How Myriad Genetics achieved quick, correct, and cost-efficient doc processing utilizing the AWS open-source Generative AI Clever Doc Processing Accelerator

This publish was written with Martyna Shallenberg and Brode Mccrady from Myriad Genetics.

Healthcare organizations face challenges in processing and managing excessive volumes of complicated medical documentation whereas sustaining high quality in affected person care. These organizations want options to course of paperwork successfully to satisfy rising calls for. Myriad Genetics, a supplier of genetic testing and precision drugs options serving healthcare suppliers and sufferers worldwide, addresses this problem.

Myriad’s Income Engineering Division processes hundreds of healthcare paperwork each day throughout Girls’s Well being, Oncology, and Psychological Well being divisions. The corporate classifies incoming paperwork into courses comparable to Take a look at Request Kinds, Lab Outcomes, Medical 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 Data Extraction (KIE) together with insurance coverage particulars, affected person data, and check outcomes to find out Medicare eligibility and help downstream processes.

As doc volumes elevated, Myriad confronted challenges with its present system. The automated doc classification resolution labored however was expensive and time-consuming. Data extraction remained guide attributable to complexity. To handle excessive prices and gradual processing, Myriad wanted a greater resolution.

This publish explores how Myriad Genetics partnered with the AWS Generative AI Innovation Heart (GenAIIC) to rework their healthcare doc processing pipeline utilizing Amazon Bedrock and Amazon Nova basis fashions. We element the challenges with their present resolution, and the way generative AI decreased prices and improved processing pace.

We study the technical implementation utilizing AWS’s open supply GenAI Clever Doc Processing (GenAI IDP) Accelerator resolution, the optimization methods used for doc classification and key data extraction, and the measurable enterprise affect on Myriad’s prior authorization workflows. We cowl how we used immediate engineering strategies, mannequin choice methods, and architectural choices to construct a scalable resolution that processes complicated medical paperwork with excessive accuracy whereas lowering operational prices.

Doc processing bottlenecks limiting healthcare operations

Myriad Genetics’ each day operations depend upon effectively processing complicated medical paperwork containing important data for affected person care workflows and regulatory compliance. Their present resolution mixed Amazon Textract for Optical Character Recognition (OCR) with Amazon Comprehend for doc classification.

Regardless of 94% classification accuracy, this resolution 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

Data extraction was fully guide, requiring contextual understanding to distinguish important scientific distinctions (like “is metastatic” versus “just isn’t metastatic”) and to find data like insurance coverage numbers and affected person data throughout various doc codecs. This processing burden was substantial, with Girls’s Well being customer support requiring as much as 10 full-time workers contributing 78 hours each day within the Girls’s Well being enterprise unit alone.

Myriad wanted an answer to:

• Scale back doc classification prices whereas sustaining or enhancing accuracy
• Speed up doc processing to remove workflow bottlenecks
• Automate data extraction for medical paperwork
• Scale throughout a number of enterprise models and doc sorts

Amazon Bedrock and generative AI

Trendy massive language fashions (LLMs) course of complicated healthcare paperwork with excessive accuracy attributable to pre-training on huge textual content corpora. This pre-training allows LLMs to know language patterns and doc constructions with out function engineering or massive labeled datasets. Amazon Bedrock is a totally managed service that provides a broad vary of high-performing LLMs from main AI corporations. It offers the safety, privateness, and accountable AI capabilities that healthcare organizations require when processing delicate medical data. For this resolution, we used Amazon’s latest basis fashions:

• Amazon Nova Professional: An economical, low-latency mannequin ultimate for doc classification
• Amazon Nova Premier: A complicated 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 offers a scalable, serverless structure that converts unstructured paperwork into structured information. The accelerator processes a number of paperwork in parallel by configurable concurrency limits with out overwhelming downstream companies. Its built-in analysis framework lets customers present anticipated output by the consumer interface (UI) and consider generated outcomes to iteratively customise configuration and enhance accuracy.

The accelerator gives 1-click deployment with a alternative of pre-built patterns optimized for various workloads with totally different configurability, price, and accuracy necessities:

• Sample 1 – Makes use of Amazon Bedrock Information Automation, a totally managed service that provides wealthy out-of-the-box options, ease of use, and easy per-page pricing. This sample is really helpful for many use instances.
• 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 good for complicated 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 good for paperwork requiring specialised classification.

Sample 2 proved most fitted for this challenge, assembly the important requirement of low price whereas providing flexibility to optimize accuracy by immediate engineering and LLM choice. This sample gives a no-code configuration – customise doc sorts, extraction fields, and processing logic by configuration, editable within the net UI.

