This publish was written with Martyna Shallenberg and Brode Mccrady from Myriad Genetics.
Healthcare organizations face challenges in processing and managing excessive volumes of advanced medical documentation whereas sustaining high quality in affected person care. These organizations want options to course of paperwork successfully to fulfill rising calls for. Myriad Genetics, a supplier of genetic testing and precision medication options serving healthcare suppliers and sufferers worldwide, addresses this problem.
Myriad’s Income Engineering Division processes hundreds of healthcare paperwork day by day throughout Ladies’s Well being, Oncology, and Psychological Well being divisions. The corporate classifies incoming paperwork into lessons akin to Take a look at Request Varieties, Lab Outcomes, Scientific Notes, and Insurance coverage to automate Prior Authorization workflows. The system routes these paperwork to acceptable exterior distributors for processing primarily based on their recognized doc class. They manually carry out Key Info Extraction (KIE) together with insurance coverage particulars, affected person data, and check outcomes to find out Medicare eligibility and assist downstream processes.
As doc volumes elevated, Myriad confronted challenges with its current system. The automated doc classification answer labored however was pricey and time-consuming. Info extraction remained guide on account of complexity. To handle excessive prices and gradual processing, Myriad wanted a greater answer.
This publish explores how Myriad Genetics partnered with the AWS Generative AI Innovation Heart (GenAIIC) to remodel their healthcare doc processing pipeline utilizing Amazon Bedrock and Amazon Nova basis fashions. We element the challenges with their current answer, and the way generative AI lowered prices and improved processing velocity.
We look at the technical implementation utilizing AWS’s open supply GenAI Clever Doc Processing (GenAI IDP) Accelerator answer, the optimization methods used for doc classification and key data extraction, and the measurable enterprise influence on Myriad’s prior authorization workflows. We cowl how we used immediate engineering methods, mannequin choice methods, and architectural selections to construct a scalable answer that processes advanced medical paperwork with excessive accuracy whereas lowering operational prices.
Doc processing bottlenecks limiting healthcare operations
Myriad Genetics’ day by day operations rely on effectively processing advanced medical paperwork containing crucial data for affected person care workflows and regulatory compliance. Their current answer mixed Amazon Textract for Optical Character Recognition (OCR) with Amazon Comprehend for doc classification.
Regardless of 94% classification accuracy, this answer had operational challenges:
- Operational prices: 3 cents per web page leading to $15,000 month-to-month bills per enterprise unit
- Classification latency: 8.5 minutes per doc, delaying downstream prior authorization workflows
Info extraction was fully guide, requiring contextual understanding to distinguish crucial scientific distinctions (like “is metastatic” versus “is just not metastatic”) and to find data like insurance coverage numbers and affected person data throughout various doc codecs. This processing burden was substantial, with Ladies’s Well being customer support requiring as much as 10 full-time staff contributing 78 hours day by day within the Ladies’s Well being enterprise unit alone.
Myriad wanted an answer to:
- Cut back doc classification prices whereas sustaining or bettering accuracy
- Speed up doc processing to get rid of workflow bottlenecks
- Automate data extraction for medical paperwork
- Scale throughout a number of enterprise models and doc varieties
Amazon Bedrock and generative AI
Trendy massive language fashions (LLMs) course of advanced healthcare paperwork with excessive accuracy on account of pre-training on large textual content corpora. This pre-training allows LLMs to know language patterns and doc buildings with out function engineering or massive labeled datasets. Amazon Bedrock is a completely managed service that gives a broad vary of high-performing LLMs from main AI firms. It gives the safety, privateness, and accountable AI capabilities that healthcare organizations require when processing delicate medical data. For this answer, we used Amazon’s latest basis fashions:
- Amazon Nova Professional: An economical, low-latency mannequin preferrred for doc classification
- Amazon Nova Premier: A sophisticated mannequin with reasoning capabilities for data extraction
Resolution overview
We carried out an answer with Myriad utilizing AWS’s open supply GenAI IDP Accelerator. The accelerator gives a scalable, serverless structure that converts unstructured paperwork into structured information. The accelerator processes a number of paperwork in parallel by way of configurable concurrency limits with out overwhelming downstream providers. Its built-in analysis framework lets customers present anticipated output by way of the person interface (UI) and consider generated outcomes to iteratively customise configuration and enhance accuracy.
