FHIR vs Proprietary Formats: How to Model Health Screening Payloads

For insurance platforms moving health screening into digital underwriting, the payload model is no longer a back-office detail. It shapes decision speed, partner onboarding, auditability, and how often engineers end up writing one-off translation code. That is why the argument around FHIR vs proprietary health screening payloads keeps coming up in carrier architecture reviews. The real choice is not between "healthcare standard" and "insurance standard." It is between a reusable clinical data model that already knows how to represent observations and a custom format that may fit one workflow well but can become expensive each time the platform adds another carrier, policy admin system, or data consumer.

"The standards are designed to function across any technology interfaces to support data transfer mechanisms including microservices and RESTful APIs." — Bill Pieroni, CEO of ACORD, on the release of ACORD's Next-Generation Digital Standards in 2020

FHIR vs proprietary health screening payloads

A lot of underwriting teams start with proprietary payloads for understandable reasons. A custom JSON object is quick to ship. It can mirror the exact fields the scoring engine expects. It can also avoid the learning curve that comes with clinical standards. Early on, that feels efficient.

Then reality shows up. One partner wants blood pressure split into systolic and diastolic components. Another needs provenance metadata. A policy admin system wants a compact decision summary. Audit wants the original measurement context. Suddenly the neat custom payload turns into a family of near-duplicates.

FHIR gives teams a different starting point. Instead of inventing the structure for every observation, it provides standard resources like Patient and Observation, plus established coding systems and rules for units, timestamps, and interpretation. In practice, that means a heart-rate reading is not just a number. It can carry code, effective time, measurement method, subject reference, device or source metadata, and quality context in a format many downstream teams already recognize.

That does not mean FHIR should replace every proprietary object inside an underwriting stack. The stronger pattern is usually a hybrid one:

• use FHIR-shaped objects for the canonical clinical record
• use proprietary insurance payloads for quote-time orchestration and scoring responses
• map between them in a controlled layer instead of rewriting the data model for every integration

| Modeling approach | Best fit | Main strength | Main weakness | Typical insurance outcome | |---|---|---|---|---| | Pure proprietary payload | Single workflow, one scoring engine, limited partners | Fastest to launch | Hard to reuse, weak interoperability | Quick initial delivery, rising integration debt | | Pure FHIR payload end to end | Clinical exchange-heavy environments | Strong semantic consistency | Too heavy for every quote-time call | Better data governance, slower implementation in some flows | | FHIR canonical + proprietary edge payloads | Digital underwriting platforms with multiple consumers | Balances structure and speed | Requires disciplined mapping layer | Best fit for scaling insurer and partner integrations | | Document-based payloads or PDFs | Legacy handoff environments | Easy for archival workflows | Poor machine readability | High manual review burden |

There is a practical reason this hybrid model keeps winning. Health screening data has clinical meaning, but underwriting systems still have insurance-specific needs such as decision tiers, refer flags, pricing attributes, and partner IDs. Trying to force either side to fully impersonate the other usually creates friction.

Why canonical clinical structure matters more than teams expect

HL7 designed FHIR so healthcare data could move as structured resources rather than as static documents and screenshots of meaning. That matters for underwriting because the same health screening payload often has to do three jobs at once: feed a scoring service, survive audit, and remain understandable when another system reads it months later.

The FHIR vital-signs framework is useful here because it treats an observation as a real object with context. Blood pressure is not stored as one vague field; it can be represented with a panel code and component values for systolic and diastolic pressure. Oxygen saturation likewise has established codes and quantity fields. That level of structure helps when teams need to answer simple but expensive questions later, like which reading was used, when it was taken, and under what measurement type.

Proprietary formats often skip that context during the first build because it looks optional. In underwriting, though, optional context becomes mandatory the minute a case is disputed or a downstream system needs to reuse the data.

A good payload model usually keeps these elements explicit:

• observation type and standardized code
• value and unit
• measurement time
• subject and session identifiers
• source or capture method
• confidence, exception, or quality metadata
• downstream decision status kept separate from the raw observation

That last point matters. Teams get into trouble when the clinical observation and the underwriting decision are collapsed into the same field family. It becomes harder to explain what was measured versus what the rules engine concluded.

Industry applications

Digital underwriting orchestration

For quote-time scoring, proprietary payloads are often still the right surface area. They are smaller, easier to version, and can be tuned to the insurer's decision engine. But the payload should point back to a richer canonical record rather than pretending to be the record itself.

