eHealth competence building for future doctors and nurses - Attitudes and capabilities.

BACKGROUND: Digitalisation is rapidly changing health care processes and the health care sector, thus increasing the need to improve the digital competence of future health care professionals. PURPOSE: The aim of this study was to describe the attitudes of medical and nursing students towards digital health based on self-evaluation as well as to compare the differences in perceptions between the two student groups. METHODS: A cross-sectional study was conducted as an online survey using the Webropol in April 2021 at the University of Oulu and Oulu University of Applied Sciences in Finland. The survey questionnaire consisted of seven background questions and 16 statements on a five-point Likert scale (fully disagree to fully agree) to survey student attitudes towards eHealth, and their digital capabilities. RESULTS: A total of 250 medical and nursing students were invited to participate in the study and 170 of them took the survey (response rate 68 %). Of those answered, 38 % (n = 64) were nursing and 32 % (n = 106) medical students. Students generally had a positive attitude towards eHealth and health care digitalisation. The differences in perceptions and preparedness between medical and nursing students were surprisingly small in the two student groups. There was a statistically significant difference between the two groups in three out of 16 statements: these were related to changes in the roles of health care professionals and patients as well as the students' knowledge of information contained in the national patient portal. CONCLUSIONS: The results of this study provide a good starting point for further harmonisation of the curriculum for both health professional groups regarding the teaching of eHealth and telemedicine.

System Architecture of a European Platform for Health Policy Decision Making: MIDAS.

Background: Healthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner. Methods: The MIDAS platform was developed in an iterative and collaborative way with close involvement of academia, industry, healthcare staff and policy-makers, to solve tasks including data storage, data harmonization, data analytics and visualizations, and open and social data analytics. The platform has been piloted and tested by health departments in four European countries, each focusing on different region-specific health challenges and related data sources. Results: A novel health data platform solving the needs of Public Health decision-makers was successfully implemented within the four pilot regions connecting heterogeneous healthcare datasets and open datasets and turning large amounts of previously isolated data into actionable information allowing for evidence-based health policy-making and risk stratification through the application and visualization of advanced analytics. Conclusions: The MIDAS platform delivers a secure, effective and integrated solution to deal with health data, providing support for health policy decision-making, planning of public health activities and the implementation of the Health in All Policies approach. The platform has proven transferable, sustainable and scalable across policies, data and regions.

NewsMeSH: A new classifier designed to annotate health news with MeSH headings.

MOTIVATION: In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease. METHODS: We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics. RESULTS: A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus. CONCLUSIONS: The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar.