AWS for Industries
Reinventing medical education with personalized video streaming
Companies of all types are innovating in the digital media space, from traditional media and entertainment (M&E) companies to Healthcare and Life Sciences (HCLS), to deliver streaming media experiences for consumers based on their preferences.
In the medical space, for a long time, congresses have been a critical source of education for Health Care Professionals (HCPs). There are examples of digital platforms using video streaming like Evermed, Vivid TV, MEDICALLY by Roche, and WebMD offering medical education and digital congresses. However, issues with content availability, fragmentation and searchability still remain and physicians end up struggling to filter through large volumes of medical information. Novartis has recently announced a partnership with Evermed to develop video assets and explore the potential of on demand video/audio services for doctors with encouraging results.
In contrast, as video on demand and streaming services are emerging in the medical space, we have seen traditional media and entertainment (M&E) companies accelerating their move to the Cloud to deliver innovative personalized experiences to consumers.
On July 15, 2020, Comcast Corporation launched Peacock, a streaming service conceptualized as an over-the-top (OTT) service that offers video on demand with both paid and free subscription levels. Peacock also has the capability to support live broadcasting of events around the globe. In only one year, Peacock created a simple, de-risked, scalable, and highly available system on Amazon Web Services (AWS). Despite unforeseen obstacles like the COVID-19 pandemic and the postponement of the 2020 Olympic Games, Peacock achieved a flawless launch right on time. As of December 2020, Peacock had already amassed over 26 million sign-ups.
The amount of medical information has been estimated, in 2021, to double every 73 days. Physicians are looking for ways to consume medical information faster and more seamlessly through personalized user experiences. They would want to access relevant content, preferably video and audio, in a self-service mode while being able to leverage a community of peers or industry experts. This would cut down the time needed for determining a diagnosis and the best care pathway for their patients.
Key question for us was: can we take the lessons learned and best-practices from M&E to create a personalized video streaming service for physicians at scale?
In essence, we would leverage prepackaged AWS media and AI/ML capabilities, primarily designed for M&E customers, to help pharma companies build and operate a personalized video medical service that delivers hard hitting scientific content with a consistently entertaining and unbiased perspective.
The service would offer frequently updated and highly curated content that has been peer reviewed by an expert editorial board to enable doctors to access educational content. Each major specialty area would have its own channel and qualified host. All hosts would have a combination of clinical credentials and the ability to entertainingly deliver informative content.
Depending on the interests of a given physician within a specialty, episodes could be seamlessly edited down to be as short as 5 minutes or as long as an hour. Doctors would be able to watch live streams or recordings from prior congresses anytime, anywhere, and from any device.
Ultimately, the portfolio of content will include various formats like continuing medical education accredited courses, journals, research articles, podcast or webinars. The ease of content discovery and searchability will be facilitated through additional intelligent content management capabilities.
The differentiator of this platform will be surfacing content based on a users’ past engagement along with suggesting related content. We foresee interesting features similar to Prime Video’s “X-Ray”, where the content being discussed in any frame of the video can be used to display more information—such as links to specific articles and connections to subject matter experts.
This experience will require a vast amount of downstream work building knowledge graphs. They will be based on meta-data that can store the relationships between the topics and “discover” similar content, which can be recommended to the physicians.
AWS offers the most purpose-built services for direct-to-consumer and streaming, helping leaders like Netflix, Disney+, HBO Max, Discovery+, HULU and Prime Video reliably deliver and support live and on-demand media over the internet to screens everywhere.
To help companies deliver a better consumer experience along their content dissemination via streaming media, AWS Professional Services has developed reference implementation and solution accelerator for direct-to-consumer (D2C) streaming media. This implementation integrates AWS Media Services, AWS AI/ML Services along with AWS Partner ISVs to deliver on a direct-to-consumer (D2C) offering.
To develop use cases in the medical information space, Pharma companies can set up a low cost innovation sandbox in a few weeks. They can quickly refine and validate the experience they want to deliver to Health Care Professionals through experimentation.
With the help of AWS M&E experts, they will be able to review and assess their streaming workflow components as part of their content supply chain optimization and define a plan to achieve a flexible, scalable, and production-ready VOD and/or Live workflow. They will be also introduced to the benefits of AWS’s comprehensive set of cloud capabilities for content production and partner’s offerings.
