Life insurance coverage corporations 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 complicated medical paperwork.
One main life settlement underwriter discovered their course of breaking below new pressures. Their two-part workflow — an inside crew labeled paperwork earlier than docs reviewed them to calculate life expectancy — was struggling to maintain up as their enterprise grew and healthcare documentation grew to become more and more complicated. Medical specialists had been spending extra time sorting via paperwork as an alternative 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 charge. With correct underwriting immediately impacting coverage pricing, even small errors may result in hundreds of thousands in losses. Now, because the medical trade concurrently faces worsening workforce shortages, they wanted an answer that might rework their doc processing whereas sustaining the precision their enterprise will depend on.Â
This can be a story of how they did it.
When medical report volumes get out of hand
Processing 200+ affected person case recordsdata weekly may sound manageable. Nonetheless, every case contained a affected person’s whole medical historical past — from physician visits and lab outcomes to hospital stays and specialist consultations. These recordsdata 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 stress from a number of instructions. Rising trade volumes meant they’d extra instances to course of. On the flip aspect, the healthcare trade staffing shortages meant they needed to pay docs and different medical specialists prime {dollars}. Their current handbook workflow merely could not scale to satisfy these calls for. It was made worse by the truth that they needed to keep near-perfect doc classification accuracy for dependable life expectancy calculations.
The enterprise affect was evident:
- Slower processing occasions meant delayed underwriting choices
- Inaccurate life expectancy calculations resulted in hundreds of thousands in mispriced insurance policies
- Doubtlessly dropping enterprise to extra agile rivals
- Larger processing prices immediately affected profitability
- Rising prices as docs hung out on paperwork as an alternative of research
Their medical specialists’ time was their most respected useful resource. And but, regardless of the 2-step workflow, the sheer quantity of paperwork compelled these extremely skilled professionals to behave as costly doc sorters relatively than making use of their experience to threat evaluation.Â
The mathematics was easy: each hour docs spent organizing papers as an alternative of analyzing medical circumstances price the corporate considerably. This not solely elevated prices but in addition restricted the variety of instances they may deal with, immediately constraining income progress.
What makes healthcare doc processing sophisticated
Let’s break down their workflow to know why their medical report processing workflow was notably difficult. It started with doc classification — sorting a whole lot to 1000’s of pages into classes like lab reviews, ECG reviews, and chart notes. This crucial first step was carried out by their six-member crew.
Every member may course of ~400 digital pages per hour. Which means, a single case file of two,000 pages would take over 5 hours to finish. Additionally, the pace tends to range closely based mostly on the complexity of the paperwork and the aptitude of the worker.
The method was labor-intensive and time-consuming. With digital medical data coming from over 230 completely different methods, every with its personal codecs and constructions, the crew needed to take care of plenty of variation. It additionally made automation via conventional template-based knowledge extraction practically inconceivable.
The complexity stemmed from how medical info is structured:
- Crucial particulars are unfold throughout a number of pages
- Data wants chronological ordering
- Context from earlier pages is usually required
- Dates are typically lacking or implied
- Duplicate pages with slight variations
- Every healthcare supplier makes use of completely different documentation strategies
After classification, the crew would manually determine pages containing info 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 principally 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 knowledge from medical paperwork as a result of solely they possessed the specialised medical data wanted to appropriately interpret complicated medical terminology, lab values, and scientific findings.
Some case recordsdata had been enormous — reaching past 10,000 pages. Simply think about the sheer endurance and a spotlight to element required from the crew and docs sifting via all that. That is why when the agency was on the lookout for automation options, there was a robust emphasis on attaining practically 100% classification accuracy, self-learning knowledge extraction, and decreasing person-hours.Â
How the underwriter applied clever doc processing for medical data
Medical report volumes had been rising, and physician evaluation prices had been mounting. The underwriting crew 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 throughout the transition.
Their necessities had been particular and demanding:
- Skill to course of 1000’s of pages of medical data day by day
- Understanding of complicated 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 knowledge key factors, and guarantee seamless knowledge flows throughout 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 interior employees combines all medical data— lab reviews, 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 day by day
Be aware: This method ensures safe dealing with of affected person info and maintains clear group all through the method.
