Learning Health Systems Machine Intelligence for Clinical Prediction and Healthcare Optimization
DOI:
https://doi.org/10.15662/IJARCST.2023.0602006Keywords:
Learning Health Systems, Real-Time Clinical Feedback Loops, Intelligent Healthcare Decision Support, Predictive Patient Trajectory Analytics, AI-Driven Healthcare Optimization, Data-Driven Clinical Learning, Adaptive Care Delivery Systems, Healthcare Resource Intelligence, Machine Learning in Clinical Practice, Continuous Healthcare ImprovementAbstract
Learning health systems (LHSs) continually and automatically seek ways to improve healthcare delivery and outcomes through a real-time feedback loop. The LHS paradigm encourages the integration of data-driven learning into everyday clinical practice, informing the routine evaluation and refinement of clinical prediction models and optimizing the allocation of resources and workflows. The LHS concept has gained increasing prominence and is now emerging as a mature domain of research and practice.
Three factors have contributed to this evolution. First, the enormous volume of readily available patient data, accumulated through decades of routine clinical practice, has enabled machine learning and artificial intelligence techniques to be successfully applied for predicting patient trajectories, risks, and outcomes at multiple time-scales; indeed, patient trajectories are now being predicted at a more granular scale than ever before. Second, health systems are continually seeking ways to allocate scarce resources more intelligently, both for planning purposes and to meet real-time demand; such work can now be informed by the predictions of patient arrivals at an operational level, as well as by predictions of the expected trajectories of patients within the care delivery process. Third, as the introduction of intelligent decision support systems becomes more common in various aspects of healthcare delivery, research is emerging on how best to integrate such decision support systems with clinical teams for practical impact.
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