Session ID: 
MLAI11

How AI Enabled a Community Hospital to Tackle Clinical Variation and Reduce Length-of-Stay

11:40am - 12:00pm Monday, February 11
Orlando - Rosen Centre
Rosen Centre Jr. Ballroom F

Description

Clinical Variation is one of the key challenges facing healthcare today, and represents trillions of dollars per year in costs that don't improve the patient experience or outcomes. Flagler Hospital, a 335-bed facility in St. Augustine, Fla., tackled this challenge head on with artificial intelligence.

Applying AI to pneumonia, Flagler saved an average of $1,350 per case, reduced the average length of stay by two days and readmissions by seven times – eliminating nearly $850,000 in unnecessary costs. 

In this session, attendees will learn how Flagler, drawing data from its EMR and using the FHIR standard, tapped powerful unsupervised learning capabilities to understand the optimal events, sequence and timing of care. These results were presented to physician teams via an intuitive interface that allowed them to understand exactly why each step (and the timing of the step) was recommended. Final care paths were deployed as new order sets to the EMR. The intelligent application has also been applied to sepsis and will be used with 18 other conditions in the coming months.

Learning Objectives:
• Learn how to use an unsupervised AI application to reduce clinical variations.
• Discover how to extract the right source data from multiple clinical and financial systems, and prepare the data to be fed into the AI algorithm.
• Learn how to use the data produced by the algorithm to improve a clinical care pathway to cut costs, shorten length of stay, and reduce readmissions.

This session is part of a special program called HIMSS19 Machine Learning & AI for Healthcare. Extra fees and separate registration is required.

Speaker(s): 

CMIO,
Flagler Hospital

Audience: 

CIO, CTO
Chief Quality, Chief Clin Transformation Officer
CMIO/CMIO

Level: 

Intermediate