2. Doc import
- The system checks for brand spanking new recordsdata each hour
- Every case can include 2000-10,000 pages of medical data
- Recordsdata are readied for secured processing via our platform
Be aware: This automated monitoring ensures constant processing occasions and helps keep the 24-hour turnaround requirement.
3. Doc classification
Our AI mannequin analyzes every web page based mostly on fastidiously drafted pure language prompts that assist determine medical doc sorts. These prompts information the AI in understanding the particular traits of lab reviews, ECG reviews, and chart notes.
The classification course of entails:
- Figuring out doc sorts based mostly 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 constantly refined based mostly on suggestions and new doc sorts, making certain the system maintains excessive classification accuracy.
4. Knowledge extraction
Our system handles three primary doc sorts: lab reviews, ECG reviews, and chart notes. We now have two specialised extraction fashions to course of these paperwork – one for lab/ECG knowledge and one other for chart notes.
Mannequin 1 extracts roughly 50 fields from lab reviews and ECG knowledge, together with affected person title, blood glucose stage, creatinine worth, glomerular filtration charge, hemoglobin worth, prostate particular antigen, white blood cell depend, hepatitis worth, ldl cholesterol worth, and lots of different crucial lab measurements.Â
Mannequin 2 processes chart notes to extract 13 key fields together with blood stress, heartbeat charge, O2 supply, O2 circulation charge, temperature, date of delivery, gender, top, weight, and smoking standing. Every knowledge level is linked to its supply web page and doc for verification.
5. Knowledge export
The extracted info is exported as three separate CSV recordsdata again to the S3 Bucket — one every for doc classification, lab outcomes and ECG, and chart notes.
The classification CSV incorporates 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 incorporates related medical info from docs’ notes.
In every file title, an identifier, like ‘lab outcomes’ and ‘ECG’ or ‘chart notes’, shall be routinely added to determine the content material sort. And for consistency, CSV recordsdata are generated for all classes, even when no related pages are present in a case doc. Every affected person’s knowledge shall be saved within the Export folder on the S3 bucket below the identical figuring out quantity.
6. ValidationÂ
The CSV outputs are imported into their inside utility, the place a two-member validation crew (diminished from the unique six) critiques the automated classifications. Right here, they will evaluate the extracted knowledge 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 research medical histories and calculate life expectancy. As a substitute of spending hours organizing and reviewing paperwork, they now work with structured, verified info at their fingertips.
Be aware: For safety and compliance causes, all processed recordsdata are routinely purged from Nanonets servers after 21 days.
The affect of automated medical report processing
With structured knowledge and an environment friendly validation course of, the underwriting supplier has been in a position to reduce the operational bottlenecks concerned within the course of.
Right here’s a fast overview of how a lot they’ve been in a position to obtain inside only a month of implementation:
- 4 members on the information validation crew had been reassigned to different roles, so validation now runs easily with simply 2 folks
- Classification accuracy maintained at 97-99%
- Automated workflow is dealing with ~20% of the overall workload
- Full knowledge classification and extraction for every case file inside 24 hours
- Obtain a 5X discount within the variety of pages docs have to evaluation per case to compute life expectancy
- Freed medical specialists to concentrate on their core experience
These numbers do not inform the entire story. Earlier than automation, docs needed to sift via 1000’s of pages as a result of they had been the one ones with the mandatory context to know affected person knowledge. 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.Â
This modification means they will deal with extra instances with out having to rent costlier docs. That is an enormous benefit, particularly with healthcare going through employees shortages whereas the trade continues to develop.
Wanting forward
This profitable implementation has helped the underwriting supplier perceive what’s doable with clever doc processing. They now need to scale their medical report processing to cowl all ~200 instances weekly. That is not all. They’re already exploring the way to automate different document-heavy workflows, like belief deed processing.
Fascinated with what this implies in your group? The time to modernize doc processing is now. Healthcare documentation is turning into extra complicated, with a 41% progress in high-acuity care and rising persistent situation administration. Add to this the rising staffing challenges in healthcare, and it is clear— in case you do not modernize, your group will wrestle to maintain up.
Wish to see related outcomes along with your medical report processing? Let’s discuss how Nanonets can assist. Schedule a demo now.
