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Dr Amy Nelson builds machine learning models for scheduling optimization at University College London Hospital (UCLH) and Great Ormond Street Hospital. Her recent work is on the prediction of MRI appointment no-shows, which highlights the dimensionality of complex behavior prediction and the need for high capacity modeling. She is also developing novel research impact metrics focused on translational outcomes at the National Institute of Health Research UCLH BRC. She obtained a medical degree and first class honors pharmacology degree from the University of Edinburgh, was a visiting student at Columbia University Biomedical Informatics Department with Professor Herbert Chase, and worked as a foundation doctor at UCLH.
Optimizing Hospital Scheduling With Artificial Intelligence
Non-attendance is common, delays management, and costs £1 billion annually in the UK alone. Though complex, the field of causal factors is intelligible with machine-learning assisted models, enabling individually-tailored optimization of scheduling. Drawing on experience from UCL Hospitals, I demonstrate how artificial intelligence could transform hospital operations generally.
Data science in combination with new tools help predict which patients will benefit most from health care interventions. Session contributors are representatives from medical organizations discussing various data science applications and their approaches to using data and predictive modeling to analyze and identify meaningful patterns that result in better patient outcomes.