Life insurance coverage firms depend on correct medical underwriting to find out coverage pricing and threat. These calculations come from specialised underwriting corporations that analyze sufferers’ medical data intimately. As healthcare digitization has surged from 10% in 2010 to 96% in 2023, these corporations now face overwhelming volumes of advanced medical paperwork.
One main life settlement underwriter discovered their course of breaking underneath new pressures. Their two-part workflow — an inside group labeled paperwork earlier than docs reviewed them to calculate life expectancy — was struggling to maintain up as their enterprise grew and healthcare documentation turned more and more advanced. Medical consultants had been spending extra time sorting by paperwork as a substitute of analyzing medical histories, making a rising backlog and rising prices.
This bottleneck threatened their aggressive place in an trade projected to develop at twice its historic fee. With correct underwriting straight impacting coverage pricing, even small errors may result in thousands and thousands in losses. Now, because the medical trade concurrently faces worsening workforce shortages, they wanted an answer that might remodel their doc processing whereas sustaining the precision their enterprise depends upon.
It is a story of how they did it.
When medical report volumes get out of hand
Processing 200+ affected person case information weekly would possibly sound manageable. Nevertheless, every case contained a affected person’s total medical historical past — from physician visits and lab outcomes to hospital stays and specialist consultations. These information ranged from 400 to 10,000 pages per affected person. However quantity wasn’t the one problem for the medical underwriting supplier.
Their enterprise confronted mounting strain from a number of instructions. Rising trade volumes meant that they had extra instances to course of. On the flip aspect, the healthcare trade staffing shortages meant they needed to pay docs and different medical consultants prime {dollars}. Their current handbook workflow merely could not scale to fulfill these calls for. It was made worse by the truth that they needed to preserve near-perfect doc classification accuracy for dependable life expectancy calculations.
The enterprise influence was evident:
- Slower processing instances meant delayed underwriting selections
- Inaccurate life expectancy calculations resulted in thousands and thousands in mispriced insurance policies
- Probably dropping enterprise to extra agile rivals
- Greater processing prices straight affected profitability
- Rising prices as docs hung out on paperwork as a substitute of study
Their medical consultants’ time was their most precious useful resource. And but, regardless of the 2-step workflow, the sheer quantity of paperwork compelled these extremely educated professionals to behave as costly doc sorters quite than making use of their experience to threat evaluation.
The mathematics was easy: each hour docs spent organizing papers as a substitute of analyzing medical situations price the corporate considerably. This not solely elevated prices but in addition restricted the variety of instances they may deal with, straight constraining income development.
What makes healthcare doc processing difficult
Let’s break down their workflow to know why their medical report processing workflow was notably difficult. It started with doc classification — sorting lots of to 1000’s of pages into classes like lab experiences, ECG experiences, and chart notes. This essential first step was carried out by their six-member group.
Every member may course of ~400 digital pages per hour. That means, a single case file of two,000 pages would take over 5 hours to finish. Additionally, the velocity tends to differ closely primarily based on the complexity of the paperwork and the potential of the worker.
The method was labor-intensive and time-consuming. With digital medical data coming from over 230 totally different programs, every with its personal codecs and buildings, the group needed to cope with loads of variation. It additionally made automation by conventional template-based information extraction almost unattainable.
The complexity stemmed from how medical data is structured:
- Vital particulars are unfold throughout a number of pages
- Data wants chronological ordering
- Context from earlier pages is commonly required
- Dates are generally lacking or implied
- Duplicate pages with slight variations
- Every healthcare supplier makes use of totally different documentation strategies
After classification, the group would manually establish pages containing data related to life expectancy calculation and discard irrelevant ones. This meant their employees wanted to have an understanding of medical terminology and the importance of assorted take a look at outcomes and diagnoses. There was little or no margin for error as a result of even the slightest errors or omissions may result in incorrect calculations downstream.
The paperwork would then be despatched to docs for all times expectancy calculation. Docs largely did this throughout their non-clinical hours, which already made them a scarce useful resource. To make issues worse, regardless of having workers to deal with preliminary classification, docs had been nonetheless compelled to spend vital time extracting and verifying information from medical paperwork as a result of solely they possessed the specialised medical information wanted to appropriately interpret advanced medical terminology, lab values, and scientific findings.
Some case information had been big — reaching past 10,000 pages. Simply think about the sheer persistence and a focus to element required from the group and docs sifting by all that. That is why when the agency was searching for automation options, there was a powerful emphasis on reaching almost 100% classification accuracy, self-learning information extraction, and lowering person-hours.
How the underwriter applied clever doc processing for medical data
Medical report volumes had been rising, and physician overview prices had been mounting. The underwriting group knew they wanted to automate their course of. However with life expectancy calculations depending on exact medical particulars, they could not threat any drop in accuracy in the course of the transition.
Their necessities had been particular and demanding:
- Means to course of 1000’s of pages of medical data each day
- Understanding of advanced medical relationships throughout paperwork
- Classification accuracy needed to be near-perfect
- Fast and safe processing with out compromising high quality
- Combine out-of-the-box with Amazon S3
That’s when their VP of Operations reached out to us at Nanonets. They found that we may assist classify medical data with excessive accuracy, present a filtered view of great pages, extract information key factors, and guarantee seamless information flows inside the workflow. This satisfied them we may deal with their distinctive challenges.
