PTP Enables Medical Document Automation for Moyae
Moyae partnered with PTP to automate medical document processing and patient intake workflows in a life sciences environment. Built on AWS Textract, AWS HealthLake, Amazon S3, and a secure review workflow, the solution replaced manual form handling with a scalable system that extracts, validates, and structures patient data while improving accuracy, compliance, and operational efficiency.
“Working with PTP on this project was a great experience. Brantley [PTP Account Manager] and the team brought a thoughtful and innovative approach that really stood out, and we shopped across several groups! Overall, the collaboration was seamless, and the customers were very happy with the product.”
- Douglas Phung, Co-Founder, Moyae
Overview of Moyae’s Medical Document Automation Project
Moyae is a healthcare technology company operating in the optical medical space, working with a network of approximately 100–200 clinical offices. Their platform supports clinics and providers that collect patient intake forms and medical documentation during the onboarding and treatment process.
These forms often arrive in multiple formats including PDFs, Word documents, and handwritten paperwork scanned from physical forms creating operational challenges for digitizing and standardizing patient data across their platform.
To address this challenge, Moyae partnered with PTP to design a scalable, secure, and automated document ingestion workflow that could streamline the process of extracting patient information from intake forms and integrating it into their healthcare data systems.
The Challenge of Manual Patient Intake Processing
Before engaging PTP, Moyae relied heavily on manual workflows to process patient intake forms. Clinicians or administrative staff would: a) collect patient intake forms from clinics or patients; b) manually read the information from the forms; c) enter the data into spreadsheets such as CSV or Excel files; and d) upload those files into the platform to be ingested into their system.
This process created operational challenges and inefficiencies:
- Manual data entry: Staff were required to transcribe large amounts of patient information.
- Inconsistent formats: Each clinic uses different intake form layouts and formats.
- Handwritten information: Many forms contained handwritten fields that were difficult to process automatically.
- Scalability concerns: With hundreds of clinics contributing documents, the volume of records could quickly reach thousands of forms.
- Security requirements: Patient data required for secure handling and storage.
Moyae needed a system that could automate document ingestion while maintaining accuracy, compliance, and scalability.
The AWS Solution for Medical Document Automation
Several alternative proposals evaluated by Moyae relied on complex generative AI architecture that would require significant engineering investment and ongoing operational costs.
PTP instead implemented a solution built on proven, rules-based machine learning services, including AWS Textract and medical data processing tools designed for healthcare workloads. This approach delivered several advantages:
- Lower development complexity
- Reduced operational costs
- Compliance with healthcare data requirements
- Faster deployment timelines
PTP designed and implemented an automated document processing platform built on AWS services that could extract, validate, and structure patient information from uploaded forms. The system included several core components.
1. Automated Document Extraction
The platform uses AWS Textract to process uploaded patient intake documents. Textract converts scanned documents into structured text data by analyzing the layout of the form and identifying fields within the document. This allowed the system to extract key information such as patient names, dates of birth, insurance identifiers, and other structured intake form data.
Because intake forms vary widely in layout, the system also analyzes bounding blocks and field positions within documents to help identify and interpret information more accurately.
2. Template-Based Field Mapping
To handle the variation between intake forms used by different clinics, PTP developed a template creation system. Administrators could update a simple intake form, identify and select the important fields on the document, map those fields to a standardized data model, and once created, a template can be saved for that clinic form type.
Future documents from that clinic automatically use the same template for data extraction. This approach allows Moyae to support hundreds of different form structures without rebuilding the system each time.
3. Secure Document Upload and Storage
The platform includes a secure document ingestion pipeline that allowed for clinicians to upload intake documents through a web portal, files to be securely stored in Amazon S3, documents to be processed through the extraction workflow, extracted data to be mapped to structured patient records, and S3 buckets that are segregated by client, enabling secure isolation between different clinical organizations.
