Vincent J. Major, PhD, is an Assistant Professor of Population Health at NYU Grossman School of Medicine who works closely with NYU Langone Health’s Predictive Analytics Unit. Vincent’s work focuses on applied machine learning for healthcare and involves the development, validation, and deployment of predictive models using electronic health record (EHR) data.
Vincent received his PhD from New York University in 2020 for his thesis work using EHR data to develop machine learning models to estimate risk of death within 60 days of hospitalization to encourage end-of-life planning. For his Master’s research, Vincent studied the respiratory dynamics of critical care patients supported by invasive mechanical ventilation using mathematical modeling and signal processing methods.
PhD in Medical Informatics, 2020
New York University
ME in Bioengineering, 2015
University of Canterbury, New Zealand
BE(Hons) in Mechanical Engineering, 2014
University of Canterbury, New Zealand
An AI-based system for UTI antibiotic selection generalized fairly well to data from NYU Langone Health. However, utility in practice unclear due to a concerning trend of disparities against non-white patients. The tradeoff between inappropriate antibiotic treatment (IAT; how often the prescribed antiobiotic is ineffective) and broad-spectum usage (BSU; how often a stronger, more broad antibiotic is prescribed) is improved by the AI but white patients benefit significantly whereas non-white groups continue to receive ineffective and aggressive treatment.
Background We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown. Objectives The aim of the study is to estimate the effect of displaying risk scores on length of stay (LOS). Methods We integrated model output into the electronic health record (EHR) at four hospitals in one health system by displaying a green/orange/red score indicating low/moderate/high-risk in a patient list column and a larger COVID-19 summary report visible for each patient. Display of the score was pseudo-randomized 1:1 into intervention and control arms using a patient identifier passed to the model execution code. Intervention effect was assessed by comparing LOS between intervention and control groups. Adverse safety outcomes of death, hospice, and re-presentation were tested separately and as a composite indicator. We tracked adoption and sustained use through daily counts of score displays. Results Enrolling 1,010 patients from May 15, 2020 to December 7, 2020, the trial found no detectable difference in LOS. The intervention had no impact on safety indicators of death, hospice or re-presentation after discharge. The scores were displayed consistently throughout the study period but the study lacks a causally linked process measure of provider actions based on the score. Secondary analysis revealed complex dynamics in LOS temporally, by primary symptom, and hospital location. Conclusion An AI-based COVID-19 risk score displayed passively to clinicians during routine care of hospitalized adults with COVID-19 was safe but had no detectable impact on LOS. Health technology challenges such as insufficient adoption, nonuniform use, and provider trust compounded with temporal factors of the COVID-19 pandemic may have contributed to the null result. Trial registration ClinicalTrials.gov identifier: NCT04570488.
Patients with serious, life-limiting disease benefit from end-of-life conversations, goal setting, and palliative care. Hospitalized patients at high risk of near-term death are likely to benefit from such interventions. As NYU Langone Health expanded its institutional initiatives promoting patient-centered end-of-life care, leaders developed an artificial intelligence–based system that identifies patients at high risk of dying within 2 months. Upon opening a high-risk patient’s chart, attending physicians receive an interruptive alert stating the risk. If they agree, they are encouraged to conduct and document an advance care planning discussion. In the first year of operation running live across the health system, this platform has precisely identified high-risk patients within days of admission, producing high rates of provider agreement with risk and increased adoption of advance care planning. We are continuing to refine the system and to investigate how physicians interpret the risk estimates and integrate them into their decision-making.
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