Realtime Streaming Clinical Use Engine for Medical Escalation

Summary

The escalation of care for patients in a hospitalized setting between nurse practitioner managed services, teaching services, step-down units, and intensive care units is critical for appropriate care for any patient. Often such "triggers" for escalation are initiated based on the nursing evaluation of the patient, followed by physician history and physical exam, then augmented based on laboratory values. These "triggers" can enhance the care of patients without increasing the workload of responder teams. One of the goals in hospital medicine is the earlier identification of patients that require an escalation of care. The study team developed a model through a retrospective analysis of the historical data from the Mount Sinai Data Warehouse (MSDW), which can provide machine learning based triggers for escalation of care (Approved by: IRB-18-00581). This model is called "Medical Early Warning Score ++" (MEWS ++). This IRB seeks to prospectively validate the developed model through a pragmatic clinical trial of using these alerts to trigger an evaluation for appropriateness of escalation of care on two general inpatients wards, one medical and one surgical. These alerts will not change the standard of care. They will simply suggest to the care team that the patient should be further evaluated without specifying a subsequent specific course of action. In other words, these alerts in themselves does not designate any change to the care provider's clinical standard of care. The study team estimates that this study would require the evaluation of \~ 18380 bed movements and approximately 30 months to complete, based on the rate of escalation of care and rate of bed movements in the selected units.

Conditions

• Clinical Deterioration
• Hospital Medicine
• Monitoring, Physiologic

Interventions

OTHER

MEWS++ Monitoring

Patient's electronic medical record data will undergo processing by a machine learning algorithm (MEWS++).

OTHER

Predictor Score

A score predicting the likelihood that the patient will experience a deterioration in their clinical condition within six hours will be generated. If the prediction score exceeds a predetermined threshold, an alert will be sent to the provider. The alerting protocol is tiered, with both a low and high threshold. If the score is above the low threshold, nursing will be notified. If the score is above the high threshold, RRT will be notified.

Sponsors & Collaborators

• Icahn School of Medicine at Mount Sinai

  lead OTHER

Principal Investigators

• Matthew A Levin, MD · Icahn School of Medicine at Mount Sinai

Study Design

Allocation

NON_RANDOMIZED

Purpose

PREVENTION

Masking

NONE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2019-06-18

Primary Completion

2020-03-19

Completion

2020-03-19

Countries

• United States

Study Locations