Detection of Opioid over Prescription

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Business Goals

  • Detect and prevent overprescription of opioids.

Challenge

  • The usage of opioids is associated with severe risks. These risks include misuse, addiction, overdoses, and death.
  • According to the US Centers for Disease Control and Prevention (CDC): ”The number of drug overdose deaths increased by nearly 5% from 2018 to 2019 and has quadrupled since 1999. Over 70% of the 70,630 deaths in 2019 involved an opioid.”
  • Detect doctors that are overprescribing opioids.
  • Detect patients that are taking addictive substances on demand rather than genuine need.

Results

  • The service detected 367 cases of opioid overprescription in the sample of nearly 500 care settings and 12 200 people under the study. The cases are under closer scrutiny now.

Implementation Details

  • Years of medical prescriptions history and a stack of machine learning tools for anomaly detection were used to detect doctors that prescribed and patients that got excessive amounts of opioids.
  • Then, the detected cases of opioid misuse were analyzed by a team of experts. Such factors as patient age, gender, route of administration, dosage form, location, etc. were taken into account. As a result, the system of post-filtering for the anomaly detection algorithm was designed.
  • The anomaly detection model and guard rails bearing domain knowledge produced an effective tandem allowing reliable detecting and preventing cases of opioid misuse.

Industry

Service

Type

  • Case Study

Keywords

  • Precision Medicine
  • Decision Support
  • Patient Care
  • Anomaly Detection
Roadmap
Business Goal Validation
AI Solutions Architect
Solution Design
AI Solutions Architect
Data Collection
Data Architect, Data Engineer
Exploratory Data Analysis
Data Scientist
Data Preprocessing
Data Scientist, Data Engineer
Advanced Analytics
Data Scientist
Findings Delivery
AI Solutions Architect, Data Scientist
Features Engineering
Data Scientist
Anomaly Detection Model Development
Data Scientist
Model Performance Evaluation
Data Scientist
Hyperparameters Tuning
Data Scientist
Standalone AI Service Design
AI Solutions Architect, MLOps
Standalone AI Service Coding
MLOps
Release

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    Michael Gurbych Director,
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    Roadmap
    Business Goal Validation
    AI Solutions Architect
    Solution Design
    AI Solutions Architect
    Data Collection
    Data Architect, Data Engineer
    Exploratory Data Analysis
    Data Scientist
    Data Preprocessing
    Data Scientist, Data Engineer
    Advanced Analytics
    Data Scientist
    Findings Delivery
    AI Solutions Architect, Data Scientist
    Features Engineering
    Data Scientist
    Anomaly Detection Model Development
    Data Scientist
    Model Performance Evaluation
    Data Scientist
    Hyperparameters Tuning
    Data Scientist
    Standalone AI Service Design
    AI Solutions Architect, MLOps
    Standalone AI Service Coding
    MLOps
    Release

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