Machine Learning
The Clinical Excellence Research Center studies applications of machine learning to electronic health record data and to administrative claims data for improving clinical care. These efforts use machine learning to provide forecasts, such as patients likely to incur high medical costs in the future, and patients at high risk of mortality as well as fundamental methods that enable the making of such forecasts via models that are stable of time, sites, and populations.
Clinical Excellence Research Center's faculty study the interplay of the AI model’s output, the decision-making protocol based on that output, and the capacity of the stakeholders involved to take the necessary subsequent action determines the clinical usefulness of model-guided care.
Publications and News
- – JAMA Network
Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review
CERC researchers examined how health care applications of large language models (LLMs) are currently evaluated and found that only 5% used real patient care data for LLM evaluation. Administrative tasks such as writing prescriptions and natural language processing and natural language understanding tasks such as summarization were understudied; accuracy was the predominant dimension of evaluation, while fairness, bias, and toxicity assessments were less studied.
- – Journal of Biomed Inform
Language models are an effective representation learning technique for electronic health record data
We demonstrated that using patient representation schemes inspired from techniques in natural language processing can increase the accuracy of clinical prediction models by transferring information learned from the entire patient population to the task of training a specific model, where only a subset of the population is relevant.
- – Nature Medicine
Estimate the hidden deployment cost of predictive models to improve patient care
Although examples of algorithms designed to improve healthcare delivery abound, for many, clinical integration will not be achieved. The deployment cost of machine learning models is an underappreciated barrier to success. Experts propose three criteria that, assessed early, could help estimate the deployment cost.
- – Health Affairs
A 'green button' for using aggregate patient data at the point of care. - PubMed - NCBI
Stanford's Clinical Excellence Research Center's faculty also study the interplay of the AI model’s output, the decision-making protocol based on that output, and the capacity of the stakeholders involved to take the necessary subsequent action determines the clinical usefulness of model-guided care.
- – BMJ Open
Predicting patient ‘cost blooms’ in Denmark: a longitudinal population-based study
The Clinical Excellence Research Center conducted a population-based study of newly high-cost patients using the Danish national database. Machine learning was used to predict future high-cost patients by identifying those whose costs will "bloom" within 12 months. The analysis found that we improved positive predictive power by more than 30%, compared with standard risk-stratification tools.