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 information 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 inner group 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 complicated. Medical consultants have 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 grow at twice its historical rate. With correct underwriting straight impacting coverage pricing, even small errors may result in tens of millions 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 is dependent upon.
This can be a story of how they did it.
When medical report volumes get out of hand
Processing 200+ affected person case information weekly may sound manageable. Nevertheless, every case contained a affected person’s complete 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 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 consultants prime {dollars}. Their present guide workflow merely could not scale to fulfill 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 impression was evident:
- Slower processing instances meant delayed underwriting choices
- Inaccurate life expectancy calculations resulted in tens of millions in mispriced insurance policies
- Doubtlessly dropping enterprise to extra agile rivals
- Greater 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 precious 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 moderately than making use of their experience to danger evaluation.
The mathematics was easy: each hour docs spent organizing papers as an alternative of analyzing medical situations value the corporate considerably. This not solely elevated prices but additionally restricted the variety of instances they might deal with, straight constraining income development.
What makes healthcare doc processing sophisticated
Let’s break down their workflow to grasp why their medical report processing workflow was significantly difficult. It started with doc classification — sorting a whole lot to hundreds of pages into classes like lab studies, ECG studies, and chart notes. This crucial first step was carried out by their six-member group.
Every member may course of ~400 digital pages per hour. That means, a single case file of two,000 pages would take over 5 hours to finish. Additionally, the pace tends to differ 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 information coming from over 230 different systems, every with its personal codecs and constructions, the group needed to cope with a whole lot of variation. It additionally made automation via conventional template-based knowledge extraction practically not possible.
The complexity stemmed from how medical data is structured:
- Important 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 completely different documentation strategies
After classification, the group 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 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 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 staff to deal with preliminary classification, docs have been nonetheless compelled to spend important 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 information have been enormous — reaching past 10,000 pages. Simply think about the sheer endurance and a focus to element required from the group and docs sifting via all that. That is why when the agency was in search of 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 information
Medical report volumes have been rising, and physician evaluation prices have 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 danger any drop in accuracy in the course of the transition.
Their necessities have been particular and demanding:
- Capacity to course of hundreds of pages of medical information 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 information with excessive accuracy, present a filtered view of great pages, extract knowledge key factors, and guarantee seamless knowledge flows inside the workflow. This satisfied them we may deal with their distinctive challenges.
Here is what the brand new automated medical information automation workflow appeared like:
1. Doc preparation
- The interior workers combines all medical information— lab studies, ECG, chart notes, and different miscellaneous paperwork — for every affected person right into a single file
- Every affected person is assigned a singular 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 strategy 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 information
- Information are readied for secured processing via our platform
Be aware: This automated monitoring ensures constant processing instances 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 establish medical doc varieties. These prompts information the AI in understanding the precise traits of lab studies, ECG studies, and chart notes.
The classification course of includes:
- Figuring out doc varieties 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 repeatedly refined based mostly on suggestions and new doc varieties, guaranteeing the system maintains excessive classification accuracy.
4. Knowledge extraction
Our system handles three principal doc varieties: lab studies, ECG studies, 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 studies and ECG knowledge, together with affected person identify, blood glucose degree, 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.
Mannequin 2 processes chart notes to extract 13 key fields together with blood strain, heartbeat charge, O2 supply, O2 movement charge, 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. Knowledge 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 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 data from docs’ notes.
In every file identify, an identifier, like ‘lab outcomes’ and ‘ECG’ or ‘chart notes’, will probably be robotically 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 will probably 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 inner utility, the place a two-member validation group (diminished from the unique six) opinions the automated classifications. Right here, they will examine the extracted knowledge 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 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.
Be aware: For safety and compliance causes, all processed information are robotically purged from Nanonets servers after 21 days.
The impression of automated medical report processing
With structured knowledge 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 group 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 whole workload
- Full knowledge classification and extraction for every case file inside 24 hours
- Obtain a 5X discount within the variety of pages docs must 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 via hundreds of pages as a result of they have been the one ones with the mandatory context to grasp 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.
This transformation means they will deal with extra instances 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.
Trying forward
This profitable implementation has helped the underwriting supplier perceive what’s potential 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 best way to automate different document-heavy workflows, like belief deed processing.
Fascinated with what this implies to 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 continual 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 report processing? Let’s speak about how Nanonets may also help. Schedule a demo now.
