Healthcare administrators steer their organizations' strategic direction with an emphasis on quality, value and efficiency, aiming to improve patient outcomes and ensure operational sustainability. Artificial intelligence (AI) has become a transformative force in healthcare in the past decade, with Canadian health systems and research institutions investing in AI solutions to address critical healthcare challenges. This primer delivers a fundamental guide to essential AI concepts in healthcare and provides practical guidance to prepare organizations for AI readiness.
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| http://dx.doi.org/10.12927/hcq.2024.27325 | DOI Listing |
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Lateral End-Range Movement Profile and Shot Effectiveness During Grand Slam Tennis Match-Play.
Eur J Sport Sci
February 2025
School of Human Sciences (Exercise and Sport Science), The University of Western Australia, Perth, Australia.
End-range movements are among the most demanding but least understood in the sport of tennis. Using male Hawk-Eye data from match-play during the 2021-2023 Australian Open tournaments, we evaluated the speed, deceleration, acceleration, and shot quality characteristics of these types of movement in men's Grand Slam tennis. Lateral end-range movements that incorporated a change of direction (CoD) were identified for analysis using k-means (end-range) and random forest (CoD) machine learning models.
View Article and Find Full Text PDFPredictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning.
J Med Syst
January 2025
Department of Computing, University of North Florida, 1 UNF Dr., Jacksonville, 32246, FL, USA.
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions.
View Article and Find Full Text PDFDevelopment of Predictive Model of Surgical Case Durations Using Machine Learning Approach.
J Med Syst
January 2025
Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation methods by developing Random Forest models tailored to specific surgical departments.
View Article and Find Full Text PDFMachine learning validation of a simple prediction model for the correlation between advanced age and clinical outcomes in patients with aneurysmal subarachnoid hemorrhage.
Neurosurg Rev
January 2025
Kobayashi Hospital, 510 Imaichi, Izumo City, Shimane, 693-0001, Japan.
Adverse effects of advanced age and poor initial neurological status on outcomes of patients with aneurysmal subarachnoid hemorrhage (SAH) have been documented. While a predictive model of the non-linear correlation between advanced age and clinical outcome has been reported, no previous model has been validated. Therefore, we created a prediction model of the non-linear correlation between advanced age and clinical outcome by machine learning and validated it using a separate cohort.
View Article and Find Full Text PDFCharacterization and design of dipeptide media formulation for scalable therapeutic production.
Appl Microbiol Biotechnol
January 2025
School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-Ro, Jangan-GuGyeonggi-Do 16419, Suwon-Si, South Korea.
Process intensification and simplification in biopharmaceutical manufacturing have driven the exploration of advanced feeding strategies to improve culture performance and process consistency. Conventional media design strategies, however, are often constrained by the stability and solubility challenges of amino acids, particularly in large-scale applications. As a result, dipeptides have emerged as promising alternatives.
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