Objective: This study evaluated a state psychiatric hospital's algorithm for prescribing antipsychotic drugs for inpatients with schizophrenia to determine whether its emphasis on cost efficiency is compatible with quality of care.
Methods: Outcomes were compared for patients who received medication that was algorithm adherent or nonadherent. Risperidone and ziprasidone were first-step oral antipsychotics. Documentation of clinical rationale was acceptable for nonpreferred drug use. Outcomes of interest were length of hospitalization and "much improved" or "very much improved" status on the Clinical Global Impression severity scale (CGI-S).
Results: Of 401 patients, 70% were male. The CGI-S modal rating of severity was "markedly ill." Duration of illness was longer for patients given algorithm-nonadherent (17.6±9.7 years) versus -adherent (14.9±11.6 years, p=.013) medication. No statistically significant between-group differences were observed for mean length of stay (51.4±35.5 days versus 43.8±27.4 days, adjusted difference p=.18) or median improvement time (adherent, 41 days; nonadherent, 42 days; CI=34-48 days for both group medians).
Conclusions: Prescription algorithm adherence was not associated with significantly increased length of inpatient stay or delayed time to improvement.
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http://dx.doi.org/10.1176/ps.62.8.pss6208_0963 | DOI Listing |
Biomed Phys Eng Express
January 2025
Shandong University, No. 72, Binhai Road, Jimo, Qingdao City, Shandong Province, Qingdao, 266200, CHINA.
U-Net is widely used in medical image segmentation due to its simple and flexible architecture design. To address the challenges of scale and complexity in medical tasks, several variants of U-Net have been proposed. In particular, methods based on Vision Transformer (ViT), represented by Swin UNETR, have gained widespread attention in recent years.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, DC, United States.
Artificial intelligence-enabled ambient digital scribes may have many potential benefits, yet results from our study indicate that there are errors that must be evaluated to mitigate safety risks.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Department of Computer Science, University of California, Irvine, Irvine, CA, United States.
Background: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Unitat de Recerca i Innovació, Gerència d'Atenció Primària i a la Comunitat de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain.
Background: The COVID-19 pandemic reshaped social dynamics, fostering reliance on social media for information, connection, and collective sense-making. Understanding how citizens navigate a global health crisis in varying cultural and economic contexts is crucial for effective crisis communication.
Objective: This study examines the evolution of citizen collective sense-making during the COVID-19 pandemic by analyzing social media discourse across Italy, the United Kingdom, and Egypt, representing diverse economic and cultural contexts.
J Med Internet Res
January 2025
Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.
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