File #: 22-5103    Version: 1 Name: Machine learning approaches
Type: Grant Award Status: Approved
File created: 8/26/2022 In control: Board of Commissioners
On agenda: 9/22/2022 Final action: 9/22/2022
Title: PROPOSED GRANT AWARD Department: Cook County Health Grantee: Cook County Health Grantor: Department of Health & Human Services/National Institute of Health Request: Authorization to accept grant Purpose: Machine learning approaches for the detection of emergency department patients with opioid misuse. Grant Amount: $198,000.00 Grant Period: 4/15/2022-3/31/2023 Fiscal Impact: N/A Accounts: N/A Concurrences: The Budget Department has received all requisite documents and determined the fiscal impact on Cook County, if any Summary: Opioid overdose is a leading cause of death in the United States with approximately 49,000 deaths and 1.5 million Emergency Department (ED) encounters related to opioid misuse annually. ED-based interventions for opioid misuse are effective but limited in scale by barriers to the accurate detection of all patients who could benefit from these interventions. This program seeks to use machine learning approaches to better identify and...
Indexes: (Inactive) ISRAEL ROCHA JR., Chief Executive Officer, Cook County Health & Hospitals System

title

PROPOSED GRANT AWARD

 

Department:  Cook County Health

 

Grantee:  Cook County Health

 

Grantor:  Department of Health & Human Services/National Institute of Health 

 

Request: Authorization to accept grant 

 

Purpose:  Machine learning approaches for the detection of emergency department patients with opioid misuse. 

 

Grant Amount:  $198,000.00

 

Grant Period:  4/15/2022-3/31/2023

 

Fiscal Impact:  N/A

 

Accounts: N/A

 

Concurrences:

The Budget Department has received all requisite documents and determined the fiscal impact on Cook County, if any

 

Summary:  Opioid overdose is a leading cause of death in the United States with approximately 49,000 deaths and 1.5 million Emergency Department (ED) encounters related to opioid misuse annually. ED-based interventions for opioid misuse are effective but limited in scale by barriers to the accurate detection of all patients who could benefit from these interventions. This program seeks to use machine learning approaches to better identify and characterize ED patients with opioid misuse so that timely treatments can be targeted to decrease opioid-related mortality.

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