Medical document automation

Claims Acceleration Suite expedites prior authorization submission and review by transforming unstructured documents into structured datasets.It uses Document AI and Healthcare NLP API to extract essential clinical and demographic data, then stores structured data in BigQuery, Firestore, and Cloud Healthcare API (FHIR) for near-real-time payer/provider exchange.The solution supports providers in preparing prior authorization requests, activating review workflows, and expediting manual review through Pega Care Management on Google Cloud Marketplace.

MyndshftHealthcare

Brillio built an AWS-based claims workflow for a large national insurer to automate claim intake, document extraction, image-based damage assessment, policy validation, and guided settlement recommendations.The solution normalizes unstructured submissions into structured records, scores confidence, routes exceptions to adjusters, and stores artifacts for auditability.

A large national insurerInsurance

Reveleer, a healthcare data and analytics company, uses AWS services to analyze medical records at scale for value-based care workflows.The platform scans patient records, extracts clinical facts from unstructured medical text and scanned documents, and highlights findings for medical coders.The article states the system processed over 45 million pages of medical chart data in Q1 2024 and serves 90% of responses in under 8 seconds with 100% uptime.

ReveleerHealthcare

Exact Sciences, through its PreventionGenetics subsidiary, uses AWS to accelerate variant curation and phenotype abstraction for genetic testing.The company manually reviewed scientific literature and patient clinical notes to interpret genetic variants that may cause rare disease or indicate elevated risk, and needed to speed turnaround for clinicians and patients.Working with the AWS Generative AI Innovation Center, Exact Sciences built the Variant Curation Accelerator and a phenotype abstraction tool on Amazon Bedrock, with human review, citations, source PDFs, and highlighted supporting text to improve trust and accuracy.

Exact SciencesHealthcare

Huron, a healthcare consulting firm, uses generative AI to accelerate the analysis of clinical trial coverage documents.The company needed to extract critical billing and coverage information from complex unstructured documents, including graphs, charts, text, and images, which previously required manual search across multiple documents.Huron built a generative AI solution on AWS with Amazon Bedrock, Amazon Bedrock Knowledge Bases, and Amazon OpenSearch Serverless to support faster, more accurate clinical trial administration.

HuronHealthcare

One Medical is a US-based membership primary care company focused on delivering high-quality, human-centered healthcare through a purpose-built technology platform.The challenge was to reduce administrative burden on healthcare providers to improve patient care experience and provider satisfaction amidst physician burnout due to systemic inefficiencies and administrative overload.One Medical developed its proprietary Electronic Health Record (EHR) system called 1Life, hosted on AWS and built with Amazon Bedrock and Amazon SageMaker machine learning services to automate workflows including document processing and patient record summarization.The solution offers a seamless clinical experience allowing providers to see all patient details and actions from one screen and supports secure in-person and virtual care interactions.Impact includes clinicians spending more time with patients, improved operational efficiency, scalable infrastructure supporting expansion, and ongoing exploration of generative AI for telehealth communication enhancements.

One MedicalHealthcare

Amazon Textract is used by organizations including Change Healthcare, Symbeo (a CorVel company), Elevance Health, Healthfirst, nib Group, Wrapped Insurance, and others primarily in the healthcare and insurance industries.These organizations faced the challenge of tedious, time-consuming, and error-prone manual data entry and processing of forms, claims, and documents.Amazon Textract's AI-powered OCR and ML capabilities automate extraction of printed text, handwriting, and structured data from scanned documents, significantly reducing document processing time from hours to minutes.The technology is integrated with AWS infrastructure and services as part of scalable solutions that improve operational efficiency, reduce manual labor by up to 97%, and enhance customer experience.Common document types processed include insurance claims, medical charts, invoices, bank statements, and more, with automation rates frequently exceeding 60%.AWS services used include Amazon Textract and Amazon Comprehend Medical for data extraction and analysis.

Change HealthcareHealthcare

Capital District Physicians' Health Plan Inc. (CDPHP) struggled with manual processing of unstructured medical records for deriving insights to improve care.CDPHP deployed an automated, modular, serverless AI/ML pipeline on AWS using Amazon Textract to extract data, Amazon Comprehend Medical to extract and normalize medical info, and Amazon SageMaker for ML model development.The solution improved processing speed and accuracy, automating 3,000 records weekly with plans to double volume, cutting HEDIS report generation from 4-5 days to twice daily, and increasing efficiency by 60%.

