How Digital Health Data Integrates With Rx and MIB Checks

Life insurance underwriting used to run on a pretty simple data diet: an application, maybe a blood draw, an APS request that took three weeks to come back, and an MIB check. That was the stack. It worked, but it was slow, expensive, and left carriers guessing on a lot of applicants who fell into gray zones between "clearly healthy" and "clearly not."

That picture has changed fast. Carriers now pull from prescription history databases, electronic health records, MIB's expanded data platform, and — increasingly — real-time biometric signals from remote screening tools. The question is no longer whether to use digital health data alongside traditional Rx and MIB checks. The question is how these data sources talk to each other, where they overlap, and where one fills gaps the others miss.

A Munich RE study reexamining 525 life insurance applications found that underwriting decisions made on the basis of EHR data were rarely changed when reappraised with the benefit of additional APS data — suggesting that digital health records capture most of what matters for risk classification without the weeks-long wait for attending physician statements.

What MIB and Rx databases actually tell you (and what they miss)

MIB's checking code system has been a cornerstone of underwriting for decades. When an applicant applies for individual life insurance, MIB flags prior applications where medical impairments, hazardous activities, or other risk factors were reported by a previous carrier. It is a fraud detection and consistency check rolled into one. If someone told Carrier A they had no history of diabetes but Carrier B flagged insulin use two years ago, MIB catches that.

Prescription databases — Milliman IntelliScript being the most widely used — work differently. They pull pharmacy fill records going back years. An Rx history can reveal conditions an applicant never disclosed: antidepressants suggest mental health treatment, ACE inhibitors point to hypertension, and metformin signals diabetes management. RGA's research team has noted that prescription data is instant, available for almost everyone, and serves as what they call the "minimum data backbone" for accelerated programs.

But both sources have blind spots. MIB only captures information from prior insurance applications — a first-time applicant has no MIB record. Prescription data misses conditions treated through non-pharmaceutical approaches, specialist visits without prescriptions, or conditions managed outside the pharmacy system entirely. Neither source gives you real-time physiological data.

That is where the integration question gets interesting.

How the data layers stack up

Data Source	What It Captures	Speed	Coverage Gap	Best For
MIB Checking Codes	Prior insurance application disclosures, impairments flagged by other carriers	Real-time query	First-time applicants have no record; only captures what was previously underwritten	Fraud detection, application consistency
Rx History (IntelliScript)	Pharmacy fill records, medication types, dosages, fill dates	Minutes	Misses non-pharmaceutical treatments, OTC medications, conditions without prescriptions	Chronic condition identification, undisclosed condition detection
Electronic Health Records	Diagnoses, lab results, procedures, clinical notes	Minutes to hours	Requires patient consent and provider connectivity; availability varies by region	Comprehensive medical picture, lab values
Attending Physician Statement	Full medical chart review by treating physician	2-6 weeks	Slow, expensive, and sometimes incomplete depending on provider responsiveness	Complex cases, high face amounts
Remote Biometric Screening (rPPG)	Heart rate, respiratory rate, blood pressure indicators, stress biomarkers	30 seconds	Newer technology, still building actuarial history	Real-time physiological snapshot, engagement screening

The integration problem nobody talks about enough

Here is what makes digital health data integration harder than it sounds: these data sources were not designed to work together. MIB operates on a proprietary code system that maps impairments to numeric codes. Rx databases deliver structured pharmacy records. EHR data arrives in varying formats depending on the provider network. And biometric screening tools produce real-time physiological measurements that have no direct analog in the traditional underwriting manual.

MIB has been working to solve part of this problem. Their Medical Data Solutions platform, which expanded in 2025, now serves as a single integration point where carriers can access multiple data sources through one connection. As MIB described it, the goal is to eliminate the need for carriers to build and maintain separate integrations with every data vendor in the market. Ryan Coleman from Equifax noted when they partnered with MIB that extending verification capabilities to the life insurance sector helps "carriers streamline underwriting and improve decisioning."

RGA has taken a different approach, partnering with MIB to combine their Digital Health Data scoring capability with MIB's data network. The idea is that raw data alone is not enough — you need a scoring engine that can interpret EHR records and translate clinical information into underwriting risk classes. RGA's research team published findings showing that when carriers stack data sources (claims data plus EHR records, for example), the quality of automated underwriting decisions improves without a proportional increase in data cost.

Milliman IntelliScript's Irix platform represents yet another approach. Their claims-driven EHR data acquisition model uses prescription histories and medical claims data as a trigger to pull the right EHR records. The logic is straightforward: if someone's Rx history flags a condition worth investigating, pull the relevant clinical records to confirm or clarify. This targeted approach avoids the cost of pulling full EHR records on every applicant.

Where contactless biometrics fit in the stack

The data sources above — MIB, Rx, EHR — are all retrospective. They tell you what happened in someone's medical past. What they do not give you is a current physiological reading at the point of application.

This is where camera-based vital sign measurement through rPPG technology enters the picture. An applicant completes a 30-second smartphone scan, and the system captures heart rate, respiratory rate, blood pressure indicators, and stress-related biomarkers. No equipment, no appointment, no waiting period.

For carriers building accelerated underwriting programs, the appeal is that rPPG data fills a different layer than Rx or MIB. Prescription history tells you what medications someone takes. MIB tells you what a prior carrier found. EHR records tell you what a doctor documented. Biometric screening tells you what is happening right now in the applicant's body.

