Efficient patient monitoring on the medical-surgical wards is crucial, because up to 5% of hospitalized adult
patients deteriorate, requiring transfer to the intensive care unit (ICU) or intervention of a rapid response team
(RRT). Currently, vital sign measurement is performed on all patients every 4-6 hours, even the most stable.
For stable patients, this monitoring is often unnecessary, whereas for higher-risk patients, vital sign monitoring
every 4-6 hours is often not adequate. To address this need, we will leverage one of the largest, most diverse
clinical datasets in the country, using electronic health record (EHR) data from 2.4M hospitalized patients to
generate machine learning (ML) predictive models, designed to optimize patient monitoring. We will use
continuous monitoring (CM) devices to identify in advance patients likely to deteriorate and specify the clinical
underlying reasons of deterioration to enable timely interventions. We have applied and published similar ML
approaches on other cohorts, including: 1) deep recurrent neural networks (RNNs) to avoid unnecessary
overnight vitals; 2) deep learning models that use continuous monitoring data to predict clinical alerts up to 4
hours ahead of time; and 3) natural language processing on medical notes and unsupervised clustering of
patients. Our approach involves collecting prospectively CM data from a targeted population of 2,000
hospitalized patients, and developing and validating models, both retrospectively and prospectively. Our
approach will allow us to: Identify stable patients admitted on the medical-surgical wards to optimize
vital signs monitoring. We will train a RNN model using EHR data from 2.4M hospitalizations, to predict, after
vital signs are measured, stable patients for the next 8 hours, and enable eliminating the next vitals
measurement. We retrospectively will validate the model, using cross-affiliation validation, and prospectively,
silently validate it in 5 different hospitals. Develop a clinical deterioration algorithm, based on continuous
monitoring data and clinical hard outcomes. We will collect prospective data from a targeted population of
2,000 inpatients, who are admitted on medical-surgical floors in our largest hospital, with a modified early
warning score higher than 5. The CM patches will start collecting data upon admission. We will use combined
clinical hard outcomes (death, intubation, cardiac arrest, unplanned ICU transfer, RRTs) to train two deep-
learning models to predict deterioration up to 4 hours and up to 24 hours before. Define the early and late
phenotypic substrates of hospitalized patient deterioration. Using the clinical data of 56K deteriorated
patients from Aim 1 (EHR variables and extracted presenting symptoms) 4 hour and 24 hours prior to
deterioration, we will perform unsupervised cluster analysis to identify unique clusters linked to phenotypes of
deterioration. We will associate derived phenotype groups to clinical outcomes and treatments, to inform more
targeted treatment and intervention strategies. We aim to develop new tools to align patient needs with
resources, and deliver more efficient, effective, personalized, and proactive care to hospitalized patients.
