Life insurance coverage corporations 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 advanced 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 medical doctors reviewed them to calculate life expectancy — was struggling to maintain up as their enterprise grew and healthcare documentation turned more and more advanced. Medical consultants had 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 business projected to grow at twice its historical rate. With correct underwriting instantly impacting coverage pricing, even small errors may result in tens of millions in losses. Now, because the medical business concurrently faces worsening workforce shortages, they wanted an answer that would remodel their doc processing whereas sustaining the precision their enterprise will depend on.
It is 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 whole 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 that they had extra circumstances to course of. On the flip aspect, the healthcare business staffing shortages meant they needed to pay medical doctors and different medical consultants prime {dollars}. Their current 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 influence 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 opponents
- Greater processing prices instantly affected profitability
- Rising prices as medical doctors hung out on paperwork as an alternative of study
Their medical consultants’ time was their most precious 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 quite than making use of their experience to danger evaluation.
The mathematics 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 in addition restricted the variety of circumstances 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 file processing workflow was notably difficult. It started with doc classification — sorting lots of to 1000’s of pages into classes like lab reviews, ECG reviews, 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 range 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 take care of plenty of variation. It additionally made automation via conventional template-based information extraction almost inconceivable.
The complexity stemmed from how medical info is structured:
- Important particulars are unfold throughout a number of pages
- Info wants chronological ordering
- Context from earlier pages is usually required
- Dates are generally 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 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 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 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 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 appropriately interpret advanced medical terminology, lab values, and medical findings.
Some case recordsdata had been large — reaching past 10,000 pages. Simply think about the sheer persistence and a focus to element required from the group and medical doctors sifting via 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 information extraction, and lowering 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 through the transition.
Their necessities had been particular and demanding:
- Skill to course of 1000’s of pages of medical information 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 information with excessive accuracy, present a filtered view of serious pages, extract information key factors, and guarantee seamless information flows throughout the workflow. This satisfied them we may deal with their distinctive challenges.
This is what the brand new automated medical information automation workflow appeared like:
1. Doc preparation
- The interior workers 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 circumstances are uploaded day by day
Observe: 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 comprise 2000-10,000 pages of medical information
- Information are readied for secured processing via our platform
Observe: 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 based mostly on fastidiously drafted pure language prompts that assist determine medical doc sorts. These prompts information the AI in understanding the particular traits of lab reviews, ECG reviews, and chart notes.
The classification course of includes:
- 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
Observe: The prompts are constantly refined based mostly on suggestions and new doc sorts, making certain 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 information and one other for chart notes.
Mannequin 1 extracts roughly 50 fields from lab reviews and ECG information, together with affected person identify, blood glucose degree, creatinine worth, glomerular filtration price, hemoglobin worth, prostate particular antigen, white blood cell rely, 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 stress, heartbeat price, O2 supply, O2 move price, temperature, date of start, gender, top, 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 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 info from medical doctors’ notes.
In every file identify, 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 information can 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 (decreased from the unique six) opinions the automated classifications. Right here, they’ll examine 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 research 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.
Observe: 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 information 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 information validation group had been reassigned to different roles, so validation now runs easily with simply 2 folks
- Classification accuracy maintained at 97-99%
- Automated workflow is dealing with ~20% of the full workload
- Full information 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 consultants to give attention to their core experience
These numbers do not inform the entire story. Earlier than automation, medical doctors needed to sift via 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 might be prepared for evaluation. It is a full shift from sorting papers to doing precise medical evaluation.
This modification means they’ll deal with extra circumstances 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 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 the best way to automate different document-heavy workflows, like belief deed processing.
Occupied with what this implies in 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— 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 may also help. Schedule a demo now.
