9 Ways to Apply Predictive Analytics to Healthcare

Here are nine ways using predicitve analytics can improve results for both the patient and businesses participating in the complex healthcare market:

1. Model drug development collaborations that maximize IP and drug discovery.

2. Simulate PRO (Patient Reported Outcomes) for care quality improvement and outcomes.

3. Accelerate time to market for new therapies with strategic portfolio modeling.

4. Predict market access and optimize resource allocation for new therapies.

5. Predict high risk patients for ACO (accountable care organization) and hospitals.

6. Leverage advanced analytics to reduce hospital readmissions.

7. Simulate connected health consumer and recommend technology interventions that drive healthy behavior change.

8. Simulate the financial risks and incentives of emerging reimbursement models for ACO.

9. Quantify health costs & productivity of simulated workforce while recommending the most appropriate wellness intervention.

 

Learn more about applying predictive analytics to healthcare by downloading Eric Bonabeau’s ‘A More Rational Approach to New-Product Development‘.  In this free paper, you’ll discover how Eli Lilly approached this complex problem by structuring their research to seek truth first and success second.

 

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