Part of our Underwriter-to-Underwriter blog series
By Carolyn McAvinn, FLMI, AALU, PMC-IV
January 29, 2021
I have recently had several conversations with MIB's members focused on how to measure success using EHR data within various stages of the underwriting process. Each member has unique directives and execution plans for the implementation of EHR data into their respective workflows and/or engines. Some are jumping right into production with EHR due to challenges in retrieving medical data as a result of Covid-19 and their need to process a large influx of business quickly. Others are methodically analyzing initial pilot results against traditional underwriting assessments and/or performing detailed cost benefit analysis. The what, where, when and how to weave EHR into their workflows are just a few of the items that our members are wrestling with as we leap into Q1- 2021.
EHR data is quickly maturing to be able to support underwriting risk assessment beyond the ability to act as a triage tool only. Because of the expanded EHR data access, it is the perfect time to explore all that EHR data has to offer to help you improve cycle time and reduce costs. Based on my conversations with MIB's members, I have quickly learned that there is no optimal pilot or template that will work for every organization. However, there are common themes that have enabled carriers to smoothly implement an EHR strategy and quickly adopt the benefits of EHR. You may find these helpful as you contemplate where to start - or how to present your own EHR data analysis to your leadership team:
Define a Search Strategy.
Developed to be in line with the states where your products are sold and your customer base resides. (Hint- MIB can help identify your personalized regional focus based on our internal analysis of our Checking Service data).
Use a Time Bound Period to Gather EHR Data for Evaluation.
New data sources are continuously being added and the providers (healthcare organizations or individual healthcare providers) within the data sources are constantly evolving. Set an established time parameter to gather and evaluate the data, such as four-six weeks. More time that that may render your results obsolete.
Create Consistency with Internal Terminology.
Save time and confusion by ensuring that your team uses standardized language when talking about record retrieval. Various data sources have their own terminology with regard to the facilities that are feeding data through their platforms (examples of terms include health care organization, provider, site, vendor, data source, record source... it gets confusing!). Trust me, having a standardized language will save time in the end. (Hint- MIB can help sort through the terminology).
Use a clear definition when discussing 'hit rate'. This may seem trivial but it is important when measuring results among data sources and communicating findings with data vendor partners. I posed the question "how does your organization define (hit rate)" to several members and received a variety of responses. This confirmed what I suspected... the term is vague and can cause confusion. (Hint- It is ok to have various definitions, as long as your organization and vendor partners are using the same language).
Establish a Measurement of Success.
Create a document that allows your Underwriters to measure each EHR record objectively and with consistent metrics. (Hint- MIB can help you create this document as an MIB EHR customer).
Save time by tapping into MIB to determine which states will yield the highest EHR release rates so your team can focus efforts to drive the best results.
Be focused in your approach and spend your medical data budget dollars in the best way possible by searching for medical data in a systematic and streamlined manner.
As much as we would like it to be, EHR data is not a digitized copy of an APS (Attending Physician Statement). It is a collection of structured data (and sometimes unstructured data such as clinical notes and images) created for easy access to patient information and ease of billing for the 'treatment' use case. That does not mean that it does not hold value today in the life/disability space. The opportunities for EHR data to be significantly valuable to underwriting within the insurance space continue to grow based on ongoing improvements to speed, lower costs than traditional APS methods, and the expanded access of information within the records. Based on the flurry of headlines related to EHR data access, partnerships and usability, it is clear that our industry is dedicated to its’ growth moving forward.
While today most initial EHR pilots are measuring data on whether it can be used solely as an APS replacement tool, the future of EHR is much more robust. As you look to achieve your goals in the upcoming year, consider not only how you are capturing medical information today, but also what potential exists as your underwriting process evolves. While EHR data today may not be a complete replacement for the APS you are accustom to, I am confident that at maturity, EHR data will hold enough value to stand on its own as a single source of medical data. We have seen huge strides in the last 12 months as data sources have expanded, record access has grown, and quality continues to improve. We expect even more progress in the next 12 months in all of these areas, which means carriers may need to reconsider their roadmap and processes in order to best leverage this new level of data, stay on top of the ever evolving world of underwriting, and win in a race amongst carriers to stay competitive.
For information about how we can help you pilot the MIB EHR, please reach out to firstname.lastname@example.org.
About Carolyn McAvinn
Carolyn McAvinn is the Director of Business Development for MIB, Inc. 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.
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