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Aging Well: Using Precision to Drive Down Costs and Increase Health Quality
Rhoda Au1,2,3,4, Marina Ritchie4, Spencer Hardy4
1Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA.
Summary
Precision medicine alone cannot combat rising chronic diseases. Integrating data science and AI for digital biomarkers can shift healthcare to precision health, enabling early disease detection and prevention.
Area of Science:
- Digital Health
- Biomarkers
- Healthcare Innovation
Background:
- Precision medicine advances have improved individualized treatments but fail to curb the rise of chronic non-communicable diseases in aging populations.
- Current healthcare models face challenges in managing increasing disease prevalence and healthcare costs.
- Early detection of disease indicators is crucial for effective intervention.
Purpose of the Study:
- To propose a shift from precision medicine to precision health.
- To explore the potential of data science and artificial intelligence in identifying digital biomarkers.
- To outline strategies for preventing chronic diseases and improving health quality.
Main Methods:
- Leveraging technological advancements for collecting digital metrics.
- Utilizing data science and artificial intelligence for discovering digital biomarkers.
- Analyzing current healthcare models and identifying challenges.
Main Results:
- Digital metrics can detect disease indicators earlier, potentially identifying pre-symptomatic stages.
- Data science and AI can uncover digital biomarkers before clinical symptoms manifest.
- A transition to precision health offers a path towards disease prevention.
Conclusions:
- A comprehensive precision health approach, driven by digital biomarkers, is essential for combating chronic diseases.
- Early detection through digital health technologies can redefine disease monitoring and prevention.
- Addressing current healthcare challenges requires a proactive strategy focused on preventing disease altogether.
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