Friday, April 26, 2024
4/26/2024
Project Summary/Abstract Patients with opioid misuse disproportionately utilize emergency health services and are at increased risk for premature death. The timely and accurate identification of patients with opioid misuse in the Emergency Department (ED) is critical to provide evidence-based interventions to decrease mortality. Challenges to opioid misuse detection in the ED include provider time constraints, inconsistent screening approaches, and patient barriers to self-reporting. Advanced analytic techniques such as machine learning and cluster analyses offer promise in efficiently characterizing and identifying patients with opioid misuse during their ED encounter by leveraging data within the electronic health record (EHR) and the prescription drug monitoring program (PDMP). The role of machine learning approaches utilizing multiple data sources to identify ED patients with opioid misuse has yet to be fully explored. In aim 1, multiple machine learning algorithms using ED encounter data will be developed for the identification of opioid misuse. Models will be systematically assessed for social biases and mitigation strategies implemented to ensure equity in model performance. In aim 2, the inclusion of longitudinal PDMP data for the identification of ED patients with opioid misuse will be evaluated by building models from both data sources utilizing ensemble stacking methods. Finally, in aim 3, an unsupervised latent class analysis model will be built to identify clinically relevant subphenotypes of ED patients with opioid misuse, describe their characteristics, and determine patient-oriented outcomes. An innovative approach to the detection of ED patients with opioid misuse will be pursued by rigorously testing machine learning models utilizing multiple data sources, conducting social bias assessments prior to clinical deployment, and characterizing latent groups of patients with opioid misuse. The candidate for this Mentored Patient-Oriented Career Development Award (Dr. Neeraj Chhabra) possesses a strong foundation in emergency care, medical toxicology, substance use research, and biostatistics. Through this K23, he will further develop skills in data science to build comprehensive and scalable models spanning multiple data domains for the identification of patients with opioid misuse. The multidisciplinary mentorship team led by his primary mentor (Dr. Niranjan Karnik) and co-mentors (Dr. Majid Afshar, Dr. Harold Pollack, and Dr. Gail D’Onofrio) consists of nationally renowned experts in the fields of substance use research, machine learning, natural language processing, and clinical ethics. Through an integrated program of formal coursework, ethics training, mentorship, and research, Dr. Chhabra will develop the skillset necessary to complete these aims and transition to independent investigation. His proposal takes full advantage of the combined resources provided by the affiliated institutions of Cook County Health and Rush University Medical Center. Dr. Chhabra’s long-term goal is to utilize machine learning techniques to focus treatments and resources towards patients with opioid misuse within the ED setting. This K23 award provides the necessary foundation to pursue this goal and will form the basis for future R01 proposals evaluating the clinical impact of these models.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).