This is what the brand new automated medical data automation workflow seemed like:
1. Doc preparation
- The inner employees combines all medical data— lab experiences, ECG, chart notes, and different miscellaneous paperwork — for every affected person right into a single file
- Every affected person is assigned a novel quantity
- A folder with this quantity is created within the S3 enter folder
- 7-10 such instances are uploaded each day
Be aware: This strategy ensures safe dealing with of affected person data and maintains clear group all through the method.
2. Doc import
- The system checks for brand new information each hour
- Every case can include 2000-10,000 pages of medical data
- Information are readied for secured processing by our platform
Be aware: This automated monitoring ensures constant processing instances and helps preserve the 24-hour turnaround requirement.
3. Doc classification
Our AI mannequin analyzes every web page primarily based on rigorously drafted pure language prompts that assist establish medical doc sorts. These prompts information the AI in understanding the particular traits of lab experiences, ECG experiences, and chart notes.
The classification course of entails:
- Figuring out doc sorts primarily based on content material and construction
- Understanding medical context and terminology
- Sustaining doc relationships and chronological order
- Recognizing when context from earlier pages is required
Be aware: The prompts are repeatedly refined primarily based on suggestions and new doc sorts, making certain the system maintains excessive classification accuracy.
4. Knowledge extraction
Our system handles three fundamental doc sorts: lab experiences, ECG experiences, and chart notes. We’ve two specialised extraction fashions to course of these paperwork – one for lab/ECG information and one other for chart notes.
Mannequin 1 extracts roughly 50 fields from lab experiences and ECG information, together with affected person title, blood glucose degree, creatinine worth, glomerular filtration fee, hemoglobin worth, prostate particular antigen, white blood cell rely, hepatitis worth, ldl cholesterol worth, and plenty of different essential lab measurements.
Mannequin 2 processes chart notes to extract 13 key fields together with blood strain, heartbeat fee, O2 supply, O2 stream fee, temperature, date of delivery, gender, peak, weight, and smoking standing. Every information level is linked to its supply web page and doc for verification.
5. Knowledge export
The extracted data is exported as three separate CSV information again to the S3 Bucket — one every for doc classification, lab outcomes and ECG, and chart notes.
The classification CSV comprises file names, web page numbers, classifications, and hyperlinks to entry the unique pages. The lab outcomes and ECG CSV include extracted medical values and measurements, whereas the chart notes CSV comprises related medical data from docs’ notes.
In every file title, an identifier, like ‘lab outcomes’ and ‘ECG’ or ‘chart notes’, shall be routinely added to establish the content material sort. And for consistency, CSV information are generated for all classes, even when no related pages are present in a case doc. Every affected person’s information shall be saved within the Export folder on the S3 bucket underneath the identical figuring out quantity.
6. Validation
The CSV outputs are imported into their inside utility, the place a two-member validation group (lowered from the unique six) critiques the automated classifications. Right here, they’ll examine the extracted information in opposition to the unique paperwork, making the verification course of fast and environment friendly.
As soon as the information is validated, the docs are notified. They’ll go forward to investigate medical histories and calculate life expectancy. As a substitute of spending hours organizing and reviewing paperwork, they now work with structured, verified data at their fingertips.
Be aware: For safety and compliance causes, all processed information are routinely purged from Nanonets servers after 21 days.
The influence of automated medical report processing
With structured information and an environment friendly validation course of, the underwriting supplier has been capable of decrease the operational bottlenecks concerned within the course of.
Right here’s a fast overview of how a lot they’ve been capable of obtain inside only a month of implementation:
- 4 members on the information validation group had been reassigned to different roles, so validation now runs easily with simply 2 individuals
- Classification accuracy maintained at 97-99%
- Automated workflow is dealing with ~20% of the whole workload
- Full information classification and extraction for every case file inside 24 hours
- Obtain a 5X discount within the variety of pages docs must overview per case to compute life expectancy
- Freed medical consultants to concentrate on their core experience
These numbers do not inform the entire story. Earlier than automation, docs needed to sift by 1000’s of pages as a result of they had been the one ones with the mandatory context to know affected person information. Now docs get precisely what they want – detailed medical histories sorted chronologically which are prepared for evaluation. It is a full shift from sorting papers to doing precise medical evaluation.
This modification means they’ll deal with extra instances with out having to rent dearer docs. That is an enormous benefit, particularly with healthcare dealing with employees shortages whereas the trade continues to develop.
Wanting forward
This profitable implementation has helped the underwriting supplier perceive what’s potential with clever doc processing. They now wish to scale their medical report processing to cowl all ~200 instances weekly. That is not all. They’re already exploring how one can automate different document-heavy workflows, like belief deed processing.
Eager about what this implies to your group? The time to modernize doc processing is now. Healthcare documentation is changing into extra advanced, with a 41% development in high-acuity care and rising persistent situation administration. Add to this the rising staffing challenges in healthcare, and it is clear— should you do not modernize, your group will battle to maintain up.
Need to see comparable outcomes together with your medical report processing? Let’s discuss how Nanonets may help. Schedule a demo now.
