Reported impact
Business outcome
Moving toward increased provider and patient satisfaction with AI tools
- Cost Reduction
- Customer Experience
- Productivity Improvement
MicrosoftHealthcareDocument ProcessingAI and Machine LearningIndustry CloudCost ReductionCustomer ExperienceProductivity Improvement2024 MicrosoftMS#0078d4HealthcareUnited StatesHealthcare technology provider eClinicalWorks sought to improve efficiency and cut time spent for healthcare workers who had to go through thousands of faxes each year to match the faxed data with current patient files.So far, 427 practices have implemented this solution and scanned nearly 2.2 million faxes, saving up to one…Dec 11, 20246Document ProcessingAzure AI Document Intelligence · Azure AI Services0 2024-12-11 6
eClinicalWorks
Document Processing
Healthcare technology provider eClinicalWorks sought to improve efficiency and cut time spent for healthcare workers who had to go through thousands of faxes each year to match the faxed data with current patient files.
So far, 427 practices have implemented this solution and scanned nearly 2.2 million faxes, saving up to one minute per incoming fax.
Today, eClinicalWorks serves over 180,000 providers and 1 million medical professionals with the latest innovations in cloud-based and AI-driven electronic health records and practice management solutions.
eClinicalWorks has helped healthcare providers reduce time spent on paperwork and spend more time with patients by using AI to improve efficiency and analyze nearly 2.2 million faxes.
“We’ve worked with Microsoft for over 10 years,” says Bharat Satyanarayan, Vice President of Technology and Quality Assurance at eClinicalWorks.
Solution
Healthcare technology provider eClinicalWorks sought to improve efficiency and cut time spent for healthcare workers who had to go through thousands of faxes each year to match the faxed data with current patient files. The company developed a tool using Azure AI Services and Azure AI Document Intelligence to scan and sort through thousands of faxes using advanced machine learning. This tool analyzes and matches each fax to simplify the process for healthcare workers. Artificial intelligence is augmenting the workflows and making things more efficient so that providers and staff can do what they really love to do—take care of patients. In many healthcare practices and facilities, physicians and staff spend hours each day sorting through paper documentation related to patient health records. This keeps them from spending as much time with patients as they would like and leads to feelings of burnout.
Data flow
Incoming faxed documents are analyzed with Azure AI Document Intelligence and Azure AI Services, matched to the right records, and then surfaced back to healthcare staff through the operational workflow.
Solution architecture
- Compute
- Azure AI Document Intelligence Extracts structured fields and text from forms or document images to reduce manual processing.
- Azure AI Services Supplies language, vision, and other AI APIs that classify, interpret, or enrich source content.
Architecture clues
- Azure AI Document Intelligence: Extracts structured fields and text from forms or document images to reduce manual processing.
- Azure AI Services: Supplies language, vision, and other AI APIs that classify, interpret, or enrich source content.
- Integrates with faxed documents.
- Today, eClinicalWorks serves over 180,000 providers and 1 million medical professionals with the latest innovations in cloud-based and AI-driven electronic health records and practice management solutions.
- “We’ve worked with Microsoft for over 10 years,” says Bharat Satyanarayan, Vice President of Technology and Quality Assurance at eClinicalWorks.
Evidence from the source
Artificial intelligence is augmenting the workflows and making things more efficient so that providers and staff can do what they really love to do—take care of patients.
Healthcare technology provider eClinicalWorks sought to improve efficiency and cut time spent for healthcare workers who had to go through thousands of faxes each year to match the faxed data with current patient files.
In many healthcare practices and facilities, physicians and staff spend hours each day sorting through paper documentation related to patient health records. This keeps them from spending as much time with patients as they would like and leads to feelings of burnout.
Moving toward increased provider and patient satisfaction with AI tools
The company developed a tool using Azure AI Services and Azure AI Document Intelligence to scan and sort through thousands of faxes using advanced machine learning. This tool analyzes and matches each fax to simplify the process for healthcare workers.
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value91/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth25/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity16/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact25/253 metrics describe measurable impact.
- Deployment scale5/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/205 direct source quotes retained.
- Date provenance10/10The publication date comes from the source page or publisher.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity8/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://www.microsoft.com/en/customers/story/20380-eclinicalworks-azure-ai-servicesMicrosoftFirst-party
eClinicalWorks customer story
Supports- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date
Date evidence
- Published on source page Dec 11, 2024 · Microsoft · First-partyOpen date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
Related
Similar use cases
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