Using this D2C streaming media accelerator, they will be able to deploy a Minimum Value Proposition in about six months by leveraging pre-built assets based on the following video-on-demand (VOD) Reference Implementation:
AWS Streaming Media Reference Architecture
This architecture includes a set of services originally designed for M&E such as:
AWS Elemental MediaLive is a broadcast-grade live video processing service. It lets you create high-quality video streams for delivery to broadcast televisions and internet-connected multi-screen devices, like connected TVs, tablets, smart phones, and set-top boxes.
AWS Elemental MediaPackage (MediaPackage) is a just-in-time video packaging and origination service that runs in the AWS Cloud. With MediaPackage, you can deliver highly secure, scalable, and reliable video streams to a wide variety of playback devices and content delivery networks.
AWS Elemental MediaTailor is a channel assembly and personalized ad insertion service for video providers. It can create linear OTT, internet delivered, channels using existing video content and monetize those channels, or other live streams and VOD content, with personalized advertising.
Amazon Personalize enables developers to build applications with the same machine learning (ML) technology used by Amazon.com for real-time personalized recommendations. Machine learning expertise is not required to utilize this service.
Amazon Personalize makes it uncomplicated for developers to build applications capable of delivering a wide array of personalization experiences, including specific product recommendations, personalized product re-ranking, and customized direct marketing. Amazon Personalize is a fully managed ML service that goes beyond rigid, static rule-based recommendation systems. It trains, tunes, and deploys custom ML models to deliver highly customized recommendations to customers across industries such as retail, media, and entertainment.
Further customization available to Health Care and Life sciences
The above listed assets can be complemented by additional AWS services to enable expert collaboration and provide intelligent content management services.
Amazon Pinpoint is a flexible and scalable outbound and inbound marketing communications service. You can connect with customers over channels like email, SMS, push, or voice. Segment your campaign audience for the right customer and personalize your messages with the right content. Delivery and campaign metrics in Amazon Pinpoint measure the success of your communications. Amazon Pinpoint can grow with you and scales globally to billions of messages per day across channels.
Amazon Kendra is an intelligent search service powered by machine learning. Amazon Kendra reimagines enterprise search for your websites and applications. Your employees and customers will easily find the content they are looking for, even when it’s scattered across multiple locations and content repositories within your organization.
Using Amazon Kendra, you can stop searching through troves of unstructured data and discover the right answers to your questions when you need them. Amazon Kendra is a fully managed service, so there are no servers to provision, and no machine learning models to build, train, or deploy.
Amazon Neptune (Neptune) is a fast, reliable, fully managed graph database service that makes it clear how to build and run applications that work with highly connected datasets. The core of Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. Neptune powers graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security.
Amazon Lex is a service for building conversational interfaces into any application using voice and text. Amazon Lex provides the advanced deep learning functionalities of automatic speech recognition for converting speech to text, and natural language understanding to recognize the intent of the text. This enables you to build applications with highly engaging user experiences and lifelike conversational interactions. With Amazon Lex, the same deep learning technologies, which power Amazon’s Alexa, are now available to any developer. This empowers you to easily build sophisticated, natural language, conversational bots (“chatbots”).
Amazon SageMaker Autopilot (SageMaker Autopilot) eliminates the heavy lifting of building ML models, and helps you automatically build, train, and tune the best ML model based on your data. With SageMaker Autopilot, you simply provide a tabular dataset and select the target column to predict, which can be a number (such as a house price, called regression), or a category (such as spam/not spam, called classification).
SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click, or iterate on the recommended solutions with Amazon SageMaker Studio to further improve the model quality.
Pharma Companies today have a unique opportunity to reinvent the way they provide curated, educational content to HCPs and patients. They can leverage prepackaged AWS M&E assets to deliver a personalized media streaming experience at scale, in a self-service mode, with the appropriate content search and social networking capabilities.
By using various AWS services, and working backwards from physicians needs, AWS can accelerate the incremental delivery of your medical services hub. You can build a repeatable, innovative way to personalize medical content to physicians. This new content access stream could help physicians in their clinical decision making and, ultimately, in their care management of patients.
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