Predicting Readmission Risks

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Business Goals

  • Reduce the number of readmissions
  • Upgrade care management operations

Challenge

  • One readmission costs ~$20 000 for the healthcare provider. Some patients have 11 readmissions during a 90-day episode, totaling ~$220 000 loss.
  • Nurses are overloaded and need to prioritize patient care. One nurse can have ~400 beds.

Results

  • In 90 days of the A/B testing period, the number of readmissions dropped by 32% in the experimental group nursed with the AI service, compared to the control group under ordinary care.
  • Recalculated to the number of patients, the savings were ~$8 000 000 for the observation period (90 days).

Implementation Details

  • 6 years of medical history records and machine learning tools were used to uncover reasons, predict and prevent readmissions.
  • 3 groups of patients have been identified according to the readmission risk rate.
  • Detected readmission reasons became the foundation for personalized health plans.
  • Each group of patients gets a target health plan to reduce readmission risks.
  • Each patient is tracked by predictive AI models and allocated to one of the risk cohorts in real-time.
  • Nurses get push notifications about patient risks.
  • Nurses can add patient notes taken into account by the AI service.

Industry

Service

Type

  • Case Study

Keywords

  • Precision Medicine
  • Decision Support
  • Patient Care
  • Healthcare Provider
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
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
Deployment Infrastructure Design
AI Solutions Architect, MLOps
Web Development
Frontend Developer
Backend Development
Backend Developer
Deployment Infrastructure Rollout
MLOps
AI Service Deployment
MLOps
AI Service Integration
MLOps
Setting Up CI/CD
MLOps
Model Training Automation
MLOps
Release

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    Michael Gurbych Director,
    Operations and Finance
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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
    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
    Deployment Infrastructure Design
    AI Solutions Architect, MLOps
    Web Development
    Frontend Developer
    Backend Development
    Backend Developer
    Deployment Infrastructure Rollout
    MLOps
    AI Service Deployment
    MLOps
    AI Service Integration
    MLOps
    Setting Up CI/CD
    MLOps
    Model Training Automation
    MLOps
    Release

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