A manual system of microbiology reporting with a National Cash Register (NCR) form with printed names of bacteria and antiboitics required less time to compose reports than a previous manual system that involved rubber stamps and handwriting on plain report sheets. The NCR report cost 10-28 pence and, compared with a computer system, it had the advantages of simplicity and familarity, and reports were not delayed by machine breakdown, operator error, or data being incorrectly submitted. A computer reporting system for microbiology resulted in more accurate reports costing 17-97 pence each, faster and more accurate filing and recall of reports, and a greater range of analyses of reports that was valued particularly by the control-of-infection staff. Composition of computer-readable reports by technicians on Port-a-punch cards took longer than composing NCR reports. Enquiries for past results were more quickly answered from computer printouts of reports and a day book in alphabetical order.
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http://dx.doi.org/10.1136/jcp.29.6.553 | DOI Listing |
Cien Saude Colet
January 2025
Departamento de Nutrição e Saúde, Universidade Federal de Viçosa. Viçosa MG Brasil.
This article describes the construction and validation of an instruction manual geared toward nutritional care (NC) for people with severe obesity in the Brazilian Unified Health System (SUS). In the production of this instruction manual, a broad literature review was conducted for the identification and discussion of topics to be treated. The content and appearance validity were conducted according to the Delphi technique and to focus groups, respectively, with evaluators who were nutritionists and practitioners, from different regions of Brazil.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Background: Social media has become a widely used way for people to share opinions about health care and medical topics. Social media data can be leveraged to understand patient concerns and provide insight into why patients may turn to the internet instead of the health care system for health advice.
Objective: This study aimed to develop a method to investigate Reddit posts discussing health-related conditions.
Front Public Health
January 2025
Department of Pharmacy, Personalized Drug Therapy Key Laboratory of Sichuan Province, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Objective: To optimize the construction of pharmaceutical services in medical institutions, advance the development of clinical pharmacy as a discipline, enhance the level of clinical pharmacy services, systematically implement and evaluate clinical pharmacy practices, and improve patient therapeutic outcomes, we have developed the Practice Guidelines for the Value Evaluation of Clinical Pharmacy Services (Version 2).
Methods: This guideline was designed following the World Health Organization (WHO) Guideline Development Manual. The Delphi method was employed to identify clinical questions.
Brain Behav Immun Health
February 2025
Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases-IRCAD, University of Eastern Piedmont, 28100, Novara, Italy.
Major Depressive Disorder (MDD) is a widespread psychiatric condition impacting social and occupational functioning, making it a leading cause of disability. The diagnosis of MDD remains clinical, based on the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 criteria, as biomarkers have not yet been validated for diagnostic purposes or as predictors of treatment response. Traditional treatment strategies often follow a one-size-fits-all approach obtaining suboptimal outcomes for many patients who fail to experience response or recovery.
View Article and Find Full Text PDFCureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
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