Despite the problems we encountered, which are not uncommon with the development and implementation of any data system, we are confident that our success in achieving our goals is due to the following: establishing a reliable information database connecting several related departments; interfacing with registration and billing systems to avoid duplication of data and chance for error; appointing a qualified Systems Manager devoted to the project; developing superusers to include intensive training in the operating system (UNIX), parameters of the information system, and the report writer. We achieved what we set out to accomplish: the development of a reliable database and reports on which to base a variety of hospital decisions; improved hospital utilization; reliable clinical data for reimbursement, quality management, and credentialing; enhanced communication and collaboration among departments; and an increased profile of the departments and staff. Data quality specialists, Utilization Management and Quality Management coordinators, and the Medical Staff Credentialing Supervisor and their managers are relied upon by physicians and administrators to provide timely information. The staff are recognized for their knowledge and expertise in their department-specific information. The most significant reward is the potential for innovation. Users are no longer restricted to narrow information corridors. UNIX programming encourages creativity without demanding a degree in computer science. The capability to reach and use diverse hospital database information is no longer a dream.
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Scand J Trauma Resusc Emerg Med
January 2025
Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
Background: One way to measure emergency department (ED) performance is using key performance indicators (KPIs). Thus, identifying reliable KPIs can be critical in appraising ED performance. This study aims to introduce and classify the KPIs related to ED in simulations through the Balanced Scorecard (BSC) framework.
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Prehospital Center Region Zealand, Ringstedgade 61, 14th Floor, Naestved, 4700, Denmark.
Background: Effective interventions to reduce drowning incidents require accurate and reliable data for scientific analysis. However, the lack of high-quality evidence and the variability in drowning terminology, definitions, and outcomes present significant challenges in assessing studies to inform drowning guidelines. Many drowning reports use inappropriate classifications for drowning incidents, which significantly contributes to the underreporting of drowning.
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January 2025
HPTLC Association, Rheinfelden, Switzerland.
High-performance thin-layer chromatography (HPTLC) plays a crucial role in establishing the chemical fingerprint of natural products (NPs) during analysis. This technique involves standardized procedures for each step, ensuring the reproducibility of results. In this context, we present a comprehensive standard operating procedure (SOP) encompassing both manual instruments and suitable devices.
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January 2025
H&TRC, Health & Technology Research Center, Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, Av. D. João II, Lote 4.69.01, Parque das Nações, Lisboa, 1990-096, Portugal; CICPSI, Faculdade de Psicologia, Universidade de Lisboa, Alameda da Universidade, Lisboa, 1649-013, Portugal.
Introduction: Advancements in medical imaging with ionizing radiation have significantly transformed the field and enhanced the education and training of medical professionals. A notable development in this educational landscape is the use of social media, which engages millions of users worldwide. This scoping review aims to explore the potential of social media as an educational tool for healthcare professionals and students in medical imaging with ionizing radiation, highlighting its benefits and disadvantages.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy.
Plant viruses pose a significant threat to global agriculture and require efficient tools for their timely detection. We present AutoPVPrimer, an innovative pipeline that integrates artificial intelligence (AI) and machine learning to accelerate the development of plant virus primers. The pipeline uses Biopython to automatically retrieve different genomic sequences from the NCBI database to increase the robustness of the subsequent primer design.
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