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DataFramed
#244 Using Data to Optimize Costs in Healthcare with Travis Dalton and Jocelyn Jiang President/CEO & VP of Data & Decision Science at MultiPlan
Sep 16, 2024
39:06
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Travis Dalton, President/CEO at MultiPlan, and Jocelyn Jiang, VP of Data & Decision Science, dive into the complex U.S. healthcare landscape. They discuss the crucial role of data and AI in enhancing cost transparency and empowering patient decision-making. The conversation highlights how decision trees can predict healthcare spending effectively and the need for early intervention to improve outcomes. They also address the ethical implications of data privacy and the transformative potential of data science in healthcare delivery.
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Quick takeaways
- Data transparency in healthcare pricing is crucial for empowering consumers to make informed decisions and understand their costs.
- Leveraging predictive analytics and AI can transform patient care by enabling early interventions and fostering a preventative healthcare model.
Deep dives
The Complexity of Healthcare Pricing
The pricing structure of healthcare in the United States is complex and often confusing, with two main types of insurance: government-funded and commercial. Consumers face difficulties knowing what they will pay out-of-pocket before receiving care, as costs can vary significantly from one provider to another. For instance, an x-ray can cost ten times more in a hospital than at an imaging center, leading to financial strain and unpredictable expenses. This lack of price transparency complicates educated decision-making for consumers, making it essential to develop systems that clarify costs and provide comprehensive information about services.
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