PDMP-EHR Risk Analytics for Opioid Use Disorder Prevention
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Solution Overview
Problem
Current systems fail to effectively identify and prevent patients from developing opioid use disorder (OUD), particularly those who initially started using opioids via prescription, contributing to the rising crisis.
Innovation Solution
A system utilizing explainable artificial intelligence models for predictive and prescriptive analytics, integrating data from Prescription Drug Monitoring Program (PDMP) and Electronic Health Record (EHR) to assess patient risk and provide personalized interventions, including dosage adjustments, alternate medications, and behavioral interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional opioid prescribing practices are used to treat pain, then patient pain relief is improved, but patient risk of developing opioid use disorder increases
Solution Approach 1:
The system performs preliminary risk assessment before opioid prescription by analyzing patient data from multiple sources (EHR, PDMP, claims data) to identify patients at high risk of developing OUD. This preliminary action enables clinicians to take preventive measures such as selecting alternative pain management strategies or implementing additional monitoring for at-risk patients before they start opioid therapy.
Solution Approach 2:
The AI/ML risk assessment tool serves as an intermediary between patient care needs and opioid prescribing decisions. It processes complex patient data and provides risk scores that guide clinician decision-making, acting as a mediator that balances pain relief needs against OUD prevention without requiring clinicians to manually analyze all risk factors.
2Object-affected harmful factors
If opioid prescription numbers are reduced to combat the epidemic, then OUD development is prevented, but pain treatment effectiveness deteriorates
Solution Approach 1:
The system applies differentiated prescribing strategies based on individual patient risk profiles rather than uniform restrictions. Low-risk patients receive appropriate opioid therapy for pain management, while high-risk patients receive alternative treatments or enhanced monitoring. This local quality approach ensures that opioid restrictions do not uniformly impact all patients, maintaining effectiveness for those who benefit most from opioid therapy.
Solution Approach 2:
The system changes the parameter of prescribing decisions from blanket reduction to risk-stratified prescribing. By introducing risk scores as a new parameter, clinicians can adjust prescribing intensity, duration, and type of opioid medication based on individual patient characteristics, thereby maintaining pain treatment effectiveness while reducing overall misuse.
3Measurement precision
If comprehensive patient data is collected to improve risk prediction accuracy, then prediction precision is improved, but system complexity increases
Solution Approach 1:
The system uses multi-functional data aggregation that collects information from multiple sources (EHR, PDMP, claims data) for a single integrated risk assessment purpose. This universal data collection approach enables the system to evaluate multiple risk factors simultaneously without requiring separate systems for each data source, thereby managing complexity while improving prediction accuracy.
Solution Approach 2:
The system replaces manual data analysis and risk assessment with automated AI/ML algorithms. Instead of clinicians manually reviewing complex patient data from multiple sources, machine learning models automatically process and integrate the data, reducing the operational complexity burden on healthcare providers while maintaining high prediction accuracy.
4Ease of operation
If AI/ML models provide detailed risk explanations to improve clinician trust, then ease of operation is improved, but computational requirements increase
Solution Approach 1:
The system extracts and highlights only the most relevant risk factors and contributing variables from the comprehensive analysis, presenting them to clinicians in a prioritized format. Instead of displaying all computational details, the system extracts key insights such as top risk factors and suggested interventions, reducing computational presentation overhead while maintaining clinician trust through explainable AI.
Data Source
AI summary
Embodiments relate to a system comprising a processor that is configured to: receive input data of a patient, wherein the input data comprises one or more of a patient data, a prescription data, a drug data, a dispenser data, and a prescriber data; derive one or more attribute variables, based on the input data; predict using a predictive analytics, a risk score based on the attribute variables, wherein the risk score is a probability of the patient developing an opioid use disorder; and determine a subset of the attribute variables according to a percentage contribution to the risk score; provide using a prescriptive analytics module, a treatment recommendation based on the risk score and the attribute variables, and wherein the system is configured to identify and/or prevent opioid use disorder in the patient being treated for pain with a prescription drug, and wherein the prescription drug is an opioid.