Upgrading Analytics and Interop for VBC with Anna Taylor of MultiCare Connected Care

This is part of our weekly executive insights series where Elion CEO Bobby Guelich speaks with healthcare leaders about their tech priorities and learnings. For more, become a member and sign up for our email here.

Role: AVP, Population Health & Value-Based Care

Organization: MultiCare Connected Care & MultiCare Health System

Can you tell me a bit about MultiCare Connected Care and your role there?

MultiCare Connected Care is an accountable care organization (ACO) wholly owned by MultiCare Health System. We have a network of about 5,200 providers and manage around 380,000 lives across various risk models. In my role I oversee the strategic plan and digital solutions necessary for success in both fee-for-service and value-based care.

I’m interested in the digital solutions you’re focusing on, especially those that work in both fee-for-service and value-based environments. What are some key technology areas you’re prioritizing?

To succeed in both revenue models, the key is ensuring that data intelligence is available at decision points, whether at the point of care or in patients’ hands. It’s all about getting more mature data so a better decision can be made in the moment. Our main focus has been on interoperability, investing heavily in the infrastructure needed to move data efficiently across different applications.

Can you provide examples of specific technology solutions you’ve implemented around interoperability?

In healthcare, we’re still catching up to other industries when it comes to exchanging data through APIs. We’ve been focusing on building infrastructure to manage API front doors, developer portals, manage our security around key issuing, and to generally manage APIs across our entities, whether that be internal or external.

Specifically, we’re using Azure API Management and FHIR services. FHIR allows for a standardized way to exchange health data, enabling different applications to speak the same language. Beyond that, we’re also investing in tools like Azure Purview for data governance, which helps us track and manage where data is moving. As the healthcare landscape evolves, we’re preparing for regulations that will require more accountability in how long data is kept and how it’s used.

Outside of interoperability, are there any other impactful technology solutions you’re using for population health and value-based care?

We use Innovaccer for care management and analytics. It helps us with risk stratification, ensuring we focus on the patients who need the most care. This has been instrumental in managing the total cost of care and improving population health outcomes by identifying high-risk patients and making sure they receive appropriate interventions.

What are your thoughts on AI’s role in value-based care? Are there specific areas where you see AI being especially useful?

We’ve used AI for years, but generative AI’s real impact lies in its ability to process vast amounts of data and deliver insights instantly. In value-based care, this means providing comprehensive insights at the point of care, beyond what a human can compute. This can significantly enhance decision-making and outcomes not only for value-based care, but with healthcare in general.

Additionally, AI can reduce burdens on providers and administrative staff by automating tasks like prior authorization and risk coding, allowing us to do more with less.

Are there any overhyped solutions or lessons you’ve learned that you think others in similar roles should know?

Mastering data management and being able to set up your infrastructure to plug and play with these billions of solutions in the marketplace is going to be key to success in the future, no matter which model you’re in. I would say, focus on your infrastructure first, make the investments there, and in parallel, you can still be doing the work of population health.

As for things that are overhyped, we need to be cautious about how we use AI. There’s a risk of over-relying on AI without understanding its limitations. We need to improve digital literacy to ensure that clinicians and decision-makers understand AI as just one sophisticated tool among many. I think we need more research into what needs to be in educational programs to understand how to use these tools in decision-making.

Anything else we didn’t cover that would be valuable to note?

I think I would be remiss if I didn’t say something about standardizing data exchange. I think there’s a lot of hope in the FHIR HL7 standards and the implementation guides that are out there. I would ask that people get curious there and ensure that their products can leverage those standards so that it makes healthcare easier for all of us. You’re going to be more valuable to the ecosystem if you can get us actually caring for patients, instead of taking nine months to build an integration that then breaks all the time.