The life of a prescription at Amazon Pharmacy

From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money.

Pharmacies play a vital role in ensuring patients’ health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial intelligence (AI) and cutting-edge technologies to remove this complexity and improve patients’ experiences.

The pharmacy challenge

When a prescription arrives at a pharmacy, its details must be entered into the pharmacy's software system. Then, a licensed pharmacist reviews the prescription to verify the patient's information, check for potential drug interactions or allergies, and confirm that the prescribed medication, dosage, and instructions are appropriate and accurate.

This process is susceptible to errors — even if the prescription arrives electronically. A U.S. study estimated that there are approximately 51.5 million dispensing errors annually in community pharmacies, with a meta-analysis supporting an error rate of around 1.5%.

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Pharmacies must also handle billing and insurance claims for their patients. These are involved calculations based on patients’ specific insurance policies, their copay responsibilities, and the rates that the pharmacy has negotiated with different insurance providers. In fact, just identifying a patient's insurance can be challenging. The result is that patients often do not know the prices they will pay for their medications until the end of the process, when they’re picking up their prescriptions at a retail pharmacy or checking out online.

After a prescription has been validated and the purchase completed, the pharmacy staff must locate the specific medication in their inventory. However, it's possible that at this stage, the prescribed medication, the required strength, or the preferred brand may no longer be available. Providing a substitute could necessitate contacting the prescribing physician again for approval. Additionally, depending on the substitution, the billing and insurance process may need to be re-initiated to account for any changes in pricing or coverage.

Once the medication is ready for dispensing, the pharmacist provides the patient with detailed instructions on how to properly take it. Patients may also have questions regarding their insurance coverage or costs. However, these conversations often take place in public areas, which can be uncomfortable for patients who have personal or sensitive questions.

Finally, patients need convenient access to pharmacists at any time, day or night. This allows patients to report how they are feeling while taking their medications, which can help pharmacists provide better guidance and support throughout the treatment process.

The AI-powered pharmacy

Amazon Pharmacy uses large language models (LLMs) to enhance the accuracy, safety, and speed of prescription processing. First, we use LLMs to transcribe raw prescription data into structured, standardized formats that are seamlessly processed by software and more easily understood by patients. For example, medical abbreviations like "PRN" and "QID" are transformed into their full-text equivalents, such as "take as needed" and "take four times a day," respectively.

After standardizing the prescription data, the system performs a validation step that includes checking the medication names, dosage forms, strengths, and directions for use against an industry database. After validation, all prescriptions are still carefully reviewed and verified by licensed pharmacists. By leveraging this automated process, Amazon Pharmacy has reduced the number of near-miss events (potential medication errors) by 50% and improved processing speed by up to 90%. This allows our pharmacists to focus their time and attention on critical tasks, such as providing personalized care and addressing complex medication-related issues.

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The AI workflow at Amazon Pharmacy.

Amazon Pharmacy understands the importance of price transparency for customers. When a patient is using insurance to cover the cost of medication, Amazon Pharmacy will first try to obtain the exact price directly from the insurance provider. However, if this real-time pricing information is not available, Amazon Pharmacy will provide an estimated out-of-pocket cost for the patient's copay, without requiring the customer to go through the entire checkout process first.

To generate accurate price estimates, Amazon Pharmacy uses an ensemble of decision-tree-based models. These models take into account factors such as historical claims data (time series features) and static information such as the specific medication, the number-of-days' supply, and the quantity prescribed. By providing upfront pricing information, either the exact cost or a reliable estimate, Amazon Pharmacy aims to increase transparency and help patients understand their out-of-pocket expenses before committing to purchases. Additionally, Amazon Pharmacy searches for applicable industry coupons and automatically applies them to orders. We also use ML to validate the patient's insurance registration and claim requests to insurance providers.

Amazon is known for its extensive logistics and fulfillment capabilities, and Amazon Pharmacy takes advantage of Amazon's vast network of same-day and local delivery facilities, as well as innovative transportation methods like Prime Air drones. Additionally, Amazon Pharmacy employs specialized automation technologies, such as robotic vial-filling systems, to streamline the medication-dispensing process, enabling prompt delivery of medications to patients across the nation.

Beyond the physical logistics infrastructure, Amazon Pharmacy has developed its own order fulfillment system to handle complex medication-routing and -dispensing logic, while ensuring compliance with over 160 different pharmacy regulatory bodies across the United States. For example, if a medication for an order is no longer available at the closest fulfillment center, Amazon Pharmacy can identify the next best eligible facility to fulfill the order, even if it's in a different state, provided that the relevant state regulations allow for such cross-state fulfillment.

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Moreover, Amazon Pharmacy's order fulfillment algorithm takes into account regional variations in insurance eligibility. In such cases, Amazon Pharmacy will first validate that there are no changes to the patient's copay. If changes are required, Amazon Pharmacy will work with the insurance providers to clarify the benefits. To accomplish all of this, Amazon Pharmacy's order fulfillment solution employs a combination of operations research techniques, such as optimization solvers, and deep-learning models such as variational autoencoders and diffusion models. These models help simulate different scenarios and optimize the fulfillment process to ensure efficient and compliant delivery of medications to patients.

