Diagnose a sudden drop in response time for project health dashboard

Problem Statement Description

Clinic administrators rely on a project health dashboard to monitor clinic operations, identify at-risk projects, review alerts, and coordinate follow-up actions. A sudden negative change has been observed in response time for these administrators, affecting how quickly they can use the dashboard or respond to project-health signals.

Your task is to diagnose the issue before proposing fixes. Focus on clarifying what “response time” means in this context, validating whether the anomaly is real, identifying which users/workflows are affected, and narrowing down likely causes through data, segmentation, and operational context.

Treat this as an RCA interview: the emphasis is on structured investigation, not jumping to solutions. Consider both product-performance issues, such as dashboard load/API latency, and workflow-response issues, such as admins taking longer to acknowledge or act on project-health alerts.

The experience should consider:

- The exact metric definition, including numerator, denominator, timestamp logic, and expected baseline

- Whether the drop is visible across all clinic administrators or limited to specific clinics, regions, roles, devices, browsers, or network conditions

- Which dashboard workflows are affected, such as login, project list loading, alert review, filtering, drill-downs, or task acknowledgment

- Recent changes in product releases, data pipelines, integrations, permissions, alert logic, or clinic operating processes

- Instrumentation checks to rule out tracking bugs, missing events, delayed ingestion, or changed metric definitions

- User and system cohorts, including new versus returning admins, high-volume versus low-volume clinics, and projects with different health statuses

- Evidence needed to prioritize hypotheses, such as logs, funnel data, latency traces, support tickets, session recordings, and admin feedback

- Immediate containment, communication, and prevention considerations once the root cause is better understood

The goal is to present a clear diagnostic approach that separates symptom from cause, validates the anomaly, identifies the affected scope, and builds confidence in the root-cause hypothesis before recommending any remediation.