This put up 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 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 resembling Check Request Types, 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 take a look at 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. Info extraction remained guide resulting from complexity. To handle excessive prices and gradual processing, Myriad wanted a greater resolution.
This put up explores how Myriad Genetics partnered with the AWS Generative AI Innovation Middle (GenAIIC) to remodel 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 lowered prices and improved processing pace.
We look at 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 selections to construct a scalable resolution that processes advanced medical paperwork with excessive accuracy whereas decreasing operational prices.
Doc processing bottlenecks limiting healthcare operations
Myriad Genetics’ each day operations rely on effectively processing advanced medical paperwork containing vital 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
Info extraction was solely guide, requiring contextual understanding to distinguish vital medical distinctions (like “is metastatic” versus “will not be 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 bettering accuracy
- Speed up doc processing to get rid of workflow bottlenecks
- Automate data extraction for medical paperwork
- Scale throughout a number of enterprise items and doc varieties
Amazon Bedrock and generative AI
Fashionable massive language fashions (LLMs) course of advanced healthcare paperwork with excessive accuracy resulting from pre-training on large textual content corpora. This pre-training permits LLMs to grasp 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 firms. 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: A cheap, low-latency mannequin supreme for doc classification
- Amazon Nova Premier: A sophisticated mannequin with reasoning capabilities for data extraction
Resolution overview
We applied 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 knowledge. The accelerator processes a number of paperwork in parallel by configurable concurrency limits with out overwhelming downstream providers. 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 presents 1-click deployment with a alternative of pre-built patterns optimized for various workloads with totally different configurability, value, 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 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 best suited for this venture, assembly the vital requirement of low value whereas providing flexibility to optimize accuracy by immediate engineering and LLM choice. This sample presents a no-code configuration – customise doc varieties, extraction fields, and processing logic by configuration, editable within the net UI.
We personalized the definitions of doc courses, key attributes and their definitions per doc class, LLM alternative, LLM hyperparameters, and classification and extraction LLM prompts by way of 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:
- Doc Ingestion: Incoming order occasions set off doc retrieval from supply doc administration methods, with cache optimization for beforehand processed paperwork.
- Concurrency Administration: DynamoDB tracked concurrent AWS Step Operate jobs whereas Amazon Easy Queue Service (SQS) queues information exceeding concurrency limits for doc processing.
- Textual content Extraction: Amazon Textract extracted textual content, structure data, tables and kinds from the normalized paperwork.
- Classification: The configured LLM analyzed the extracted content material primarily based on the personalized 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 by way of RESTful operations.
Doc classification with generative AI
Whereas Myriad’s present resolution achieved 94% accuracy, misclassifications occurred resulting from 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 strategies that used LLM’s contextual understanding capabilities. This strategy moved past sample matching to allow semantic evaluation of doc context and function, figuring out distinguishing options that human specialists acknowledge however earlier automated methods 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 permits the mannequin to reveal its step-by-step evaluation course of). The mannequin recognized distinguishing options between related doc courses, which Myriad’s subject material specialists 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 resembling structure constructions, area preparations, and visible components, permitting the system to distinguish between paperwork that textual content material alone can not distinguish. For instance, lab studies and take a look at outcomes each comprise affected person data and medical knowledge, however lab studies show numerical values in tabular format whereas take a look at outcomes comply with a story format. We instructed the LLM: “Lab studies comprise numerical outcomes organized in tables with reference ranges and items. Check outcomes current findings in paragraph format with medical interpretations.”
Implementing adverse prompting for enhanced accuracy
We applied adverse prompting strategies to resolve confusion between related paperwork by explicitly instructing the mannequin what classifications to keep away from. This strategy added exclusionary language to classification prompts, specifying traits that shouldn’t be related to every doc kind. Initially, the system regularly misclassified Check Request Types (TRFs) as Check Outcomes resulting from confusion between affected person medical historical past and lab measurements. Including a adverse immediate like “These kinds comprise affected person medical historical past. DO NOT confuse them with take a look at outcomes which comprise present/latest lab measurements” to the TRF definition improved the classification accuracy by 4%. By offering specific steering on frequent misclassification patterns, the system averted 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 courses: Check Request Types, Lab Outcomes, and Insurance coverage. We assessed every mannequin utilizing three vital metrics: classification accuracy, processing latency, and price 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 stability for Myriad’s use case. We transitioned from Myriad’s Amazon Comprehend implementation to Amazon Nova Professional with optimized prompts for doc classification, attaining vital enhancements: classification accuracy elevated from 94% to 98%, processing prices decreased by 77%, and processing pace improved by 80%—decreasing 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 workers contributing 78 hours each day within the Girls’s Well being unit alone, which created operational bottlenecks and scalability constraints. Automating healthcare KIE introduced challenges: checkbox fields required distinguishing between marking kinds (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 medical distinctions and find data throughout various doc codecs. We labored with Myriad to develop an automatic KIE resolution, implementing the next optimization strategies 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 capability to detect and interpret marking kinds present in medical kinds.
