Electronic Health Record (EHR) utilization continues to grow as more carriers are integrating EHRs into their underwriting processes. Growth has been spurred by increasing hit rates, improvements to the underlying data available, and users growing more comfortable working with EHR records across a broader array of use cases.  To understand the best uses and potential outcomes for EHRs in underwriting, Munich Re Life US completed a series of large-scale, wide-ranging retrospective studies to determine the protective and operational value of EHRs in multiple underwriting scenarios.

The full series of studies considered over 800 underwriting files consisting of business from multiple carriers targeting middle to high-net-worth markets. Broker, general agency, and direct-to-consumer distribution channels were included, as were traditional and fluidless underwriting paths.

The use cases studied included:

• Adding EHRs to accelerated underwritten cases, i.e., no labs or attending physician statement (APS)
• Adding EHRs to fully underwritten business without APS
• Assessing the impact of replacing traditional APSs with EHRs

This paper presents findings from the studies on the protective and operational value of EHRs in accelerated underwriting workflows. It will be followed by separate papers covering the other use cases.

This paper focuses on determining the potential benefits of adding EHR data to accelerated underwriting business (AUW), which includes disclosures (applicant admitted history), MIB check and prescription (Rx), and/or medical claims (Dx) history. Underwriters reviewed the files to determine the final risk assessment on each case before and after the addition of the EHR data.

This section of the study included life insurance applications from multiple carriers totaling 525 cases.¹ Face amounts were limited to a maximum of $2m and issue ages were capped at age 65 to align with typical AUW program eligibility parameters. The cases included middle and high-net-worth markets with distribution representing brokers, general agents, and direct-to-consumer channels. For this analysis, we included cases with EHR hits only. EHRs were ordered and received from Clareto, a Munich Re company. 

To ensure an independent review, we engaged external and internal underwriting consultants to help complete the risk assessments. For consistency, they utilized the Munich Re EDGE underwriting manual to assess each risk in the scenarios indicated.

The study captures the underwriting risk class assessment for each policy before and after adding EHRs to the underwriting evidence in an AUW setting without labs or APS. Munich Re calculated the expected mortality based on the underwriting risk class decisions in both scenarios and assessed the protective value from incorporating EHRs. A cost-benefit analysis was conducted to illustrate the net financial impact in dollar amount.

The following methodology was used in quantifying mortality saving and performing cost-benefit analysis.

• Table 1 shows the relative mortality assumptions by risk class that were used in the analysis.
• A count-based average expected mortality was calculated for both the AUW scenario and the AUW+EHR scenario based on the relative mortality factors outlined above. There were policies for which an assessment could not be made given the available underwriting evidence in the AUW scenario and/or the AUW+EHR scenario. These undetermined underwriting risks were excluded from the mortality calculation, and therefore, the average expected mortality includes all policies that received an assessment in that scenario.
• Mortality saving was calculated as follows:
  Mortality saving % = Average expected mortality for (AUW+EHR) / Average expected mortality for AUW - 1
• Mortality saving was further quantified in a dollar amount on a present value of future death benefits (PVDB) basis, which was used as a surrogate for the present value of premiums. The 2015 Society of Actuaries (SOA) Valuation Basic Table (VBT) with no mortality improvement was used as the underlying mortality basis. Lapse and interest rates were set to best estimate assumptions. Projection length was set to a 10-year time horizon.
• The net saving per policy was calculated as follows:
  Net saving per policy = Mortality saving per policy (PVDB basis) - EHR cost per policy

Mortality savings and cost-benefit analysis

The overall mortality saving percentage based on this study is 35%. This means that decision changes from information in the EHRs yielded a 35% higher average mortality compared to the original AUW decision. In other words, the additional medical information uncovered in EHRs enables more accurate risk class assessment that offsets potential mortality slippage of 35%.

The dollar amount of the mortality saving is quantified to be $971 for an average policy.² For this study, the average cost of EHRs per policy, accounting for multiple EHR data sources per life and summarization services, is assessed to be $55 per policy.³ This demonstrates clear net positive protective value, with an average net saving of $916 per policy after factoring in the cost of EHRs.

While the study demonstrates positive net savings on aggregate, we further analyzed how the net savings vary across issue age and face amount groups. Table 2 illustrates the average net savings per policy at varying issue age and face amount buckets.⁴

We observe that net savings are higher at older issue age buckets and higher face amount bands, which indicate the pockets where adding EHRs could deliver the most financial impact.

