Project Details - Ended
- Grant Number:R21 HS024977
- Funding Mechanism:
- AHRQ Funded Amount:$296,567
- Principal Investigator:
- Project Dates:9/30/2016 to 9/29/2018
- Care Setting:
- Medical Condition:
- Type of Care:
- Health Care Theme:
Epilepsy is one of the leading neurological disorders in the United States, affecting more than 479,000 children and over 2 million adults. Approximately 30 percent of epileptic patients have poor seizure control despite antiepileptic medications and are potential candidates for neurosurgical intervention. Early identification and referral of individuals who are potential surgical candidates is complex and, while relevant guidelines exist, there is no standard process to efficiently identify those patients meeting criteria for neurosurgical intervention. Given the large quantity of note-based data available in the electronic health record (EHR), it is challenging for providers to efficiently retain and process all the pertinent patient information. Natural Language Processing (NLP) and machine learning techniques have been successfully used to evaluate clinical notes and make recommendations in the research setting. However, NLP techniques are rarely integrated into practice to provide real-time clinical decision support.
The investigators previously developed and evaluated an NLP system to provide a candidate list of patients potentially eligible for surgical consult. In this followup project, the investigators will integrate this work into neurology clinical practice and develop a decision support mechanism to alert providers of patients potentially eligible for surgical intervention, with a goal of shortening the time to surgical evaluation.
The specific aims of this project are as follows:
- Implement and prospectively evaluate the existing NLP system by integrating the system with the EHR for patients identified as potential surgical candidates.
- Perform a clinical pilot test to evaluate the effectiveness of electronic alerts, honest broker reminders, and no intervention (standard of care) for eligible patients.
After the existing NLP tool is implemented and fully integrated into the electronic health record, the project team will perform a prospective evaluation on the NLP system’s recommendations to assess its accuracy. The investigators will then develop an integrated alert in the EHR that will provide clinicians with timely identification of patients with intractable seizures eligible for surgical consult. The use of this provider-driven alert will be compared to standard of care.