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Data hubs: The next step for plans that want the most value from their data

Get more from your member data

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Data hubs: The next step for plans that want the most value from their data

This post is sponsored by NTT DATA.

Karen Way

Health plans often struggle to make the best use of their vast stores of data. Past solutions have included data lakes but it is now possible to go further with data hubs. Data hubs present a clearer picture to guide decisions and enable personalized, predictive care for members. SmartBrief spoke with Karen Way of NTT DATA Services, to learn how plans can get the most value from their data.

What is the best solution for health plans trying to manage both structured and unstructured data? 

There are many technical solutions to this dilemma. Big data lakes are an example, but they don’t solve all of the challenges. It’s not just a matter of loading all the data into a single repository — that’s really the easiest part. The data has to be curated to ensure added value to the end user; it’s not just dumping all the puzzle pieces out of the box into a pile, it’s putting those pieces together into a comprehensive and complete picture to be able to drive decision making. 

Many health plans are now looking at solutions that use the concept of a data hub — the ability to collect data from multiple sources and curate it for distribution and sharing, often in multiple formats or in subsets. It’s the curation piece that makes data hubs different from big data lakes as it is during those processes that value can be added through matching, merging, data quality validation, etc. Data hubs are becoming the “go-to” for enterprise-level data to facilitate connected care.

How can health plans use predictive and/or prescriptive models to improve care? 

Predictive and/or prescriptive models allow a health plan to anticipate what events may occur with their members, such as ER visits, and determine the likelihood of that event happening to particular members. Using this information, they can make decisions on which members should participate in care management programs, which members are at higher risk for an adverse event and what potential care pathways might be most effective for these high-risk members. These predictive models can also be used to help medium- or lower-risk members remain in those risk stratifications by assisting them in appropriate next steps.

How does having a more complete view of members support value-based care? 

Value-based care is all about improving the health outcomes for members/patients; the right care at the right time for the right cost. Having the more complete view allows greater visibility for both health plans and providers to make the decisions/recommendations that drive the right care for a member at that point in time, which should lead to more manageable healthcare costs in the long run. If Provider A isn’t aware that their patient is participating in a care management program with their health plan, and also isn’t aware of a medication that Provider B has prescribed for the same patient, how can we even begin to hope that we can improve outcomes with so many gaps in knowledge? 

How are health plans using social determinants of health data to address member needs?  

Use of SDOH indicators adds even more clarity and focus to the picture of the member that is created from all the different sources of data. It helps the health plan to recognize and attempt to remove the barriers (such as lack of transportation, or access to fresh produce) that are likely impacting member outcomes. For example, is a member likely to adhere to their medication regime if it’s difficult for them to get to a pharmacy to pick it up, or they can’t afford the medication initially prescribed? Knowledge of these factors can assist health plans (and providers) in coordinating the best possible care pathways for their members.

Karen Way is the Global Practice Lead, Data & Intelligence for NTT DATA Services. NTT DATA offers expertise and technology solutions to help health plans succeed in an environment where payment models, technology and regulations change rapidly. It helps plans integrate data sources and harmonize the data, with the approach of querying multiple data sources only once. Learn more at