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 beneath new pressures. Their two-part workflow — an inside group categorised 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 way of paperwork as an alternative 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 straight impacting coverage pricing, even small errors might result in hundreds of thousands 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 depends upon.
This can be a story of how they did it.
When medical document volumes get out of hand
Processing 200+ affected person case information weekly may 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 business volumes meant they’d extra instances to course of. On the flip facet, the healthcare business staffing shortages meant they needed to pay docs and different medical consultants prime {dollars}. Their current guide 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 affect was evident:
- Slower processing instances meant delayed underwriting selections
- Inaccurate life expectancy calculations resulted in hundreds of thousands in mispriced insurance policies
- Probably shedding enterprise to extra agile opponents
- Larger processing prices straight affected profitability
- Rising prices as docs hung out on paperwork as an alternative of research
Their medical consultants’ time was their most dear 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 slightly than making use of their experience to threat evaluation.
The mathematics was easy: each hour docs spent organizing papers as an alternative of analyzing medical circumstances value 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 sophisticated
Let’s break down their workflow to grasp why their medical document processing workflow was significantly difficult. It started with doc classification — sorting lots of to 1000’s of pages into classes like lab stories, ECG stories, and chart notes. This vital first step was carried out by their six-member group.
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 velocity tends to fluctuate closely based mostly 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 different systems, every with its personal codecs and constructions, the group needed to take care of numerous variation. It additionally made automation by way of conventional template-based information extraction practically not possible.
The complexity stemmed from how medical info is structured:
- Important 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 determine pages containing info 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 varied 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 staff 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 accurately interpret advanced medical terminology, lab values, and scientific findings.
Some case information had been enormous — reaching past 10,000 pages. Simply think about the sheer persistence and a spotlight to element required from the group and docs sifting by way of 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 information extraction, and decreasing person-hours.
How the underwriter carried out clever doc processing for medical data
Medical document 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 every 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 might assist classify medical data with excessive accuracy, present a filtered view of great pages, extract information key factors, and guarantee seamless information flows throughout the workflow. This satisfied them we might deal with their distinctive challenges.
Here is what the brand new automated medical data automation workflow regarded like:
1. Doc preparation
- The interior workers combines all medical data— lab stories, 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 every day
Notice: This strategy ensures safe dealing with of affected person info and maintains clear group all through the method.
2. Doc import
- The system checks for brand new information each hour
- Every case can comprise 2000-10,000 pages of medical data
- Information are readied for secured processing by way of 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 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 stories, ECG stories, and chart notes.
The classification course of includes:
- 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
Notice: The prompts are constantly refined based mostly on suggestions and new doc sorts, guaranteeing the system maintains excessive classification accuracy.
4. Information extraction
Our system handles three important doc sorts: lab stories, ECG stories, and chart notes. We’ve got 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 stories and ECG information, together with affected person title, blood glucose degree, creatinine worth, glomerular filtration price, hemoglobin worth, prostate particular antigen, white blood cell depend, hepatitis worth, ldl cholesterol worth, and lots of different vital lab measurements.
Mannequin 2 processes chart notes to extract 13 key fields together with blood strain, heartbeat price, O2 supply, O2 circulate price, temperature, date of delivery, gender, top, weight, and smoking standing. Every information level is linked to its supply web page and doc for verification.
5. Information export
The extracted info 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 comprise extracted medical values and measurements, whereas the chart notes CSV accommodates 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 kind. 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 beneath the identical figuring out quantity.
6. Validation
The CSV outputs are imported into their inside software, the place a two-member validation group (lowered from the unique six) evaluations the automated classifications. Right here, they’ll evaluate the extracted information towards the unique paperwork, making the verification course of fast and environment friendly.
As soon as the info is validated, the docs are notified. They’ll go forward to investigate medical histories and calculate life expectancy. As an alternative of spending hours organizing and reviewing paperwork, they now work with structured, verified info at their fingertips.
Notice: For safety and compliance causes, all processed information are routinely purged from Nanonets servers after 21 days.
The affect of automated medical document 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 info 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 way of 1000’s of pages as a result of they had been the one ones with the mandatory context to grasp 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 variation means they’ll deal with extra instances 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 document processing to cowl all ~200 instances weekly. That is not all. They’re already exploring automate different document-heavy workflows, like belief deed processing.
Occupied with what this implies to your group? The time to modernize doc processing is now. Healthcare documentation is turning 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— for those who do not modernize, your group will battle to maintain up.
Wish to see related outcomes together with your medical document processing? Let’s speak about how Nanonets may help. Schedule a demo now.