We custom-made the definitions of doc courses, key attributes and their definitions per doc class, LLM alternative, LLM hyperparameters, and classification and extraction LLM prompts through Sample 2’s config file. In manufacturing, Myriad built-in this resolution into their present event-driven structure. The next diagram illustrates the manufacturing pipeline:

1. Doc Ingestion: Incoming order occasions set off doc retrieval from supply doc administration programs, with cache optimization for beforehand processed paperwork.
2. Concurrency Administration: DynamoDB tracked concurrent AWS Step Operate jobs whereas Amazon Easy Queue Service (SQS) queues recordsdata exceeding concurrency limits for doc processing.
3. Textual content Extraction: Amazon Textract extracted textual content, format data, tables and varieties from the normalized paperwork.
4. Classification: The configured LLM analyzed the extracted content material primarily based on the custom-made doc classification immediate supplied within the config file and classifies paperwork into acceptable classes.
5. Key Data Extraction: The configured LLM extracted medical data utilizing extraction immediate supplied within the config file.
6. 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 present resolution achieved 94% accuracy, misclassifications occurred attributable to structural similarities, overlapping content material, and shared formatting patterns throughout doc sorts. This semantic ambiguity made it tough to tell apart between comparable paperwork. We guided Myriad on immediate optimization strategies that used LLM’s contextual understanding capabilities. This method moved past sample matching to allow semantic evaluation of doc context and objective, 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 comparable doc sorts. To determine these differentiators, we supplied doc samples from every class to Anthropic Claude Sonnet 3.7 on Amazon Bedrock with mannequin reasoning enabled (a function that permits the mannequin to show its step-by-step evaluation course of). The mannequin recognized distinguishing options between comparable doc courses, which Myriad’s material consultants refined and integrated 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 comparable doc sorts that shared comparable content material however differed in construction. We enabled the classification fashions to acknowledge format-specific traits comparable to format constructions, area preparations, and visible parts, 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 observe 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 unfavourable prompting for enhanced accuracy

We carried out unfavourable prompting strategies to resolve confusion between comparable 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 sort. Initially, the system continuously misclassified Take a look at Request Kinds (TRFs) as Take a look at Outcomes attributable to confusion between affected person medical historical past and lab measurements. Including a unfavourable 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 steerage on widespread misclassification patterns, the system averted typical errors and confusion between comparable doc sorts.

Mannequin choice for price and efficiency optimization

Mannequin choice drives optimum cost-performance at scale, so we performed 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 courses: Take a look at Request Kinds, Lab Outcomes, and Insurance coverage. We assessed every mannequin utilizing three important metrics: classification accuracy, processing latency, and value per doc. The accelerator’s price 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 stability 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 pace improved by 80%—lowering classification time from 8.5 minutes to 1.5 minutes per doc.

Automating Key Data Extraction with generative AI

Myriad’s data extraction was guide, requiring as much as 10 full-time workers contributing 78 hours each day within the Girls’s Well being unit alone, which created operational bottlenecks and scalability constraints. Automating healthcare KIE offered challenges: checkbox fields required distinguishing between marking kinds (checkmarks, X’s, handwritten marks); paperwork contained ambiguous visible parts 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 resolution, implementing the next optimization strategies to deal with 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 parts. These options enhanced the system’s means to detect and interpret marking kinds present in medical varieties.

We enhanced accuracy by incorporating visible cues into the extraction prompts. We up to date the prompts with directions comparable 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 picks. This mixture of enhanced OCR capabilities and focused prompting improved checkbox extraction in medical varieties.

Visible context studying by few-shot examples

Configuring Textract and enhancing prompts alone couldn’t deal with complicated visible parts successfully. We carried out a multimodal method that despatched each doc photos and extracted textual content from Textract to the inspiration mannequin, enabling simultaneous evaluation of visible format and textual content material for correct extraction choices. We carried out few-shot studying by offering instance doc photos paired with their anticipated extraction outputs to information the mannequin’s understanding of varied type layouts and marking kinds. 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 comparable paperwork inside that timeframe, Bedrock reuses cached examples as a substitute of reprocessing them, lowering each price and processing time.

