Life insurance coverage corporations depend on correct medical underwriting to find out coverage pricing and threat. These calculations come from specialised underwriting companies that analyze sufferers’ medical data intimately. As healthcare digitization has surged from 10% in 2010 to 96% in 2023, these companies now face overwhelming volumes of complicated medical paperwork.
One main life settlement underwriter discovered their course of breaking underneath new pressures. Their two-part workflow — an inside staff 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 complicated. 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 business projected to grow at twice its historical rate. With correct underwriting immediately impacting coverage pricing, even small errors might result in tens of millions in losses. Now, because the medical business concurrently faces worsening workforce shortages, they wanted an answer that might remodel their doc processing whereas sustaining the precision their enterprise will depend on.
This can be a story of how they did it.
When medical file volumes get out of hand
Processing 200+ affected person case information weekly may sound manageable. Nonetheless, 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 stress from a number of instructions. Rising business volumes meant that they had extra circumstances to course of. On the flip aspect, the healthcare business staffing shortages meant they needed to pay docs and different medical consultants high {dollars}. Their present handbook workflow merely could not scale to satisfy 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 impression was evident:
- Slower processing instances meant delayed underwriting selections
- Inaccurate life expectancy calculations resulted in tens of millions in mispriced insurance policies
- Probably dropping enterprise to extra agile rivals
- Greater processing prices immediately affected profitability
- Rising prices as docs frolicked on paperwork as a substitute of study
Their medical consultants’ time was their most beneficial 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 maths was easy: each hour docs spent organizing papers as a substitute of analyzing medical circumstances price the corporate considerably. This not solely elevated prices but in addition restricted the variety of circumstances they might deal with, immediately constraining income development.
What makes healthcare doc processing sophisticated
Let’s break down their workflow to know why their medical file processing workflow was notably difficult. It started with doc classification — sorting lots of to hundreds of pages into classes like lab reviews, ECG reviews, and chart notes. This vital first step was carried out by their six-member staff.
Every member might 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 primarily based 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 different systems, every with its personal codecs and buildings, the staff needed to take care of a number of variation. It additionally made automation by conventional template-based knowledge extraction practically inconceivable.
The complexity stemmed from how medical data is structured:
- Essential particulars are unfold throughout a number of pages
- Info 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 totally different documentation strategies
After classification, the staff would manually establish pages containing data related to life expectancy calculation and discard irrelevant ones. This meant their workers 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 might 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 important time extracting and verifying knowledge from medical paperwork as a result of solely they possessed the specialised medical information wanted to appropriately interpret complicated medical terminology, lab values, and medical findings.
Some case information had been enormous — reaching past 10,000 pages. Simply think about the sheer endurance and a spotlight to element required from the staff and docs sifting by all that. That is why when the agency was searching for automation options, there was a robust emphasis on reaching practically 100% classification accuracy, self-learning knowledge extraction, and decreasing person-hours.
How the underwriter carried out clever doc processing for medical data
Medical file volumes had been rising, and physician evaluation prices had been mounting. The underwriting staff 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 through the transition.
Their necessities had been particular and demanding:
- Skill to course of hundreds of pages of medical data every 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 might assist classify medical data with excessive accuracy, present a filtered view of serious pages, extract knowledge key factors, and guarantee seamless knowledge flows inside the workflow. This satisfied them we might deal with their distinctive challenges.
Here is what the brand new automated medical data automation workflow appeared like:
1. Doc preparation
- The interior workers 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 circumstances are uploaded every day
Notice: This method ensures safe dealing with of affected person data and maintains clear group all through the method.
2. Doc import
- The system checks for brand spanking new information each hour
- Every case can include 2000-10,000 pages of medical data
- Recordsdata are readied for secured processing by our platform
Notice: 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 varieties. These prompts information the AI in understanding the particular traits of lab reviews, ECG reviews, and chart notes.
The classification course of includes:
- Figuring out doc varieties 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
Notice: The prompts are repeatedly refined primarily based on suggestions and new doc varieties, making certain the system maintains excessive classification accuracy.
4. Information extraction
Our system handles three predominant doc varieties: 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 identify, blood glucose stage, creatinine worth, glomerular filtration price, hemoglobin worth, prostate particular antigen, white blood cell depend, hepatitis worth, ldl cholesterol worth, and plenty of different vital lab measurements.
Mannequin 2 processes chart notes to extract 13 key fields together with blood stress, heartbeat price, O2 supply, O2 move price, temperature, date of beginning, gender, peak, weight, and smoking standing. Every knowledge level is linked to its supply web page and doc for verification.
5. Information 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 accommodates 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 accommodates related medical data from docs’ notes.
In every file identify, an identifier, like ‘lab outcomes’ and ‘ECG’ or ‘chart notes’, might be mechanically 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 knowledge might 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 staff (lowered from the unique six) opinions the automated classifications. Right here, they’ll examine 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 will 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 data at their fingertips.
Notice: For safety and compliance causes, all processed information are mechanically purged from Nanonets servers after 21 days.
The impression of automated medical file processing
With structured knowledge 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 staff 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 full 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 consultants to concentrate on their core experience
These numbers do not inform the entire story. Earlier than automation, docs needed to sift by hundreds of pages as a result of they had been the one ones with the required context to know affected person knowledge. Now docs get precisely what they want – detailed medical histories sorted chronologically which might be prepared for evaluation. It is a full shift from sorting papers to doing precise medical evaluation.
This transformation means they’ll deal with extra circumstances with out having to rent dearer docs. That is an enormous benefit, particularly with healthcare dealing with workers shortages whereas the business continues to develop.
Trying forward
This profitable implementation has helped the underwriting supplier perceive what’s potential with clever doc processing. They now wish to scale their medical file processing to cowl all ~200 circumstances weekly. That is not all. They’re already exploring find out how to automate different document-heavy workflows, like belief deed processing.
Desirous about what this implies in your group? The time to modernize doc processing is now. Healthcare documentation is changing into extra complicated, 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— in the event you do not modernize, your group will battle to maintain up.
Need to see related outcomes together with your medical file processing? Let’s discuss how Nanonets can assist. Schedule a demo now.
