By Roberta Katz, Director, Healthcare-Life Sciences, EMC
There is no doubt healthcare providers are working hard to improve clinical outcomes. With healthcare reform progressing, providers are being asked to accelerate their goals and timetables to programmatically manage current and future high risk populations as their organizations make the transition to value-based payment structures. “All hands are on deck” is the cry as IT teams work to integrate EHRs, medical imaging, labs, and pathology at the same time they are building better predictive analytic models to inform clinicians at the point-of-care.
As organizations look at their IT strategy as a key component in achieving value-based care, the use of advanced analytics comes to the forefront. But how can IT build out their supporting data analytics infrastructure that joins both historical and real-time data in a meaningful way to address growing business and clinical demands for new data processing and application capabilities?
Many providers will need to quickly adopt new, enhanced methodologies to shorten their time to analytics value to deliver on improvements in efficiencies, patient engagement, and clinical outcomes. This includes predictive analytics that leverage more complete data sets to help identify which patients or cohorts are at highest risk for re-admittance due to acute myocardial infarction, heart failure, and pneumonia.
Once this risk segmentation has been conducted, richer analytics can then be applied to determine specific patient outreach programs for targeted treatment plans and to better manage behavior changes based on disease progression. For these tactics to be successful, the large volumes of unstructured and semi-structured data that are currently siloed in EHRs, PACS, and lab systems will also need to be integrated to guide informed data-driven decisions.
But with healthcare data growing 48 percent a year through 2020, providers need to find more efficient approaches to storing, managing, sharing, and analyzing all of the data being collected across the care continuum. Enter the data lake — a technology infrastructure that incorporates information generated or “tributaries” from across the health system, including data imported from outside sources and services. This enterprise-wide approach helps reveal actionable insights about an organization’s performance indicators and impacts of patient care interventions to better manage risks and deliver affordable, higher quality care.
Embracing Futurecare — Data Lakes And Advanced Analytics
A data lake offers healthcare organizations a powerful data architecture with one, unified location for all healthcare data required for mining and analysis by clinical departments, business analysts, and data science teams. The end goal — reduce time to insights — is attained through the ability to analyze multiple variables to quickly identify trends, patterns, and correlations.
Traditional environments often provided healthcare organizations with a “rear-view mirror” style of reporting — dramatically reducing the ability to ask future oriented questions. Complementing existing business intelligence and data warehouse investments, a data lake enables healthcare providers to execute analytics across disparate systems — running databases, data warehouses, and structured or unstructured data sets without impacting day-to-day operations or access to data.
The gain for healthcare organizations? Advancing accountable care initiatives through more robust exploration and discovery, thus identifying better predictors to truly impact patient care quality.
Data Lakes In Action: Use Cases Examples
As providers begin their journey to value-based care utilizing a data lake, starting with a priority use case allows your organization to demonstrate early success to build a longer-term Big Data analytics strategy. EMC has worked with many healthcare organizations to define their goals, create a vision for Big Data architecture, and implement a data lake solution across patient care delivery, clinical research, population health management, and security analytics use cases. Providers have quickly seen preliminary benefits from data lakes, such as better, measurable clinical outcomes, reduced costs, more accurate clinical research, and improved data security. Four use cases in particular demonstrate the immediate value healthcare organizations can derive from their data lake infrastructure:
Moving into the future, providers will use predictive care analytics leveraging a data lake platform to make the transition to value-based care and differentiate their organization in a competitive marketplace. With IT departments able to deploy analytics at scale, healthcare organizations can consolidate legacy systems, provide better patient outcomes, see fewer complications, and improve overall population health and wellness — all at lower costs.