The evaluation of information systems is an important topic in Clinical Informatics. It is argued that past evaluations have not been particularly informative in progressing the effective use of IT in healthcare due to their narrow focus. The different roles of evaluation in Clinical Informatics are examined, and the breadth and diversity of the available methodological tool kit highlighted. The aim is to stimulate a greater awareness of the roles and methods of evaluation. Challenges in evaluation which face the Clinical Informatics community are discussed and finally some comments made concerning the way in which evaluation might be made more effective in order to improve our knowledge of how to deliver useful systems into healthcare.
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JMIR Med Educ
January 2025
Centre for Digital Transformation of Health, University of Melbourne, Carlton, Australia.
Background: Learning health systems (LHS) have the potential to use health data in real time through rapid and continuous cycles of data interrogation, implementing insights to practice, feedback, and practice change. However, there is a lack of an appropriately skilled interprofessional informatics workforce that can leverage knowledge to design innovative solutions. Therefore, there is a need to develop tailored professional development training in digital health, to foster skilled interprofessional learning communities in the health care workforce in Australia.
View Article and Find Full Text PDFFront Psychol
January 2025
Department of Behavioral Sciences, University of Medicine and Pharmacy of Craiova, Craiova, Romania.
Objectives: The main objectives were to investigate the prevalence of ED and associated risk factors among medical students in Romania, as well as to determine which variables may predict ED and to explore the differences between medical students and the general population.
Methods: The Eating Disorders Inventory questionnaire (EDI-3) was applied. Also, the body mass index of the students was calculated, socio-demographic information regarding personal and family medical history was collected (mental and chronic diseases, self-reported sleep difficulties in the past 6 months, family history of obesity) and potentially risky events (history of ridicule, major negative events, social pressure to be thin from family, friends, media).
Front Cell Dev Biol
January 2025
Department of Medical Informatics, Nantong University, Nantong, Jiangsu, China.
Introduction: Diabetic retinopathy (DR) has long been recognized as a common complication of diabetes, making accurate automated grading of its severity essential. Color fundus photographs play a crucial role in the grading of DR. With the advancement of artificial intelligence technologies, numerous researchers have conducted studies on DR grading based on deep features and radiomic features extracted from color fundus photographs.
View Article and Find Full Text PDFHemasphere
January 2025
Université Paris Cité, Institut Cochin, INSERM U1016, CNRS UMR8104 Assistance Publique-Hôpitaux de Paris.Centre, Laboratory of Hematology, Hôpital Cochin Paris France.
Lower risk (LR) myelodysplastic syndromes (MDS) are heterogeneous hematopoietic stem and progenitor disorders caused by the accumulation of somatic mutations in various genes including epigenetic regulators that may produce convergent DNA methylation patterns driving specific gene expression profiles. The integration of genomic, epigenomic, and transcriptomic profiling has the potential to spotlight distinct LR-MDS categories on the basis of pathophysiological mechanisms. We performed a comprehensive study of somatic mutations and DNA methylation in a large and clinically well-annotated cohort of treatment-naive patients with LR-MDS at diagnosis from the EUMDS registry (ClinicalTrials.
View Article and Find Full Text PDFNat Rev Bioeng
May 2024
Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Artificial intelligence (AI) and machine learning (ML) models are being deployed in many domains of society and have recently reached the field of drug discovery. Given the increasing prevalence of antimicrobial resistance, as well as the challenges intrinsic to antibiotic development, there is an urgent need to accelerate the design of new antimicrobial therapies. Antimicrobial peptides (AMPs) are therapeutic agents for treating bacterial infections, but their translation into the clinic has been slow owing to toxicity, poor stability, limited cellular penetration and high cost, among other issues.
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