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Readers Write: Value-Based Care Arrangements: Four Ways Specialty Care Providers Can Prepare for Claims Data
Value-Based Care Arrangements: Four Ways Specialty Care Providers Can Prepare for Claims Data
By Tyler Johnson
Tyler Johnson is VP of strategic partnerships at Ursa Health of Nashville, TN.
Companies that are bringing new specialty care models to market face a big early hurdle when partnering with plans, full-risk provider groups, or self-insured employers: working with claims data. Although lacking some clinical context, clean, well-organized claims data is vital for creating longitudinal patient views and the main fuel for analytics (which of course become even more powerful when supplemented with clinical and other data sources). Trusted analytics, in turn, are the first step toward optimal operations and outcomes, as well as the financial reconciliation between partners that value-based contracts require.
With everything else involved in launching or expanding a new business, specialty care providers (SCPs) may be tempted to put data and analytics planning on the back burner. Those that delay too long, however, could find themselves scrambling to get new partnerships off the ground or to keep up with an ever-changing landscape. In the best-case scenario, late-night heroics save the day but inordinately stress the team. In the worst, lack of planning leads to lost sales, crumbling partnerships, and dwindling rather than growing healthcare impact.
SCPs must ready themselves to consume claims data from their partners in four key ways.
The first concerns the security review. Organizations are very particular about how and where their data gets shared. Convincing business or clinical leadership to try a novel intervention is tough, but convincing security and InfoSec folks that others can be trusted with their most prized possession is another obstacle altogether.
Before any data is shared, an organization will ask its potential SCP partner to submit to a comprehensive vetting process to ensure the SCP’s technical and administrative safeguards are strong enough to meet both internal and HIPAA requirements. To prepare for the review process, the SCP should:
- Create very tight and easy-to-understand documentation around its technical architecture, including where data is going to live and what people, tools, and processes are going to touch it.
- Create an overview document that summarizes its security posture.
- Organize employee business and security procedures for easy reference.
- Devise a system for retaining answers to assessment questions to expedite the next review.
- Consider being HITRUST and SOC2 certified, which can quickly ease the security team’s concerns. Because the level of effort isn’t trivial, working with a technology vendor that is already certified can help organizations that do not have the internal resources to pursue certification themselves.
Second, an SCP needs to prepare is its tech stack. The contracting and security assessment process can feel a lot like hurry up and wait, but the reality is that this is a task in a very long queue, and once the organization assigns resources to complete that task, it will expect a new partner to be ready to roll.
If the SCP can tap into and pull from the organization’s existing infrastructure for hosting data, great. If not, it needs its own secure cloud storage mechanism (e.g., Amazon S3, Azure blob storage) into which data can be dropped, as well as a pre-defined process for granting access to it. In addition to transmission mechanisms, a database/warehouse and any data modeling and transformation tools must be up and ready to use.
The potential partner is also going to expect the SCP to quickly provide feedback and ask questions about the data. If the environment is ready to go in advance, the SCP can spend more time on loading and investigation instead of provisioning cloud resources. It is also extremely helpful to get answers to data questions while the company’s technical resources are still engaged and informed.
As a final note, SCPs should think in advance about how they will assess the quality of the incoming data, both in a general sense (e.g., data completeness) and regarding specific data points or lineage that is important to their analyses.
The third way to prepare is to ensure a scalable approach exists for organizing and analyzing data. Without a proactive approach to a data model, an SCP can very easily stack up technical debt — in the form of silos of logic and code that are custom to analyzing data from a single source — that becomes a nightmare to untangle down the road and will prohibit efficient scaling of its business.
Once it has defined the data model, the SCP should apply transformation logic to all incoming raw data sources to map the data to that standardized structure. Rules and algorithms to interpret data for specific use case(s) should only be authored on top of that standardized data model, an approach called hierarchical data modeling. This approach facilitates scalability while making it easier to marry up claims data with other sources of information: for example, clinical data from the EHR, patient engagement data, and internal product data.
The final way to prepare is to identify how the claims data will be used to provide insight into their operations and business. This planning should occur before any value-based contract is signed, let alone claims data is shared, to help determine whether other sources of data will be needed — for example, other patient data or industry-available supplemental data such as value sets and provider rosters. Armed with a clear understanding of what insights need to be derived, SCPs can more intelligently articulate their needs and the anticipated value to partnering organizations.
Effective partnership in the co-management of patient populations demands a strong data foundation paired with trusted, useful analytics. Bad data in results in bad data out. SCPs embarking on new value-based partnerships can increase their chances of success and make life easier for both parties with some basic preparation. With a solid and scalable data foundation in place, technical resources can shift their attention away from non-strategic data wrangling work and focus on building the special sauce that differentiates it from competitors and adds the most value to its customers.