From Two Hours to Five Minutes: Restoring Epic Reporting for a Regional Health System

The situation

The VP of Health Informatics at a regional health system had a problem she could not explain to her board. Every morning, the clinical teams waiting on overnight Epic reports were getting the same answer: “Still running.” Discharge planners could not pull patient lists. Revenue cycle analysts could not start their day. Quality teams could not generate the metrics their accreditation reviewers expected.

What should have been routine Clarity reports — built on a well-established Epic environment with adequate hardware — were taking two hours or more to return results. On some mornings they simply timed out.

The health system had already escalated to their internal DBA team, who confirmed the queries were slow but had no clear path to fix them. A major EHR vendor advisory was consulted. The recommendation came back: hardware upgrade, six-month timeline, significant capital expenditure. The organization needed a second opinion from someone who actually lived inside Epic’s data layer.

Challenge and approach

Why this was harder than it looked

Epic’s Clarity database is not a standard SQL Server environment. Epic controls the schema, which means most standard DBA optimization techniques — restructuring tables, modifying the data model, adding columns — are off-limits. Every change has to work within what Epic permits, which is a narrow lane.

The health system’s internal team had the right instincts but not the Epic-specific knowledge. They had applied some general SQL Server tuning that actually made things worse in places, because general tuning and Epic-aware tuning are different disciplines.

The APSIS consultant brought something the team did not have: ten years of direct Clarity performance work across multiple health systems, which meant pattern recognition rather than trial and error. When the execution plans came up, the root cause was identifiable in hours, not weeks.

Results

The operational change was immediate. On the day the optimization went live, discharge planners had their patient lists before 7:00 AM. Revenue cycle staff started work at the start of their shift instead of two hours in. The accreditation quality reports that had been failing due to timeout were running cleanly within the scheduled window.

The hardware upgrade the health system had been quoted — a six-month capital project — was no longer necessary. The problem was never hardware. It was precision: knowing exactly which indexes were missing, which statistics were stale, and how TempDB needed to be structured for a Clarity workload of this scale.

Total engagement time was six weeks from initial assessment to post-optimization validation. The health system continues to use the monitoring framework APSIS put in place as a standard operating procedure.

APSIS capabilities demonstrated