Life insurance coverage corporations depend on correct medical underwriting to find out coverage pricing and danger. 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 below new pressures. Their two-part workflow — an inside crew categorized 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 specialists have been spending extra time sorting by means of 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 hundreds of thousands 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 depends upon.
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 would possibly 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 trade volumes meant they’d extra circumstances to course of. On the flip aspect, the healthcare trade staffing shortages meant they needed to pay docs and different medical specialists high {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 influence was evident:
- Slower processing occasions meant delayed underwriting selections
- Inaccurate life expectancy calculations resulted in hundreds of thousands in mispriced insurance policies
- Doubtlessly dropping enterprise to extra agile opponents
- Increased processing prices straight affected profitability
- Rising prices as docs hung out on paperwork as a substitute of study
Their medical specialists’ time was their most beneficial 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 reasonably than making use of their experience to danger evaluation.
The mathematics 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, straight constraining income progress.
What makes healthcare doc processing difficult
Let’s break down their workflow to know why their medical file processing workflow was significantly difficult. It started with doc classification — sorting a whole lot 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 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 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 methods, every with its personal codecs and buildings, the crew needed to take care of plenty of variation. It additionally made automation by means of conventional template-based information extraction practically unattainable.
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 crew would manually establish 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 assorted check 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. Medical doctors largely 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 have been nonetheless compelled to spend important time extracting and verifying information from medical paperwork as a result of solely they possessed the specialised medical data wanted to accurately interpret advanced medical terminology, lab values, and scientific findings.
Some case information have been large — reaching past 10,000 pages. Simply think about the sheer endurance and a spotlight to element required from the crew and docs sifting by means of all that. That is why when the agency was searching for automation options, there was a robust emphasis on attaining practically 100% classification accuracy, self-learning information extraction, and lowering person-hours.
How the underwriter carried out clever doc processing for medical data
Medical file volumes have been rising, and physician evaluate prices have 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 danger any drop in accuracy through the transition.
Their necessities have been particular and demanding:
- Capacity to course of 1000’s 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 may assist classify medical data with excessive accuracy, present a filtered view of serious 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 regarded like:
1. Doc preparation
- The inner workers 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 new information each hour
- Every case can comprise 2000-10,000 pages of medical data
- Recordsdata are readied for secured processing by means of 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 primarily based on fastidiously drafted pure language prompts that assist establish medical doc sorts. These prompts information the AI in understanding the precise 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 primary doc sorts: lab experiences, ECG experiences, and chart notes. We have now 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 identify, 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 stress, heartbeat fee, O2 supply, O2 stream fee, temperature, date of beginning, 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 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 comprises 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 comprises related medical info from docs’ notes.
In every file identify, an identifier, like ‘lab outcomes’ and ‘ECG’ or ‘chart notes’, can 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 can be saved within the Export folder on the S3 bucket below the identical figuring out quantity.
6. Validation
The CSV outputs are imported into their inside software, the place a two-member validation crew (diminished from the unique six) evaluations the automated classifications. Right here, they will evaluate the extracted information towards 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 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.
Be aware: For safety and compliance causes, all processed information are routinely purged from Nanonets servers after 21 days.
The influence of automated medical file processing
With structured information and an environment friendly validation course of, the underwriting supplier has been in a position to decrease 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 have 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 overall workload
- Full information classification and extraction for every case file inside 24 hours
- Obtain a 5X discount within the variety of pages docs must evaluate per case to compute life expectancy
- Freed medical specialists to give attention to their core experience
These numbers do not inform the entire story. Earlier than automation, docs needed to sift by means of 1000’s of pages as a result of they have been the one ones with the required context to know affected person information. 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 alteration means they will deal with extra circumstances with out having to rent costlier docs. That is an enormous benefit, particularly with healthcare going through workers shortages whereas the trade continues to develop.
Wanting forward
This profitable implementation has helped the underwriting supplier perceive what’s attainable 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 tips on how to automate different document-heavy workflows, like belief deed processing.
Interested by what this implies to your group? The time to modernize doc processing is now. Healthcare documentation is turning into extra advanced, 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— should you do not modernize, your group will battle to maintain up.
Wish to see related outcomes along with your medical file processing? Let’s speak about how Nanonets may help. Schedule a demo now.
