Healthcare Network Reduced Patient Processing
Time by 37%
A regional healthcare network with 14 facilities deployed ACG's process mining and AI-driven triage routing across their Epic EMR system, transforming patient throughput and operational efficiency across the entire care network.
-37%
Patient Processing Time
Average intake-to-discharge cycle reduced from 4.2 hours to 2.6 hours
+22%
Patient Throughput
Daily capacity across 14 facilities increased without adding staff
$4.2M
Annual Cost Savings
Combination of reduced rework, overtime elimination, and capacity revenue
23
Bottlenecks Resolved
Distinct bottleneck patterns identified and systematically eliminated
Client Context
A Regional Network Under Capacity Pressure
The client is a regional healthcare network operating 14 acute care facilities across three states, serving approximately 280,000 patients annually. The network had experienced 20% volume growth over the preceding three years — driven by population growth in their service area and the acquisition of two smaller hospital systems.
Despite this volume growth, the network had not added proportional operational capacity. Administrative and clinical leadership were under significant pressure to improve patient throughput and reduce wait times without proportional headcount expansion or capital investment in new facilities.
Patient satisfaction scores had declined 12 points year-over-year, with wait times and discharge delays cited as primary negative factors in patient experience surveys. Payors were beginning to flag the network's readmission rates and length-of-stay metrics in contract renewal discussions.
The network's COO engaged ACG following an internal assessment that confirmed operational inefficiency as the primary constraint on throughput — but was unable to pinpoint exactly where the inefficiency resided or what was driving it.
Engagement Profile
Services Deployed
Process Mining & Workflow Intelligence
AI Process Optimization
AI Implementation Consulting
Implementation
The 16-Week Transformation Journey
Phase 1
Process Mining & Discovery
Weeks 1–4ACG connected to the client's Epic EMR system via read-only API access, extracting 18 months of patient event log data across all 14 facilities. Process mining algorithms reconstructed actual patient flow paths — from registration through triage, clinical assessment, treatment, and discharge.
Key Findings
Average patient journey involved 47 discrete steps across systems and departments
Actual process conformance to designed pathways was only 23%
Top 5 bottleneck points accounted for 68% of total cycle time
Discharge delays alone added an average of 94 minutes per patient
Phase 2
Root Cause Analysis & Prioritization
Weeks 5–7With the full process map established, ACG's team conducted root cause analysis on the 23 identified bottleneck patterns. Each was scored by frequency, cost impact, and resolution complexity. Clinical operations leadership validated findings and provided operational context.
Key Findings
8 bottlenecks were immediately resolvable through process redesign (no technology change)
9 bottlenecks required workflow automation or routing logic changes
6 bottlenecks required new predictive routing capability
Combined impact of top 12 bottlenecks: $3.8M annual cost and 34% of excess cycle time
Phase 3
AI-Driven Triage Routing System
Weeks 8–13ACG developed and deployed a predictive triage routing model that assigns incoming patients to the optimal care pathway based on presenting symptoms, historical case patterns, current facility load, and staff availability. The system integrates directly with Epic and surfaces routing recommendations to triage nursing staff.
Key Findings
Model trained on 380,000 historical patient encounters across all facilities
Routing accuracy of 91% against clinical gold-standard assignments
Average triage decision time reduced from 14 minutes to under 3 minutes
System handles 94% of routing decisions autonomously; exceptions escalate to charge nurse
Phase 4
Discharge Workflow Automation
Weeks 14–16The single largest waste category identified was discharge delay — patients medically cleared for discharge but waiting for administrative processes to complete. ACG implemented automated discharge checklists, parallel documentation workflows, and predictive discharge readiness alerts.
Key Findings
Discharge cycle reduced from average 94 minutes to 31 minutes
Transportation coordination automated for 78% of discharge cases
Insurance authorization pre-triggered during clinical assessment phase
Bed availability notifications reduced room turnover time by 26 minutes
Key Learnings
What This Engagement Taught Us
Process Conformance Is the Core Problem
The client had extensive clinical protocols and workflow guidelines in place. The problem was not a lack of process design — it was that actual execution diverged dramatically from designed pathways. The process mining discovery was the pivotal finding that shifted the engagement from generalist improvement work to targeted, precise intervention.
Clinical Validation Before Technology Build
Every identified bottleneck was validated with clinical operations leadership before any remediation work began. This validation step added two weeks to the engagement but saved significantly more time by ensuring recommendations were clinically sound and operationally feasible before implementation began.
Discharge Optimization Delivered Disproportionate Impact
The team initially expected triage routing to be the primary value driver. In practice, discharge workflow optimization delivered 47% of total cycle time reduction — a finding that only emerged from the data-driven discovery process. Pre-determined solutions would have missed this opportunity.
Change Management Is a Clinical Competency
Clinical staff adoption required engagement approaches tailored to healthcare environments — peer champions among senior nursing staff, phased rollout starting with one facility before network-wide deployment, and tight feedback loops during the first 30 days of live operation.
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