Health Service Quality and its Relationship to Team Collaboration and Communication during the COVID-19 Pandemic

BACKGROUND: In the search for innovative methods to improve the quality and efficiency of health services, integrated clinical pathways (ICPs) have been introduced. AIM: As there is a gap in research on ICP efficiency, the aim of the study was to investigate the role and impact of collaboration and communication among three interprofessional ICP teams on the self-assessment of efficiency of ICPs. METHOD(S): A cross-sectional study was conducted using a descriptive quantitative with a survey (n = 152) and qualitative methods with a focus group (n = 27) and in-depth interviews (n = 22) in a typical general hospital in Slovenia. RESULT(S): The results showed that health-care professionals found patient health care and the work of healthcare professionals' better quality with ICP than without ICP. The ICPs team members assessed communication, cooperation, and effectiveness in the ICP team as relatively good but identified the lack of staff as the main reason for their limitations. The impact of ICP team collaboration and communication on ICP safety exists but it does not explain a sufficient proportion of the variance and the corelation is medium strong. The result also revealed that the COVID-19 pandemic did not primarily affect ICP team members' fear of possible infection, as studies have shown in the first wave of the COVID-19 pandemic, but rather staff shortages leading to increased fear of errors and possible complaints and lawsuits from patients and relatives. CONCLUSION(S): Measures are needed for the additional employment of team members and the retention of current staff through financial compensation and the promotion of supportive workplace characteristics.Copyright © 2023 Mateja Simec, Sabina Krsnik, Karmen Erjavec.

Core competencies of healthcare professionals in Oman: Research and evidence-based practice needs attention.

AIM: The aim of the study was to examine (1) the perceptions on core competencies of healthcare professionals working at clinical settings in Oman and (2) which demographic characteristics explain the overall core competency. DESIGN: A cross-sectional design. METHODS: Healthcare Professional Core Competency Instrument, consisting of 11 sub-scales with 81 items, was distributed to healthcare professionals (n = 1,543; 826 nurses and 717 physicians) who worked at primary, secondary and tertiary healthcare institutions. Descriptive statistics, t-test, ANOVA and linear regression were used for data analysis. RESULTS: Altogether 1,078 healthcare professionals (628 nurses and 450 physicians) responded representing 70% overall response rate. Healthcare professionals perceived their overall core competence as excellent, safety being the highest, and research and evidence-based practice was the lowest. The multiple linear regression analysis revealed that ethnicity, gender and years of working experience were the characters that explained the overall core competence, where expatriate senior professionals reported higher competency levels compared with counterparts.

Training, testing and benchmarking medical AI models using Clinical AIBench

AI technology has been used in many clinical research fields, but most AI technologies are difficult to land in real-world clinical settings. In most current clinical AI research settings, the diagnosis task is to identify different types of diseases among the given ones. However, the diagnosis in real-world settings needs dynamically developing inspection strategies based on the existing resources of medical institutions and identifying different kinds of diseases out of many possibilities. To promote the development of different clinical AI technologies and the implementation of clinical applications, we propose a benchmark named Clinical AIBench for developing, verifying, and evaluating clinical AI technologies in real-world clinical settings. Specifically, Clinical AIBench can be used for: (1) Model training and testing: Researchers can use the data to train and test their models. (2)Model evaluation: Researchers can use Clinical AIBench to objectively, fairly, and comparably evaluate various models of different researchers. (3) Clinical value evaluation: Researchers can use the clinical indicators provided by Clinical AIBench to evaluate the clinical value of models, which will be applied in real-world clinical settings. For convenience, Clinical AIBench provides three different levels of clinical settings: restricted clinical setting, which is named closed clinical setting, data island clinical setting, and real-world clinical setting, which is called open clinical setting. In addition, Clinical AIBench covers three diseases: Alzheimer’s disease, COVID-19, and dental. Clinical AIBench provides python APIs to researchers. The data and source code are publicly available from the project website https://www.benchcouncil.org/clinical_aibench/.