Amazon Pharmacy also introduced personalized AI-powered chatbots to assist users. These virtual assistants can answer frequently asked questions about Amazon Pharmacy, such as how to enroll in the service. In a first for the industry, Amazon Pharmacy's chatbot also provides personalized support, allowing patients to ask questions about their medication orders, delivery status, prescription transfers, and inventory availability. If a patient prefers, there is always 24/7 access to direct pharmacist support and the customer care team.

Implementing a personalized AI chatbot in the healthcare setting is a complex task. It's crucial to safeguard patients' privacy and ensure the highest level of accuracy, avoiding LLM hallucinations. To address these challenges, Amazon Pharmacy has enhanced the typical retrieval-augmented generation (RAG) approach used for LLM chatbots. The enhancements include input and output guardrails, the use of ensembles of specialized (mini) AI models, and a continuous model improvement process through reinforcement learning using human feedback (RLHF).

The digital pharmacy counter

Amazon Pharmacy is leveraging ML and optimization algorithms to streamline the complex process of dispensing medications. By addressing long-standing challenges such as data entry errors, lack of price transparency, intelligent nationwide medication fulfillment, and personalized-AI-based experiences, Amazon Pharmacy enables patients to save time, save money, and stay healthy.

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For example, it is not uncommon for medications to go into short-term back order (STBO), especially new medications entering the market, as was recently the case with the GLP-1 line of medications used for diabetes and weight loss. Amazon Pharmacy’s intelligent-fulfillment solution has enabled an 85% decrease in delivery estimate misses for unforeseen reasons, including STBOs.

The AI-powered Amazon Pharmacy assistant helps customers navigate the complexities of the pharmacy industry, providing 24/7 assistance on topics like prescription tracking, insurance coverage, medication availability, and cost-saving strategies. Half of the customers who interact with the assistant don't require additional human support, which saves them time and effort. (For customers who still need assistance, Amazon Pharmacy likewise provides 24/7 access to pharmacist support.) Additionally, the assistant provides real-time medication transfer or shipment status updates in response to patient queries, handling follow-up questions to recommend next steps.

For all the success of our AI-based systems, however, Amazon Pharmacy’s research and engineering teams remain hard at work. We will continue pushing the envelope in scaling medication dispensing, improving personalized AI-based chatbots and assistants, and transitioning toward a longitudinal pharmacy that is proactively looking out for patients.

Research areas

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Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As an Applied Scientist, you will develop and improve machine learning systems that help robots perceive, reason, and act in real-world environments. You will leverage state-of-the-art models (open source and internal research), evaluate them on representative tasks, and adapt/optimize them to meet robustness, safety, and performance needs. You will invent new algorithms where gaps exist. You’ll collaborate closely with research, controls, hardware, and product-facing teams, and your outputs will be used by downstream teams to further customize and deploy on specific robot embodiments. Key job responsibilities As an Applied Scientist in the Foundations Model team, you will: - Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization. - Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments. - Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes. - Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes. - Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies. About the team We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities
US, CA, San Francisco
Amazon is seeking an exceptional Sr. Applied Scientist to lead the development of perception systems that harness the power of radar and thermal imaging — enabling robots to perceive and operate reliably in conditions where conventional vision alone falls short. In this role, you will develop ML-driven perception pipelines for non-traditional sensing modalities, pushing the boundaries of what robots can see, understand, and act upon in challenging real-world environments. At Amazon, we leverage advanced robotics, machine learning, and artificial intelligence to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence. As a Sr. Applied Scientist in Multi-Modal Perception, you will apply deep computer vision expertise alongside classical signal processing techniques for radar and thermal imaging — modalities that provide robustness in adverse conditions and sensing capability beyond the visible spectrum. You will develop ML-based methods to extract semantic and geometric information from radar point clouds, radar tensors, and thermal imagery, and fuse these with camera and depth data to build perception systems that are reliable, comprehensive, and ready for deployment at scale. Your work will unlock new capabilities for our robots — enabling reliable detection, classification, and scene understanding in low-visibility conditions, cluttered environments, and scenarios where traditional RGB-based perception is insufficient. You will lead research that translates cutting-edge advances in deep learning and computer vision to these underexplored but high-impact sensing modalities. Join us in building the next generation of multi-modal perception systems that will define the future of autonomous robotics at scale. Key job responsibilities - Lead the research, design, and development of ML-based perception pipelines for radar and thermal/infrared imaging modalities - Develop deep learning models for object detection, classification, segmentation, and tracking using radar data (point clouds, range-Doppler maps, radar tensors) and thermal imagery - Design and implement multi-modal fusion architectures that combine radar, thermal, camera, and depth data for robust, all-condition perception - Develop novel representations and feature extraction methods tailored to the unique characteristics of radar and thermal sensors (sparsity, noise profiles, spectral properties) - Build end-to-end perception systems — from raw sensor data processing and calibration to model training, evaluation, and real-time deployment - Collaborate closely with Hardware, Navigation, Planning, and Controls teams to define sensor configurations and deliver integrated autonomy solutions - Establish benchmarks, datasets, and evaluation frameworks for radar and thermal perception - Mentor scientists and engineers; foster a culture of scientific rigor, innovation, and high-impact delivery - Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Our team is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.