Capital District Physicians' Health Plan Inc.Healthcare

Natera, a global cell-free DNA testing company focused on oncology, women’s health, and organ health, modernized its data and analytics platform on AWS to support high-volume diagnostic operations and secure handling of patient data.The implementation aimed to reduce data silos, improve access to clinical and genomic data, and accelerate extraction of information from unstructured documents to support precision medicine and earlier detection of cancer recurrence.

NateraHealthcare

Covered California automated healthcare insurance eligibility verification using Google Cloud Document AI integrated with their CalHEERS platform.The AI-powered system provides real-time on-screen feedback and human-in-the-loop review, handling 25 new document types.This automation reduced manual tasks by 40%, accelerated eligibility determinations, enhanced fraud detection, and improved compliance in the regulated healthcare insurance environment.

Covered CaliforniaHealthcare

LivTech partnered with Innovative Solutions to implement the Tailwinds AI solution using Generative AI and Weaviate AI-First Vector Database integrated with LivTech's claims management system.The solution automates repetitive tasks, improves accuracy in claims validation, accelerates decision-making, and supports scaling of claims processing operations.This implementation involved AWS services including Amazon SageMaker, AWS CloudFormation, AWS CloudWatch, AWS KMS, Amazon Redshift, and AWS Security Hub to build and secure the AI-powered claims processing workflow.

LivTechHealthcare

Myriad Genetics faced costly, slow manual document processing bottlenecks handling complex medical documents across oncology, women's health, and mental health divisions.They partnered with AWS GenAI Innovation Center to implement the open-source AWS GenAI Intelligent Document Processing (IDP) Accelerator using Amazon Bedrock foundation models, Amazon Textract, and specialized LLMs.The solution employed custom prompt engineering, model selection, and scalable serverless architecture to optimize document classification and automated key information extraction.Document classification accuracy improved from 94% to 98%, processing cost reduced by 77%, and processing time cut by 80%.Automated key information extraction achieved 90% accuracy matching human baseline, saving 78 daily labor hours and projecting $132K annual savings.The system uses Amazon Nova Pro for classification and Amazon Nova Premier for complex extraction tasks, with multimodal prompts and few-shot learning for visual context understanding.

Myriad GeneticsHealthcare

This AWS blog post presents a vehicle insurance claims workflow demo built by Snowflake using LangGraph and Streamlit.The workflow extracts structured fields from driver’s licenses and claim forms with Snowflake Document AI, analyzes accident photos with Amazon Nova Lite on Amazon Bedrock, cross-checks extracted values against policy records, and generates a customer-facing claim decision email.

SnowflakeInsurance

UTHealth Houston faced inefficient legacy fax workflows unable to handle growing healthcare data volumes.They developed iDFax, a generative AI-powered medical fax processing system using Amazon Bedrock, Amazon EC2, S3, and SQS.iDFax automates document categorization, data extraction, integration with Epic EHR, with OCR accuracy over 95%.The system reduced fax processing times by over 50%, processing over 220,000 faxes in a pilot, with projected annual savings exceeding $2 million.The solution operates securely to meet HIPAA compliance and dynamically scales with AWS cloud services.

UTHealth HoustonHealthcare

Verisma, a provider of health data lifecycle solutions, integrated AI and Microsoft Azure OpenAI to automate and safeguard patient record release management.The Verisma Release Manager (VRM) platform leverages Azure OpenAI and Azure AI Foundry to automate tasks like request logging, record retrieval, risk identification, and compliance review, minimizing manual intervention.With features like custom workflow alerts, robotic process automation for record retrieval, and predictive selection of fees and delivery methods, the platform enhances efficiency while supporting regulatory compliance.Analytical tools deliver at-a-glance summaries and ROI trends, arming healthcare organizations with actionable operational insights for improved resource planning.Automation frees up health information staff to focus on clinical expertise, while a human-in-the-loop guarantees accuracy at every stage of sensitive operations.VRM’s integration with Microsoft technology is designed to offer health facilities an agile, data-rich document release process—positioned to handle scaling request volumes and shifting compliance demands.

