AWS Healthcare India

Eka Care

Document Processing

Eka Care needed to reduce the clinical time lost to administrative work, structure large volumes of unstructured medical data, and preserve compliance while scaling digital healthcare services for doctors and patients across India.

just 3 minutes per patient
millions across India
over 100 patients a day
over a billion people

Reported impact

Business outcome

Eka Care gives doctors more time with patients, improves access to personal health data for patients, and creates a scalable path to better health outcomes for millions across India by automating documentation and real-time insight generation.

  • Compliance
  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Eka Care built AI-driven healthcare workflows on AWS to turn unstructured clinical interactions into usable patient context for doctors and patients. Its Eka Doc, DocAssist, and Voice2RX experiences use Amazon Bedrock to power generative-AI features that support decision-making and automation, while Amazon Transcribe converts doctor-patient conversations into structured notes for prescriptions and follow-up care. These capabilities sit alongside Eka Care's personal health record application so patient data, prescriptions, and administrative workflows can be handled through a single digital experience. By combining transcription, generative AI, and real-time analytics on patient information, the platform helps doctors move faster inside high-volume practices while maintaining HIPAA-aligned and India DPDP-aligned handling of sensitive health data. The result is more time with patients and broader access to digital health records and care support.

Data flow

Doctor-patient conversations are captured and transcribed by Amazon Transcribe, then Amazon Bedrock-based applications use the resulting medical context to generate structured notes, support clinical decisions, and surface patient insights inside Eka Care's doctor and personal-health-record workflows.

Solution architecture

2 components · 2 layers
  1. Ingest
    • Amazon Transcribe Converts doctor-patient conversations into structured notes for prescriptions and follow-up care.
  2. Serving
    • Amazon Bedrock Powers generative-AI features in Eka Doc, DocAssist, and Voice2RX.

Architecture clues

  • Platform targets chronic diseases such as diabetes and hypertension.
  • Uses HIPAA-compliant AWS services and aligns with India's DPDP Act guidelines.
  • Voice2RX links speech transcription directly to structured clinical notes and prescriptions.

Evidence from the source

Eka Care also deployed Amazon Transcribe ... to enhance its Voice2RX tool.
The company developed new solutions such as Eka Doc, DocAssist and Voice2RX ... using Amazon Bedrock
Using the generative AI tools we have built, we have been able to give doctors more time with their patients.
administrative burdens further reduced patient interaction to just 3 minutes per patient
doctors often manage over 100 patients a day
Transparency Evidence & methodology 95/100 reference value · 90% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

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 value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence90%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency9/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    Eka Care customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/generative-ai-ekacare/
    Source type
    Vendor-published customer story
    Published
    Oct 1, 2024
    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
  2. LinkedIn

    Eka Care | LinkedIn

    Independent secondary
    https://www.linkedin.com/company/eka-care/

    The Organization JSON-LD lists numberOfEmployees value 5, and sameAs 'http://eka.care'.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.