Project description:The National Institutes of Health (NIH) is the largest public research funder in the world. In an effort to make publicly funded data more accessible, the NIH established a new Data Management and Sharing (DMS) Policy effective January 2023. Though the new policy was available for public comment, the patient perspective and the potential unintended consequences of the policy on patients' willingness to participate in research have been underexplored. This study aimed to determine: (1) participant preferences about the types of data they are willing to share with external entities, and (2) participant perspectives regarding the updated 2023 NIH DMS policy. A cross-sectional, nationally representative online survey was conducted among 610 English-speaking US adults in March 2023 using Prolific. Overall, 50% of the sample identified as women, 13% as Black or African American, and 7% as Hispanic or Latino, with a mean age of 46 years. The majority of respondents (65%) agreed with the NIH policy, but racial differences were noted with a higher percentage (28%) of Black participants indicating a decrease in willingness to participate in research studies with the updated policy in place. Participants were more willing to share research data with healthcare providers, yet their preferences for data sharing varied depending on the type of data to be shared and the recipients. Participants were less willing to share sexual health and fertility data with health technology companies (41%) and public repositories (37%) compared to their healthcare providers (75%). The findings highlight the importance of adopting a transparent approach to data sharing that balances protecting patient autonomy with more open data sharing.
Project description:BackgroundSince the national big data strategy was unveiled at the fifth plenary session of the 18th CPC (Communist Party of China) Central Committee, the big data industry has been flourishing in China. Various successful industrial data governance systems have emerged with the rapid development of big data technologies and data management theories. City Brain and Enterprise Data Middle Platform are considered the best data governance systems in urban and corporate governance, respectively. However, in the health and medical sectors, issues of data operation occur frequently due to a lack of systematic data governance. These problems need to be urgently addressed, as health and medical data have been defined as national fundamental strategic resources. Clinical researchers have an increasing demand for data analysis.MethodsTherefore, the Medical Data Governance System (MDGS) has been designed to improve data quality and provide simple and convenient data analysis tools for the National Clinical Research Center for Child Health. The MDGS consists of the Medical Data Platform (MDP) and Operation Management System (OMS). The MDP comprises acquisition layer, middle platform, and application layer that persistently elevates data quality and significantly shortens data analysis duration. Organization construction, management regulations, and technical standards are included in the OMS, which guarantees the sustainable operation of the MDGS. The MDGS was established to advance state-of-the-art and state-of-practice data governance for the health and medical sectors in China.ResultsWith the first phase of the MDGS, the quantity and quality of research projects increase, research transformation speeds up, and the researchers' job satisfaction increased.ConclusionsBased on our preliminary achievements, it was necessary and feasible to establish the MDGS. It is important to have comprehensive requirement study, top-level design, refined planning, phase-by-phase implementation, and continual optimization.
Project description:BackgroundDigital innovations in medicine are disruptive technologies that can change the way diagnostic procedures and treatments are delivered. Such innovations are typically designed in teams with different disciplinary backgrounds. This paper concentrates on 2 interdisciplinary research teams with 20 members from the medicine and engineering sciences working jointly on digital health solutions.ObjectiveThe aim of this paper was to identify factors on the individual, team, and organizational levels that influence the implementation of interdisciplinary research projects elaborating on digital applications for medicine and, based on the results, to draw conclusions for the proactive design of the interdisciplinary research process to make these projects successful.MethodsTo achieve this aim, 2 interdisciplinary research teams were observed, and a small case study (response rate: 15/20, 75%) was conducted using a web-based questionnaire containing both closed and open self-report questions. The Spearman rank correlation coefficient was calculated to analyze the quantitative data. The answers to the open-ended questions were subjected to qualitative content analysis.ResultsWith regard to the interdisciplinary research projects investigated, the influencing factors of the three levels presented (individual, team, and organization) have proven to be relevant for interdisciplinary research cooperation.ConclusionsWith regard to recommendations for the future design of interdisciplinary cooperation, management aspects are addressed, that is, the installation of a coordinator, systematic definition of goals, required resources, and necessary efforts on the part of the involved interdisciplinary research partners. As only small groups were investigated, further research in this field is necessary to derive more general recommendations for interdisciplinary research teams.Trial registrationGerman Clinical Trials Register, DRKS00023909, https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00023909 ; German Clinical Trials Register, DRKS00025077, https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00025077.
Project description:BackgroundSerious illness is characterised by uncertainty, particularly in older age groups. Uncertainty may be experienced by patients, family carers, and health professionals about a broad variety of issues. There are many evidence gaps regarding the experience and management of uncertainty.AimWe aimed to identify priority research areas concerning uncertainty in serious illness, to ensure that future research better meets the needs of those affected by uncertainty and reduce research inefficiencies.MethodsRapid prioritisation workshop comprising five focus groups to identify research areas, followed by a ranking exercise to prioritise them. Participants were healthcare professionals caring for those with serious illnesses including geriatrics, palliative care, intensive care; researchers; patient/carer representatives, and policymakers. Descriptive analysis of ranking data and qualitative framework analysis of focus group transcripts was undertaken.ResultsThirty-four participants took part; 67% female, mean age 47 (range 33-67). The highest priority was communication of uncertainty, ranked first by 15 participants (overall ranking score 1.59/3). Subsequent priorities were: 2) How to cope with uncertainty; 3) healthcare professional education/training; 4) Optimising clinical approaches to uncertainty; and 5) exploring in-depth experiences of uncertainty. Research questions regarding optimal management of uncertainty were given higher priority than questions about experiences of uncertainty and its impact.ConclusionsThese co-produced, clinically-focused research priorities map out key evidence gaps concerning uncertainty in serious illness. Managing uncertainty is the most pressing issue, and researchers should prioritise how to optimally manage uncertainty in order to reduce distress, unlock decision paralysis and improve illness and care experience.