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Predictive Analytics for Healthcare

Author Jaquie Finn , Gavin Troughton , Paul Baxter
Release Date: 2020/09/01
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Book Description

Before the onset of COVID-19, the healthcare community was already moving to meet the challenges of a growing global population. By collecting record amounts of clinical data electronically and making significant progress on neural network-based AI approaches, the industry now has the potential to build powerful predictive analytics systems. The focus will accelerate the shift from a one-size-fits-all approach to individualized medicine.

But several questions remain. What are the plausible outcomes for the world of predictive analytics in both the short and long term? What does the care pathway look like if everything is predicted? And with patient populations and healthcare needs increasing exponentially, how can the industry deliver care in a sustainable and cost-effective way?

This comprehensive report, written by Jaquie Finn and Dr. Gavin Troughton with Cambridge Consultants, explores the possibilities. You’ll learn:

  • How predictive analytics plays a part across all stages of the care pathway
  • The foundational enablers for predictive analytics
  • How healthcare economics figure into the equation
  • Predictive analytics and today’s healthcare system
  • The future of predictive analytics in healthcare

Table of Contents

  1. Predictive Analytics for Healthcare
    1. Introducing Predictive Analytics
      1. Drawing the Line Between Predictive Analytics and Diagnosis
      2. What This Report Covers
    2. PA Plays a Part Across All Stages of the Care Pathway
      1. Care Pathways
      2. Characteristics of Chronic Care Pathways
      3. Characteristics of Acute Care Pathways
    3. Foundational Enablers for PA
      1. Data for PA: Three Crucial Aspects to Get Right
      2. Fundamental Principles of PA: Data Is Critical, but Start with the Outcomes
    4. Healthcare Economics
      1. Need for More Efficient Use of Resources
      2. Growth of Chronic Conditions
      3. Personalized Therapies
      4. Economic Pressures in the Hospital
      5. Economic Pressures in Wider Healthcare Settings
      6. Who Pays for PA Innovation?
    5. Predictive Analytics and the Healthcare System
      1. Machine Learning
      2. The Rise of Deep Learning
      3. Availability of Toolsets
    6. Predicting What Conditions People Will Get
      1. Chronic Conditions
      2. Oncology
      3. Acute Conditions
    7. Predicting Which Drugs and Treatments Will Work
      1. The Effect of Genetics on the Response to Drugs
      2. Alternatives to PGx
      3. Current Knowledge and Challenges to Implementation
      4. Deep Phenotyping
    8. Predicting Who Will Comply and Engage Through the Care Pathway
      1. Psychographic Segmentation
      2. Digital Therapeutics
      3. Hospital No-Shows
      4. Not Taking Medicine/Rehabilitation
      5. Patient Engagement Strategies
    9. Healthcare Efficiency Through Predictive Analytics
      1. Data Sources as Enabling Technologies for Efficiency
      2. Overcoming Inefficiencies Across the Patient Journey
      3. Continuing Opportunities for Healthcare Efficiency
    10. The Future of Predictive Analytics in Healthcare
      1. Good Data Will Always Be Key
      2. Increasing Availability of Data for Predictive Analysis
      3. The Increasing Range of Usable Data
      4. Synthetic Versus Real-World Data
      5. Increasing Processing Power and Tools Available for Training and Running Predictive Analytics
      6. Signal in the Noise
      7. Bias in Aggregated Information
      8. Digital Twins
    11. Summary
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