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How Pangaea Data uses Azure for a medical AI tool that improves patient outcomes

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Starting a company is never easy. But it can be especially painful in the healthcare industry. There are numerous regulations, HIPAA compliance requirements, and strict rules regarding patient confidentiality that you need to navigate. These factors, while important, make it extremely difficult to introduce new technology into hospitals and healthcare companies.

However, these challenges don’t mean that entrepreneurs shouldn’t—or can’t—pursue this path. Many startups have succeeded in changing healthcare through innovation, and Pangaea Data (Pangaea) is one of them.

Pangaea Data, founded in 2018, is a life sciences technology business that provides PIES (Pangaea’s Intelligence Extraction and Summarization), which is driven by novel unsupervised artificial intelligence (AI) to extract and summarize intelligence from patient records in a federated privacy preserving manner. It then uses this intelligence to characterize hard to diagnose conditions and make predictions that can help catch undiagnosed or miscoded patients.

This is no small task, though, and it’s compounded by the fact that there’s an underlying distrust of AI in the healthcare field due to earlier AI technologies that failed to deliver on their promises.  Pangaea Data has taken approach to using AI in healthcare: Instead of trying to replace human doctors and nurses with computers, they’re focusing on helping them do their jobs better by providing insights derived from big data sets. By combining the best aspects of clinical medicine and cutting-edge computer science, they’ve developed an AI platform that can identify and diagnose diseases faster and less expensively than traditional methods. And they’re doing this with Microsoft Azure.

To learn more, we spoke with Pangaea Data co-founder and CEO Vibhor Gupta. Let’s take a closer look at what Pangaea Data is doing, and find out how they’re using Azure.

Proof of concept: Detecting Cachexia

One challenge of using AI in the medical industry comes when trying to build a system that works well even when data is complex and unstructured. To prove that applying unsupervised learning to extract actionable intelligence and insights is viable, Pangaea Data needed to work with a real-world data set to try to accurately detect a frequently undiagnosed or misdiagnosed condition. Cachexia is a perfect example of a condition that is significantly underdiagnosed.

Cachexia is characterized by weight loss and muscle wasting. It’s one of the most common symptoms of cancer and has been linked to high mortality rates in cancer patients. The problem is that patient data is messy, and it’s difficult for doctors to identify the subtle indicators that a patient has begun to be affected by cachexia.

To solve this problem, Pangaea Data decided to use cachexia detection as a proof of concept to validate their AI approach to early diagnosis. They analyzed the electronic health records of thousands of oncology patients to see if they were able to spot any hints (like specific clinical features) through their AI driven product (PIES), which might indicate cachexia earlier than conventional means.

In a small dataset of 100 patients which Pangaea used, 19 cancer patients had previously been identified as having cachexia based on ICD codes in their patient records. Pangaea’s AI driven product, PIES, correctly identified the initial 19 patients, and then found an additional 51 (268% more) cancer patients who were suffering from cachexia but were undiagnosed. Following this study, PIES was deployed on a larger dataset of circa 29,000 patients where it found 1052% more cancer patients with cachexia, who were hidden (or missed) in plain sight. These results were validated with help from clinicians.

It’s incredible — and important! Early detection of cachexia would have allowed those 51 patients to begin undergoing treatment sooner, which translates directly into saved lives and reduced healthcare costs.

And detecting cachexia was just the beginning. With proof that its technology works, Pangaea is now able to partner with healthcare providers and pharmaceutical companies, allowing them to begin delivering real-world improvements to patient outcomes.

Scaling with Azure

For startups, the ability to scale quickly and cost-effectively is critical to success. Azure offers secure, reliable, and cost-effective computing power. Building an AI system that works and improves patient outcomes is great, but it’s only useful if you can reliably run and scale it.

Pangaea Data relies on Azure virtual machines to host their servers and AI models. They use both CPU and GPU instances to run their models, using Azure Storage Accounts to store and manage data. They also rely on Azure networking tools such as VPNs and network security groups. Azure cloud management features automate the provisioning and maintenance of their servers, allowing the Pangaea team to scale up easily without worrying about running out of capacity.

This highlights a key part of the value Azure provides younger businesses: it enables them to focus on building their product instead of spending time managing infrastructure. Such younger businesses can also use Azure’s pay-as-you-go pricing model to minimize capital expenditures.

Microsoft and Azure: Trusted by industry

Azure is also trusted by enterprises and healthcare providers around the world. By working with Microsoft and Azure, Pangaea Data can provide a level of built-in trust: Potential clients are reassured when they hear that Pangaea Data runs on Azure.

Pangaea Data’s clients who already use Azure know they can deploy their AI driven product (PIES) on Azure and it will work as expected and assure compliance with privacy regulations. And, clients who haven’t yet moved to the cloud may already be interested in doing so because they know that moving their data to the cloud will make it easier for doctors, researchers, and scientists to collaborate in compliance with privacy regulations.

Running on Azure has an additional benefit: It makes it easy for Pangaea to deploy its product (PIES) for end users across multiple pharmaceutical companies and healthcare providers for various diseases and purposes, including early diagnosis, patient stratification, precision medicine, drug discovery and clinical trials in a federated privacy preserving manner. This means that patients everywhere achieve better outcomes, while Pangaea Data can ensure that every organization’s data remains secure and is never touched, copied or shared. In the privacy-conscious healthcare industry, this is critical to building trust and growing a client base.

Pangaea’s advice for Startups

Vibhor Gupta, the co-founder and CEO of Pangaea Data, has a few pieces of advice for other founders:

  • Focus on solving real problems and creating value for users and stakeholders.
  • Build a team with complementary skill sets to ensure that goals can be achieved.
  • If it’s possible, partner with other companies to increase the probability of success.

And finally, startups can turn to Azure as part of their solution stack to reduce costs and speed up the time to market.


Regardless of the industry you’re in, launching and scaling a business is never easy. But it’s especially difficult in healthcare, where privacy and regulatory concerns can slow things down. Pangaea Data’s use of Azure has been one of the keys to their early success.

Using Azure’s powerful computing and storage resources, Pangaea Data can process massive amounts of data quickly and reliably. With Azure, Pangaea Data can focus on building its core business instead of dealing with technical headaches.

If you’re a founder, consider Azure when you need to scale quickly and cost-effectively. With Azure, you can focus on creating a great product that solves real problems and adds value to users instead of stressing about IT infrastructure. Azure provides a level of trust and credibility that can help you attract customers and grow your business.

In this post, we learned about Pangaea Data’s use of Azure to build an AI driven product (PIES) focusing on patient intelligence that detects and diagnoses cachexia in cancer patients earlier. We also saw how Azure helped Pangaea Data to launch its product and scale it to meet customer demand.

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