Run more intelligent clinical trials

  • Overview
  • Resources
  • Transcript
Ryan Stastny
Product Manager, MuleSoft


Clinical trial management is the most expensive and time-consuming segment of the research and development process. In this demo, learn how customers can surface critical study and subject data from Veeva to power visualizations — in tools like Tableau — using MuleSoft Accelerator for Life Sciences. As a result, organizations can run more efficient and intelligent clinical trials.

Ryan Stastny (00:07):
Thank you for investing your time to learn more about our Accelerator for Life Sciences clinical trial analytics use case. Today I'll share why we invested in developing these capabilities, demonstrate a part of the solution, and close by sharing some additional resources to help you get started.

Ryan Stastny (00:25):
If you're in the life sciences industry, none of these metrics will surprise you. These insights, as well as input from our customers and partners led us to developing this use case. First, 75% of pharma companies reported a need for a more agile method to ingest and consolidate data. Second, 93% of pharma companies shared they're not using AI for data, in large part due to the first shared metric; that without access to quality data, the potential for AI technologies is stunted. Third, only around 10% of drugs that enter the clinical trial phase reach approved status. Life sciences organizations across the globe are seeking ways to run more intelligent, efficient trials to increase the likelihood of success and decrease time to market.

Ryan Stastny (01:16):
For today's demonstration, we'll be using a fictitious situation at an organization called Mythical Pharma. First, let's meet Amy, a Clinical Trails Operations Manager at Mythical Pharma, who's responsible for ensuring her study runs according to plan, so her organization is able to bring their new investigational drug to market as quickly as possible. The study she's currently assigned to is ESS1001, which has sites in multiple countries and a number of subjects that have already begun to receive treatment. She identifies a need to develop a report to monitor subject status across the different sites of the study when she reaches out to her colleague in Mythical Pharma's IT team, Lisa. Lisa is an IT System Specialist who will help Amy create the view she is looking for in their analytics platform, Tableau, but before doing so will need to stand up the integration with their CTMS system from Veeva.

Ryan Stastny (02:15):
Now that we've set the scene, let's run it through a demonstration of the assets provided by the Accelerator for Life Sciences that will be used by Lisa. To start, Lisa heads to Anypoint Exchange, Mulesoft's marketplace for reusable assets like connectors, templates, and accelerators. She searches life sciences and clicks into the Mulesoft Accelerator for Life Sciences homepage. After reviewing the overview and reference architecture, as well as the supported use cases, she realizes that the clinical trial analytics use case is in line with Amy's requirements. She then clicks into the use case tab to get additional description into the use case capabilities, a glossary of terms, as well as a high level architecture that provides visibility into the different assets that are provided by the solution. As she scrolls further, she sees there's a sequence diagram that provides visibility into the data flow from the various systems supported by the accelerator. As she scrolls further, she is able to access the assets themselves, which include both API specifications and implementation templates.

Ryan Stastny (03:35):
As you remember, Amy has a requirement to surface data from Veeva CTMS, so Lisa begins by clicking into the API specification. From here, she's able to get a better understanding of how the API is organized, what endpoints are available, as well as what methods can be used. Next, she is ready to begin implementation work, so she navigates to Mulesoft's IDE Anypoint Studio, where she's able to import the templates or prebuilt applications that include the integration logic that she needs to connect to the different systems.

Ryan Stastny (04:12):
For this example, we will import the Veeva CTMS system API, where she loads the template into her local [inaudible 00:04:25] studio and opens the implementation dot XML file to look at the sub-flows associated with this asset. This asset, as you can see, provides capability to both extract in bulk, as well as to query incremental changes to Veeva CTMS data. After providing Mythical Pharma's credentials and making slight configuration changes to this asset, she's ready to deploy it to Cloud Hub, as well as deploy the other APIs associated with this flow, including the clinical trial process API and the AWS system API. After these APIs have been deployed, she's now ready to open Tableau desktop to begin creating the visualization for Amy.

Ryan Stastny (05:13):
First, Lisa begins by setting up the connection to her AWS S3 bucket using a new data source with Amazon Athena. In the interest of time for this demonstration, I've pre-configured the connection with Amazon Athena. As you can see, Lisa is able to see a number of the required fields to support Amy's request for this report. After clicking update, she's now reflecting the latest data available from her Athena data store, and she's now ready to create Amy's report. She'll click create new worksheet and start to drag in the fields that Amy needs. First, she'll pull a count of all the subjects into her rows to reflect the number of subjects in Amy's study. She then will add a filter to only include the study that Amy is responsible for, which as we discussed earlier is ESS1001. Now the visualization is just showing the count of subjects in Amy's study.

Ryan Stastny (06:18):
As a reminder, Amy is looking for a report that provided her visibility into subject status by site. Next, Lisa pulls the site as a column in her visualization. She's now able to see subjects by site. Lastly, she needs to see the subject status, so she adds a color label to allow for Amy to quickly be able to determine the subject status by site.

Ryan Stastny (06:50):
Amy is ecstatic to see what Lisa has delivered, and the two of them start to discuss some other ways to manipulate the data to provide other beneficial views. To simplify the experience for Amy and her colleagues, Lisa publishes this report to a dashboard in Tableau Online. As you can see, we see a similar report that was provided in Tableau desktop, as well as a number of other views, including subject pipeline, subject pipeline by site country, as well as a geographic view of her different sites in the study. In addition, Lisa provides the ability for Amy and her colleagues to filter on different subject statuses to include things like looking at just subjects that have withdrawn from the study to allow for her to determine where she may need to focus her attention to ensure the study has enough participants to succeed.

Ryan Stastny (07:55):
Before closing, we wanted to mention that while this initial use case for the Accelerator for Life Sciences focuses on analytics use case, the value of the solution goes far beyond that. The primary objective of accelerators are to serve as an example for our customers of how our API led methodology can come to life and serve as a foundation for your application network. Additionally, by providing system APIs to a number of critical systems like Veeva CTMS, our customers can use these assets to accelerate implementation of other related use cases, such as non-Veeva EDC to Veeva CTMS integration. Lastly, since we provide our customers with direct access to each of these assets, one can easily swap out the systems used in our design while adhering to this framework and identified integration patterns.

Ryan Stastny (08:44):
Now, how can you learn more? First, head to Anypoint Exchange to access these assets and their associated documentation. Second, feel free to reach out to our team to ask any questions, share your feedback, or let us know what you were able to accomplish through the use of our accelerators at Thank you again for your time.

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