Life insurance coverage firms 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 advanced medical paperwork.
One main life settlement underwriter discovered their course of breaking beneath new pressures. Their two-part workflow — an inner group categorised paperwork earlier than medical doctors reviewed them to calculate life expectancy — was struggling to maintain up as their enterprise grew and healthcare documentation grew to become more and more advanced. Medical specialists 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 might result in tens of millions in losses. Now, because the medical trade concurrently faces worsening workforce shortages, they wanted an answer that would rework their doc processing whereas sustaining the precision their enterprise is determined by.
This can be a story of how they did it.
When medical file volumes get out of hand
Processing 200+ affected person case recordsdata 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 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 that they had extra circumstances to course of. On the flip aspect, the healthcare trade staffing shortages meant they needed to pay medical doctors and different medical specialists high {dollars}. Their present 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 impression was evident:
- Slower processing occasions meant delayed underwriting selections
- Inaccurate life expectancy calculations resulted in tens of millions in mispriced insurance policies
- Doubtlessly shedding enterprise to extra agile rivals
- Increased processing prices straight affected profitability
- Rising prices as medical doctors hung out on paperwork as a substitute of study
Their medical specialists’ time was their most useful 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 maths was easy: each hour medical doctors spent organizing papers as a substitute of analyzing medical circumstances value the corporate considerably. This not solely elevated prices but additionally restricted the variety of circumstances 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 file processing workflow was notably difficult. It started with doc classification — sorting a whole bunch to hundreds of pages into classes like lab experiences, ECG experiences, 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 pace tends to range 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 techniques, every with its personal codecs and buildings, the group needed to cope with loads of variation. It additionally made automation by conventional template-based knowledge extraction almost not possible.
The complexity stemmed from how medical info is structured:
- Vital particulars are unfold throughout a number of pages
- Data wants chronological ordering
- Context from earlier pages is commonly 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 group 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 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 medical doctors 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, medical doctors 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 advanced medical terminology, lab values, and scientific findings.
Some case recordsdata had been big — reaching past 10,000 pages. Simply think about the sheer endurance and a spotlight to element required from the group and medical doctors sifting by all that. That is why when the agency was on the lookout for automation options, there was a robust emphasis on attaining almost 100% classification accuracy, self-learning knowledge extraction, and lowering person-hours.
How the underwriter carried out clever doc processing for medical data
Medical file volumes had been rising, and physician assessment 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 throughout the transition.
Their necessities had been particular and demanding:
- Potential to course of hundreds of pages of medical data day by 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 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 regarded 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 circumstances are uploaded day by day
Be aware: 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 spanking new recordsdata each hour
- Every case can include 2000-10,000 pages of medical data
- Recordsdata are readied for secured processing by our platform
Be aware: This automated monitoring ensures constant processing occasions 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 determine 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 includes:
- 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. Information extraction
Our system handles three fundamental doc sorts: lab experiences, ECG experiences, 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 experiences and ECG knowledge, together with affected person title, blood glucose stage, creatinine worth, glomerular filtration fee, hemoglobin worth, prostate particular antigen, white blood cell rely, 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 fee, O2 supply, O2 movement fee, temperature, date of beginning, gender, top, weight, and smoking standing. Every knowledge level is linked to its supply web page and doc for verification.
5. Information 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 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 info from medical doctors’ notes.
In every file title, an identifier, like ‘lab outcomes’ and ‘ECG’ or ‘chart notes’, can 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 can 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 inner software, the place a two-member validation group (diminished from the unique six) opinions the automated classifications. Right here, they’ll evaluate the extracted knowledge in opposition to the unique paperwork, making the verification course of fast and environment friendly.
As soon as the info is validated, the medical doctors 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 info at their fingertips.
Be aware: For safety and compliance causes, all processed recordsdata are routinely 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 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 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 knowledge classification and extraction for every case file inside 24 hours
- Obtain a 5X discount within the variety of pages medical doctors have to assessment per case to compute life expectancy
- Freed medical specialists to deal with their core experience
These numbers do not inform the entire story. Earlier than automation, medical doctors 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 medical doctors 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 alteration means they’ll deal with extra circumstances with out having to rent dearer medical doctors. That is an enormous benefit, particularly with healthcare going through employees shortages whereas the trade continues to develop.
Trying forward
This profitable implementation has helped the underwriting supplier perceive what’s attainable with clever doc processing. They now need to scale their medical file processing to cowl all ~200 circumstances weekly. That is not all. They’re already exploring learn how to automate different document-heavy workflows, like belief deed processing.
Fascinated about what this implies on 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 power situation administration. Add to this the rising staffing challenges in healthcare, and it is clear— when you do not modernize, your group will wrestle to maintain up.
Need to see comparable outcomes along with your medical file processing? Let’s speak about how Nanonets may also help. Schedule a demo now.