• Issue age: Older issue ages are typically associated with higher impairments, hence higher potential mortality slippage in an AUW environment where fluids and APS are not obtained. Furthermore, as the base mortality rate increases with age, older age policies have a larger financial impact compared to younger age policies.
• Face amount: The mortality saving percentage impact (i.e., slippage) generally decreases with face amount, as higher face amount policies typically undergo more underwriting scrutiny and, therefore, have lower opportunities for misrepresentation. However, given that the face amount is directly correlated with financial impact when looking at impacts on a dollar amount basis, the net savings go up as face amount increases. 

Decision rate

In addition to providing protective value on mortality, the study showed EHRs can add operational value by driving a net increase in the number of risk class decisions that can be made without requesting additional underwriting evidence. In the context of AUW programs, this means that incorporating EHRs at the point of sale can reduce kick-out or refer-to-underwriter cases, improving instant decision or light touch rates.

In this study, the percentage of cases that received a risk class decision prior to adding EHRs is 68%.

The medical information in the EHRs was able to:

1. help make a decision on additional cases that were otherwise undetermined (19% of total cases), or
2. flag additional cases as undetermined due to new conditions being uncovered in EHRs (8% of total cases).

The net impact is an increase in the decision rate from 68% to 79% (Table 3). Note that the decision rate includes decline and postpone assessments. Therefore, the percentage of policies that receive an actual offer will be lower.

Common conditions EHRs help uncover

This study captures the reasons and corresponding evidence sources for why a risk class decision was made (or not made). We were able to analyze the reasons for policies whose risk class decisions are different after adding EHRs.

For cases that were undetermined in the AUW scenario and received a decision post-EHRs:

There were 102 cases, or 19% of the study sample, that did not receive an underwriting decision under the AUW scenario but received a decision after incorporating EHRs. Some of the common reasons resulting in an undetermined assessment in the AUW scenario included information found in the AUW requirements, such as the Rx or Dx, MIB, or disclosures, that raised potential concerns for which additional information was needed to determine the appropriate risk class.

Table 4 shows the risk class distribution after incorporating EHRs. Within this group, we see a higher-than-average prevalence of decline and postpone cases, and the average mortality is 203% of standard mortality.

As illustrated in Table 5, EHRs identify a variety of medical conditions as well as build and/or weight changes. When incorporated into accelerated underwriting, EHRs can help confirm applicant disclosures and also highlight any inconsistencies. In addition, these results indicate that EHRs enable review and assessment of conditions that historically have been triaged out to full underwriting with fluids and/or APSs, allowing for a more customer-friendly experience with faster decisions and less invasive underwriting.

For cases that received a decision in the AUW scenario and the post-EHR decision is different:

Out of the 311 cases (59% of the study sample) that received decisions both pre- and post-EHR, 61 cases received a different decision after adding EHRs. Table 6 below summarizes the degree of risk class variance for the 311 cases.

Build and/or weight changes are the top driver of risk class differences between the original AUW decision and the AUW plus EHR decision. This aligns with our monitoring data in which build/BMI is a leading misclassification reason for AUW programs. Following build/weight changes are medical histories, including substance use and cancer, which have notable mortality impacts. Another top reason for differences in risk class uncovered by the EHR is tobacco use, which is also a leading misrepresentation reason found in AUW monitoring reviews. In this study, we observed that 76% of the EHRs contained tobacco details, and 60% included dates with the tobacco information. 

Whether it is tobacco use, build, or medical history, our findings indicate EHRs can be a quick and informative tool for confirming disclosures or uncovering discrepancies with meaningful mortality impacts. These findings also demonstrate the value of adding EHRs in accelerated or non-fluid underwriting, whether at point of sale or post-issue audit. 

The results of this comprehensive EHR study highlight the effectiveness of EHRs as a core underwriting tool, showing their value in accelerated underwriting for both point-of-sale and post-issue audit use cases. Based on these study results, adding EHR as an additional source of evidence in AUW processes can bring impressive benefits both financially and operationally through mortality and underwriting improvements.

Our analysis demonstrated significant mortality protection when including EHRs in AUW programs. These results show that the financial benefit of adding an EHR could far exceed the cost of ordering an EHR. In addition to overall savings, EHRs can add operational value by driving a net increase in the number of risk class decisions that can be accelerated without fluids. 

Each carrier’s AUW program is unique, and we recommend that carriers complete their own analysis to determine the specific business impact. We are happy to support these efforts and have the tools, such as Automated EHR Summarizer and alitheia, that can effectively integrate EHRs into AUW programs.

We look forward to sharing our next paper in this series, where we will review the impact of EHRs in the fully underwritten or fluid underwriting space. Please subscribe to our EHR Retro Study Series to receive future study papers via email.