By Carolyn McAvinn, FLMI, AALU, PMC-IVApril 28, 2022
Lately, I have been fielding many questions from members and clients who are trying to understand the nuances about electronic medical data. It has become apparent that our members and clients may need some help understanding these details when navigating the new world of digital data connections. Unless you ask the right questions, and understand how to interpret the answers, you could become confused or misled. So I have compiled a list of the five most frequently asked ‘themes’ of questions and I am offering tips and examples to help clear some of the confusion.
1. If a sales presentation includes statistics that lack context, what follow-up questions should I ask to test the accuracy or to ensure that I will experience similar results?
Tip: Context is everything with statistics. Ask questions about which data is included in the statistics, especially when contrasting options to ensure you are comparing apples to apples. Check that the information being compared includes the same products and workflows, and that it is not comparing a single source of information to a more robust set of potential results.
Example: A 50% release rate that is a combined statistic for both an EHR direct solution (automated with HIPAA consent) & and patient portal solution (access granted via credentials provided by an applicant/patient/consumer) is not meaningful when compared to a release rate that only factors in an EHR direct workflow. From a metric perspective, this is similar to measuring the number of apples vs the number of oranges.
Note: MIB provides clear and concise metric information to members and clients to offer full transparency of how a product typically perform. We provide release rates by state, cycle times and records released per search, etc.
2. I recently heard that an EHR record with less than five pages has no value, is this a fair and accurate statement?
Tip: Another example of ‘context’ proving to be critical. I would start by asking for details. Ask if there could ever be a use case or circumstance where a smaller record could add value.
Example: For a company trying to assess risk with a product that has multiple levels of preferred, it may be true. For a company that is simply trying to test for disclosure or use as a triage tool, a short record may prove to be adequate information.
Note: MIB has found that EHR records with limited pages often provide predictable and valuable information such as vitals, tobacco use status and health concerns and supports many use cases.
3. What is the best way to calculate the ‘usability’ or ‘sufficiency’ rate of an EHR record?
Tip: Our industry often uses the terms usability and/or sufficiency when referring to the usefulness of electronic medical data. Some vendors provide a “usability rate” to express the percentage of records that they return that are useful to underwriters. However, calculating usability requires insights that only a carrier can provide. If a vendor claims to have a certain “usability rate” – ask them to describe the source of the data used to calculate the figure. To be statistically significant, the percent needs to be based on a large quantity of underwriting information, but otherwise the percent can only be conjecture.
Example: Today, the most common use case for EHR data is as a quicker and less expensive replacement of an (APS) Attending Physician’s Statement. In a very simple example, let’s say 10 EHR search requests result in 10 records released. The underwriter finds that 3 of those 10 records provide data that isn’t useful (for example, it’s for a negative COVID test). If the remaining 7 records are useful in making a risk assessment without the need for an APS or additional data - then the usability/sufficiency rate is 7/10 or 70%.
Note: In reality, calculating a “usability rate” is not this simple as release rates and other variables come into play. MIB does get anecdotal feedback from members suggesting that ‘usability/sufficiency’ rates tend to increase over time as carriers progress through the stages of electronic medical data adoption: Pilot---Early production use--- Experienced production use---Full underwriter adoption.
4. What are the potential fees associated with adopting electronic medical data?
Tip: Beware of fees, they may come in numerous and/or at various phases of the workflow and may be tough to anticipate. This is especially true since you may not know if a particular record is ‘usable’ until it is ultimately received and reviewed by the underwriter or rules engine.
Example: Types of fees may include:
• Licensing / Subscription fee
• Search fee
• Sequencing fee
• Summary fee
• Record retrieval fee
• DocuSign or other special authorization handling fee
• Additional requirement fee to finalize the review/assessment if the original piece of data is deemed insufficient
Note: MIB only charges for a record retrieval fee of $30 and an optional DocuSign processing fee of $5, charged only if a member company/client chooses to activate this workflow option.
5. What is the best way to navigate the confusing new jargon and terms unique to electronic medical data products?
Tip: If a sales pitch includes heavy acronym and jargon use, request that a definition of terms accompany the sales material.
Example: The term ‘hit rate’ when describing electronic medical data tends to cause confusion. It could pertain to a found record that does not include any clinical data, a released record, or in some cases, the robustness of a record.
Note: MIB provides a data dictionary in our sales materials and uses the term ‘release rate’ vs ‘hit rate’ as it provides more clarity.
As virtual and in-person conference season is upon us, I hope that you find this ‘know before you go’ list of tips to be a useful tool as you explore the various electronic medical data options.
If you have any questions, please feel free to reach out to the MIB Team. We are more than happy to field questions as you navigate this maturing landscape of data options.
Carolyn McAvinn is the Director of Underwriting Innovations for MIB. Prior to joining MIB in late 2018, she held various underwriting roles supporting multiple companies, product lines and distribution platforms. These included underwriting management, direct line production underwriting in the life, disability and long-term care markets and assisting with the development of underwriting engine automation and accelerated underwriting programs. Carolyn is a graduate of the University of Massachusetts - Amherst and currently serves as a board member of the MUD (Metropolitan Underwriting Discussion) Group in NYC.