The accelerator provides 1-click deployment with a selection of pre-built patterns optimized for various workloads with totally different configurability, value, and accuracy necessities:
- Sample 1 – Makes use of Amazon Bedrock Knowledge Automation, a completely managed service that gives wealthy out-of-the-box options, ease of use, and simple per-page pricing. This sample is beneficial for many use circumstances.
- Sample 2 – Makes use of Amazon Textract and Amazon Bedrock with Amazon Nova, Anthropic’s Claude, or customized fine-tuned Amazon Nova fashions. This sample is right for advanced paperwork requiring customized logic.
- Sample 3 – Makes use of Amazon Textract, Amazon SageMaker with a fine-tuned mannequin for classification, and Amazon Bedrock for extraction. This sample is right for paperwork requiring specialised classification.
Sample 2 proved most fitted for this undertaking, assembly the crucial requirement of low value whereas providing flexibility to optimize accuracy by way of immediate engineering and LLM choice. This sample provides a no-code configuration – customise doc varieties, extraction fields, and processing logic by way of configuration, editable within the net UI.
We custom-made the definitions of doc lessons, key attributes and their definitions per doc class, LLM selection, LLM hyperparameters, and classification and extraction LLM prompts through Sample 2’s config file. In manufacturing, Myriad built-in this answer into their current event-driven structure. The next diagram illustrates the manufacturing pipeline:
- Doc Ingestion: Incoming order occasions set off doc retrieval from supply doc administration programs, with cache optimization for beforehand processed paperwork.
- Concurrency Administration: DynamoDB tracked concurrent AWS Step Operate jobs whereas Amazon Easy Queue Service (SQS) queues recordsdata exceeding concurrency limits for doc processing.
- Textual content Extraction: Amazon Textract extracted textual content, structure data, tables and varieties from the normalized paperwork.
- Classification: The configured LLM analyzed the extracted content material primarily based on the custom-made doc classification immediate offered within the config file and classifies paperwork into acceptable classes.
- Key Info Extraction: The configured LLM extracted medical data utilizing extraction immediate offered within the config file.
- Structured Output: The pipeline formatted the leads to a structured method and delivered to Myriad’s Authorization System through RESTful operations.
Doc classification with generative AI
Whereas Myriad’s current answer achieved 94% accuracy, misclassifications occurred on account of structural similarities, overlapping content material, and shared formatting patterns throughout doc varieties. This semantic ambiguity made it troublesome to tell apart between related paperwork. We guided Myriad on immediate optimization methods that used LLM’s contextual understanding capabilities. This method moved past sample matching to allow semantic evaluation of doc context and function, figuring out distinguishing options that human consultants acknowledge however earlier automated programs missed.
AI-driven immediate engineering for doc classification
We developed class definitions with distinguishing traits between related doc varieties. To determine these differentiators, we offered doc samples from every class to Anthropic Claude Sonnet 3.7 on Amazon Bedrock with mannequin reasoning enabled (a function that enables the mannequin to display its step-by-step evaluation course of). The mannequin recognized distinguishing options between related doc lessons, which Myriad’s material consultants refined and included into the GenAI IDP Accelerator’s Sample 2 config file for doc classification prompts.
Format-based classification methods
We used doc construction and formatting as key differentiators to tell apart between related doc varieties that shared comparable content material however differed in construction. We enabled the classification fashions to acknowledge format-specific traits akin to structure buildings, area preparations, and visible components, permitting the system to distinguish between paperwork that textual content material alone can’t distinguish. For instance, lab studies and check outcomes each include affected person data and medical information, however lab studies show numerical values in tabular format whereas check outcomes comply with a story format. We instructed the LLM: “Lab studies include numerical outcomes organized in tables with reference ranges and models. Take a look at outcomes current findings in paragraph format with scientific interpretations.”