Policy administration and servicing

Policy admin systems rarely need the full clinical object on every transaction. What they usually need is a summary: observation date, normalized result set, status, source reference, and decision outcome. FHIR helps upstream normalization, while a slimmer proprietary schema helps PAS ingestion.

BPO and audit operations

BPO teams feel the downside of proprietary sprawl quickly. If every carrier integration describes the same health screening a little differently, exception queues grow. Standardized observation modeling reduces that translation burden and makes review easier for operations teams that do not want to decipher custom field names on every file.

• FHIR helps normalize meaning across carriers and vendors.
• Proprietary schemas help tailor decisions to a specific underwriting workflow.
• The highest-friction environments are usually the ones with neither a canonical model nor strict mapping rules.

Current research and evidence

The argument for FHIR is not only theoretical. Maryam Y. Garza, Michael Rutherford, Sahiti Myneni, and colleagues evaluated FHIR coverage for multi-site clinical research data exchange and found the standard useful enough to support broad portions of study data collection across very different sites and studies. In related site-level work published in 2022, Garza, Michael W. Rutherford, Bhargav Adagarla, Eric Eisenstein, Karan R. Kumar, Kanecia Zimmerman, Umit Topaloglu, and Meredith Zozus reported that 52% to 67% of total study data elements were available through FHIR across three academic medical centers. That is not full coverage, but it is a meaningful signal: standardized resources can already handle a large share of structured health data without custom redesign every time.

A separate public-health analysis reached an even stronger number. Researchers measuring FHIR coverage for Trauma, Stroke, and NSQIP registry collection found that about 80% of required data elements were available in FHIR on average, including 71% for Trauma, 77% for Stroke, and 92% for NSQIP. For insurance architects, the takeaway is straightforward. FHIR will not eliminate every custom field, but it can dramatically shrink the amount of custom modeling work.

The insurance side has its own parallel standardization story. When ACORD released Version 1.0 of its Next-Generation Digital Standards in April 2020, CEO Bill Pieroni said the goal was streamlined exchange across technologies including microservices and RESTful APIs. That is the insurance architecture context teams should keep in mind. ACORD is useful for insurance transactions. FHIR is useful for clinical observations. They solve adjacent problems, not identical ones.

That is why "FHIR versus proprietary" is often the wrong framing. The more durable question is this: where should standardized clinical semantics stop, and where should insurance-specific orchestration begin?

The future of health screening payload modeling

I do not think most underwriting platforms will settle on pure FHIR or pure custom JSON. The economics point the other way. Platforms need fast decision payloads at the edge, but they also need a canonical structure that survives partner growth, regulator questions, and downstream analytics.

The likely end state looks like this:

• a FHIR-aligned observation layer for normalized health screening data
• an insurance-specific API layer for quote, decision, and policy workflows
• explicit transformation rules between the two
• versioning policies that change edge contracts without corrupting the canonical record

That is a more realistic architecture than asking a policy admin system to behave like an EHR or asking clinical data to squeeze itself into ad hoc underwriting fields forever.

For insurtech CTOs and underwriting platform vendors, the win is not ideological purity. It is lower rework. When a new carrier, BPO, or policy system arrives, the team with a canonical observation model usually maps once and moves on. The team with five proprietary payload families usually opens another exception document.

Frequently Asked Questions

Should insurers use FHIR for every underwriting API?

No. FHIR works best as a canonical health-data layer. Many insurers still benefit from smaller proprietary payloads for quote-time scoring, decision responses, and PAS handoffs.

What is the biggest advantage of FHIR in health screening payloads?

It gives teams a standardized way to represent observations with context such as codes, units, timestamps, and provenance. That makes reuse, audit, and downstream mapping much easier.

Are proprietary payloads always a bad idea?

Not at all. They are often useful at the edge of an underwriting platform where speed and workflow-specific design matter. The problem starts when proprietary payloads become the only source of truth.

How should teams model blood pressure or SpO2 in insurance workflows?

A common pattern is to store the clinical measurement in a FHIR-aligned Observation structure, then map a simplified summary into the insurance workflow payload that drives scoring and policy processing.

For digital underwriting teams that need both structured health observations and insurance-ready API design, Circadify's custom underwriting and scoring environments are built for that middle layer between clinical data capture and carrier workflow execution. Related reading: 5 Integration Patterns for Adding Vitals Data to Policy Admin Systems and Underwriting Platform Latency: How to Keep Risk Scoring Under 500ms.