Chain of thought reasoning for complicated extraction

Whereas this multimodal method improved extraction accuracy, we nonetheless confronted challenges with overlapping and ambiguous tick marks in complicated type layouts. To carry out effectively in ambiguous and complicated conditions, we used Amazon Nova Premier and carried out Chain of Thought reasoning to have the mannequin assume by extraction choices step-by-step utilizing pondering tags. For instance:

Analyze the checkbox marks on this type:


1. What checkboxes are current? [List all visible options]
2. The place are the marks positioned? [Describe mark locations]
3. Which marks are clear vs ambiguous? [Assess mark quality]
4. For overlapping marks: Which checkbox comprises many of the mark?
5. Are marks positioned within the heart or touching edges? [Prioritize center positioning]

Moreover, we included reasoning explanations within the few-shot examples, demonstrating how we reached conclusions in ambiguous instances. This method enabled the mannequin to work by complicated visible proof and contextual clues earlier than making last determinations, enhancing efficiency with ambiguous tick marks.

Testing throughout 32 doc samples with various complexity ranges through the GenAI IDP Accelerator revealed that Amazon Textract with Structure, TABLES, and FORMS options enabled, paired with Amazon Nova Premier’s superior reasoning capabilities and the inclusion of few-shot examples, delivered the perfect outcomes. The answer achieved 90% accuracy (similar as human evaluator baseline accuracy) whereas processing paperwork in roughly 1.3 minutes every.

Outcomes and enterprise affect

Via our new resolution, we delivered measurable enhancements that met the enterprise targets established on the challenge outset:

Doc classification efficiency:

• We elevated accuracy from 94% to 98% by immediate optimization strategies for Amazon Nova Professional, together with AI-driven immediate engineering, document-format primarily based classification methods, and unfavourable prompting.
• We decreased 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 decreased 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 resolution.

New automated Key Data Extraction efficiency:

• We achieved 90% extraction accuracy (similar because the baseline guide course of): Delivered by a mix of Amazon Textract’s doc evaluation capabilities, visible context studying by few-shot examples and Amazon Nova Premier’s reasoning for complicated 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 workers working 78 hours each day per enterprise unit.

Enterprise affect and rollout

Myriad has deliberate a phased rollout starting with doc classification. They plan to launch our new classification resolution within the Girls’s Well being enterprise unit, adopted by Oncology and Psychological Well being divisions. Because of our work, Myriad will understand 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 a substitute of six minutes attributable to sooner entry to tagged paperwork. This enchancment saves 300 hours month-to-month throughout 9,000 prior authorizations in Girls’s Well being alone, equal to 50 hours per prior authorization specialist.

These measurable enhancements have remodeled Myriad’s operations, as their engineering management confirms:

“Partnering with the GenAIIC emigrate our Clever Doc Processing resolution from AWS Comprehend to Bedrock has been a transformative step ahead. By enhancing each efficiency and accuracy, the answer is projected to ship financial savings of greater than $10,000 per thirty days. The crew’s shut collaboration with Myriad’s inside engineering crew delivered a high-quality, scalable resolution, 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 affect.”

– Martyna Shallenberg, Senior Director of Software program Engineering, Myriad Genetics

Conclusion

The AWS GenAI IDP Accelerator enabled Myriad’s speedy implementation, offering a versatile framework that decreased growth time. Healthcare organizations want tailor-made options—the accelerator delivers in depth customization capabilities that permit customers adapt options to particular doc sorts and workflows with out requiring in depth code adjustments or frequent redeployment throughout growth. Our method demonstrates the ability of strategic immediate engineering and mannequin choice. We achieved excessive accuracy in a specialised area by specializing in immediate design, together with unfavourable prompting and visible cues. We optimized each price and efficiency by deciding on Amazon Nova Professional for classification and Nova Premier for complicated extraction—matching the correct mannequin to every particular process.

Discover the answer for your self

Organizations seeking to enhance their doc processing workflows can expertise these advantages firsthand. The open supply GenAI IDP Accelerator that powered Myriad’s transformation is accessible to deploy and check in your setting. 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 affect in your workflows, attain out to the AWS GenAIIC crew to discover how the GenAI IDP Accelerator may be custom-made and optimized in your particular use case. This hands-on method ensures you may make knowledgeable choices about implementing clever doc processing in your group.

In regards to the authors

Priyashree Roy is a Information Scientist II on the AWS Generative AI Innovation Heart, the place she applies her experience in machine studying and generative AI to develop revolutionary options for strategic AWS prospects. She brings a rigorous scientific method to complicated 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 prospects deal with 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 prospects design and deploy generative AI purposes to unravel complicated enterprise challenges. Her experience spans massive language fashions (LLMs), multimodal studying, and AI-driven automation. She holds a Grasp’s in Utilized Information 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 purposes, 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 revolutionary 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 drugs 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 complicated 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 give attention to AI/ML purposes in healthcare, he helps enterprises design and implement environment friendly cloud options that deal with 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 choices and steerage all through.