Implementing destructive prompting for enhanced accuracy
We carried out destructive prompting methods to resolve confusion between related paperwork by explicitly instructing the mannequin what classifications to keep away from. This method added exclusionary language to classification prompts, specifying traits that shouldn’t be related to every doc kind. Initially, the system often misclassified Take a look at Request Varieties (TRFs) as Take a look at Outcomes on account of confusion between affected person medical historical past and lab measurements. Including a destructive immediate like “These varieties include affected person medical historical past. DO NOT confuse them with check outcomes which include present/latest lab measurements” to the TRF definition improved the classification accuracy by 4%. By offering specific steering on frequent misclassification patterns, the system prevented typical errors and confusion between related doc varieties.
Mannequin choice for value and efficiency optimization
Mannequin choice drives optimum cost-performance at scale, so we carried out complete benchmarking utilizing the GenAI IDP Accelerator’s analysis framework. We examined 4 basis fashions—Amazon Nova Lite, Amazon Nova Professional, Amazon Nova Premier, and Anthropic Claude Sonnet 3.7—utilizing 1,200 healthcare paperwork throughout three doc lessons: Take a look at Request Varieties, Lab Outcomes, and Insurance coverage. We assessed every mannequin utilizing three crucial metrics: classification accuracy, processing latency, and value per doc. The accelerator’s value monitoring enabled direct comparability of operational bills throughout totally different mannequin configurations, guaranteeing efficiency enhancements translate into measurable enterprise worth at scale.
The analysis outcomes demonstrated that Amazon Nova Professional achieved optimum steadiness for Myriad’s use case. We transitioned from Myriad’s Amazon Comprehend implementation to Amazon Nova Professional with optimized prompts for doc classification, reaching important enhancements: classification accuracy elevated from 94% to 98%, processing prices decreased by 77%, and processing velocity improved by 80%—lowering classification time from 8.5 minutes to 1.5 minutes per doc.
Automating Key Info Extraction with generative AI
Myriad’s data extraction was guide, requiring as much as 10 full-time staff contributing 78 hours day by day within the Ladies’s Well being unit alone, which created operational bottlenecks and scalability constraints. Automating healthcare KIE offered challenges: checkbox fields required distinguishing between marking types (checkmarks, X’s, handwritten marks); paperwork contained ambiguous visible components like overlapping marks or content material spanning a number of fields; extraction wanted contextual understanding to distinguish scientific distinctions and find data throughout various doc codecs. We labored with Myriad to develop an automatic KIE answer, implementing the next optimization methods to handle extraction complexity.
Enhanced OCR configuration for checkbox recognition
To handle checkbox identification challenges, we enabled Amazon Textract’s specialised TABLES and FORMS options on the GenAI IDP Accelerator portal as proven within the following picture, to enhance OCR discrimination between chosen and unselected checkbox components. These options enhanced the system’s skill to detect and interpret marking types present in medical varieties.
We enhanced accuracy by incorporating visible cues into the extraction prompts. We up to date the prompts with directions akin to “search for seen marks in or across the small sq. bins (✓, x, or handwritten marks)” to information the language mannequin in figuring out checkbox alternatives. This mix of enhanced OCR capabilities and focused prompting improved checkbox extraction in medical varieties.
Visible context studying by way of few-shot examples
Configuring Textract and bettering prompts alone couldn’t deal with advanced visible components successfully. We carried out a multimodal method that despatched each doc photographs and extracted textual content from Textract to the inspiration mannequin, enabling simultaneous evaluation of visible structure and textual content material for correct extraction selections. We carried out few-shot studying by offering instance doc photographs paired with their anticipated extraction outputs to information the mannequin’s understanding of assorted kind layouts and marking types. A number of doc picture examples with their right extraction patterns create prolonged LLM prompts. We leveraged the GenAI IDP Accelerator’s built-in integration with Amazon Bedrock’s immediate caching function to cut back prices and latency. Immediate caching shops prolonged few-shot examples in reminiscence for five minutes—when processing a number of related paperwork inside that timeframe, Bedrock reuses cached examples as an alternative of reprocessing them, lowering each value and processing time.
