Life insurance coverage corporations depend on correct medical underwriting to find out coverage pricing and threat. 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 inside 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 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 business projected to grow at twice its historical rate. With correct underwriting straight impacting coverage pricing, even small errors may result in hundreds of thousands in losses. Now, because the medical business concurrently faces worsening workforce shortages, they wanted an answer that might rework their doc processing whereas sustaining the precision their enterprise depends upon.
This can be a story of how they did it.
When medical report volumes get out of hand
Processing 200+ affected person case recordsdata weekly would possibly 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 facet, the healthcare business staffing shortages meant they needed to pay medical doctors 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 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 hundreds of thousands in mispriced insurance policies
- Probably shedding enterprise to extra agile opponents
- Larger processing prices straight affected profitability
- Rising prices as medical doctors 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 pressured these extremely educated professionals to behave as costly doc sorters moderately than making use of their experience to threat evaluation.
The mathematics was easy: each hour medical doctors 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 circumstances they may deal with, straight constraining income progress.
What makes healthcare doc processing difficult
Let’s break down their workflow to grasp why their medical report processing workflow was notably difficult. It started with doc classification — sorting tons of to 1000’s 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. Which 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 aptitude 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 lots of variation. It additionally made automation by means of conventional template-based information extraction practically not possible.
The complexity stemmed from how medical info is structured:
- Crucial 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 totally different documentation strategies
After classification, the group 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 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 medical doctors 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 workers to deal with preliminary classification, medical doctors had been nonetheless pressured to spend vital time extracting and verifying information from medical paperwork as a result of solely they possessed the specialised medical data wanted to appropriately interpret complicated medical terminology, lab values, and medical findings.
Some case recordsdata had been enormous — reaching past 10,000 pages. Simply think about the sheer endurance and a spotlight to element required from the group and medical doctors 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 information
Medical report volumes had been rising, and physician overview 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 threat any drop in accuracy through the transition.
Their necessities had been particular and demanding:
- Capability to course of 1000’s of pages of medical information each 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 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 information automation workflow seemed like:
1. Doc preparation
- The inner 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 circumstances are uploaded each 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 spanking 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
Be aware: 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 establish medical doc sorts. These prompts information the AI in understanding the particular traits of lab studies, ECG studies, 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
Be aware: The prompts are repeatedly refined based mostly on suggestions and new doc sorts, guaranteeing the system maintains excessive classification accuracy.
4. Knowledge extraction
Our system handles three most important doc sorts: lab studies, ECG studies, and chart notes. We now 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 title, blood glucose stage, creatinine worth, glomerular filtration charge, hemoglobin worth, prostate particular antigen, white blood cell depend, hepatitis worth, ldl cholesterol worth, and plenty of different crucial lab measurements.
Mannequin 2 processes chart notes to extract 13 key fields together with blood stress, heartbeat charge, O2 supply, O2 circulation charge, 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 title, an identifier, like ‘lab outcomes’ and ‘ECG’ or ‘chart notes’, will likely be routinely added to establish 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 will likely 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 inside software, the place a two-member validation group (diminished from the unique six) critiques the automated classifications. Right here, they will 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 investigate medical histories and calculate life expectancy. As a substitute 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 recordsdata are routinely purged from Nanonets servers after 21 days.
The influence of automated medical report 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 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 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 overview 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, 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 might be prepared for evaluation. It is a full shift from sorting papers to doing precise medical evaluation.
This variation means they will deal with extra circumstances with out having to rent costlier medical doctors. That is an enormous benefit, particularly with healthcare dealing with workers shortages whereas the business 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 circumstances weekly. That is not all. They’re already exploring how you can automate different document-heavy workflows, like belief deed processing.
Desirous 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% progress in high-acuity care and rising power 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 comparable outcomes along with your medical report processing? Let’s speak about how Nanonets will help. Schedule a demo now.
