Modivcare

Catalyte apprentices’ problem-solving skills and tech expertise improved efficiencies and reduced talent acquisition timelines

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Challenge

Unfilled tech positions were affecting Modivcare’s purpose of making connections to care and Modivcare wanted immediate solutions.

Outcome

The client’s initial contract for an initial resource quickly grew into a fully staffed team of with junior apprentices and supporting senior resources. Modivcare found that apprentices repeatedly proved their tech capabilities in managing and streamlining complicated data processes from disparate sources. Catalyte apprentices’ contributions helped to improve the ride experience for members and users. Catalyte reduced Modivcare’s talent fulfillment period from an average of three months to just two weeks.

Modivcare aims to improve medical outcomes by providing non-emergency medical transportation to members of vulnerable and underserved communities. Modivcare’s collaboration with transportation vendors and care facilities results in more members seeking treatment and keeping existing appointments. The company turned to Catalyte to meet its demand for qualified tech workers who could streamline and process large amounts of data from different vendors.

Becoming full believers in the quality of Catalyte’s apprentices

Modivcare was new to sourcing apprentice talent – and was a bit skeptical of the quality of work Catalyte could deliver or the time it would require to ramp up. Catalyte’s flexible model allowed Modivcare to contract with a single apprentice before committing to others. The first apprentice integrated with an existing team and began contributing immediately. Within two months, the client returned to Catalyte to add seven more resources, including one with senior-level experience to assist with ramp up.

Applying knowledge to deliver value

Catalyte’s first team of seven apprentices, plus one senior, supported a variety of data streams between members, transportation providers and health care providers. A few examples of the team’s work included:

  • Automating a manual vendor integration process that allowed new drivers to begin servicing patients more quickly;
  • Aggregating rides with billing complexities to identify and solve challenges;
  • Creating a Ride Flow tool to centralize key components of data collected during the ride;
  • Proactively addressing challenges with the API documentation to ensure it was up to date and intuitive for users;
  • Solving a migration issue between different systems.

Catalyte also quickly upskilled an additional six apprentice developers and placed them in QA roles. The combined efforts helped to improve efficiencies and allowed Modivcare to make better connections to care.

Speed to productivity

Modivcare came to Catalyte because traditional hiring methods were costly and couldn’t fill positions quickly enough. The result of open positions was felt across the company. The first apprentice, and all that followed, proved that Catalyte could deliver a pipeline of qualified talent on par with Modivcare’s high expectations. Catalyte apprentices quickly began to apply their tech skills to Modivcare’s complex systems and data integrations. Their competency grew so rapidly that Modivcare was able to roll off the senior resource and continue to the team with apprentices only.

Key takeaways

Proof of quality talent
After an initial trial with one apprentice, Modivcare quickly requested additional resources.
Speed to productivity
Catalyte apprentices arrived on site qualified and ready to apply their skills immediately to client projects.
A more efficient way to fill workforce needs
Catalyte reduced Modivcare’s talent fulfillment period from an average of three months to just two weeks.
Ability to source junior- to senior-level talent
The client sourced apprentices for entry-level positions as well as roles that required deep thinking and problem-solving skills with higher technical proficiency.

Key technologies/skills

  • Django
  • Redis
  • QA
  • AWS
  • Datadog
  • Twilio
  • Selenium

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