Health Care Science最新文献

Recently, the emergence of ChatGPT, an artificial intelligence chatbot developed by OpenAI, has attracted significant attention due to its exceptional language comprehension and content generation capabilities, highlighting the immense potential of large language models (LLMs). LLMs have become a burgeoning hotspot across many fields, including health care. Within health care, LLMs may be classified into LLMs for the biomedical domain and LLMs for the clinical domain based on the corpora used for pre-training. In the last 3 years, these domain-specific LLMs have demonstrated exceptional performance on multiple natural language processing tasks, surpassing the performance of general LLMs as well. This not only emphasizes the significance of developing dedicated LLMs for the specific domains, but also raises expectations for their applications in health care. We believe that LLMs may be used widely in preconsultation, diagnosis, and management, with appropriate development and supervision. Additionally, LLMs hold tremendous promise in assisting with medical education, medical writing and other related applications. Likewise, health care systems must recognize and address the challenges posed by LLMs.

Quality of life at work is an important and widely discussed concept in the literature. Several instruments can be used to measure it, but with regard to healthcare and social services, the existing instruments are not well known. A review of available instruments intending to capture the quality of life of healthcare and social services workers (QoLHSSW) is necessary to better assess their working conditions and promote programs/guidelines to improve these conditions. The aim of this study was to identify the existing instruments used in measuring QoLHSSW and explore their characteristics. Particular attention was given to instruments adapted to the province of Quebec, Canada, which enabled the determination of which instruments are adapted for the measurement of QoLHSSW in Quebec and possibly elsewhere. A systematic review of the literature was conducted according to the JBI methodological guide. The articles' selection procedure was performed according to the PRISMA flowchart. The search was conducted up to October 28, 2021, and then updated on January 25, 2023, in four databases: PsycINFO, Medline, Embase, and CINAHL. The selection and extraction were performed independently by two researchers. The analysis of the quality of the studies was performed with the COnsensus-based Standards for the selection of health Measurement Instruments. From a total of 8178 entries, 13 articles corresponding to 13 instruments were selected. Among these instruments, the common aspects that were considered were work conditions, job satisfaction, stress at work, relationship/balance, and career development. Most instruments used a 5-point Likert scale. Various validation methods were used, including reporting Cronbach's alpha for overall scale reliability; factor analysis to test construct validity; different model fit indices to test model superiority; different language comparisons to test cross-cultural validity; and qualitative expert reviews to assess content validity.

Since the identification of the first case of pneumonia of unknown cause in 2019, the COVID-19 pandemic has spread the globe for over 3 years. As the most populous country in the world, China's disease prevention policies and response plans concern the health of the country's 1.4 billion people and beyond. During the course of the pandemic, scientific research has been accumulated and given evidence-based support to the official guidance of COVID-19 management. The National Health Commission of China have compiled, published, and updated a total of 10 versions of the “Diagnosis and Treatment Protocol for COVID-19 Patients” to better inform clinical practitioners and staff to effectively screen, diagnose, manage, treat, and care for cases of severe acute respiratory syndrome coronavirus 2 infection. This paper compares and summarizes each version of the protocol in terms of etiology and epidemiology, clinical manifestation and diagnosis, treatment and nursing, disease control and management, presenting detailed changes, additions, deletions, and refinement of the protocols.

In a prior practice and policy article published in Healthcare Science, we introduced the deployed application of an artificial intelligence (AI) model to predict longer-term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home (H2H) program that has been operating since 2017. In this follow on practice and policy article, we further elaborate on Singapore's H2H program and care model, and its supporting AI model for multiple readmission prediction, in the following ways: (1) by providing updates on the AI and supporting information systems, (2) by reporting on customer engagement and related service delivery outcomes including staff-related time savings and patient benefits in terms of bed days saved, (3) by sharing lessons learned with respect to (i) analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants, (ii) balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables, and (iii) the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems, (4) by highlighting how this H2H effort supported broader Covid-19 response efforts across Singapore's public healthcare system, and finally (5) by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards. For the convenience of the reader, some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.