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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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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:
  1. De-identified tools and data marts
  2. Delivery of customized data sets
  3. EDW-facilitated study recruitment
  4. Centralized, research-specific data collection
  5. Automated research operations reporting
  6. Biobank/genomic data integration
  7. Multi-site data sharing
  8. Translational Analytics

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A New Era of Personalized Medicine: The Power of Analytics and AI

Healthcare is looking towards an era of personalized medicine in which providers customize treatments for the individual patient. Realizing this tailored level of care s a new level of data volume and analytics and AI capabilities that, while novel to healthcare, other industries are thriving in. Choosing the right role models as healthcare works towards the analytics- and AI-driven territory of personalized medicine will guide informed strategies and establish best practices. With experience and expertise in these key areas, the military, aerospace, and automotive industries can serve as healthcare’s best examples:

  1. The human cognitive processes of complex decision making.
  2. The digitization of their industries, with the “health” of their assets as key drivers.
  3. Operating in a “big data” ecosystem.

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Bridging the Data and Trust Gaps: Why Health Catalyst Entered the Life Sciences Market

Why would a healthcare data warehousing and analytics company partner with the life sciences industry? Because trust and collaboration across the industry—between life sciences, healthcare delivery systems, and insurance—is the only path to real healthcare transformation. Health Catalyst recognizes an industrywide improvement opportunity in collaborating with life sciences to build mutual trust, integrate data, and leverage analytics insights for a common interest (i.e., patient outcomes). By aligning themselves around human health fulfillment, Health Catalyst, their provider partners, and life sciences will advance important healthcare goals:

  1. Improving clinical trial design and execution.
  2. Stimulating clinical innovation.
  3. Supporting population health.
  4. Reducing pharmaceutical costs.
  5. Improving drug safety and pharmacovigilance.

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Extended Real-World Data: The Life Science Industry’s Number One Asset

The life science industry has historically relied on sanitized clinical trials and commoditized data sources (largely claims) to inform its drug development process—an under-substantiated approach that didn’t reflect how a new drug would affect broader patient populations. In an effort to gain more accurate insight into the patient experience and bring drugs to market more efficiently and safely, the industry is now expanding into extended real-world data (RWD). To access the needed breadth and depth of patient-centric data, life science companies must partner with a healthcare transformation company that has three key qualities:

  1. A broad and deep data asset.
  2. Extensive provider partnerships.
  3. An outcomes-improvement engine to support the next generation of drug development.

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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:

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

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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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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:

  1. Enables early discovery and exploration to test the validity of the hypothesis before committing time and resources.
  2. Enables researchers to explore and refine their cohorts and determine whether the patient population can support the scope of the project.
  3. 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.
  4. 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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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:
  1. De-identified tools and data marts
  2. Delivery of customized data sets
  3. EDW-facilitated study recruitment
  4. Centralized, research-specific data collection
  5. Automated research operations reporting
  6. Biobank/genomic data integration
  7. Multi-site data sharing
  8. Translational Analytics

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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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