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Big Data and Predictive Analytics in Health Care.

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|July 22, 2016
PubMed
Summary
This summary is machine-generated.

Predictive analytics in healthcare offer personalized care and risk reduction but face adoption challenges. Actionable insights from these systems can drive prevention strategies for better population and individual health outcomes.

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Area of Science:

  • Health Informatics
  • Clinical Decision Support
  • Preventive Medicine

Background:

  • Predictive analytics demonstrate significant potential for advancing healthcare delivery.
  • Widespread adoption of these advanced analytical tools in clinical settings faces considerable obstacles.

Purpose of the Study:

  • To review the current state of predictive analytics within the healthcare domain, focusing on clinical applications.
  • To explore methods for translating predictive system outputs into actionable preventive health strategies.

Main Methods:

  • Review of current literature and clinical practices in predictive health-care analytics.
  • Conceptual framework development for actionable output integration.

Main Results:

  • Predictive analytics can identify at-risk populations and individuals for targeted interventions.
  • Differentiated processes are needed to make predictive model outputs actionable for prevention.

Conclusions:

  • Implementing predictive analytics can lead to more personalized healthcare.
  • These systems hold the potential to significantly reduce health risks at both population and individual levels.