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 data 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 beneath new pressures. Their two-part workflow — an inside crew categorized paperwork earlier than medical doctors 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 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 instantly impacting coverage pricing, even small errors might result in tens of millions 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 is dependent upon.
It is a story of how they did it.
When medical document volumes get out of hand
Processing 200+ affected person case recordsdata weekly may 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 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 business volumes meant they’d extra instances to course of. On the flip facet, the healthcare business staffing shortages meant they needed to pay medical doctors and different medical specialists prime {dollars}. Their current guide workflow merely could not scale to fulfill 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 occasions meant delayed underwriting selections
- Inaccurate life expectancy calculations resulted in tens of millions in mispriced insurance policies
- Doubtlessly dropping enterprise to extra agile rivals
- Larger processing prices instantly affected profitability
- Rising prices as medical doctors hung out on paperwork as an alternative of research
Their medical specialists’ time was their most beneficial useful resource. And but, regardless of the 2-step workflow, the sheer quantity of paperwork pressured these extremely educated professionals to behave as costly doc sorters quite than making use of their experience to danger evaluation.
The maths was easy: each hour medical doctors spent organizing papers as an alternative of analyzing medical circumstances price the corporate considerably. This not solely elevated prices but additionally restricted the variety of instances they might deal with, instantly constraining income progress.
What makes healthcare doc processing difficult
Let’s break down their workflow to grasp why their medical document processing workflow was notably difficult. It started with doc classification — sorting a whole bunch to 1000’s of pages into classes like lab studies, ECG studies, and chart notes. This important first step was carried out by their six-member crew.
Every member might 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 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 different systems, every with its personal codecs and constructions, the crew needed to cope with plenty of variation. It additionally made automation by means of conventional template-based information extraction practically unimaginable.
The complexity stemmed from how medical info is structured:
- Vital particulars are unfold throughout a number of pages
- Info 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 crew 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 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 medical doctors for all times expectancy calculation. Medical doctors 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, medical doctors had been nonetheless pressured 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 complicated medical terminology, lab values, and scientific findings.
Some case recordsdata had been large — reaching past 10,000 pages. Simply think about the sheer endurance and a spotlight to element required from the crew and medical doctors sifting by means 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 evaluation prices had 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 in the course of the transition.
Their necessities had been particular and demanding:
- Means to course of 1000’s of pages of medical data 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 might 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 might deal with their distinctive challenges.
Here is what the brand new automated medical data automation workflow appeared like:
1. Doc preparation
- The inner workers combines all medical data— 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
Notice: This method 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 recordsdata each hour
- Every case can include 2000-10,000 pages of medical data
- Recordsdata are readied for secured processing by means of our platform
Notice: 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 fastidiously drafted pure language prompts that assist determine 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 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
Notice: The prompts are constantly refined primarily based on suggestions and new doc varieties, guaranteeing the system maintains excessive classification accuracy.
4. Information extraction
Our system handles three primary doc varieties: lab studies, ECG studies, and chart notes. Now we have 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 studies and ECG information, together with affected person identify, 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 important lab measurements.
Mannequin 2 processes chart notes to extract 13 key fields together with blood stress, heartbeat price, O2 supply, O2 stream 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 recordsdata 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 include extracted medical values and measurements, whereas the chart notes CSV accommodates related medical info from medical doctors’ notes.
In every file identify, an identifier, like ‘lab outcomes’ and ‘ECG’ or ‘chart notes’, shall be robotically added to determine the content material kind. 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 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 crew (lowered from the unique six) opinions the automated classifications. Right here, they’ll evaluate the extracted information in opposition to the unique paperwork, making the verification course of fast and environment friendly.
As soon as the information is validated, the medical doctors 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.
Notice: For safety and compliance causes, all processed recordsdata are robotically 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 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 crew 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 information classification and extraction for every case file inside 24 hours
- Obtain a 5X discount within the variety of pages medical doctors must evaluation per case to compute life expectancy
- Freed medical specialists to concentrate on their core experience
These numbers do not inform the entire story. Earlier than automation, medical doctors needed to sift by means 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 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 modification means they’ll deal with extra instances with out having to rent costlier medical doctors. That is an enormous benefit, particularly with healthcare going through workers shortages whereas the business continues to develop.
Wanting forward
This profitable implementation has helped the underwriting supplier perceive what’s doable 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 find out how to automate different document-heavy workflows, like belief deed processing.
Eager about what this implies to your group? The time to modernize doc processing is now. Healthcare documentation is turning into extra complicated, with a 41% progress in high-acuity care and rising continual 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 along with your medical document processing? Let’s discuss how Nanonets may help. Schedule a demo now.
