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Dashboard / Cycle Time / High-Latency Handoffs in Follow-up SUSAR Cases
Opportunity Surfaced
High Impact Integration

High-Latency Handoffs in Follow-up SUSAR Cases

Opportunity Lifecycle

1
Surfaced
2
Accepted
3
Remediating
4
Remediated
Status persists in your browser. In production, these actions notify team members, trigger workflows, and begin value-realization monitoring.

★ Savings Opportunity

Assumes $75/hr fully loaded cost. Pilot: 19 days. See methodology.
Pilot Period (19d)
1,079 hrs
Annual (17 users)
14,200 hrs
$1,065,022
Projected (1,000 users)
835,312 hrs
$62,648,385
Risk-Adjusted (17 users, annual)
13,064 hrs
0.92× composite · see breakdown ↓

Description & Data Evidence

Sequential handoffs between users average 59h wait time (median 34h, max 307h). 41% of the 61 sequential handoffs exceed 3 days. Total of 3,597 hours of wait time across 44 cases in the pilot period.

Self-Evaluation Scores

The platform grades each finding on four dimensions (1–5 scale). Low scores flag findings that need more data or clearer remediation before acceptance.

Overall 5/5
Actionability 5/5
Specificity 5/5
Remediation Alignment 5/5

Key Findings

  • Avg 59h inter-user wait, max 307h across 61 handoffs
  • 41% of sequential handoffs exceed 3 days (25 of 61)
  • 3,597 total wait hours across 44 cases
  • Follow-up SUSAR cases show highest handoff latency per case type analysis

Case Evidence

Specific case IDs pulled from the pilot data where this pattern is most pronounced. In production, clicking a case opens its full event timeline.

Case ID Signal Context
2346915 307h wait sequential handoff
2317182 243h wait sequential handoff
2352236 202h wait sequential handoff
2350797 184h wait sequential handoff
2291921 179h wait sequential handoff

Validation Questions

0 of 4 answered
Before accepting this opportunity, work through the questions below with the relevant subject-matter experts. Your answers lock in the acceptance criteria and — when you toggle Share with Pyze — inform how our agents surface similar patterns in the future.
1
Follow-up SUSARs wait 5x longer than Initial SAEs (131h vs 25h). Is there a regulatory or medical reason why follow-up reviews require a multi-day cooling-off period before document selection?
Rules out compliance as root cause before proposing notification automation.
2
For Case 2346915, there was a 307-hour gap after 'View Combined Review'. Does this activity signify the end of internal work, or is there an offline process (e.g., waiting for an external physician's input) that occurs before 'Select Documents'?
Determines whether the delay is under Merck's control or dependent on external stakeholders.
3
Are users notified via email or only via the Veeva task list? The data suggests users are not aware a case is ready for them until they manually check the queue.
Identifies whether push notifications would eliminate the wait time entirely.
4
Is the 'Select Documents' activity typically performed by a different functional group than the 'Review' activity?
If yes, a simple UI notification won't solve it — needs routing integration.

Remediation Ideas

  • Automated case routing based on analyst availability and skill
  • Real-time notifications when cases are ready for handoff
  • SLA-based escalation for handoffs exceeding 24h
  • Workload balancing dashboard for supervisors
  • Pre-populate next analyst's queue with case context summary

Implementation Roadmap

Effort
Medium
Timeline
8-12 weeks
Primary Owner
Engineering + Ops
Dependencies
  • Veeva Safety notification config access
  • SLA policy sign-off from PV operations
  • Email/Teams integration review
Phased Delivery
  1. Discovery & BA interviews (1-2 weeks)
  2. Notification design & SLA policy (2 weeks)
  3. Veeva config + testing (3-5 weeks)
  4. Rollout + monitoring (2-3 weeks)

How Risk-Adjusted Savings Is Calculated

The risk-adjusted number is the annual savings multiplied by a composite factor of four independent dimensions. Each dimension is rated High (1.0×), Medium (0.8×), or Low (0.5×). See full methodology.

Detection 40% weight
High
Confidence the agent-detected pattern is real
Feasibility 25% weight
Medium
Ease of building the remediation
Adoption 20% weight
High
Likelihood users change workflow
Compliance 15% weight
Medium
Simplicity of PV validation path
14200 hrs × 0.92 = 13064 hrs / year
At 1,000 users: 768,487 hrs / year · $57.6M