AI & Machine Learning

Deployed AI: How Lumiata is using AI to make healthcare smarter

AI Hub.png

Editor’s note: Today we hear from Miguel Alvarado, CTO at Lumiata, which is using Google Cloud AI to provide intelligent health analytics for improved risk awareness and cost management in healthcare. 

Lumiata was founded on a vision of smarter, more cost-effective healthcare, and AI plays a central role in making it a reality. We provide sophisticated machine learning applications that elevate a healthcare organization’s ability to manage spend forecasting, stop loss, disease prediction and more, out of the box. 

We recently unveiled the Lumiata AI Platform for healthcare organizations who are seeking to apply machine learning to improve affordability and quality of care. Our platform helps healthcare organizations in two ways. Firstly, they can streamline their data to get a comprehensive longitudinal person record (LPR) for personalized analytics that uses different signals including claims, eligibility, labs, pharmacy, EHR/EMR, unstructured data, and more. Secondly, they get tools and access to pre-trained models through Lumiata AI Studio that healthcare data scientists can use to build and deploy machine learning models for use-cases like predicting medical costs and events.

It takes powerful infrastructure to deliver ease of use and simplicity to our customers. After careful evaluation, we chose Google Cloud to help us deliver meaningful AI capabilities to transform healthcare organizations. Google Cloud’s security infrastructure, wide selection of intuitive AI tools, and technologies like Anthos that make multi-cloud environments utterly straightforward were some of the key factors behind our decision.

As we moved to Google Cloud and built our platform, there are a few lessons we learned along the way that can help others who are in the middle of deploying AI in business. 

Lesson #1: AI doesn’t “just work”

The first misconception about AI—and probably the most common—is that it’s a kind of silver bullet that “just works”. The truth is, AI must be purpose-built to extract the most value and insights from data. It requires a deep understanding of the problem at hand and the unique challenges it presents. With a goal to improve the affordability and quality of care, we focus on helping healthcare organizations use AI to understand the individual and provide personalized experiences. Google addresses this learning head-on with Deployed AI, a uniquely pragmatic approach that echoes our belief that although AI is incredibly powerful—and often transformative—it’s not magic.

It also takes patience to iterate, experiment, and learn from mistakes, from stakeholders at every level of an organization. Operating ML infrastructure and live production models is hard.

The CI/CD philosophy you apply to traditional software can be applied to models as well. These practices—also called “MLOps”—may not be the most attractive parts of AI, but they’re an essential part of successful deployments. We're doing the ML Ops heavy lifting on behalf of our customers. Through Lumiata’s MLOps framework, models can be automatically monitored, validated, and tested so model performance is continuously optimized.

Lesson #2: AI is about much more than models

Next, I believe a disproportionate amount of attention is paid to machine learning models themselves, making it easy to underestimate the universe of supporting components that surrounds them and makes them work. There’s the data that trains the model, for instance, as well as the infrastructure that connects everything—two areas in which Google has deeper roots than just about anyone. Healthcare organizations are sitting on large volumes of data, but its visibility and access to the data science and analytics teams are limited. This impedes their ability to apply AI to meaningfully solve relevant problems. BigQuery, Google’s serverless data warehouse, enables us to solve this challenge for our users by providing access to large volumes of data with no operational hurdle at all.

In addition to data, building, deploying, and maintaining machine learning models requires significant computational horsepower. Our customers often struggle to get buy-in from their technology counterparts to scale their infrastructure requirements for machine learning. With the help of Google Kubernetes Engine, our platform helps them meet their compute requirements for machine learning with ease and flexibility. 

Lesson #3: AI is a team sport

The bottom line is that even the most sophisticated machine learning model is but a single part of a larger system. Building that system—an AI deployment that makes a real-world difference—takes software engineering muscle, a culture of DevOps, and the tooling developers need to rapidly automate, test, deploy, and measure the results.

Smaller companies often benefit from generalists that wear a number of hats, while bigger companies can afford to hire a range of specialists. Either way, engagement between disciplines is key: for instance, it’s vital to encourage machine learning experts to participate in development activities like testing and automation. There’s also the question of proportion; both engineering talent and AI vision are vital, but the former is generally needed in larger numbers than the latter. There’s no substitute for at least one qualified voice to push the envelope with advanced models that optimize for multiple variables or deliver transparency reasoning—sometimes known as “explainability”—along with their predictions; but it takes a far larger group of engineers to bring such a vision into production in a robust, efficient way.

Adoption of AI in healthcare is limited partially due to the talent gap. Recruiting and retaining top talent to build an AI-architecture can be challenging. We strive to alleviate this challenge for our customers through our platform by automating many aspects of the machine learning pipeline, and thereby enabling them to deploy AI faster.  

Finally, be ready to continually test your deployment once it goes into production. Real-time monitoring helps ensuring models perform as expected and remain consistent. 

Conclusion

AI may be complex, but we remain big believers in its remarkable potential to transform business. That’s why we founded Lumiata—to address these challenges ourselves so healthcare organizations can focus on innovations that improve affordability and quality of care.

We’re helping to make healthcare smarter. And with Google Cloud, we’re getting there faster than ever.

Learn more about AI on Google Cloud.