As the move toward value-based care and alternative payment models continues to gain momentum, additional investments in associated business processes and overhaul of technology infrastructure are key, many experts say.

To find sustainable success in value-based care, healthcare provider organizations need digitized data and analytics at the individual patient journey level, they contend.

Artificial intelligence technologies, coupled with machine learning algorithms in a robust data engineering framework that enables to/from integration between systems with this digitized data, are needed to make this transition a reality, said Lynn Carroll, chief operating officer at HSBlox, a technology and services company that assists provider organizations with value-based care.

Healthcare IT News sat down with Carroll to discuss all of these issues and more, especially as artificial intelligence has exploded in the healthcare sector.

Q. Why do you believe an overhaul of technology infrastructure is imperative to deal with value-based care?

A. As the move toward value-based care and alternative payment models gains momentum, it is increasingly vital that payers and providers have comprehensive, accurate patient data – along with the ability to analyze and share that data – to support these new models of care. This will necessitate more investments into associated business processes and overhaul of infrastructure.

A strong data engineering framework is essential to a patient longitudinal health record. Integration of digitized data with structured and external data sets can provide a holistic view of the patient that enables actionable insights, allowing payers and providers to reduce costs and improve care quality under value-based care contracts.

Such a data engineering framework should be able to process both standard and nonstandard data sets, external data sets like those recommended by the CDC and other major public healthcare organizations, and unstructured data such as images, charts, clinician notes and freeform text. The ability to extract unstructured data is critical because more than 70% of digital health data is in unstructured form.

In addition to providing interoperability for exchanging clinical information, a data engineering framework must support a multi-stakeholder "network of networks" in a way that includes payment capabilities.

Q. What roles do artificial intelligence technologies and a powerful data engineering framework play in your vision for the future of health IT and value-based care?

A. To find sustainable success in value-based care, we need digitized data and analytics at the individual patient journey level. Patient data sets typically are fragmented across different systems, requiring them to be digitized and combined with structured and external data sets to create the 360-degree view of a patient’s health necessary for clinically sound decision-making in value-based arrangements.

Artificial intelligence coupled with machine learning algorithms in a robust data engineering framework enables integration between systems with this digitized data. AI and machine learning can be deployed to better automate tasks and decision-making processes, helping to provide scalability.

More specifically, AI-based technologies, such as natural language processing and computer vision, can drive data digitization by converting unstructured information from notes and images into structured data that can inform care delivery and coordination.

Machine learning algorithms also can help reduce claims denials by improving error detection in billing and coding. And by accelerating the processing of large data sets, AI and machine learning help to inform precise and comprehensive value-based risk forecasting and provide recommended actions to improve patient outcomes.

Q. You've said moving to a fully digitalized offline-to-online patient journey is key. How can this open new opportunities in the delivery of healthcare services while resolving barriers to treatment and improving patient outcomes?

A. Interoperability and access to data are key to the success of value-based care and other alternative payment models.

Digitized patient data, supported by a cloud-based data engineering framework that powers a "network of networks," can provide a 360-degree view of the patient. This allows healthcare organizations to identify and proactively address health equity issues.

Data digitization and interoperability also facilitate care coordination and the delivery of care services in nontraditional locations such as retail environments and the home. And when healthcare organizations leverage data at scale that is digitized and can be queried, they can practice evidence-based medicine, reduce or eliminate inequities, optimize care pathways, streamline workflows for different entities, and proactively identify and address gaps in the care of patients.

On the administrative side, a data engineering framework should support reimbursements and other processes required for the many-to-many stakeholder relationships between providers, payers and community-based organizations in a value-based care model.

Community-based organizations in particular may need help with onboarding and integration of their systems and data with the network of networks. It’s not a stretch to say that without this type of value-based administration, value-based care can’t succeed.

Q. When will all of this happen? You mentioned that we will start seeing an increased focus on build/buy investments toward technology infrastructure as well as the human capital to make value-based care programs a success.

A. It’s happening now. Healthcare organizations are slowly but definitively migrating toward alternative payment models and care delivery that require a cloud-based data engineering framework on which a network of networks can run.

Barriers certainly remain: Healthcare organizations historically are slow to adopt technology, while data standards that meet health data privacy and security standards are evolving. However, the pandemic was a warning to healthcare organizations that the traditional fee-for-service reimbursement model is both risky and not built to optimize patient outcomes or efficient resource allocation.

So, as we head into 2024, I expect a greater commitment to value-based reimbursement models and technologies that support nontraditional care scenarios as healthcare organizations seek to improve patient outcomes, reduce health inequities and control costs.

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