Using the Electronic Health Record to Identify Epilepsy Patients for Care Coordination: The Epilepsy Risk Score
Abstract number :
1.138
Submission category :
13. Health Services (Delivery of Care, Access to Care, Health Care Models)
Year :
2024
Submission ID :
791
Source :
www.aesnet.org
Presentation date :
12/7/2024 12:00:00 AM
Published date :
Authors :
Presenting Author: David Ficker, MD – University of Cincinnati
Anne Paul, MA, MBA – UC Health
Steven Cogorno, MD – University of Cincinnati
Rationale: Epilepsy specialty centers care for patients ranging from those with well-controlled seizures to those who are medication resistant. Even those with well-controlled seizures may have significant challenges such as mental health disorders or medication adherence issues. We created an automated scoring system in our Epic electronic health record (EHR) to stratify patients, to allow care teams to identify those with the highest needs and to develop care coordination efforts targeted to those patients.
Methods:
An epilepsy registry was created in the EHR based on the following metrics: patients seen by an epileptologist in our center with a billing diagnosis (primary or secondary) or problem list code of epilepsy (using Epic’s grouper “Seizure disorders and convulsions” which contains all seizure-related ICD-10 codes).
The epilepsy clinical team was then surveyed to identify important clinical parameters that might indicate challenges in our epilepsy patients. We chose parameters that had a corresponding structured data elements in the EHR and used our patient reported outcomes: seizure frequency, barriers to medications, depression (NDDI-E) and anxiety (GAD-&) screening scores. Each parameter was assigned points based on importance. Table 1 summarizes the scoring system.
Patients are stratified as follows:
Low risk = score < or equal to 4
Medium risk = score of 5 to 7
High risk = score > or equal to 8
Risk scores are available to view in the provider’s daily schedule and are color coded: green (low risk), yellow (medium risk), red (high risk) and are part of a clinical dashboard created for care coordination work.
Results:
Over 6 months, our Epilepsy Center saw 3529 unique patients. 1949 (55%) had a risk score calculated. Of the patients with a risk score (n=1949), 1055 patients had a low risk score (54%), 649 had a medium risk score (33% ) and 245 had a high risk score (13%).
Conclusions:
Risk stratification in epilepsy patients can be performed using structured data in the EHR. The Epilepsy Risk Score will allow our center to identify those patients who may benefit from care coordination efforts including social work support, medication assistance or referral to epilepsy support agencies.
Funding: none
Health Services (Delivery of Care, Access to Care, Health Care Models)