Case Study: Improving Referral Submission Efficiency & Accuracy

Problem

Referral coordinators were spending a significant amount of time submitting referrals across numerous portals. This work was highly manual, varied by coordinator, and prone to data entry errors, leading to cases of rework, delays and patient dissatisfaction.

Discovery & Research

To understand the core problem, I worked very closely with a large group of referral coordinators to observe and document their end-to-end workflow.

My key activities included:

• Conducting interviews and shadowing referral coordinators to not only learn the role but also to surface pain points and variations in the process.
• Creating SIPOC diagrams to define scope, inputs, outputs and dependencies.
• Mapping detailed As-Is workflows to capture handoffs, decision points, and ownership of steps.
• Identify common sources of errors and delays, particularly around manual data entry & location and provider mismatch.

This work revealed that a significant portion of time was spent repeatedly manually entering information across different systems, sifting through unstructured data to answer complex questions, and learning different workflows. This increased both the cognitive load of the referral coordinators as well as error and rework rates.

Definition & Requirements

Upon my process discovery findings, I helped define and evaluate problem statements and success criteria centered on:

• Minimizing manual data entry and improving referral quality.
• Reducing time to submit a referral.
• Integrating easily with existing workflows to avoid disruption.

I translated my workflow insights into clear functional requirements including things like a point of singular data entry to initiate the process, automatic navigation, as well as pre-population of referral information.

Solution

As a result, a browser extension-based automation utilizing AI was implemented to support the requirements. The solution allowed coordinators to enter a patient's MRN only once, launching the navigation to the correct referral portal the patient required, and submitting all required information without manual entry.

The approach aligned with existing workflows, reducing change friction while addressing the highest impact pain points.

Outcome

The solution significantly reduced the time it required to submit a quality referral and lowered the occurrence of errors caused by manual entry. To continue, the extension also provided a solution regarding provider-location mismatch as I ensured to include Human-In-The-Loop and other reinforcements after integrating with AI APIs and address normalization.

Impact included:

• Faster referral submission
• Improved consistency and quality of referrals submitted
• Reducing cognitive load and frustration for coordinators and patients alike

The workflow documentation created during the discovery phase also became a reference for onboarding and future process improvements surrounding referrals.