Background: The growing availability of electronic data offers practitioners increased opportunities for reusing clinical data for research and quality improvement. However, relatively little is known about what clinical data practitioners keep on their computers regarding patients.
Methods: The authors conducted a web-based survey of 991 U.S. and Scandinavian practitioner-investigators (P-Is) in The Dental Practice-Based Research Network to determine the extent of their use of computers to manage clinical information; the type of patient information they kept on paper, a computer or both; and their willingness to reuse electronic dental record (EDR) data for research.
Results: A total of 729 (73.6 percent) of 991 P-Is responded.A total of 73.8 percent of U.S. solo practitioners and 78.7 percent of group practitioners used a computer to manage some patient information, and 14.3 percent and 15.9 percent, respectively, managed all patient information on a computer. U.S. practitioners stored appointments, treatment plans, completed treatment and images electronically most frequently, and the periodontal charting, diagnosis, medical history, progress notes and the chief complaint least frequently.More than 90 percent of Scandinavian practitioners stored all information electronically.A total of 50.8 percent of all P-Is were willing to reuse EDR data for research, and 63.1 percent preferred electronic forms for data collection.
Conclusion: The results of this study show that the trend toward increased adoption of EDRs in the United States is continuing, potentially making more data in electronic form available for research. Participants appear to be willing to reuse EDR data for research and to collect data electronically.
Clinical Implications: The rising rates of EDR adoption may offer increased opportunities for reusing electronic data for quality improvement and research.
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http://dx.doi.org/10.14219/jada.archive.2013.0013 | DOI Listing |
Sensors (Basel)
November 2024
Transport Faculty, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania.
Integrating road vehicles into broader Internet of Things (IoT) ecosystems is an important step in the development of fully connected and smart transportation systems. This research explores the potential of using communication technologies that achieve a balance between low-power and long-range (LPLR) capabilities while remaining cost-effective, specifically Bluetooth Classic BR-EDR, Bluetooth LE, ZigBee, nRF24, and LoRa-for Vehicle-to-Infrastructure (V2I) and Vehicle-to-IoT (V2IoT) ecosystem interactions. During this research, several field tests were conducted employing different types of communication modules, across three distinct environments: an open-field inter-urban road, a forest inter-urban road, and an urban road.
View Article and Find Full Text PDFJ Dent
November 2024
Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, The Netherlands; Royal Dutch Dental Association (KNMT), Utrecht, The Netherlands.
Objective: This study aimed to develop a machine-learning (ML) model to predict the risk for Periodontal Disease (PD) based on nonimage electronic dental records (EDRs).
Methods: By using EDRs collected in the BigMouth repository, dental patients from the US were included. Patients were labeled as cases or controls, based on PD diagnosis, treatment and pocketing.
Turk J Pharm Sci
November 2024
Hacettepe University Faculty of Pharmacy, Department of Pharmacology, Ankara, Türkiye.
BMC Genomics
November 2024
Precision Medicine & Computational Biology, Sanofi, Cambridge, MA, USA.
Background: Therapeutic targets supported by genetic evidence from genome-wide association studies (GWAS) show higher probability of success in clinical trials. GWAS is a powerful approach to identify links between genetic variants and phenotypic variation; however, identifying the genes driving associations identified in GWAS remains challenging. Integration of molecular quantitative trait loci (molQTL) such as expression QTL (eQTL) using mendelian randomization (MR) and colocalization analyses can help with the identification of causal genes.
View Article and Find Full Text PDFEarly Interv Psychiatry
November 2024
Mental Health Organization 'GGZ Oost Brabant', Centre for Eating Disorders, Helmond, The Netherlands.
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