Case Studies/Healthcare
Process OptimizationHealthcare· 16-week engagement

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

Client TypeRegional Healthcare Network
Facilities14 acute care sites
Annual Volume~280,000 patients
SystemsEpic EMR, custom scheduling
Duration16 weeks
ServicesProcess Mining, AI Implementation

Services Deployed

Process Mining & Workflow Intelligence

AI Process Optimization

AI Implementation Consulting

Implementation

The 16-Week Transformation Journey

Phase 1

Process Mining & Discovery

Weeks 1–4

ACG 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 5–7

With 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 8–13

ACG 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 14–16

The 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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