Vital is redefining patient experience with software that gives more control, clarity, and predictability to emergency department visits and hospital stays. Using advanced AI, Vital transforms complex health record data into easy-to-use, personalized interfaces that inform and engage over one million patients per year.

Hospitals across the U.S. use Vital to improve patient satisfaction, drive growth and patient loyalty, achieve better clinical outcomes, and reduce workload for care teams. However, Vital found that its previous solution’s data infrastructure was not up to the task —they struggled to make updates to their AI models, and spent more time managing data pipelines than innovating on their core product. After deploying Chalk, Vital was able to stop wrangling infrastructure and focus on improving its models, launching new products, and delivering a world-class patient experience.

Vital began their machine learning journey with another managed feature platform which ultimately did not serve their needs. Heavily dependent on Spark and Databricks, the previous solution created challenges for Vital due to its poor developer experience and architectural limitations:

• Experimentation was a slow, tedious process filled with guess work and unreliable results, which made them unable to re-train their models.
• Changes to feature pipelines required deep knowledge of Spark and Databricks in addition to the feature store architecture, which was limited to a small number of engineers.
• Data processing occurred on third-party infrastructure.

Vital selected Chalk to address each of these challenges. With Chalk, Vital dramatically increased the pace of its product development.

• Rapid iteration and experimentation: Vital now deploys model updates 2-4 times per month.
• Vital has up to nine engineers working concurrently on Chalk without being blocked on infrastructure.
• Full control of customer data: patient data never leaves Vital’s cloud infrastructure.

One of Vital’s early models was trained on hospital data collected during COVID-19. Unsurprisingly, hospital activity during the early pandemic was vastly different from hospital activity post-COVID-19 vaccine, so it was crucial to re-train models to deliver accurate predictions. Vital recognized the importance of updating their models, but they were unable to release updates with the feature engineering solution they had.

There were several reasons why model releases were challenging. To start, Vital’s engineers needed to manage Spark, Databricks, various custom data pipelines, and the feature store itself. Modifying a single feature required full re-computation of the entire feature view, so data scientists needed to coordinate with infrastructure engineers throughout experimentation.

Even after data scientists were ready to bring an idea to production, they were unable to reuse training feature code for production feature generation, leading to training-serving skew. Re-implementing feature definitions led to inconsistency between training and production, which made it difficult for the team to be confident in the accuracy of production results.

Chalk solved these problems. It unlocked rapid iteration cycles and empowered a broad range of engineers to contribute code. Critically, Chalk enabled users to reuse the same feature code between notebooks, training, and serving. Today, when Vital engineers want to experiment with new feature pipelines, they create branch deployments with a single Chalk command. Later, they can deploy the exact same code to production. Engineers no longer have to manually update Spark and Databricks — updating features is as simple as editing a few lines of code. “Anyone who can write Python can work with Chalk,” says Mack Delany, Director of Machine Learning at Vital.

Vital handles sensitive healthcare information, which is regulated by HIPAA and requires strict data control. Their previous solution required data processing to occur on third-party cloud infrastructure.

When they set out to replace their machine learning partner, they searched for an enterprise-grade solution that could guarantee data would never leave their own cloud infrastructure. Chalk was a natural fit because it was designed with data privacy and security in mind from day one.

• Chalk is deployed in Vital’s own cloud infrastructure and no production traffic exits Vital’s virtual private cloud (VPC).
• Real-time data processing, feature caching, and query serving all happen from within Vital’s infrastructure.

By deploying Chalk into a customer’s own cloud infrastructure, customers are able to retain control of their data while still benefiting from Chalk’s best-in-class developer experience.

Vital now uses Chalk to process and serve features powering seven different models in production. These models are used in real-time applications, such as Vital’s ERAdvisor software, which guides patients through emergency room visits with personalized wait times and next steps.

ERAdvisor uses Chalk to provide accurate wait time estimates based on real-time, hospital-specific factors, including each hospital’s latest wait time data. Vital has seen improvements in:

Vital is looking forward to expanding its product offerings with machine learning powered by Chalk. They aim to continue guiding patients through their healthcare journeys even after the emergency room. Vital’s latest offering, AccessAdvisor, helps patients connect with specialized doctors for follow-up care while considering insurance matching, proximity, and relevant experience. Vital will use Chalk to predict the best doctor matches for patients. Additionally, they plan to increase the granularity of their existing guided products, with new predictions such as wait time estimates for lab results.