The vision

Our prestigious academic medical center client is on a transformative journey to implement competency-based medical learning practices. This meaningful transition to improve its already-renowned ability to educate future healthcare professionals impacts all organizational stakeholders. Its education leadership team envisioned a future where students, educators, and evaluators have access to near real-time performance insights and thoughtfully designed self-discovery.

The challenge

This greenspace project is a critical transformation, replacing an existing manual process and challenging user experience. Complexity increased due to the sensitivity of student performance data, as well as a wide stakeholder scope. This created a highstakes delivery environment with a highly-visible outcome.

The plan

Our team worked closely with varying levels of client stakeholders to understand and document the current processes and user experiences. We drove effective process reengineering and data engineering automation through the design and implementation of a layered cloud data fabric: a custom application allowing for machine co-piloting, as well as Tableau for visualizing near real-time dashboards.

Major outcomes

Our priority was to deliver a best-in-class solution that maximized our client’s capabilities while minimizing their costs. By leveraging our expertise in Artificial Intelligence, Platform Engineering and Machine Learning, we were able to satisfy our client’s vision and deliver four major outcomes:

• Exceptional User Experience
  We created a superior user experience to any other competency-based learning reporting system in the country.
• Optimized Labor Focus
  We enabled our client to transition 200+ labor hours per month from manual data curation to direct student support operations
• Human/AI Collaboration
  The platform enabled machine co-pilots for human users and decision-makers, creating certainty in stakeholders’ data visibility while eliminating unnecessary confusion and support requests.
• Iterative Innovation
  We introduced a data fabric approach to pave the way for the next iterative step of AI (machine learning, NLP, LLM) for optimization of data curation efforts and student support.