and the optimization of interfaces and the solution to care issues. New care concepts need to be developed, in particular regarding the lack of personal and structural capacities. Greater cooperation across settings can prevent discontinuities in care and improve the perception of the care experienced. Also, there is a need for further monitoring of the effects of the GKV-IPReG and the implementation of the necessary conditions in patient care.

Introduction: Medical reports contain critical information and require concise language, yet often display errors despite advances in digital tools. This study compared the effectiveness of ChatGPT 4.0 in reporting orthographic, grammatical, and content errors in German neurology reports to a human expert.

Materials and methods: Ten neurology reports were embedded with ten linguistic errors each, including typographical and grammatical mistakes, and one significant content error. The reports were reviewed by ChatGPT 4.0 using three prompts: (1) check the text for spelling and grammatical errors and report them in a list format without altering the original text, (2) identify spelling and grammatical errors and generate a revised version of the text, ensuring content integrity, (3) evaluate the text for factual inaccuracies, including incorrect information and treatment errors, and report them without modifying the original text. Human control was provided by an experienced medical secretary. Outcome parameters were processing time, percentage of identified errors, and overall error detection rate.

Results: Artificial intelligence (AI) accuracy in error detection was 35% (median) for Prompt 1 and 75% for Prompt 2. The mean word count of erroneous medical reports was 980 (SD = 180). AI-driven report generation was significantly faster than human review (AI Prompt 1: 102.4 s; AI Prompt 2: 209.4 s; Human: 374.0 s; p < 0.0001). Prompt 1, a tabular error report, was faster but less accurate than Prompt 2, a revised version of the report (p = 0.0013). Content analysis by Prompt 3 identified 70% of errors in 34.6 seconds.

Conclusions: AI-driven text processing for medical reports is feasible and effective. ChatGPT 4.0 demonstrated strong performance in detecting and reporting errors. The effectiveness of AI depends on prompt design, significantly impacting quality and duration. Integration into medical workflows could enhance accuracy and efficiency. AI holds promise in improving medical report writing. However, proper prompt design seems to be crucial. Appropriately integrated AI can significantly enhance supervision and quality control in health care documentation.