VerismaHealthcare

Pharma and life sciences organizations are undergoing digital transformation to accelerate clinical discoveries, improve agility, modernize engagement, and enhance patient experiences. Through Microsoft Azure, Dynamics 365, Microsoft 365, Nuance Dragon Ambient eXperience, and Copilot, these organizations drive innovation focused on precision medicine.Nuance AI boosts efficiency in text analytics and automates medical record documentation, while AI-powered radiology solutions provide instant clinical insights. AI and Copilot features are embedded in everyday business applications, enhancing productivity and collaboration workflows.Microsoft's $1B annual security investment secures sensitive patient and operational data, meeting HIPAA, HITECH, and international standards. The Dynamics 365 Guides GxP Playbook offers step-by-step deployment for compliance in regulated pharma environments.Cloud standards have shifted to automation-focused GxP operations, reducing costs and upholding data protection. Platform governance, advanced controls, and environment safeguards further assist regulated companies in simplification and risk reduction.Overall, Microsoft technologies enable pharma firms to deliver more personalized patient experiences, faster innovations, and improved compliance.

Genpact offers a generative AI powered contents claims solution for property and casualty insurance.The solution includes talk-to-text inventory capture, automated pricing and valuation, and integrated fulfillment capability powered by AWS GenAI technology and Amazon Business partnership.It integrates directly with carrier and estimating platforms to manage the end-to-end claims process.

GenpactInsurance

Doctolib, a leading European e-health company, implemented an advanced AI-powered customer care solution to enhance its support services. Their journey began with deploying Retrieval Augmented Generation (RAG) to power customer FAQs, using GPT-4o through Azure OpenAI Service and OpenSearch vector databases for dynamic knowledge retrieval. The team built robust data pipelines for continuous FAQ embedding and leveraged machine learning classifiers to increase answer precision. An evaluation tool measured key metrics such as context precision, recall, faithfulness, and answer relevancy to optimize the system. With iterative improvements, including prompt engineering and reranking, Doctolib reduced the volume of deflected cases and improved user satisfaction. Key challenges like system latency were addressed through architectural adjustments and model optimization. The article outlines a path toward more sophisticated agentic AI frameworks capable of handling even more complex queries and actions.Limitations of conventional scripted bots were overcome as LLMs (Large Language Models) enhanced response adaptability. The integration of RAG enabled context-aware responses using up-to-date internal documentation. However, the system exposed bottlenecks in handling complex, non-FAQ scenarios, motivating explorations into multi-agent agentic architectures for future expansion.The solution underscores Doctolib’s ongoing development, aiming to further streamline healthcare customer care while providing a scalable and secure support framework that protects user data privacy.

DoctolibHealthcare

Austin Health, a major healthcare provider in Australia, faced mounting challenges from the growing complexity and volume of healthcare data. Clinicians struggled with significant cognitive burdens, requiring them to access data from multiple sources, slowing down processes and reducing face-to-face patient time.Austin Health conducted trials using Microsoft generative AI technologies to read and index two decades of scanned medical records, including handwritten notes, and to improve accessibility and searchability of clinical guidelines vital to daily operations.The AI system leveraged GPT-4 Turbo with vision capabilities and GPT-3 models to accurately recognize handwritten terms and rapidly retrieve precise internal guidelines.A key focus for Austin Health has been ensuring ethical, secure, and responsible AI usage. Data privacy controls restricted external internet access for AI models, and strict organizational policies prevented health data from being used to train third-party models.The initiative prioritized building evidence around AI's value, with early trials demonstrating faster and more accurate information retrieval for clinicians and strong support from medical staff.While production rollout is on hold due to government policy, internal tests highlight a foundation for future healthcare transformation, promising a shift toward non-document-based, knowledge-driven systems with voice capabilities for medical record-keeping and search.Austin Health is also preparing to integrate data from platforms like Oracle Health and Microsoft Dynamics, aiming for holistic patient and operational insights.The project underscores the importance of privacy, governance, and trust in clinical AI, laying the groundwork for future advances that could fundamentally change healthcare delivery in Australia.

Austin HealthHealthcare

Fujita Health University used Amazon Bedrock to reduce discharge summary creation time by up to 90%, improving doctor workflows and allowing more patient communication time.Genomics England leveraged Claude 3 on Amazon Bedrock to accelerate gene-disease research by processing massive volumes of research literature, identifying potential gene associations faster than manual methods.AlayaCare automated extraction and summarization of patient data using AWS AI technologies to assist home care providers, enabling early identification of at-risk clients and reducing costs.Amazon Bedrock foundation models and AI services underpin these use cases, enhancing productivity, accelerating research, and enabling innovative healthcare interactions.