Chain of thought reasoning for advanced extraction
Whereas this multimodal method improved extraction accuracy, we nonetheless confronted challenges with overlapping and ambiguous tick marks in advanced kind layouts. To carry out nicely in ambiguous and sophisticated conditions, we used Amazon Nova Premier and carried out Chain of Thought reasoning to have the mannequin assume by way of extraction selections step-by-step utilizing considering tags. For instance:
Moreover, we included reasoning explanations within the few-shot examples, demonstrating how we reached conclusions in ambiguous circumstances. This method enabled the mannequin to work by way of advanced visible proof and contextual clues earlier than making remaining determinations, bettering efficiency with ambiguous tick marks.
Testing throughout 32 doc samples with various complexity ranges through the GenAI IDP Accelerator revealed that Amazon Textract with Format, TABLES, and FORMS options enabled, paired with Amazon Nova Premier’s superior reasoning capabilities and the inclusion of few-shot examples, delivered one of the best outcomes. The answer achieved 90% accuracy (identical as human evaluator baseline accuracy) whereas processing paperwork in roughly 1.3 minutes every.
Outcomes and enterprise influence
Via our new answer, we delivered measurable enhancements that met the enterprise objectives established on the undertaking outset:
Doc classification efficiency:
- We elevated accuracy from 94% to 98% by way of immediate optimization methods for Amazon Nova Professional, together with AI-driven immediate engineering, document-format primarily based classification methods, and destructive prompting.
- We lowered classification prices by 77% (from 3.1 to 0.7 cents per web page) by migrating from Amazon Comprehend to Amazon Nova Professional with optimized prompts.
- We lowered classification time by 80% (from 8.5 to 1.5 minutes per doc) by selecting Amazon Nova Professional to offer a low-latency and cost-effective answer.
New automated Key Info Extraction efficiency:
- We achieved 90% extraction accuracy (identical because the baseline guide course of): Delivered by way of a mixture of Amazon Textract’s doc evaluation capabilities, visible context studying by way of few-shot examples and Amazon Nova Premier’s reasoning for advanced information interpretation.
- We achieved processing prices of 9 cents per web page and processing time of 1.3 minutes per doc in comparison with guide baseline requiring as much as 10 full-time staff working 78 hours day by day per enterprise unit.
Enterprise influence and rollout
Myriad has deliberate a phased rollout starting with doc classification. They plan to launch our new classification answer within the Ladies’s Well being enterprise unit, adopted by Oncology and Psychological Well being divisions. Because of our work, Myriad will notice as much as $132K in annual financial savings of their doc classification prices. The answer reduces every prior authorization submission time by 2 minutes—specialists now full orders in 4 minutes as an alternative of six minutes on account of quicker entry to tagged paperwork. This enchancment saves 300 hours month-to-month throughout 9,000 prior authorizations in Ladies’s Well being alone, equal to 50 hours per prior authorization specialist.
These measurable enhancements have reworked Myriad’s operations, as their engineering management confirms:
“Partnering with the GenAIIC emigrate our Clever Doc Processing answer from AWS Comprehend to Bedrock has been a transformative step ahead. By bettering each efficiency and accuracy, the answer is projected to ship financial savings of greater than $10,000 per 30 days. The staff’s shut collaboration with Myriad’s inner engineering staff delivered a high-quality, scalable answer, whereas their deep experience in superior language fashions has elevated our capabilities. This has been a superb instance of how innovation and partnership can drive measurable enterprise influence.”
– Martyna Shallenberg, Senior Director of Software program Engineering, Myriad Genetics
Conclusion
The AWS GenAI IDP Accelerator enabled Myriad’s fast implementation, offering a versatile framework that lowered improvement time. Healthcare organizations want tailor-made options—the accelerator delivers intensive customization capabilities that permit customers adapt options to particular doc varieties and workflows with out requiring intensive code adjustments or frequent redeployment throughout improvement. Our method demonstrates the facility of strategic immediate engineering and mannequin choice. We achieved excessive accuracy in a specialised area by specializing in immediate design, together with destructive prompting and visible cues. We optimized each value and efficiency by choosing Amazon Nova Professional for classification and Nova Premier for advanced extraction—matching the correct mannequin to every particular process.
