Transforming Payer Infrastructure to Accelerate Value-based Care

There’s good news and bad news when it comes to the evolution of value-based care in the U.S. Let’s start with the good news. A 2021 study from the Health Care Payment Learning and Action Network (HCPLAN) shows a continued move from fee-for-service models to value-based care (VBC). According to the study, 39.3% of healthcare dollars spent are tied to traditional fee-for-service payments, 19.8% are tied to pay-for-performance, and 40.9% are part of some type of value-based contract (e.g., shared savings, shared risk, bundled payment, population-based payments, etc.).¹

Now for the bad news. While the trend toward VBC is promising, research also shows it can take a long time for healthcare payers to effectively implement these new payment models. According to a 2018 study, only 21% of payers say they can roll out a new episode of care in three to six months, more than 33% say they need a year, 21% need 18 months, and 13% require two years or more.²

Rahul Sharma

This inconsistent implementation pace can impact the overall adoption and success of VBC programs nationwide. Addressing this issue requires resolving several universal challenges that currently hinder the ability for many healthcare payers to quickly implement new VBC payment models.

A common implementation barrier is legacy claims and workflow technology investments that don’t support the management of the complex multi-stakeholder care networks involved in VBC or the episodic requirements of payment models that aren’t claim centric. Another barrier is the timeliness of data reporting and the inability to understand contract performance more prospectively as opposed to after-the-fact.

While technology and data exchange challenges impact both payers and providers, payers will likely need to take the lead in this area to make progress. This is primarily because payers are often responsible for not only educating providers on VBC models, but also bridging the gap between fee-for-service and VBC. Serving as a catalyst for this change and positioning the industry for VBC nimbleness requires payers to rethink the existing infrastructure paradigm. 

Complex hierarchy support a must for VBC

Successful administration of value-based programs requires an infrastructure that enables the many-to-many relationships between VBC stakeholders and their counterparts. This includes health insurance carriers as well as risk-bearing entities such as accountable care urganizations (ACOs), clinically integrated networks, carve-out programs for chronic disease management, primary care, care management programming, social service networks, and community-based organizations (CBOs). The administration of funding pools, including downstream distribution of funds and data exchange to participating partners, is one of the most critical functions of successful value-based execution.

Lynn Carroll

For both payers and providers, traditional approaches and legacy systems do not support the hierarchical relationship structures necessary for onboarding stakeholders in value-based contracts. Likewise, orchestration of cascading payment models, where payer/provider collaborations incorporate risk-bearing entities and downstream participating providers, are not easily scaled, reducing the ability to accelerate adoption of varied alternative payment models. This hierarchical approach to partner onboarding, scaling of contract operationalization, and permissioned data sharing is a necessity for alignment of medical, social, behavioral, and environmental components of successful value-based program administration and high-performance networks that confidently deliver on whole-health patient outcomes.

Enabling LHR data sharing for VBC

An infrastructure for VBC not only requires hierarchy support, but also the ready exchange of a more comprehensive set of health data. Traditional electronic health record (EHR) systems were built to solve the challenges of transactional data related to claims submission, billing, and processing of adjudication, clinical data, and pharmacy data. VBC requires more insight into patient health and outcomes. It requires the creation of a longitudinal health record (LHR) that not only manages and shares traditional structured EHR data (e.g., data that fits neatly into fixed fields), but also enables the proper digitization and exchange of clinically rich data that resides in unstructured form as charts, notes, images, audio and video files, as well as freeform text of character large objects (CLOBs).

Advanced technologies such as artificial intelligence (AI) and machine learning (ML) make this possible, providing a unified view of the patient that allows for a move from transactional processing to an outcome-driven, value-based model focused on patient-centered care. However, a data infrastructure truly enabled for VBC also needs to be based on ontology mapping rather than traditional relational or nosql databases.

An ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse. For example, changing a property from single-valued to multi-valued in a relational database requires the entire column for that property to be deleted and an entirely new table to be created that holds all the new property values. By contrast, all data modeling statements in ontological languages are incremental. Enhancing or modifying a data model after the fact can be accomplished simply by modifying the concept, making the synthetization of patient health data across entities much easier.

When it comes to ontologies for the patient data sets, there are 18 different sets that these would fall into:

• Immunity tests
• Disease registry
• Demographics data
• Employment
• Claims & remittance data
• Pharmacy data
• Eligibility & benefits data
• Clinical data (e.g., notes, charts, X-Ray, MRI, radiographs etc.)
• Financial Data (from banks for loans, AR reports, patient responsibility from claims)
• Internet of Things (IOT)/sensors data
• DNA/genome data
• Schedule/referral data
• Social Determinants of Health (SDOH) Data
• Surveys
• Consent management processes
• Notifications and alerts
• Clinical trials data
• External summary data sources for analysis (WHO, CDC, Johns Hopkins etc.)

All this data needs to have an underlying Enterprise Master Patient Index (EMPI) to tie the data sets to the right patient together and tie it to the unique identity enumeration done for that patient.

Microservices aid permission-based sharing

The underlying data infrastructure outlined in the previous sections can then be exposed via a secure and scalable Data as a Service (DaaS) layer on which different applications and integrations are built, creating a network of networks for permissioned-based data sharing that enables a move to VBC. This DaaS layer is important because it allows an adaptable VBC infrastructure to be realized without a rip-and-replace strategy. Data layers can instead be integrated and extended seamlessly, allowing stakeholders to use existing applications served up via microservices and extend or create microservices and business applications for their own needs.

The trend toward VBC is driven by a desire to improve patient outcomes while lowering healthcare costs. Payers play a central role in establishing the hierarchical infrastructure necessary to accelerate and scale the implementation of new VBC models throughout the continuum of care.

1. APM Measurement: Progress of Alternate Payment Models – HCPLAN – 2020-2021
2. Payers moving to value-based care faster than expected – Healthcare Dive – Len Masterson – June 18, 2018

Rahul Sharma is CEO of HSBlox, a healthcare technology in suburban Atlanta that supports value-based technology. Lynn Carroll is the chief operations officer of the company.