Key takeaways:
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An ever-increasing influx of clinico-omic data is generating momentum in the flywheel between clinical care and research
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At the same time, operational challenges create friction for health systems looking to keep pace with emerging trends
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A data-enabled enterprise strategy eliminates points of friction and separates leaders from laggards in precision medicine
The research that fuels precision medicine is rooted in population health, requiring access to large-scale cohorts that offer the statistical power necessary to derive insights about specific subgroups of patients. When those insights make their way into clinical care, new data is acquired about these patients’ experiences and outcomes, leading healthcare teams to formulate new questions that prompt the next wave of precision medicine research. This cycle accelerates with scale - more patients means a greater breadth and depth of clinico-omic data, and with that comes a greater potential for advancement in individualized care.
Caption: Data creates momentum in the flywheel between clinical care and research.
At the same time, precision medicine is also becoming a point of operational friction for health systems. Notable contributing factors include:
- Variability in reporting and EHR integration across vendors
… which creates inconsistencies in the record - A rapidly evolving genetic testing landscape
… which leads to differences in clinical practice as individual physicians work to keep pace - A lack of purpose-built IT infrastructure
… which leave health systems ill-equipped to handle genomic data at scale - Disconnects between systems supporting clinical care and research
… which slow the flywheel that drives both clinical insights and new research hypotheses
Caption: Slow-to-change clinical and operational clinical practices contribute to the “negative scale” of the enterprise data asset. As the scale of clinical care delivery increases, so does the quantity and breadth of patient data. However, increasing data scale naturally comes at the expense of quality and usability due to the operational factors described above.
These precision medicine challenges are not isolated to specific areas of clinical care - they affect the entire organization and are embedded within clinical, education, research, operational, and commercial functions. This means that data strategy is inherently enmeshed with enterprise strategy - one cannot succeed without the other.
By intentionally committing to a data-enabled precision medicine strategy, health systems can position themselves for excellence and future-proof their data-dependent initiatives.
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Inconsistently documented records can be transformed into usable, proprietary data sets with research and commercial value
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Differences in clinical practice can be addressed through institutional best practice sharing, ensuring alignment on the latest standards of care
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Insufficient IT infrastructure can be upgraded to a flexible, scalable supporting infrastructure
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Previously disconnected systems can be integrated, accelerating the flywheel between research and clinical care
City of Hope’s DNAnexus-enabled POSEIDON platform is a prime example of the value that can come from a proactive, data-enabled enterprise strategy. POSEIDON provides seamless integration of multi-modal clinical and research data on a common platform, transforming cancer care for City of Hope’s patients and reducing the length of the research-to-clinical care pathway. In our recent case study, we highlight Dr. Aritro Nath’s impactful research related to therapy selection for patients with ER+ breast cancer and how a purpose-built tech stack made it possible.
To learn more about what’s required to enable precision medicine as a strategic initiative, join us for a webinar featuring Jeff Keller, PhD, SVP & GM of Health Systems at DNAnexus.