Background: The uptake of complex clinical decision support systems (CDSS) in daily practice remains low, despite the proven potential to reduce medical errors and to improve the quality of care. To improve successful implementation of a complex CDSS this study aims to identify the factors that hinder, or alleviate the acceptance of, clinicians toward the use of a complex CDSS for treatment allocation of patients with chronic low back pain.
Methods: We tested a research model in which the intention to use a CDSS by clinicians is influenced by the perceived usefulness; this usefulness, in turn is influenced by the perceived service benefits and perceived service risks. An online survey was created to test our research model and the data was analysed using Partial Least Squares Structural Equation Modelling. The study population consisted of clinicians. The online questionnaire started with demographic questions and continued with a video animation of the complex CDSS followed by the set of measurement items. The online questionnaire ended with two open questions enquiring the reasons to use and not use, a complex CDSS.
Results: Ninety-eight participants (46% general practitioners, 25% primary care physical therapists, and 29% clinicians at a rehabilitation centre) fully completed the questionnaire. Fifty-two percent of the respondents were male. The average age was 48 years (SD ± 12.2). The causal model suggests that perceived usefulness is the main factor contributing to the intention to use a complex CDSS. Perceived service benefits and risks are both significant antecedents of perceived usefulness and perceived service risks are affected by the perceived threat to autonomy and trusting beliefs, particularly benevolence and competence.
Conclusions: To improve the acceptance of complex CDSSs it is important to address the risks, but the main focus during the implementation phase should be on the expected improvements in patient outcomes and the overall gain for clinicians. Our results will help the development of complex CDSSs that fit more into the daily clinical practice of clinicians.
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http://dx.doi.org/10.1186/s12911-021-01502-0 | DOI Listing |
Diagnostics (Basel)
December 2024
Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium.
: Orofacial pain (OFP) encompasses a complex array of conditions affecting the face, mouth, and jaws, often leading to significant diagnostic challenges and high rates of misdiagnosis. Artificial intelligence, particularly large language models like GPT4 (OpenAI, San Francisco, CA, USA), offers potential as a diagnostic aid in healthcare settings. : To evaluate the diagnostic accuracy of GPT4 in OFP cases as a clinical decision support system (CDSS) and compare its performance against treating clinicians, expert evaluators, medical students, and general practitioners.
View Article and Find Full Text PDFPLOS Digit Health
December 2024
Mike Petryk School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada.
Periodontitis is a complex and microbiome-related inflammatory condition impacting dental supporting tissues. Emphasizing the potential of Clinical Decision Support Systems (CDSS), this study aims to facilitate early diagnosis of periodontitis by extracting patients' information collected as dental charts and notes. We developed a CDSS to predict the stage and grade of periodontitis using natural language processing (NLP) techniques including bidirectional encoder representation for transformers (BERT).
View Article and Find Full Text PDFSovrem Tekhnologii Med
December 2024
MD, DSc, Professor, Head of the Department of Neurosurgery and Innovative Medicine; Irkutsk State Medical University, 1 Krasnogo Vosstaniya St., Irkutsk, 664003, Russia; Chief of the Center of Neurosurgery; Russian Railways-Medicine Clinical Hospital, 10 Botkin St., Irkutsk, 664005, Russia; Professor, Department of Traumatology, Orthopedics and Neurosurgery; Irkutsk State Medical Academy of Postgraduate Education, 100 Yubileyny Microdistrict, Irkutsk, 664049, Russia.
Unlabelled: is to assess the effectiveness of a new neuroanesthetic protocol for treating degenerative lumbar spine diseases in high-risk patients.
Materials And Methods: Two groups of patients with a high risk of anesthesia and surgery determined by the authors' clinical decision support system (CDSS) have been prospectively studied. A new neuroanesthetic protocol was used in the experimental group (EG, n=25), while the control group (CG, n=25) underwent intravenous anesthesia based on propofol and fentanyl.
Sci Rep
December 2024
Department of Emergency medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea.
The array of complex and evolving patient data has limited clinical decision making in the emergency department (ED). This study introduces an advanced deep learning algorithm designed to enhance real-time prediction accuracy for integration into a novel Clinical Decision Support System (CDSS). A retrospective study was conducted using data from a level 1 tertiary hospital.
View Article and Find Full Text PDFSensors (Basel)
October 2024
Institute of Biomedical Engineering & Technology, Liaquat University of Medical and Health Sciences, Jamshoro 76060, Pakistan.
Respiratory disorders are commonly regarded as complex disorders to diagnose due to their multi-factorial nature, encompassing the interplay between hereditary variables, comorbidities, environmental exposures, and therapies, among other contributing factors. This study presents a Clinical Decision Support System (CDSS) for the early detection of respiratory disorders using a one-dimensional convolutional neural network (1D-CNN) model. The ICBHI 2017 Breathing Sound Database, which contains samples of different breathing sounds, was used in this research.
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