Objectives: To examine the current state of unique device identifier (UDI) implementation, including barriers and facilitators, among eight health systems participating in a research network committed to real-world evidence (RWE) generation for medical devices.
Design: Mixed methods, including a structured survey and semistructured interviews.
Setting: Eight health systems participating in the National Evaluation System for health Technology research network within the USA.
Participants: Individuals identified as being involved in or knowledgeable about UDI implementation or medical device identification from supply chain, information technology and high-volume procedural area(s) in their health system.
Main Outcomes Measures: Interview topics were related to UDI implementation, including barriers and facilitators; UDI use; benefits of UDI adoption; and vision for UDI implementation. Data were analysed using directed content analysis, drawing on prior conceptual models of UDI implementation and the Exploration, Preparation, Implementation, Sustainment framework. A brief survey of health system characteristics and scope of UDI implementation was also conducted.
Results: Thirty-five individuals completed interviews. Three of eight health systems reported having implemented UDI. Themes identified about barriers and facilitators to UDI implementation included knowledge of the UDI and its benefits among decision-makers; organisational systems, culture and networks that support technology and workflow changes; and external factors such as policy mandates and technology. A final theme focused on the availability of UDIs for RWE; lack of availability significantly hindered RWE studies on medical devices.
Conclusions: UDI adoption within health systems requires knowledge of and impetus to achieve operational and clinical benefits. These are necessary to support UDI availability for medical device safety and effectiveness studies and RWE generation.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9872505 | PMC |
http://dx.doi.org/10.1136/bmjsit-2022-000167 | DOI Listing |
Nat Photonics
July 2024
Laboratory of Applied Photonics Devices, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Deep neural networks have achieved remarkable breakthroughs by leveraging multiple layers of data processing to extract hidden representations, albeit at the cost of large electronic computing power. To enhance energy efficiency and speed, the optical implementation of neural networks aims to harness the advantages of optical bandwidth and the energy efficiency of optical interconnections. In the absence of low-power optical nonlinearities, the challenge in the implementation of multilayer optical networks lies in realizing multiple optical layers without resorting to electronic components.
View Article and Find Full Text PDFSci Rep
September 2024
Department of Laboratory Medicine and Genetics, Center for Medical Device Safety Monitoring, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, 170 Jomaru-ro, Bucheon, 14584, Republic of Korea.
The E-Health Big Data Evidence Innovation Network (FeederNet) in Korea, based on the observational medical outcomes partnership (OMOP) common data model (CDM), had 72.3% participation from tertiary hospitals handling severe diseases as of October 2022. While this contributes to the activation of multi-institutional research, concerns about the comprehensiveness of device data persist due to the adoption of national health insurance corporation (NHIC) claim codes as device identifiers in the medical device field.
View Article and Find Full Text PDFArch Gynecol Obstet
May 2024
Department of Obstetrics and Gynecology, Faculty of Medicine, Bolu Abant İzzet Baysal University, Gölköy Yerleşkesi, 14030, Bolu, Turkey.
Clin Chem Lab Med
March 2023
Institute for Clinical Chemistry, Medical Faculty Mannheim of Heidelberg University, Mannheim, Germany.
BMJ Surg Interv Health Technol
January 2023
Center for Healthcare Delivery and Policy, Arizona State University, Phoenix, Arizona, USA.
Objectives: To examine the current state of unique device identifier (UDI) implementation, including barriers and facilitators, among eight health systems participating in a research network committed to real-world evidence (RWE) generation for medical devices.
Design: Mixed methods, including a structured survey and semistructured interviews.
Setting: Eight health systems participating in the National Evaluation System for health Technology research network within the USA.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!