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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Using predictive analytics and big data to optimize pharmaceutical outcomes.

Inmaculada Hernandez1, Yuting Zhang2

  • 1Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA. inh3@pitt.edu.

American Journal of Health-System Pharmacy : AJHP : Official Journal of the American Society of Health-System Pharmacists
|September 10, 2017
PubMed
Summary
This summary is machine-generated.

Predictive analytics, using big data, helps manage population health by identifying at-risk patients for medication issues. This enables personalized interventions for better patient outcomes.

Keywords:
big datamedication managementpharmaceutical outcomespopulation health managementpredictive analytics

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Area of Science:

  • Health Informatics
  • Data Science in Healthcare
  • Pharmacoeconomics

Background:

  • Healthcare generates vast amounts of data (big data) from electronic health records, wearables, and more.
  • Predictive analytics offers advanced methods to analyze big data, overcoming traditional limitations.
  • Understanding big data and predictive analytics is crucial for clinicians.

Purpose of the Study:

  • To describe the application of predictive analytics in healthcare.
  • To outline the necessary resources and challenges in implementing predictive analytics.
  • To review successful applications in population health management, focusing on medication-related outcomes.

Main Methods:

  • Review of successful applications of predictive analytics in population health management.
  • Analysis of big data sources including electronic health records, claims data, and data from personal devices.
  • Application of statistical techniques like predictive modeling, machine learning, and data mining.

Main Results:

  • Predictive analytics successfully identifies high-risk patients for medication noncompliance or adverse effects.
  • It provides insights into patient-specific medication-related risks.
  • Enables pharmacists to tailor interventions to individual patient needs.

Conclusions:

  • Predictive analytics leveraging big data is essential for effective population health management.
  • It empowers clinicians to map interventions and improve patient outcomes.
  • This technology will become indispensable for healthcare providers.