Fujita Health UniversityHealthcare

Ensemble Health Partners, a leading U.S. provider of revenue cycle outsourcing for healthcare, partnered with Microsoft to infuse generative AI and machine learning into its EIQ revenue cycle intelligence platform. EIQ processes data from hundreds of disparate systems, aggregating more than 800 terabytes of data, and integrates insights directly into healthcare providers' EHRs. By leveraging Microsoft Azure, Azure Machine Learning, Azure AI, and Azure Generative AI, Ensemble automates workflows, improves denial resolution, and optimizes revenue collection management at scale. The partnership supports rapid deployment of hundreds of AI models, enabling real-time automation and workflow guidance for revenue cycle operators. In 2023, Ensemble prevented over $200 million in lost revenue for healthcare clients, generated tailored appeal letters with AI, and improved overall financial outcomes across its national client base. Notably, the innovations contributed to Ensemble winning multiple industry performance awards and expanded strategic partnerships with major U.S. health systems.Ensemble's investment in AI and process automation (2 million development hours and $100 million over a decade) resulted in a robust platform that also supports continuous model improvements and data harmonization, ensuring best-in-class outcomes for healthcare organizations.

Ensemble Health PartnersHealthcare

Covered California, California's health insurance marketplace, implemented Google Cloud Document AI to automate and accelerate the verification and processing of healthcare documents.The solution automates document verification for health insurance enrollment, improving speed, accuracy, and throughput for over 50,000 documents monthly.Google Cloud's security tools ensure FedRAMP compliance and protect sensitive consumer data.The AI-powered system has increased document verification rates from around 18-20% to 84%, streamlining the enrollment experience and enhancing workflows for consumers and employees.

Covered CaliforniaPublic Sector

Cognizant, in collaboration with Microsoft, has integrated generative AI into its TriZetto software platform to enhance productivity and efficiency in healthcare administration for payers and providers. The partnership leverages Microsoft Azure, Azure OpenAI Service, and Semantic Kernel to power the TriZetto Assistant, a tool that helps automate workflows inside the TriZetto user interface. The assistant can summarize and enrich content, interpret configuration documents, provide configuration templates, and activate desktop automations that reduce manual workload. By enabling contextualized automation and data access, the solution addresses common administrative bottlenecks in healthcare, such as configuration complexity and compliance requirements. It also enhances data security and ensures regulatory compliance while maintaining granular access controls. The initiative aims to support healthcare organizations in achieving greater operational efficiency, faster processing, improved accuracy, and better patient outcomes at scale. The partnership is positioned as advancing innovation and creating tools that improve experiences for healthcare professionals and patients alike.

CognizantHealthcare

R1RCM, a leading provider of technology-driven solutions for healthcare providers across the US, expanded its collaboration with Microsoft to accelerate the integration of generative AI into its revenue cycle management platform. Using Microsoft Azure OpenAI Service and Azure AI Studio, R1RCM developed and deployed a large language model (LLM)-powered application for physician coding quality assurance. This application evaluates unstructured medical records to predict evaluation and management codes, greatly improving coding quality across patient charts. The LLM application was delivered in under four months and is already increasing productivity for healthcare revenue cycle teams. Looking forward, R1RCM plans to expand the use of generative AI to automate processes such as call centers, payer follow-up, scheduling, and accounts receivable. With a customer base comprising 95 of the largest 100 health systems in the US, R1RCM leverages insights from over 500 million medical records, supporting $900 billion in total net patient revenue. The project aims to drive cost reductions, enhance financial performance, and deliver a better experience for both healthcare providers and patients.

R1RCMHealthcare

A leading global health services company faced significant inefficiencies due to manual, disconnected processes for handling rebate accruals and disbursements. The company relied on disparate tools that necessitated manual data pulls, prolonged approval cycles through email, and increased operational overhead. These processes slowed down payment processing and made data governance challenging. To address these issues, the organization partnered with Protiviti to lead a technology and process transformation leveraging Microsoft Azure, Azure Synapse, Azure Data Lake, Azure Data Factory, Power Platform, and Power BI. The project team mapped the existing processes, migrated data to Azure, and built a singular workflow enabling automated, end-to-end rebate management. Integration of the Microsoft stack reduced manual data handling, automated approvals, and improved data quality. As a result, operational efficiency increased, team members were freed for higher-value work, data governance strengthened, and payment processing accelerated, enhancing both employee experience and customer satisfaction.

