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Managing the health of at-risk populations begins with data intelligence

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Lisa Hines
Lisa Hines

U.S. healthcare spending increased rapidly in response to the COVID-19 pandemic. The Centers for Medicare & Medicaid Services (CMS) projects that national healthcare spending will reach a whopping $6.2 trillion by 2028.   Increased spending trends are consistent across private and public payers as well as out-of-pocket spending. Population health initiatives are proven to reduce hospital admissions and cut the use of emergency departments in high-risk patient groups, lowering costs while still improving outcomes. Advanced analytics, AI, and digital health are accelerating population health management as a solution to closing the spending-quality gap and aligning with the ongoing shift from volume to value-based care reimbursement.

Combine diverse data sets with data lakes

The healthcare ecosystem is harnessing both structured and unstructured data to make real-time, informed, data-driven decisions at the point of care and beyond. But to take population health management analytics to the next level, diverse datasets are required, and it’s becoming apparent that legacy clinical data warehouses aren’t robust enough to accommodate this need. One key to solving complex population health problems is the ability to combine socioeconomic data, physical environment data, clinical data, and health behavior data into a data lake. Data lakes let you combine these structured and unstructured data elements at any scale. They’re ideal not only for supporting analytics but also for accelerating research, AI, and machine learning initiatives.

To be successful, data lakes require architecture and set of data services that provide consistent capabilities across a choice of endpoints. This architecture must allow data ingestion at scale while protecting and managing the data lifecycle. It must also provide the analytical muscle to identify at-risk populations, stratify risk, target interventions, and coordinate care management must.

Define your data fabric

A simple way to look at the infrastructure is to define a data fabric, an architecture and set of services that manage and store data consistently across the entire data estate. NetApp® ONTAP® data management software does just that, allowing you to seamlessly manage data on premises or in the cloud. A data fabric powered by NetApp optimizes the storage of files, blocks, and objects.

Tools like the NetApp StorageGRID® object-based storage solution and NetApp Cloud Volumes ONTAP help keep data is where it needs to be, when it needs to be there. NetApp delivers industry-leading data protection with Cloud Secure, data management with NetApp Cloud Insights, data governance and compliance with NetApp Cloud Data Sense, and capabilities for data science production work with the NetApp DataOps Toolkit.

Learn about NetApp solutions for healthcare and life sciences organizations.

Lisa Hines

Lisa Hines is a healthcare CIO on NetApp’s Global Healthcare and Life Sciences Team. Her 25 years of real-world experience in the industry allows her to deliver strategic insights and strategic planning for customers and partners.

Throughout her career, she has led numerous early adopter projects in the healthcare provider space and has participated in statewide collaborative efforts to improve access to high-quality health care, while effectively managing the cost of providing care.

She is an active HIMSS member and has held numerous board positions in her local chapter. In her spare time, Lisa enjoys lake life, kayaking, and standup paddleboarding.

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