FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease
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Clinical Decision Support System Used in Spinal Disorders: Scoping Review
Background Spinal disorders are highly prevalent worldwide with high socioeconomic costs. This cost is associated with the demand for treatment and productivity loss, prompting the exploration of technologies to improve patient outcomes. Clinical decision support systems (CDSSs) are computerized systems that are increasingly used to facilitate safe and efficient health care. Their applications range in depth and can be found across health care specialties. Objective This scoping review aims to explore the use of CDSSs in patients with spinal disorders. Methods We used the Joanna Briggs Institute methodological guidance for this scoping review and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) statement. Databases, including PubMed, Embase, Cochrane, CINAHL, Web of Science, Scopus, ProQuest, and PsycINFO, were searched from inception until October 11, 2022. The included studies examined the use of digitalized CDSSs in patients with spinal disorders. Results A total of 4 major CDSS functions were identified from 31 studies: preventing unnecessary imaging (n=8, 26%), aiding diagnosis (n=6, 19%), aiding prognosis (n=11, 35%), and recommending treatment options (n=6, 20%). Most studies used the knowledge-based system. Logistic regression was the most commonly used method, followed by decision tree algorithms. The use of CDSSs to aid in the management of spinal disorders was generally accepted over the threat to physicians’ clinical decision-making autonomy. Conclusions Although the effectiveness was frequently evaluated by examining the agreement between the decisions made by the CDSSs and the health care providers, comparing the CDSS recommendations with actual clinical outcomes would be preferable. In addition, future studies on CDSS development should focus on system integration, considering end user’s needs and preferences, and external validation and impact studies to assess effectiveness and generalizability. Trial Registration OSF Registries osf.io/dyz3f; https://osf.io/dyz3f
Clinical Decision Support System Used in Spinal Disorders: Scoping Review
Background Spinal disorders are highly prevalent worldwide with high socioeconomic costs. This cost is associated with the demand for treatment and productivity loss, prompting the exploration of technologies to improve patient outcomes. Clinical decision support systems (CDSSs) are computerized systems that are increasingly used to facilitate safe and efficient health care. Their applications range in depth and can be found across health care specialties. Objective This scoping review aims to explore the use of CDSSs in patients with spinal disorders. Methods We used the Joanna Briggs Institute methodological guidance for this scoping review and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) statement. Databases, including PubMed, Embase, Cochrane, CINAHL, Web of Science, Scopus, ProQuest, and PsycINFO, were searched from inception until October 11, 2022. The included studies examined the use of digitalized CDSSs in patients with spinal disorders. Results A total of 4 major CDSS functions were identified from 31 studies: preventing unnecessary imaging (n=8, 26%), aiding diagnosis (n=6, 19%), aiding prognosis (n=11, 35%), and recommending treatment options (n=6, 20%). Most studies used the knowledge-based system. Logistic regression was the most commonly used method, followed by decision tree algorithms. The use of CDSSs to aid in the management of spinal disorders was generally accepted over the threat to physicians’ clinical decision-making autonomy. Conclusions Although the effectiveness was frequently evaluated by examining the agreement between the decisions made by the CDSSs and the health care providers, comparing the CDSS recommendations with actual clinical outcomes would be preferable. In addition, future studies on CDSS development should focus on system integration, considering end user’s needs and preferences, and external validation and impact studies to assess effectiveness and generalizability. Trial Registration OSF Registries osf.io/dyz3f; https://osf.io/dyz3f
Clinical Decision Support System Used in Spinal Disorders: Scoping Review (Preprint)
BACKGROUND Spinal disorders are highly prevalent worldwide with high socio-economical costs. This cost is associated with the demand for treatment and productivity loss, prompting the exploration of technology to improve patient outcomes. Clinical decision support systems (CDSSs) are computerised systems that are increasingly used to facilitate safe and efficient healthcare. Its application ranges in depth and can be found across healthcare specialities. OBJECTIVE This review aimed to explore the use of CDSSs for patients with spinal disorders. METHODS We used the Joanna Briggs Institute methodological guidance for scoping review and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews. Included studies examined the use of digitalised CDSSs for patients with spinal disorders. RESULTS Four major CDSS functions were identified from 31 studies: preventing unnecessary imaging (n = 8), diagnosis (n = 6), prognosis (n = 11), and treatment recommendation (n = 6). Most studies utilised the knowledge-based system. Logistic regression was the most employed method followed by decision tree algorithms. The use of CDSSs to aid in the management of spinal disorders was generally accepted over feelings of threat to physicians' clinical decision-making autonomy. CONCLUSIONS Although effectiveness was frequently evaluated by examining the agreement between CDSS and healthcare providers' decisions, a preferable comparison would be to compare the CDSS recommendations with actual clinical outcomes. Additionally, future studies on CDSS development should focus on system integration, considering end-user’s needs and preferences, as well as external validation and impact studies, to assess effectiveness and generalisability.
Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time.
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