MD Clarity, a healthcare technology provider in the US, implemented a comprehensive AI-powered automation system to address the growing challenges in healthcare revenue cycle management (RCM). Facing mounting operational costs, frequent claim denials, underpayment issues, and a tightening labor market, the company adopted Microsoft Azure OpenAI services, integrating generative AI with traditional RPA, NLP, OCR, and machine learning.The new solution automates critical workflows including eligibility verification, claim submission, denial and underpayment detection, patient billing, and payer contract analysis.A generative AI-based internal chatbot, deployed using Azure OpenAI, provides clinicians and staff with real-time recommendations and enhances communication workflows. Automation was leveraged not only for repetitive administrative processes but also for advanced analytics: contract compliance, predictive analytics, and optimizing collections strategies.Implementation of these intelligent automations enabled rapid scaling of operations without additional headcount, significantly cut the cost-to-collect, and improved the financial performance of healthcare provider clients.Providers utilizing MD Clarity’s automated solutions saw a decline in claim denials, increased revenue recovery, better cash flow, and improved compliance with regulatory changes such as the No Surprises Act. The automated system also supports staff by reducing manual workloads, boosting satisfaction and productivity.Key features included real-time eligibility verification, claim processing powered by NLP and OCR, patient self-serve cost estimation, automated follow-up on claims, and system-driven identification of payer underpayments.Healthcare organizations using this system reported millions in recovered underpayments, improved payer contract negotiation leverage, and higher patient satisfaction due to streamlined billing and communications. The system demonstrates advanced synergy between automation and AI, offering a template for the future of healthcare revenue cycle operations.

MD ClarityHealthcare

US Acute Care Solutions faced the challenge of managing and processing 20 million medical records annually, resulting in significant inefficiencies and heavy manual workloads. The organization sought to streamline workflows and alleviate the burden on its staff. By implementing Microsoft Power Automate and Robotic Process Automation (RPA), US Acute Care Solutions completely automated the handling and processing of medical records. This digital transformation significantly accelerated workflows, reduced the time required for record management, and minimized manual intervention. As a direct result, the company experienced tangible improvements in operational efficiency and was able to repurpose workforce time to higher-value activities, ultimately enhancing the overall quality of patient care and service delivery.The use of cloud-based RPA delivered measurable benefits, including significant reductions in processing times and error rates, and a notable acceleration in how data is routed and archived. US Acute Care Solutions' adoption of Power Automate is recognized as a model for digital process transformation in the healthcare sector.

US Acute Care SolutionsHealthcare

Globo, the largest media company in Latin America, is using Google Cloud to modernize media production and post-production workflows.For clapperboard metadata, the company replaced manual transcription with an automated process that extracts clapper information from images and fills production templates.A Python application detects the clapper, creates a low-resolution proxy, sends the frame to Cloud Vision AI by API, and writes the returned metadata into an XML template from Cloud Storage before passing it to the media system.

GloboOther

A generic healthcare payer institution implemented an automated claims adjudication workflow using AWS technologies to process medical insurance claims with minimal manual intervention.The workflow processes claims in PNG format, validates their authenticity and correctness, extracts medical procedure details using AI, and provides analytics for clinical and claims data.The solution employs a serverless architecture using Amazon S3, Amazon Textract, AWS Lambda, Amazon QuickSight, Amazon Comprehend Medical, Amazon Athena, and Amazon Simple Notification Service to automate and streamline claims adjudication and analytics.

Generic Healthcare Payer InstitutionsHealthcare

Pharmcube, a leading pharmaceutical data service platform in China, faced the challenge of high costs and time-consuming processes in drug development and R&D, especially in extracting and processing complex pharmaceutical data from global sources. To address these inefficiencies, Pharmcube partnered with Microsoft's AI Co-Innovation Lab and leveraged Azure Form Recognizer and Azure Text Analytics for Health for automation and advanced analytics. The implementation standardized the extraction of sales data from multilingual financial reports using OCR and tabular data extraction. Additionally, the project automated the recognition and matching of clinical trial indications, achieving a high match rate and drastically reducing manual workload. Integration with Azure AI Foundry enabled further technological flexibility and scalability. The overall solution increased automation in pharmaceutical data processing, saving labor costs and dramatically improving data mining depth and efficiency for Pharmcube, signaling major progress for AI-powered digital transformation in the pharma sector.

