How InpharmD uses Amazon Kendra and Amazon Lex to drive evidence-based patient care

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This is a guest post authored by Dr. Janhavi Punyarthi, Director of Brand Development at InpharmD.

The intersection of DI and AI: Drug information (DI) refers to the discovery, use, and management of healthcare and medical information. Healthcare providers have many challenges associated with drug information discovery, such as intensive time involvement, lack of accessibility, and accuracy of reliable data. The average clinical query requires a literature search that takes an average of 18.5 hours. In addition, drug information often lies in disparate information silos, behind pay walls and design walls, and quickly becomes stale.

InpharmD is a mobile-based, academic network of drug information centers that combines the power of artificial intelligence and pharmacy intelligence to provide curated, evidence-based responses to clinical inquiries. The goal at InpharmD is to deliver accurate drug information efficiently, so healthcare providers can make informed decisions quickly and provide optimal patient care.

To meet this goal, InpharmD built Sherlock, a prototype bot that reads and deciphers medical literature. Sherlock is based on AI services including Amazon Kendra, an intelligent search service, and Amazon Lex, a fully managed AI service for building conversational interfaces into any application. With Sherlock, healthcare providers can retrieve valuable clinical evidence, which allows them to make data-driven decisions and spend more time with patients. Sherlock has access to over 5,000 of InpharmD’s abstracts and 1,300 drug monographs from the American Society of Health System Pharmacists (ASHP). This data bank expands every day as more abstracts and monographs are uploaded and edited. Sherlock filters for relevancy and recency to quickly search through thousands of PDFs, studies, abstracts, and other documents, and provide responses with 94% accuracy when compared to humans.

The following is a preliminary textual similarity score and manual evaluation between a machine-generated summary and human summary.

InpharmD and AWS

AWS serves as an accelerator for InpharmD. AWS SDKs significantly reduce development time by providing common functionalities that allow InpharmD to focus on delivering quality results. AWS services like Amazon Kendra and Amazon Lex allow InpharmD to worry less about scaling, systems maintenance, and stability.

The following diagram illustrates the architecture of AWS services for Sherlock:

InpharmD would not have been able to build Sherlock without the help of AWS. At the core, InpharmD uses Amazon Kendra as the foundation of its machine learning (ML) initiatives to index InpharmD’s library of documents and provide smart answers using natural language processing. This is superior to traditional fuzzy search-based algorithms, and the result is better answers for user questions.

InpharmD then used Amazon Lex to create Sherlock, a chatbot service that delivers Amazon Kendra’s ML-powered search results through an easy-to-use conversational interface. Sherlock uses the natural language understanding capabilities of Amazon Lex to detect the intent and better understand the context of questions in order to find the best answers. This allows for more natural conversations regarding medical literature inquiries and responses.

In addition, InpharmD stores the drug information content in the cloud via S3 buckets. AWS Lambda allows InpharmD to scale server logic and interact with various AWS services with ease. It is key in connecting Amazon Kendra to other services such as Amazon Lex.

“AWS has been essential in accelerating the development of Sherlock. We don’t have to worry as much about scaling, systems maintenance, and stability because AWS takes care of it for us. With Amazon Kendra and Amazon Lex, we’re able to build the best version of Sherlock and reduce our development time by months. On top of that, we’re also able to decrease the time for each literature search by 16%.”

– Tulasee Chintha, Chief Technological Officer and co-founder of InpharmD.

Impact

Trusted by a network of over 10,000 providers and eight health systems, InpharmD helps guide evidence-based information that accelerates decision-making and saves time for clinicians. With the help of InpharmD services, the time for each literature search is decreased by 16%, saving approximately 3 hours per search. InpharmD also provides a comprehensive result, with approximately 12 journal articles summaries for each literature search. With the implementation of Sherlock, InpharmD hopes to make the literature search process even more efficient, summarizing more studies in less time.

The Sherlock prototype is currently being beta tested and shared with providers to get user feedback.

“Access to the InpharmD platform is very customizable. I was happy that the InpharmD team worked with me to meet my specific needs and the needs of my institution. I asked Sherlock about the safety of a drug and the product gave me a summary and literature to answer complex clinical questions fast. This product does a lot of the work that earlier involved a lot of clicking and searching and trying tons of different search vendors. For a busy physician, it works great. It saved me time and helped ensure I was using the most up-to-date research for my decision-making. This would’ve been a game changer when I was at an academic hospital doing clinical research, but even as a private physician it’s great to ensure you’re always up to date with the current evidence.”

– Ghaith Ibrahim, MD at Wellstar Health System.

Conclusion

Our team at InpharmD is excited to build on the early success we have seen from deploying Sherlock with the help of Amazon Kendra and Amazon Lex. Our plan for Sherlock is to evolve it into an intelligent assistant that is available anytime, anywhere. In the future, we hope to integrate Sherlock with Amazon Alexa so providers can have immediate, contactless access to evidence, allowing them to make fast data-driven clinical decisions that ensure optimal patient care.

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About the Author

Dr. Janhavi Punyarthi is an innovative pharmacist leading brand development and engagement at InpharmD. With a passion for creativity, Dr. Punyarthi enjoys combining her love for writing and evidence-based medicine to present clinical literature in engaging ways.

Disclaimer: AWS is not responsible for the content or accuracy of this post. The content and opinions in this post are solely those of the third-party author. It is each customers’ responsibility to determine whether they are subject to HIPAA, and if so, how best to comply with HIPAA and its implementing regulations. Before using AWS in connection with protected health information, customers must enter an AWS Business Associate Addendum (BAA) and follow its configuration requirements.

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