Precision Medicine


John D. Halamka, MD, MS

Precision Medicine: Four Trends Make It Possible

When realized, the promise of precision medicine (to specifically tailor treatment to each individual) stands to transform healthcare for the better by delivering more effective, appropriate care. To date, to achieve precision medicine, health systems have faced financial, data management, and interoperability barriers. Current trends in healthcare, however, will give researchers and clinicians the quality and breadth of health data, biological information, and technical sophistication to overcome the challenges to achieving precision medicine.
Four notable trends in healthcare will bolster to growth of precision medicine in the coming years:

Decision support methods harness the power of the human genome.
Healthcare leverages big data analytics and machine learning.
Reimbursement methods incentivize health systems to keep patients well.
Emerging tools enable more data, more interoperability.

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David Crockett

Going Beyond Genomics in Precision Medicine: What’s Next

Precision medicine processes, while involving genomics, are not confined to working with data about an individual’s genes, environment, and lifestyle. Precision medicine also means putting patients on the right path of care, taking into consideration other individual tolerances, such as participation and cost. Precision medicine processes incorporate data beyond the individual, pulling in socio-economic data, as well as relevant internal and external data, to create an entire patient data ecosystem. With reusable data modules, this information is processed within a closed-loop analytics framework to facilitate clinical decision making at the point of care. This optimizes clinical workflow, thus leading to more precise medicine.

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Eric Just

The Top 4 Ways De-Identified Data Improves Research

Level 1 of the Healthcare Analytics Adoption Model is providing de-identified data marts and self-service tools. Researchers navigating the complex research process can use de-identified data in each step of the process to increase their chances of having more successful research projects.
Using de-identified data not only removes research roadblocks, but also enables researchers to navigate the four fundamental research steps with more ease. There are four specific ways de-identified data improves research:

Enables early discovery and exploration to test the validity of the hypothesis before committing time and resources.
Enables researchers to explore and refine their cohorts and determine whether the patient population can support the scope of the project.
Enables researchers to put together strong grant applications without having to tax the resources of enterprise data analysts—and without having to wait for analysts to answer relatively straightforward questions.
Enables researchers to come to the IRB with a strong, fully supported application. A data-driven research process ensures that both researchers and IRB reviewers don’t have to waste their time on projects that may not be viable.

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Eric Just

Turn Research Into Care Delivery Improvements Using the Research Analytics Adoption Model

Research is a complex yet vital component of improving care delivery, and it can be hindered by a variety of organizational and technical roadblocks:

Insufficient tools and processes
Poor infrastructure
No single source of truth for data

Health systems can overcome these common research roadblocks and turn analytics-powered research into care delivery improvements by using the Research Analytics Adoption model as a strategic roadmap.
The model consists of 8 levels designed to align operations and research priorities:

De-identified tools and data marts
Delivery of customized data sets
EDW-facilitated study recruitment
Centralized, research-specific data collection
Automated research operations reporting
Biobank/genomic data integration
Multi-site data sharing
Translational Analytics

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Eric Just
Sean Whitaker

The 3 Challenges of Translational and Clinical Research Data Management and a Strategy to Succeed

Researchers are facing problems with clinical research data management. These challenges include: 1. Accessing healthcare data due to technology barriers, regulatory barriers, and organizational barriers; 2. Inefficient use of time and resources when working with the data because of poor study recruitment, data cobbling with Excel and Access databases, and materials waste when samples can’t be found.; and 3. Translating research discovery into clinical practice because systems aren’t in place to move new best practices into everyday clinical care.

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