Academic detailing is an educational approach involving provision of evidence-based information by healthcare providers for healthcare providers with the goal of improving clinical decision-making. An interprofessional academic detailing initiative was developed to encourage rural providers to utilize guidelines when deciding which patients to vaccinate against pneumonia. This study utilized a quasi-experimental, single-group, pre-post observational design with physicians, nurses, and staff at two rural medical clinics. The 12-month academic detailing intervention included a needs assessment, workflow assessment of practice-based health information technology, vaccination training for providers and staff, and creation of exam-room posters encouraging patients to discuss vaccination with their provider. Six visits were made to deliver education, discuss needs, select priorities, and develop action plans from recommendations. Data were collected from each site for three years prior to the intervention year and for one year following the intervention. The annual rate of patients vaccinated increased during the five-year study. The cumulative proportion of the sample population that received vaccination also increased over time. Interprofessional academic detailing was well received and increased pneumococcal vaccination rates among rural-dwelling older adults. Given the alarming disparities in health outcomes for rural patients, educational outreach is needed to improve healthcare access and outcomes.
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http://dx.doi.org/10.3390/vaccines9040317 | DOI Listing |
ACS Appl Bio Mater
January 2025
Institute of Physics and Materials Science, Department of Natural Sciences and Sustainable Ressources, BOKU University, Peter Jordan-Straß 82, 1190 Vienna, Austria.
Spider silk (SPSI) is a promising candidate for use as a filler material in nerve guidance conduits (NGCs), facilitating peripheral nerve regeneration by providing a scaffold for Schwann cells (SCs) and axonal growth. However, the specific properties of SPSI that contribute to its regenerative success remain unclear. In this study, the egg sac silk of is investigated, which contains two distinct fiber types: tubuliform (TU) and major ampullate (MA) silk.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Health Services Research Centre, Singapore Health Services Pte Ltd, Singapore, Singapore.
Background: Integrating algorithm-based clinical decision support (CDS) systems poses significant challenges in evaluating their actual clinical value. Such CDS systems are traditionally assessed via controlled but resource-intensive clinical trials.
Objective: This paper presents a review protocol for preimplementation in silico evaluation methods to enable broadened impact analysis under simulated environments before clinical trials.
Cleft Palate Craniofac J
January 2025
Division of Plastic, Maxillofacial, and Oral Surgery, Department of Surgery, Duke University Health System, Durham, NC, USA.
To evaluate the feasibility of using the National Patient-Centered Clinical Research Network (PCORnet) as a source of electronic health record (EHR) data for cleft outcomes research. Exploratory retrospective analysis of multi-year, administrative and clinical, structured data stored in PCORnet. Academic institution with an ACPA-approved cleft and craniofacial team.
View Article and Find Full Text PDFBr J Ophthalmol
January 2025
Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
Background/aims: Large language models (LLMs) have substantial potential to enhance the efficiency of academic research. The accuracy and performance of LLMs in a systematic review, a core part of evidence building, has yet to be studied in detail.
Methods: We introduced two LLM-based approaches of systematic review: an LLM-enabled fully automated approach (LLM-FA) utilising three different GPT-4 plugins (Consensus GPT, Scholar GPT and GPT web browsing modes) and an LLM-facilitated semi-automated approach (LLM-SA) using GPT4's Application Programming Interface (API).
J Clin Epidemiol
January 2025
Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
Background: Having a sufficient sample size is crucial when developing a clinical prediction model. We reviewed details of sample size in studies developing prediction models for binary outcomes using machine learning (ML) methods within oncology and compared the sample size used to develop the models with the minimum required sample size needed when developing a regression-based model (N).
Methods: We searched the Medline (via OVID) database for studies developing a prediction model using ML methods published in December 2022.
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