- B
- Blockchain
- C
- Customer Churn
- Counterterrorism
- Cybersecurity in the Public Sector
- Credit Card Fraudulent Transactions
- Credit Default Rates
- Conversion Modeling
- Claim Payment Automation Modeling
- Claim Development Modeling
- D
- Drug Delivery Optimization
- Disease Propensity
- Digital Wealth Management
- Direct Marketing
- E
- Estimating Sepsis Risk
- F
- Finding Duplicate Customer Records in Your Database
- Fraud detection
- Finding New Oil and Gas Sources
- Fraudulent Claim Modeling
- G
- Google AdWords Bidding
- H
- Hospital Readmission Risk
- I
- Inventory Forecasting
- Insider Threat in Public Sector
- Insurance Pricing
- L
- Loyalty Program Usage
- Life Insurance Underwriting
- M
- Multichannel Marketing Attribution
- Modeling ICU Occupancy
- N
- Next Best Offer
- Next Best Action
- P
- Product Personalization
- Q
- Quality Assurance
- S
- Supply Chain Management
- View global site search results

Hospital Readmission Risk
Proactively identifying hospital readmittance means increasing quality of care, decreasing costs, and improving the lives of patients.
Problem/Pain
Once patients leave the hospital, it can be much more difficult to impact their health. Many patients are difficult to contact and even more difficult to influence. At the point of readmission, most likely the patient’s health has declined even further.
Solution
DataRobot models identify those patients that are likely to return to the hospital—whether due to a physical downturn, abusive relationship, or chronic disease—allowing providers to take action before the patient is discharged. Using patient information like diagnosis, length of stay, previous medical records and admissions, age, and other demographics, DataRobot models help prevent readmission, saving costs and improving quality of treatment.
Why DataRobot
DataRobot makes it easy for hospitals to process extensive patient data and identify at-risk patients before they are discharged.