AWS Healthcare United States

Amazon Pharmacy

Customer Service Automation

“Our goal is to help more people access the medications that support longer, healthier lives.” Since its launch, Amazon Pharmacy developed AI and ML solutions on AWS, beginning with demand forecasting to improve medication delivery times. In an environment where inefficient legacy systems still dominate, Amazon Pharmacy is working to improve the customer experience with the help of artificial intelligence (AI).

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Founded in 2020, Amazon Pharmacy is a full-service digital pharmacy on Amazon.com that is available in all 50 US states.

of prescriptions include up-front pricing estimates 99%

Reported impact

Business outcome

Amazon Pharmacy uses additional AWS services to help make the customer experience seamless. To transcribe prescriptions that doctors’ offices send by fax, Amazon Pharmacy uses Amazon Textract —a service that automatically extracts printed text, handwriting, and data from virtually any document with intelligent document processing. Key reported metrics include of prescriptions include up-front pricing estimates 99%; Founded in 2020, Amazon Pharmacy is a full-service digital pharmacy on Amazon.com that is available in all 50 US states..

Solution

Learn how Amazon Pharmacy reduces administrative burden in healthcare by leveraging Amazon SageMaker. With growing demand in the pharmacy industry, pharmacists face an increasingly heavy administrative burden that takes the focus away from customers. In an environment where inefficient legacy systems still dominate, Amazon Pharmacy is working to improve the customer experience with the help of artificial intelligence (AI). Founded in 2020, Amazon Pharmacy is a full-service digital pharmacy on Amazon.com that is available in all 50 US states. Using HIPAA-eligible AI and machine learning (ML) services on Amazon Web Services (AWS), Amazon Pharmacy is improving the customer experience by delivering prescription medications faster, providing up-front insurance estimates, and offering ongoing access to clinical and customer care. Source data enters the Amazon Pharmacy workflow through its application services, processed with Amazon SageMaker, Amazon SageMaker JumpStart, Amazon Bedrock.

Data flow

Source data enters the Amazon Pharmacy workflow through its application services, processed with Amazon SageMaker, Amazon SageMaker JumpStart, Amazon Bedrock.

Solution architecture

3 components · 1 layer

Compute
- Amazon SageMaker Supports model training, fine-tuning, evaluation, or managed inference for the customer workload.
- Amazon SageMaker JumpStart Accelerates prototyping with prebuilt models and starter assets.
- Amazon Bedrock Hosts the foundation models and inference APIs that power the generative AI features.

Architecture clues

For example, in 2023, Amazon Pharmacy began offering insurance pricing estimates for 99 percent of prescriptions, using deep learning statistical models hosted on Amazon SageMaker —a service where data scientists and ML

Evidence from the source

Amazon Pharmacy uses additional AWS services to help make the customer experience seamless.

For example, in 2023, Amazon Pharmacy began offering insurance pricing estimates for 99 percent of prescriptions, using deep learning statistical models hosted on Amazon SageMaker —a service where data scientists and ML engineers can build,

Founded in 2020, Amazon Pharmacy is a full-service digital pharmacy on Amazon.com that is available in all 50 US states.

To transcribe prescriptions that doctors’ offices send by fax, Amazon Pharmacy uses Amazon Textract —a service that automatically extracts printed text, handwriting, and data from virtually any document with intelligent document processing.

Using HIPAA-eligible generative AI and ML tools on AWS, Amazon Pharmacy is providing its customers with greater price transparency.

“Our goal is to help more people access the medications that support longer, healthier lives.” Since its launch, Amazon Pharmacy developed AI and ML solutions on AWS, beginning with demand forecasting to improve medication delivery times. I

Transparency Evidence & methodology 95/100 reference value · 87% confidence · 1 source

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 value95/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth20/25
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 scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence87%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
6 direct source quotes retained.
Date provenance8/10
The publication date comes from publisher-controlled metadata.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity10/10
Rewards 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.

AWS
Amazon Pharmacy customer story

First-party
https://aws.amazon.com/solutions/case-studies/amazon-pharmacy-improves-customer-care-case-study

Source type
Vendor-published customer story

Published
Jan 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

Date evidence

Publisher catalog date Jan 1, 2024 · AWS · First-party
Open date source

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

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