Executive Summary
A lightweight decision-support tool transformed dense process guidance into timely, actionable choices for a specialty operation.
At Wipro, agents process medical prior-authorization requests using ICUE and more than 30 supporting job aids. Within the genetics specialty queue, experienced agents were encountering recurring errors during case triage despite specialized training and familiarity with the broader authorization process.
As Quality Lead, I identified the trend, personally analyzed its causes, proposed the intervention, developed the tool, coordinated subject-matter review, led implementation, and measured the result.
The resulting Genetics Support Tool was intentionally modest: a completely local browser file built to meet agents at the exact moment a difficult decision had to be made.
Counts, timing, and scope in this case study are reported case assertions from the project presentation; they are not independently verified here.
The Quality Signal
Client escalations and team-lead error records were charted, classified, and traced back to the behaviors behind the numbers.
Portfolio demonstration only. The workbook contains fictional agents, cases and events created to show the analysis method—not the confidential operational dataset.
The trend involved tenured, specially trained agents. The existing job aids were comprehensive but broad, clinically written, and difficult to translate into quick action under processing-time constraints. Several related decision points created repeated opportunities for mistakes. Agents needed concise guidance at the moment of decision—not another document to study.
The Intervention
The real tool asked eight targeted questions. The abbreviated public demonstration below recreates five representative decision points with fictionalized, nonconfidential guidance.
Hello Specialty Team, Please review the fictional genetics GAP request below. Case number: Test name: CPT code: Thank you, <Agent Name>
Reported Results
The normalized index below uses April as 100. July is a partial period, so the comparison is directional rather than a like-for-like monthly result.
April 2026 baseline = 100
Validation & Adoption
A recurring genetics trend surfaced during preparation for a biweekly client quality review.
Light Python scripting narrowed broad categories; individual RCA narratives exposed the repeated decision failures underneath.
Eight error-prone points were converted into questions, conditional guidance, alerts, and a structured GAP request template.
The guidance was compared with current job aids and nurse feedback, then privately walked through with the genetics nurse team lead.
Every genetics team received a live demonstration. Team leads received separate enablement and written download, use, and redistribution instructions.
Errors were tracked against the same April baseline. Client leadership reviewed the tool, its decision logic, and its measured impact.
Reflection & Direction
It was the intervention that respected how work actually happened.
Identifying an error is only the beginning. Quality leaders must understand why the mistake makes sense from the user’s perspective, then create an intervention that fits real operational constraints. This tool succeeded because it transformed dense information into timely guidance instead of asking agents to study more material.
A second version would become a modular support center with search and dedicated process tabs. New decision guides could be added without redesigning the whole tool, preserving the original simplicity while extending the approach across other lines of business.