How a number one underwriting supplier remodeled their doc assessment course of

Life insurance coverage firms depend on correct medical underwriting to find out coverage pricing and danger. These calculations come from specialised underwriting corporations that analyze sufferers’ medical information intimately. As healthcare digitization has surged from 10% in 2010 to 96% in 2023, these corporations 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 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 complicated. Medical specialists had 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 price. 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 might remodel their doc processing whereas sustaining the precision their enterprise relies on. 

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 would possibly 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 instances 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 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 influence was evident:

• Slower processing instances meant delayed underwriting choices
• Inaccurate life expectancy calculations resulted in tens of millions in mispriced insurance policies
• Probably dropping enterprise to extra agile opponents
• Greater processing prices straight affected profitability
• Rising prices as docs frolicked 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 pressured these extremely skilled professionals to behave as costly doc sorters reasonably than making use of their experience to danger evaluation. 

The maths was easy: each hour docs spent organizing papers as a substitute of analyzing medical situations price 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 difficult

Let’s break down their workflow to grasp 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 important 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 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 completely different methods, every with its personal codecs and constructions, the group needed to take care of a number of variation. It additionally made automation by means of conventional template-based knowledge extraction almost inconceivable.

The complexity stemmed from how medical data is structured:

• Essential particulars are unfold throughout a number of pages
• Data 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 completely different documentation strategies

After classification, the group would manually determine pages containing data 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 assorted check 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. 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 had been nonetheless pressured 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 recordsdata had been big — reaching past 10,000 pages. Simply think about the sheer persistence and a focus to element required from the group 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 almost 100% classification accuracy, self-learning knowledge extraction, and decreasing person-hours. 

How the underwriter applied clever doc processing for medical information

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 danger any drop in accuracy in the course of the transition.

Their necessities had been particular and demanding:

• Skill to course of hundreds of pages of medical information 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 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 might deal with their distinctive challenges.

This is what the brand new automated medical information automation workflow appeared like:

1. Doc preparation

• The interior employees combines all medical information— lab reviews, 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 every day

Word: 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 new recordsdata each hour
• Every case can comprise 2000-10,000 pages of medical information
• Recordsdata are readied for secured processing by means of our platform

Word: 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 determine medical doc sorts. These prompts information the AI in understanding the precise traits of lab reviews, ECG reviews, and chart notes.

The classification course of entails:

• 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

Word: The prompts are constantly refined based mostly on suggestions and new doc sorts, guaranteeing the system maintains excessive classification accuracy.

4. Knowledge extraction

Our system handles three fundamental doc sorts: lab reviews, ECG reviews, and chart notes. Now we 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 rely, hepatitis worth, ldl cholesterol worth, and plenty of different important 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 delivery, gender, top, 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 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 comprise extracted medical values and measurements, whereas the chart notes CSV comprises related medical data from docs’ notes.

In every file identify, an identifier, like ‘lab outcomes’ and ‘ECG’ or ‘chart notes’, will probably 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 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 (lowered from the unique six) evaluations the automated classifications. Right here, they will evaluate 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 investigate medical histories and calculate life expectancy. As an alternative of spending hours organizing and reviewing paperwork, they now work with structured, verified data at their fingertips.

Word: For safety and compliance causes, all processed recordsdata are routinely purged from Nanonets servers after 21 days.

The influence of automated medical file processing

With structured knowledge and an environment friendly validation course of, the underwriting supplier has been capable of reduce 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 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 entire 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 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, docs needed to sift by means of hundreds of pages as a result of they had been the one ones with the required 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 dealing with employees 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 file processing to cowl all ~200 instances weekly. That is not all. They’re already exploring easy methods to automate different document-heavy workflows, like belief deed processing.

Eager 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 power 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.

Wish to see comparable outcomes along with your medical file processing? Let’s speak about how Nanonets might help. Schedule a demo now.