The integration model that seems to be emerging across the industry looks something like this:

Tier 1: Instant data (seconds)

MIB check, Rx history pull, and remote biometric scan all happen simultaneously at point of application. This gives the underwriting engine a baseline risk picture within minutes.

Tier 2: Triggered data (minutes to hours)

If Tier 1 flags anything that needs clarification — an unusual medication pattern, an MIB code that needs context, biometric readings outside normal ranges — the system pulls targeted EHR records or requests additional information.

Tier 3: Manual review (days to weeks)

For cases that cannot be resolved through automated data stacking, a human underwriter reviews the full data package and may request an APS or additional medical documentation.

Gen Re's 2025 survey found that 59% of individual life applications now qualify for an accelerated path, meaning most applicants never reach Tier 3. The economics of digital health data integration are driven by keeping as many applications as possible in Tier 1 and Tier 2.

What actuarial teams should watch

Actuaries evaluating digital health data integration face a few specific questions that do not have clean answers yet.

First, how do you build mortality tables that account for data sources that did not exist ten years ago? Prescription data has been around long enough to show actuarial lift. EHR data is newer but building credibility through studies like Munich RE's 525-application reexamination. Biometric screening through rPPG is the newest layer, and the actuarial history is still being established.

Second, data redundancy is a real cost issue. If EHR records and Rx data largely confirm the same conditions (which they often do), at what point does adding another data source produce diminishing returns? The answer depends on the product line and face amount, but it is a question more actuarial teams are starting to model.

Third, regulatory compliance is not uniform. Biometric data privacy laws vary by state, and the NAIC's Accelerated Underwriting Working Group has published guidance that specifically addresses algorithmic transparency and the impact of automated underwriting on protected classes. Carriers that build multi-source data integration pipelines need compliance frameworks that can handle the different regulatory treatment each data type receives.

Current research and evidence

The evidence base for digital health data integration is growing, though it is still concentrated among a handful of large reinsurers and data vendors.

Munich RE's retrospective study of 525 life insurance applications found that EHR-based underwriting decisions held up well against traditional APS-based decisions, with few classification changes when APS data was added on top. The study methodology used a data-stacking approach, first underwriting with claims data alone, then adding EHR records, and finally incorporating APS data to measure incremental lift.

RGA's published research on digital health data scoring shows that combining their scoring engine with MIB's data network produces underwriting recommendations that carriers can act on without manual review for a significant percentage of applications. Their partnership with MIB, announced in 2025, is specifically designed to make this combined capability available as a single integration.

Milliman IntelliScript's team published a white paper on claims-driven EHR data acquisition in April 2025, arguing that prescription and claims data should serve as the trigger layer for pulling more expensive EHR records. Their Irix platform implements this approach, and their case studies suggest it reduces per-application data costs while maintaining underwriting accuracy.

The InsurTech Express analysis of AI and data in life insurance underwriting noted that medical data — Rx, EHR, and MIB — remains the foundation, but AI scoring models are increasingly what makes the raw data actionable for real-time underwriting decisions.

The future of digital health data integration in underwriting

The trajectory is clear: carriers are moving toward unified data platforms that combine retrospective medical records with real-time biometric signals. The carriers that figure out the integration layer — how to ingest MIB codes, Rx histories, EHR records, and biometric data into a single risk scoring engine — will process applications faster, at lower cost, and with better risk selection.

MIB's platform expansion signals that the industry's shared data infrastructure is adapting. RGA's scoring partnerships show that the analytics layer is maturing. And the emergence of contactless biometric screening through companies like Circadify adds a real-time physiological dimension that traditional data sources cannot provide.

The underwriting workflow of 2027 probably looks less like a sequential checklist and more like a data mesh, where multiple sources fire simultaneously, feed into a scoring engine, and produce a recommendation in minutes rather than weeks. The building blocks are already in place. The integration work is what separates carriers that talk about accelerated underwriting from the ones actually doing it.

Frequently Asked Questions

What is MIB and how does it work in life insurance underwriting?

MIB (formerly the Medical Information Bureau) is a member-owned organization that maintains a database of coded medical impairments reported during prior life insurance applications. When someone applies for individual life insurance, the carrier queries MIB to check whether previous applications flagged any risk factors. It primarily serves as a fraud prevention and consistency tool, catching discrepancies between what applicants disclose across multiple applications.

How do prescription databases differ from EHR data in underwriting?

Prescription databases like Milliman IntelliScript pull pharmacy fill records — what medications someone has been prescribed, dosages, and fill dates. EHR data includes a broader clinical picture: diagnoses, lab results, procedures, and sometimes clinical notes. Rx data is faster and more universally available, while EHR data provides deeper clinical context but requires more complex data acquisition and patient consent workflows.

Can digital health data fully replace the attending physician statement?

Not entirely, though the gap is closing. Munich RE's research found that EHR-based underwriting decisions rarely changed when APS data was added, suggesting that for many applications, digital data captures enough of the clinical picture. However, for complex cases with unusual medical histories or very high face amounts, APS records still provide detail that aggregated digital data sources may miss. Most carriers use digital data to reduce APS requests rather than eliminate them completely.

How does contactless biometric screening complement Rx and MIB data?

Rx and MIB data are retrospective — they tell you about an applicant's medical past. Contactless biometric screening through rPPG technology captures current physiological measurements (heart rate, respiratory rate, blood pressure indicators) at the point of application. This gives underwriters a real-time health snapshot that complements the historical picture from Rx histories and MIB checks, helping identify current health status that may not yet appear in medical records or pharmacy databases.