MicrosoftProductionEvidence: Low40/100

Medical equipment supplier automates patient reorder processing with Azure AI

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.

Industry
Healthcare

Reported outcomes

Strategic outcomes

Speed & agilityAutomated patient reorder processingNew product / capabilityCaptured handwritten and structured dataBetter decisions & insightEnabled advanced reporting and analyticsScale & capacityCreated scalable foundation for automation
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
Customer
Leading Durable Medical Equipment supplier
Provider
Microsoft
Maturity
Production

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

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

  • 1Automated Processing of Patient Reorder Cards using AI
  • 2Human-in-the-Loop for Complex Healthcare Document Extraction
  • 3Centralized Data Platform for DME Analytics
  • Manual processing of patient reorder cards was labor-intensive and error-prone.
  • Backlogs and delays in updating patient records led to operational inefficiencies.
  • Handwritten and varied form formats made extraction difficult for traditional systems.
  • Existing workflows offered limited insight for analytics or downstream automation.
  • Developed a proof of concept leveraging Azure Document Intelligence to capture structured and handwritten data from reorder cards.
  • Integrated human-in-the-loop (HIL) reviews to ensure data quality and accuracy in ambiguous cases.
  • Centralized extracted data into a new platform for reporting and analytics.
  • Enabled process improvements that could be extended to patient intake and service request documentation.
  • Demonstrated high accuracy in automating data extraction from handwritten and structured documents.
  • Significantly reduced processing time of reorder cards.
  • Created a scalable foundation for automate related document-driven processes.
  • Improved operational efficiency and data quality across the organization.
Architecture

Patient reorder forms scanned into a central repository; Azure Document Intelligence classifies templates, extracts structured and handwritten field data, which is reviewed with human-in-the-loop for low-confidence cases. Data moves to a central analytics platform for reporting and downstream integration.

Sources & evidence1
Evidence: Low40/100Evidence strength
  • Customer explicitly identified
  • Deployment status explicitly supported
  • Technical implementation details available
Publisher: Baker Tilly Insights

AI-generated summary. Verify important details with the linked sources before relying on this case.

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