R01DA050676

Developing and Evaluating a Machine-Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE) - Project Summary/Abstract

An unprecedented rise in opioid overdose and opioid use disorder (OUD) has become a public health crisis in the US. In response, health systems, payers, and policymakers have developed or adopted measures and programs to target individuals at high-risk for overdose or OUD. However, significant gaps exist in the current approaches to identify individuals at high-risk for overdose or OUD.

First, the definition of 'high-risk' currently used by payers and health systems varies widely, ranging from high opioid dose to the number of pharmacies or prescribers a patient has visited. Second, little is known about how accurately these measures truly identify patients with overdose or OUD, and there is some evidence showing they perform poorly, missing 70% to 90% of individuals with an actual OUD diagnosis or overdose.

Third, our NIDA-funded work (R01DA044985) using national Medicare and Pennsylvania Medicaid claims data has shown that machine-learning algorithms can achieve better performance for risk prediction for opioid overdose and OUD. Thus, the immediate next step is to expand our algorithms to other data sources (e.g., electronic health records [EHR]), as well as to apply state-of-the-art longitudinal neural networks and natural language processing (NLP) to further improve prediction accuracy.

In addition, we aim to translate these risk scores into a clinical decision tool to be used by healthcare systems to automatically analyze and visualize the relevant information regarding risk prediction and stratification for opioid overdose or OUD, using either claims data, EHR data, or both in real time.

Leveraging our NIDA-funded work on developing machine-learning algorithms to predict opioid overdose and OUD, we propose to "Develop and Evaluate a Machine-Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE)" that can be used by healthcare systems to identify patients at high risk for opioid overdose and OUD. We have 3 specific aims.

Aim 1 will refine and validate prediction algorithms to identify patients at risk for opioid overdose/OUD using 3 different datasets (i.e., 2011-2020 Florida all-payer EHR, Florida Medicaid claims, and Florida Medicaid claims linked with EHR data) from the OneFlorida Clinical Research Consortium. We will expand our current algorithms by applying state-of-the-art methods (e.g., NLP) to improve prediction.

In Aim 2, we will design and prototype a DEMONSTRATE clinical decision support tool to incorporate the best prediction algorithms to provide automatic warnings to primary care providers of patients at high risk of overdose/OUD. An iterative user-centered design approach will be used to enhance DEMONSTRATE's functionality and usability.

In Aim 3, we will integrate DEMONSTRATE into the University of Florida Health's EHR system and deploy and pilot test DEMONSTRATE in three primary care clinics. We will assess DEMONSTRATE's usability, acceptability, and feasibility.

Our proposed research is highly innovative in its expansion, translation, and application of a promising NIDA-funded machine-learning opioid prediction and risk stratification tool into a software platform to better inform clinical practice for improving the safety of opioid use.

University Of Pittsburgh - Of The Commonwealth System Of Higher Education

TO SUPPORT BASIC AND CLINICAL NEUROSCIENCE, BIOMEDICAL, BEHAVIORAL AND SOCIAL SCIENCE, EPIDEMIOLOGIC, HEALTH SERVICES AND HEALTH DISPARITY RESEARCH. TO DEVELOP NEW KNOWLEDGE AND APPROACHES RELATED TO THE PREVENTION, DIAGNOSIS, TREATMENT, ETIOLOGY, AND CONSEQUENCES OF DRUG ABUSE AND ADDICTION, INCLUDING HIV/AIDS. TO SUPPORT RESEARCH TRAINING AND RESEARCH SCIENTIST DEVELOPMENT. TO SUPPORT DISSEMINATION OF RESEARCH FINDINGS. SMALL BUSINESS INNOVATION RESEARCH (SBIR) LEGISLATION IS INTENDED TO EXPAND AND IMPROVE THE SBIR PROGRAMS TO EMPHASIZE AND INCREASE PRIVATE SECTOR COMMERCIALIZATION OF TECHNOLOGY DEVELOPED THROUGH FEDERAL SBIR RESEARCH AND DEVELOPMENT, INCREASE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT, AND FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN THE SBIR PROGRAM. THE LEGISLATION INTENDS THAT THE SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM STIMULATE AND FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH AND DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, FOSTER TECHNOLOGY TRANSFER BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, AND FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION.

93.279 - Drug Abuse and Addiction Research Programs

National Institute on Drug Abuse (NIDA) [HHS - NIH]

Pittsburgh, Pennsylvania 152221808 United States

Single Zip Code

Research Project Grant (Parent R01 Clinical Trial Required) (PA-19-055)

Amendment Since initial award the total obligations have increased 356% from $701,725 to $3,203,374.