By Rahul Sharma

Healthcare organizations continue to accelerate their adoption of value-based care (VBC) programs, replacing the traditional volume-based fee-for-service system with value-based ones that reward better health outcomes and lower costs of care. McKinsey estimated in a recent report that “the number of patients treated by physicians within the value-based care landscape could roughly double in the next five years.”

While the benefits of VBC payment models are clear and compelling, these models in and of themselves can’t automatically transform the healthcare industry, or even an organization. They must be accompanied and enabled by several digital capabilities.
Regardless of which reimbursement model is implemented, improving quality, lowering costs, reducing inequities, and increasing healthcare coverage and access depend on:

• Deriving insights from patient data and external data sets to inform clinical decisions
• Realizing data interoperability to allow permissioned data-sharing
• Building longitudinal healthcare records (LHR) that patients can supplement using their personal/wearable data to enhance clinical insights
• Creating easy-to-use data-driven workflows for different entities in the healthcare ecosystem
• Adhering to interoperability standards for data-sharing, and
• Creating a digital infrastructure that has flexible data engineering, data processing, permissioned data sharing and enables data queries for large data sets

Daunting data challenges
Unfortunately, myriad unresolved issues have made it difficult for healthcare organizations to fully utilize data. These include:

• Unstructured data. At least 80% of data in healthcare is in unstructured form, according to industry estimates. Unstructured health data includes images, audio, video, notes, charts, faxes, freeform text, and CLOBs (Character Large Objects, or large blocks of encoded text stored in a database). Since unstructured data rarely is digitized and combined with other forms of data sets, it offers health systems and patients little or no value – despite the wealth of useful and relevant information contained within
• Lack of adherence to data standards. The inconsistency with which many healthcare organizations follow existing data standards (HL7, x12 EDI, FHIR, NCPDP, CCLF, CCDA, etc.) typically requires a lot of custom integration work. Further, data standards are non-existent for many patient data sets, including data from devices and wearables, social determinants of health (SDoH) data, and external publicly available data sets

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