Acme Analytics — Turning Security Noise into Decision-Ready Intelligence
Healthcare staffing was slowed by fragmented workflows and disconnected systems — resulting in delayed placements, higher costs, and increased risk.
Role
Lead UX Manager
Timeline
14 days · two design sprints · 2025
Team
4 Designers, 2 Researchers, 1 PM, 1 Data Scientist, 1 Dev Lead
Primary metric
58% ↓ time to fill · $4.8M annualized savings
OVERVIEW
The snapshot
Challenge
Hospitals, recruiters, and clinicians worked across 8+ disconnected systems just to fill a single shift, with credentialing alone adding 6.2 days per placement.
Approach
Ran a 14-day, two-sprint mixed-method research and design engagement with 28 stakeholders across 12 hospitals and 6 staffing agencies to map the end-to-end staffing lifecycle.
Result
Unified matching, credentialing, and scheduling into one AI-powered platform, cutting time-to-fill from 8.7 to 3.6 days and lifting fill rate from 69% to 92%.
HERO METRIC
58%
Faster time to fill critical shifts
PROBLEM
What was breaking
A compliance workflow so confusing, 1 in 3 users abandoned it mid-form.
A compliance workflow so confusing, 1 in 3 users abandoned it mid-form.
64% of hospital admins had no real-time visibility into placement status
When systems don't talk, people work harder. When people work harder, care suffers.
DISCOVERY
How I learned it
Kickoff & stakeholder alignment
28 stakeholder interviews across hospitals, recruiters & credentialing teams
Journey mapping & recruiter shadowing
Affinity synthesis & validation sessions
Recruiters average 8.6 tools per placement, ranging 6–14 tools
Found in recruiter shadowing across 3 regions
Credentialing is where good candidates go to die — slow, manual, different every time
Found in interviews with senior recruiters
59% of teams report missed or delayed information between recruiters and hospitals
Found in document analysis & stakeholder survey
INSIGHTS
The moments things clicked
Credentialing is the biggest bottleneck
Manual, repetitive verification steps create days of delay before a clinician can be placed.
Recruiters juggle too many tools and tabs
Recruiters switch between 8+ systems to complete a single placement, with heavy duplicate data entry.
Hospitals lack real-time visibility and control
Leaders don't have clear status, risk visibility, or a single source of truth into placements.
STRATEGY
How I chose what to build
People first
Design for the recruiter and clinician's actual workflow, not an idealized process.
Intelligence everywhere, human in the loop
Use AI to augment matching and verification, but keep experts reviewing every match.
Trust and compliance by default
Built-in credentialing, privacy, and audit trails so speed never comes at the cost of compliance.
THE TRADE OFF
AI matching and credential intelligence were our strategic bets — high effort, but they attacked the two biggest sources of delay directly. Predictive scheduling and the hospital dashboard followed once the core matching and verification loop was trusted and adopted.
SOLUTION
The final shape of things
1
Risk scores analysts can actually trust
Every alert ships with a confidence score and a plain-language reason code, so analysts stop re-validating what the AI already checked.
2
One incident, not a dozen alerts
Related alerts are automatically correlated into a single incident, reducing noise by up to 70% and cutting duplicate investigation work.
3
Every investigation, fully documented
A single investigation pane with timeline, evidence, and notes replaces scattered tickets and tribal knowledge.
IMPACT
What actually shifted
58%
Time to fill (8.7 → 3.6 days)
92%
Fill rate (from 69%)
31%
Cost savings, annualized
140%
Recruiter productivity (2.4x)
BEFORE
18,000+ alerts per day
6.2 days average credentialing time
18.6 manual touches per placement
AFTER
9,720 alerts per day
2.1 days average credentialing time
6.2 manual touches per placement
We didn't just ship a product. We changed how security gets done.
Chief Nursing Officer, Large Health System
Curious how this could apply to your system?
REFLECTION
What I'd carry forward
What I'd do differently
I'd bring hospital admins into the co-design sessions even earlier — their visibility problem turned out to be just as urgent as the recruiter's matching problem, and we nearly under-scoped the analytics dashboard as a result.
What this taught me
The problem isn't effort, it's fragmentation. Every team we spoke with was already working hard — what they lacked was one platform that worked for everyone. Fixing the system, not any single screen, is what moved the metrics.