PharmcubePharma

AffableBPM, a SaaS healthcare automation provider, implemented a centralized contract approval solution for a large health system facing severe delays and compliance risks. The customer's manual, distributed processes for managing 60,000 contracts resulted in 150-day approval times and exposure to fines up to $300,000. AffableBPM leveraged a suite of Microsoft Azure technologies, including Azure Cognitive Services, Azure Logic Apps, Form Recognizer, Azure Machine Learning, and Power BI, to deliver a secure, scalable, and HIPAA-compliant SaaS workflow. The solution features centralized databases, drag-and-drop uploads, electronic signatures, OCR, document search, audit trails, and Outlook integration. Automated notifications improved compliance, and integrated Power BI reporting enabled faster decision-making. The deployment supports enhanced productivity and ESG goals while reducing operating risk.Turnaround for contract approvals dropped from 150 to 35 days and compliance adherence improved, with key processes automated and auditable. The scalable architecture supports growing hospital and academic user bases with high availability, security, and disaster recovery. The deployment highlights the impact of Microsoft partner solutions and marketplace on healthcare efficiency.

AffableBPMHealthcare

PwC, a professional services firm in the UK, used Azure OpenAI Service to modernize knowledge work across multiple industries, with a particular focus on manufacturing and regulatory domains.The solution automates the classification of regulatory texts and generates automated safety narratives, providing significant touch-time savings in inventory management cases.PwC's multidisciplinary agile pods deliver generative AI solutions via a factory model, ensuring responsible AI implementation and governance.The approach encompasses ideation, data readiness, workforce enablement, model training, and deployment directly on Azure OpenAI, with continuous operational risk mitigation and governance.Key results achieved include up to 90% touch-time savings in manufacturing inventory cases, 50%+ reduction in policy review times, and an accuracy improvement in regulatory text classification and inclusion-exclusion criteria.The implementation is now live in production, with efficiency and accuracy benefits already materializing for both PwC and their clients.

PwCManufacturing

A leading US-based supplier of durable medical equipment (DME) overcame operational inefficiencies by automating the processing of patient reorder cards leveraging Azure Document Intelligence (part of Azure AI Services), with consulting support from Baker Tilly. Previously, processing patient reorder cards (often containing handwritten information) was entirely manual—leading to processing backlogs, reduced data accuracy, and high labor costs.The company and Baker Tilly developed a proof of concept using Azure Document Intelligence to classify and extract data from various reorder card templates, capturing structured, checkbox, and handwritten information. To ensure quality, a human-in-the-loop (HIL) process was implemented for low-confidence fields.Simultaneously, the solution integrated into a centralized data platform, enabling advanced reporting and analytics, highlighting new opportunities for automation in patient intake and related workflows.Impact included high accuracy of data extraction, significant process acceleration, reduction in manual intervention, and a scalable foundation for further automation. The engagement demonstrates how document intelligence and Azure-based AI can transform healthcare operations.

Leading Durable Medical Equipment supplierHealthcare

Covered California, a service from the state of California, implemented Google Cloud Document AI to automate the verification of resident documents for health insurance eligibility, significantly reducing the manual processing workload and accelerating the enrollment process.The previous manual verification process was time-consuming and delayed resident eligibility determination, burdening staff and causing delays for consumers.Covered California achieved an 84% automated document validation rate in real time during its pilot, validating 50,000 documents per month across 56 document types.The solution improves the resident and employee experience by enabling instant document verification via a photo upload, reducing errors and manual workload.Google Cloud Security Operations and compliance features ensured FedRAMP compliance and maintained high security for sensitive personally identifiable information (PII).The pilot project's positive results have led to a full launch planned for June 2024, ahead of open enrollment, aiming for a target of over 95% automated verification.

Covered CaliforniaHealthcare

Medsender provides an AI-enabled digital fax solution automating healthcare communications and document management to reduce administrative burdens.Built on Google Kubernetes Engine and Vertex AI, Medsender's platform improves document recognition, categorization, and workflow automation with HIPAA compliance.Their AI solution enables 50% more accuracy than competitors, reduces fax processing time by 20%, and allows rapid training and deployment of models, improving scalability and security.

MedsenderHealthcare