Discover the answer for your self
Organizations trying to enhance their doc processing workflows can expertise these advantages firsthand. The open supply GenAI IDP Accelerator that powered Myriad’s transformation is offered to deploy and check in your surroundings. The accelerator’s easy setup course of lets customers rapidly consider how generative AI can rework doc processing challenges.
When you’ve explored the accelerator and seen its potential influence in your workflows, attain out to the AWS GenAIIC staff to discover how the GenAI IDP Accelerator might be custom-made and optimized to your particular use case. This hands-on method ensures you can also make knowledgeable selections about implementing clever doc processing in your group.
In regards to the authors
Priyashree Roy is a Knowledge Scientist II on the AWS Generative AI Innovation Heart, the place she applies her experience in machine studying and generative AI to develop modern options for strategic AWS clients. She brings a rigorous scientific method to advanced enterprise challenges, knowledgeable by her PhD in experimental particle physics from Florida State College and postdoctoral analysis on the College of Michigan.
Mofijul Islam is an Utilized Scientist II and Tech Lead on the AWS Generative AI Innovation Heart, the place he helps clients sort out customer-centric analysis and enterprise challenges utilizing generative AI, massive language fashions (LLM), multi-agent studying, code era, and multimodal studying. He holds a PhD in machine studying from the College of Virginia, the place his work targeted on multimodal machine studying, multilingual pure language processing (NLP), and multitask studying. His analysis has been printed in top-tier conferences like NeurIPS, Worldwide Convention on Studying Representations (ICLR), Empirical Strategies in Pure Language Processing (EMNLP), Society for Synthetic Intelligence and Statistics (AISTATS), and Affiliation for the Development of Synthetic Intelligence (AAAI), in addition to Institute of Electrical and Electronics Engineers (IEEE) and Affiliation for Computing Equipment (ACM) Transactions.
Nivedha Balakrishnan is a Deep Studying Architect II on the AWS Generative AI Innovation Heart, the place she helps clients design and deploy generative AI functions to resolve advanced enterprise challenges. Her experience spans massive language fashions (LLMs), multimodal studying, and AI-driven automation. She holds a Grasp’s in Utilized Knowledge Science from San Jose State College and a Grasp’s in Biomedical Engineering from Linköping College, Sweden. Her earlier analysis targeted on AI for drug discovery and healthcare functions, bridging life sciences with machine studying.
Martyna Shallenberg is a Senior Director of Software program Engineering at Myriad Genetics, the place she leads cross-functional groups in constructing AI-driven enterprise options that rework income cycle operations and healthcare supply. With a novel background spanning genomics, molecular diagnostics, and software program engineering, she has scaled modern platforms starting from Clever Doc Processing (IDP) to modular LIMS options. Martyna can also be the Founder & President of BioHive’s HealthTech Hub, fostering cross-domain collaboration to speed up precision medication and healthcare innovation.
Brode Mccrady is a Software program Engineering Supervisor at Myriad Genetics, the place he leads initiatives in AI, income programs, and clever doc processing. With over a decade of expertise in enterprise intelligence and strategic analytics, Brode brings deep experience in translating advanced enterprise wants into scalable technical options. He holds a level in Economics, which informs his data-driven method to problem-solving and enterprise technique.
Randheer Gehlot is a Principal Buyer Options Supervisor at AWS who focuses on healthcare and life sciences transformation. With a deep concentrate on AI/ML functions in healthcare, he helps enterprises design and implement environment friendly cloud options that handle actual enterprise challenges. His work entails partnering with organizations to modernize their infrastructure, allow innovation, and speed up their cloud adoption journey whereas guaranteeing sensible, sustainable outcomes.
Acknowledgements
We wish to thank Bob Strahan, Kurt Mason, Akhil Nooney and Taylor Jensen for his or her important contributions, strategic selections and steering all through.
