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Building a Digital Health Research Platform to Enable Recruitment, Enrollment, Data Collection, and Follow-Up for a Highly Diverse Longitudinal US Cohort of 1 Million People in the All of Us Research Program: Design and Implementation Study.
J Med Internet Res
January 2025
Vibrent Health, Inc, Fairfax, VA, United States.
Background: Longitudinal cohort studies have traditionally relied on clinic-based recruitment models, which limit cohort diversity and the generalizability of research outcomes. Digital research platforms can be used to increase participant access, improve study engagement, streamline data collection, and increase data quality; however, the efficacy and sustainability of digitally enabled studies rely heavily on the design, implementation, and management of the digital platform being used.
Objective: We sought to design and build a secure, privacy-preserving, validated, participant-centric digital health research platform (DHRP) to recruit and enroll participants, collect multimodal data, and engage participants from diverse backgrounds in the National Institutes of Health's (NIH) All of Us Research Program (AOU).
Implementation of Multisource Feedback in Graduate Medical Education.
Clin Teach
February 2025
Division of Pulmonary, Critical Care, Sleep, and Occupational Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Effect of Nursing Practice Environment, Nurse Staffing, Overtime and Hand Hygiene on Hospital-Acquired Infections in a Tertiary Teaching Hospital.
J Clin Nurs
December 2024
School of Nursing, University of Wollongong, Wollongong, New South Wales, Australia.
Aims: To investigate the impact of the nursing practice environment, nurse staffing, working overtime and compliance with hand hygiene standards on hospital-acquired infections.
Design: A multi-source quantitative study.
Methods: Nursing data were collected from selected wards in one hospital between 18 January 2021 and 15 March 2021.
Investigation of Freeway Incident Duration Using Classification and Regression Trees Based on Multisource Data.
Sensors (Basel)
November 2024
Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems, School of Engineering, Huzhou University, Huzhou 313000, China.
Article Synopsis
- - This study explored the effectiveness of targeted contingency measures in reducing the time and damage from incidents using a data mining technique called Classification and Regression Trees (CART), applied to data from the Hangzhou freeway in China between 2019 to 2021.
- - A regression tree was constructed with 8 levels and 14 leaf nodes, leading to the extraction of 14 rules to guide incident assessments and emergency plans.
- - The CART model outperformed other methods like XGBoost and Random Forest in prediction accuracy, while also effectively capturing interactions between multiple variables, making it highly interpretable and relevant for real-world incident analysis.
The impact of UTeach observation protocol-based multiple-source feedback on improving the quality of online teaching in medical education.
BMC Med Educ
November 2024
School of Precision Instruments and Optoelectronics Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China.
Background: The ability to teach, as a core competency, has always suffered from a lack of systematic cultivation.
Objective: This study aimed to explore the difference in teaching quality before and after the application of the UTOP (UTeach observation protocol)-based multiple source feedback (MSF) formative assessment of the quality improvement of medical education classrooms.
Methods: This study retrospectively analyzed the teaching quality of the Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine and the Third Affiliated Hospital of Chongqing Medical University.
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