Life insurance coverage firms depend on correct medical underwriting to find out coverage pricing and threat. 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 advanced medical paperwork.
One main life settlement underwriter discovered their course of breaking beneath new pressures. Their two-part workflow — an inside workforce 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 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 business projected to develop at twice its historic charge. With correct underwriting straight 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 might rework 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 strain 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 consultants high {dollars}. Their present 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 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
- Increased processing prices straight affected profitability
- Rising prices as medical doctors frolicked 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 compelled these extremely skilled professionals to behave as costly doc sorters somewhat than making use of their experience to threat evaluation.
The maths was easy: each hour medical doctors spent organizing papers as an alternative of analyzing medical situations price the corporate considerably. This not solely elevated prices but in addition restricted the variety of circumstances they might deal with, straight constraining income progress.
What makes healthcare doc processing difficult
Let’s break down their workflow to know why their medical file processing workflow was notably difficult. It started with doc classification — sorting a whole bunch to 1000’s of pages into classes like lab stories, ECG stories, and chart notes. This vital first step was carried out by their six-member workforce.
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 completely different techniques, every with its personal codecs and buildings, the workforce needed to cope with a number of variation. It additionally made automation via conventional template-based information extraction almost unimaginable.
The complexity stemmed from how medical data is structured:
- Important particulars are unfold throughout a number of pages
- Info 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 workforce 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 may result in incorrect calculations downstream.
The paperwork would then be despatched to medical doctors for all times expectancy calculation. Docs 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, medical doctors have been nonetheless compelled to spend important time extracting and verifying information from medical paperwork as a result of solely they possessed the specialised medical information wanted to accurately interpret advanced medical terminology, lab values, and scientific findings.
Some case recordsdata have been large — reaching past 10,000 pages. Simply think about the sheer endurance and a focus to element required from the workforce and medical doctors sifting via all that. That is why when the agency was in search of automation options, there was a powerful emphasis on attaining almost 100% classification accuracy, self-learning information extraction, and lowering person-hours.
How the underwriter carried out clever doc processing for medical information
Medical file volumes have been rising, and physician evaluate prices have been mounting. The underwriting workforce 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 in the course of the transition.
Their necessities have been particular and demanding:
- Skill to course of 1000’s of pages of medical information each 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 great pages, extract information key factors, and guarantee seamless information 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 regarded like:
1. Doc preparation
- The inner employees combines all medical information— lab stories, ECG, chart notes, and different miscellaneous paperwork — for every affected person right into a single file
- Every affected person is assigned a novel quantity
- A folder with this quantity is created within the S3 enter folder
- 7-10 such circumstances are uploaded each day
Observe: 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
- Information are readied for secured processing via our platform
Observe: This automated monitoring ensures constant processing occasions 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 particular traits of lab stories, ECG stories, 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. Information extraction
Our system handles three important doc sorts: lab stories, ECG stories, and chart notes. We have now 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 stories and ECG information, together with affected person identify, blood glucose stage, creatinine worth, glomerular filtration charge, hemoglobin worth, prostate particular antigen, white blood cell rely, hepatitis worth, ldl cholesterol worth, and lots of different vital 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 information level is linked to its supply web page and doc for verification.
5. Information 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 accommodates 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 accommodates related medical data from medical doctors’ notes.
In every file identify, an identifier, like ‘lab outcomes’ and ‘ECG’ or ‘chart notes’, will likely 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 will likely 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 utility, the place a two-member validation workforce (diminished from the unique six) critiques the automated classifications. Right here, they will 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 data 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 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 workforce 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 information classification and extraction for every case file inside 24 hours
- Obtain a 5X discount within the variety of pages medical doctors must evaluate 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, medical doctors needed to sift via 1000’s of pages as a result of they have been the one ones with the mandatory context to know 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 will deal with extra circumstances with out having to rent costlier medical doctors. That is an enormous benefit, particularly with healthcare dealing with employees shortages whereas the business continues to develop.
Trying forward
This profitable implementation has helped the underwriting supplier perceive what’s doable with clever doc processing. They now wish to scale their medical file 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.
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 power situation administration. Add to this the rising staffing challenges in healthcare, and it is clear— if you happen to do not modernize, your group will wrestle to maintain up.
Need to see related outcomes together with your medical file processing? Let’s speak about how Nanonets may also help. Schedule a demo now.