We enhanced accuracy by incorporating visible cues into the extraction prompts. We up to date the prompts with directions resembling “search for seen marks in or across the small sq. bins (✓, x, or handwritten marks)” to information the language mannequin in figuring out checkbox choices. This mixture of enhanced OCR capabilities and focused prompting improved checkbox extraction in medical kinds.
Visible context studying by few-shot examples
Configuring Textract and bettering prompts alone couldn’t deal with advanced visible components successfully. We applied a multimodal strategy that despatched each doc photographs and extracted textual content from Textract to the muse mannequin, enabling simultaneous evaluation of visible structure and textual content material for correct extraction selections. We applied few-shot studying by offering instance doc photographs paired with their anticipated extraction outputs to information the mannequin’s understanding of assorted 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 scale 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, decreasing each value and processing time.
Chain of thought reasoning for advanced extraction
Whereas this multimodal strategy improved extraction accuracy, we nonetheless confronted challenges with overlapping and ambiguous tick marks in advanced type layouts. To carry out nicely in ambiguous and complicated conditions, we used Amazon Nova Premier and applied Chain of Thought reasoning to have the mannequin suppose by 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 strategy enabled the mannequin to work by advanced visible proof and contextual clues earlier than making ultimate determinations, bettering efficiency with ambiguous tick marks.
Testing throughout 32 doc samples with various complexity ranges by way of 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 the very best 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
By way of our new resolution, we delivered measurable enhancements that met the enterprise objectives established on the venture 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 adverse 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 resolution.
New automated Key Info Extraction efficiency:
- We achieved 90% extraction accuracy (similar because the baseline guide course of): Delivered by a mixture of Amazon Textract’s doc evaluation capabilities, visible context studying by few-shot examples and Amazon Nova Premier’s reasoning for advanced knowledge 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 an alternative of six minutes resulting from quicker 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 reworked 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 bettering each efficiency and accuracy, the answer is projected to ship financial savings of greater than $10,000 per thirty days. The group’s shut collaboration with Myriad’s inner engineering group delivered a high-quality, scalable resolution, whereas their deep experience in superior language fashions has elevated our capabilities. This has been a wonderful 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 fast implementation, offering a versatile framework that lowered growth time. Healthcare organizations want tailor-made options—the accelerator delivers in depth customization capabilities that allow customers adapt options to particular doc varieties and workflows with out requiring in depth code modifications or frequent redeployment throughout growth. Our strategy 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 adverse prompting and visible cues. We optimized each value and efficiency by deciding on Amazon Nova Professional for classification and Nova Premier for advanced extraction—matching the proper mannequin to every particular activity.
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 offered to deploy and take a look at in your surroundings. The accelerator’s simple setup course of lets customers shortly 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 group to discover how the GenAI IDP Accelerator could be personalized and optimized in your particular use case. This hands-on strategy ensures you can also make knowledgeable selections about implementing clever doc processing in your group.
Concerning the authors
Priyashree Roy is a Information Scientist II on the AWS Generative AI Innovation Middle, the place she applies her experience in machine studying and generative AI to develop progressive options for strategic AWS prospects. She brings a rigorous scientific strategy 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 Middle, the place he helps prospects 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 centered on multimodal machine studying, multilingual pure language processing (NLP), and multitask studying. His analysis has been revealed 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 Middle, the place she helps prospects 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 Information Science from San Jose State College and a Grasp’s in Biomedical Engineering from Linköping College, Sweden. Her earlier analysis centered 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 singular background spanning genomics, molecular diagnostics, and software program engineering, she has scaled progressive 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 methods, 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 strategy 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 deal with AI/ML functions in healthcare, he helps enterprises design and implement environment friendly cloud options that tackle actual enterprise challenges. His work includes partnering with organizations to modernize their infrastructure, allow innovation, and speed up their cloud adoption journey whereas guaranteeing sensible, sustainable outcomes.
Acknowledgements
We want to thank Bob Strahan, Kurt Mason, Akhil Nooney and Taylor Jensen for his or her vital contributions, strategic selections and steering all through.
