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Precision Medicine and Health IT: New Data, New Challenges

  • ID: 4844856
  • Report
  • 35 Pages
  • Chilmark Research
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FEATURED COMPANIES

  • 2bPrecise
  • Fabric Genomics
  • Flatiron
  • Health Catalyst
  • Syapse
  • Tempus
  • MORE

Precision medicine (PM) is slowly entering the mainstream of health and medical discourse but is still a term in need of concise definition to move beyond the origins in genomics to the present, where PM must also encompass population health management.

The most effective manner to build a bridge between past and present is to define PM as an “effort to collect, integrate, and analyze multiple sources of genetic and non-genetic data and applying data analytics and machine learning/AI to develop insights about health and disease that are tailored to an individual.”

-Kadijah Ferryman and Mikaela Pitcan in What is Precision Medicine?

This report discusses the broader definition of precision medicine, including the topics of pharmacogenomics, genotyping/sequencing, microbiomics, and radiomics & digital pathology. After introducing the various -omics data now being collected for PM purposes, the report looks at how EHRs and other health IT platforms are currently integrating this information (or not) and what major hurdles continue to limit the possibility of more broad implementation of precision medicine strategies.

The report goes on to discuss the role of new initiatives and technology that are intended to accelerate, or at least support, the continued adoption of precision medicine techniques in the provision of care. It closes with a series of seven vendor profiles and a brief list of other vendors to watch in the space.

Note: Product cover images may vary from those shown
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FEATURED COMPANIES

  • 2bPrecise
  • Fabric Genomics
  • Flatiron
  • Health Catalyst
  • Syapse
  • Tempus
  • MORE

Executive Summary

  • Key Takeaways

1. Introduction

2. Overview of Key Components of PM

  • Pharmacogenomics  
  • Genotyping, Gene Sequencing, and the Microbiome  
  • Radiomics and Digital Pathology  
  • Dramatic Growth of Biological Data and Personal Data

3. Integration of -Omic Data into EHRs

  • Collaboration for Integrating Data for Genomics and Clinical Care

4. Current Solutions for Integrating Genomic Data into EHRs

5. Scaling PM Data Sharing to Populations

6. AI/ML’s Future Role  

7. Taxonomy and Value Chain of PM Vendors

8. Future of PM and the Challenges Ahead

9. Conclusion

10. Vendor Profiles

  • Syapse
  • verily
  • Flatiron
  • Tempus
  • 2bPrecise
  • Health Catalyst  
  • Fabric Genomics  

Acronyms Used

Tables and Figures

Introduction

  • Figure 1: Layers of -Omic Data

Overview of Key Components of PM
Pharmacogenomics

  • Figure 2: Precision Medicine Cycle and Clinical Pathways

Genotyping, Gene Sequencing, and the Microbiome

  • Figure 3: Data Integration and Data Flows for PM  

Dramatic Growth of Biological Data and Personal Data

  • Figure 4: Data Sources for PM  

Integration of -Omic Data into EHRs
Collaboration for Integrating Data for Genomics and Clinical Care

  • Figure 5. Standard Medicine vs. PM
  • Figure 6: Clinical Workflow with Genomic Data  

Current Solutions for Integrating Genomic Data into EHRs

  • Figure 7: UCSF Clinical Interface Created in Partnership with Syapse

Scaling PM Data Sharing to Populations

  • Figure 8: Smart on FHIR
  • Figure 9: Precision Public Health Stakeholders

Taxonomy and Value Chain of PM Vendors

  • Figure 10: Deep Learning
  • Figure 11: Health Data Value Chain
  • Future of PM and the Challenges Ahead
  • Figure 12: PM Market Segments and Relevant Companies

Vendor Profiles

  • Figure 13: Vendors and Key Differentiators  
  • Figure 14: Data Sources and Use Cases for Vendors  

Acronyms Used

Note: Product cover images may vary from those shown
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  • 2bPrecise
  • Fabric Genomics  
  • Flatiron
  • Health Catalyst  
  • Syapse
  • Tempus
  • verily
Note: Product cover images may vary from those shown
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