Fragmented tools limit visibility as company scales mobile health platform

DocGo is a mobile healthcare company that provides medical transportation, in-home medical visits, virtual care, and chronic condition management. Using a fleet of mobile units and a virtual provider network, it offers last-mile care, including vaccinations, health screenings, labs, and telehealth-enabled field visits.

DocGo relies heavily on two critical applications. The first is a logistics platform, Dara, that helps route medical transportation and home health visits. The system is accessed by both external providers including health systems, administrators, and case management teams, as well as internal users, including dispatchers and vehicle drivers. The second application is a patient-facing portal that enables individuals to access and manage their care.

As DocGo scaled its operations, it faced observability challenges. The team was using multiple tools but lacked centralized visibility across its technology stack. The tools it was using also required significant time for planning and analytics setup, diverting resources away from product development and enhancements to improve the overall delivery of patient care.

DocGo needed an end-to-end view of the user experience to understand how customers were actually using their applications and where friction points existed. Adding to the complexity, the company was migrating from a legacy platform to Kubernetes and needed a single observability platform that could work effectively across both environments.

Cost savings were another key priority. By reducing what it spends on observability tools and improving operational efficiency, DocGo could redirect more resources toward internal staffing, as well as research and development initiatives.

Supporting a Kubernetes migration with unified observability

DocGo was already a Datadog customer, having used Application Performance Monitoring (APM) and other Datadog products for some time. As the company began migrating from its legacy system to Kubernetes, it integrated more apps and services with Datadog. Today, DocGo monitors over 80 microservices via APM.

More recently, after evaluating several competing solutions, the company adopted Datadog Real User Monitoring (RUM) and Product Analytics (PA). The core factor they assessed was how to reduce development time while gaining deeper analytics capabilities. The team wanted an auto-capture solution with the lowest development overhead. “We wanted to know how we could get the necessary metrics to understand the system-wide impact our products and features are having,” says a DocGo Product Manager. “How can we calculate ’time on task’ and understand gaps in workflows that users are facing?”

DocGo selected Datadog because it offered a single SDK, lower engineering lift, alignment with its internal security and compliance standards, and faster implementation compared to competing solutions.

“We ship code a few times a week across multiple application services, and the fact that Datadog allows us to identify a sudden spike or faulty deployment is powerful and helps us reduce time to resolution, which would take a lot longer otherwise.”

Today, DocGo’s frontend team uses RUM and PA extensively, as well as Session Replay, to identify gaps and issues in user workflows. The value of Datadog’s unified platform became clear recently when DocGo began receiving increased reports from users about a string-based search feature that wasn’t returning the responses users expected. Using a combination of RUM, PA, and APM, the team identified the root cause: white space characters were being saved in the system, causing searches to fail. By looking at both backend and frontend data in Datadog, the team quickly identified the problem and addressed it, preventing further user frustration and support tickets.

Reducing issue resolution time and improving workflows

DocGo has now transformed how it understands and improves the patient and provider experience. The team has time-on-task insights that reveal how long users spend completing workflows, faster issue diagnosis through Session Replay, clearer feature adoption analytics, and the ability to export data for deeper analysis.

“You get all the telemetry and services you need to have a modern observability stack. It's not just about logs and metrics, it's about seeing the entire pane in a unified manner.”

The engineering team achieved what they initially sought: a mature auto-capture solution with minimal development maintenance time. The results have been significant across multiple areas. From a product perspective, DocGo has achieved time savings. In addition, the quality of data they capture has improved dramatically because of its granular nature. The team can now understand interactions at the individual user level and scale those insights to understand broader impact. “We can craft more meaningful reports on product and impact our features have made,” says a DocGo Manager of DevOps.

The team is also using Watchdog, which has proven particularly valuable for deployment monitoring. “The Watchdog capabilities are incredible. It really helps identify bad deployments,” adds the Manager of DevOps. “We ship code a few times a week across multiple application services, and the fact that Datadog allows us to identify a sudden spike or faulty deployment is powerful and helps us reduce time to resolution, which would take a lot longer otherwise.”

DocGo now uses Datadog as a single platform across APM, logs, and Product Analytics. “It’s a great solution and the fact that you can have a single pane of glass for all of these tools—the cohesive nature of having APM, logs, Product Analytics, and other monitors and metrics we rely on for scaling under one umbrella is great,” says the Manager of DevOps. “You get all the telemetry and services you need to have a modern observability stack. It’s not just about logs and metrics, it’s about seeing the entire pane in a unified manner.”

“The proficiency with which Datadog surfaces insight given the volume of data that we collect and the swift manner in which the platform distills signal from noise is spectacular. It's been a game changer for us.”

Looking ahead, DocGo plans to explore Datadog Cloud Cost Management and LLM Observability to further optimize operations and support the company’s evolving technology needs. “There are a lot of observability platforms out there. Datadog does it all—and that is one of the reasons I’m so thrilled to have Datadog integrated across our tech stack,” says Hawk Newton, CTO. “The proficiency with which Datadog surfaces insight given the volume of data that we collect and the swift manner in which the platform distills signal from noise is spectacular. It’s been a game changer for us.”