4. Patient Data Integration
The extracted and verified information is ingested into Moyae’s data environment using AWS Health Lake, allowing patient data to be stored in a standardized healthcare data model. This integration enabled structured patient records, improved data accessibility, secure linking between source documents and patient data, and each document remains stored in S3 and linked to the patient record, allowing users to review the original intake forms when needed.
5. Review and Verification Interface
Because handwritten forms can introduce inconsistencies, the system includes a verification step. After data extraction, users are presented with the extracted fields, they can review the data accuracy, and corrections can be made if necessary. Once verified, the data is finalized and ingested into the system. This workflow ensures human validation while dramatically reducing manual entry time.
Technology Architecture
The solution implemented by PTP includes the following AWS services:
- AWS Textract for document text extraction
- AWS HealthLake for structured healthcare data storage
- Amazon S3 for secure document storage
- AWS Lambda for serverless processing
- Amazon RDS for application data storage
- React-based web application for document upload and management
The system integrates Moyae’s existing authentication framework so users can access the portal through the same login system used by their primary platform.
The project was also supported through AWS partner funding, allowing Moyae to significantly reduce the upfront cost of engineering development while still launching a production-ready solution.
Results: Faster, More Accurate Patient Intake Processing
The PTP solution delivered measurable operational improvements for Moyae.
- Streamlined Data Processing - manual transcription workflows were replaced with an automated document ingestion pipeline, significantly reducing administrative effort.
- Scalable Intake Processing - the system supports document ingestion from hundreds of clinics, enabling thousands of intake forms to be processed efficiently.
- Improved Data Accuracy - template-based field mapping and review workflows reduce transcription errors and standardize patient data entry.
- Secure Medical Data Handling - the architecture ensures that patient records and source documents are securely stored and accessible when needed.
- Future Platform Growth - because the system was built as a modular React application integrated with Moyae’s existing platform; the capability can be expanded across additional clients and workflows.
Conclusion: A Scalable Foundation for Healthcare Document Automation
By partnering with PTP, Moyae transformed a labor-intensive intake process into an automated, scalable document processing platform.
Using secure AWS-native services and template-based automation, the solution enables healthcare organizations to extract, validate, and integrate patient intake data more efficiently while maintaining the compliance and security required for medical information.
The result is a streamlined system that reduces administrative overhead, improves data reliability, and supports the continued growth of Moyae’s healthcare platform.
Ready to Scale Medical Document Automation in Life Sciences?
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FAQs About Medical Document Automation
What is medical document automation in healthcare?
Medical document automation in healthcare is the process of extracting, validating, and structuring information from forms such as patient intake documents, scanned records, and other healthcare paperwork. It helps reduce manual data entry, improve consistency, and make patient information easier to access and manage across healthcare workflows.
What are the benefits of automating patient intake forms?
Automating patient intake forms can reduce administrative effort, improve data accuracy, speed up document processing, and help healthcare organizations scale more efficiently. It also supports more consistent patient data capture across clinics, departments, or growing provider networks.
How does AWS Textract help with medical document processing?
AWS Textract helps healthcare organizations process scanned and digital documents by extracting text, form fields, and structured data from uploaded files. This makes it easier to turn patient intake forms and other medical documents into usable data that can be reviewed, validated, and integrated into downstream healthcare systems.
How can healthcare organizations handle different patient intake form formats?
Healthcare organizations can manage different intake form formats by using template-based field mapping, document classification, and review workflows. These approaches help standardize how data is captured from forms that vary by clinic, provider, or specialty while improving consistency across document ingestion processes.
How are patient documents and extracted data stored securely?
Patient documents and extracted data are typically stored using secure cloud storage, access controls, encryption, audit logging, and environment separation between clients or organizations. In healthcare and life sciences environments, secure storage architecture is especially important for protecting sensitive patient information and supporting compliance requirements.
What should healthcare organizations look for in a medical document automation solution?
Healthcare organizations should look for a solution that supports accurate data extraction, secure document storage, workflow scalability, human review when needed, and integration with existing healthcare systems. The best medical document automation solutions also support varying document formats and help organizations balance efficiency, security, and operational flexibility.