Careers Support
Search
1-800-873-9426
Connect with an expert

Case Study – Large Health System

Case Studies, Healthcare0 comments

Case Study

Large Health System Achieves $7.9 Million in Savings with AI-Powered Document Indexing

Large Health System

A large health system in the Mid-Atlantic region supports millions of patient interactions annually across a network of hospitals, specialty practices, and outpatient facilities. With approximately 49,000 employees, the organization is focused on delivering exceptional care and operational efficiency.

Industry

Healthcare

Challenge

Manual document indexing could not keep pace with growing volumes across ambulatory care, driving errors, staff strain, and rising cost per document.

Results

AI-powered Intelligent Document Processing automated classification and indexing, delivering $7.9 million in projected annual savings at 98% accuracy.

Solutions

Intelligent Document Processing
Workflow & Process Automation

Outcomes

$7.9M

In projected annual savings

210%

Projected return on investment

55%

Reduction in document indexing costs

98%

Document indexing accuracy

Overview

Manual document indexing had become increasingly difficult to sustain across the health system’s ambulatory care operations. Growing document volumes created bottlenecks, increased indexing errors, and strained staff resources. DataBank implemented AI-powered Intelligent Document Processing to automate classification and indexing, improving accuracy, reducing costs, and creating a scalable foundation for future growth.

Challenges

Manual indexing processes struggled to keep pace with growing document volumes

High workloads increased the risk of indexing inconsistencies and errors

Data entry mistakes impacted downstream clinical and operational workflows

Staffing shortages and burnout limited indexing capacity and scalability

Rising operational costs created pressure to improve efficiency and resource utilization

Implementation

Workflow Assessment

DataBank evaluated existing indexing processes, document volumes, and operational requirements to identify automation opportunities and establish performance benchmarks.

AI Model Configuration

Document classification and indexing models were configured and optimized to recognize healthcare-specific document types and indexing requirements.

Process Automation Deployment

Automated workflows were implemented to replace manual indexing activities while maintaining data quality and compliance standards.

Performance Optimization

Processing accuracy, exception handling, and workflow performance were continuously monitored and refined to maximize efficiency and user adoption.

Workforce Transition Support

The health system reallocated team members from repetitive indexing activities into higher-value analytical and quality-focused roles.

Key Takeaways

Automating a labor-intensive process cut the cost of handling every document and freed experienced staff for work that needed their judgement.

Cost Per Document Cut

From $1.50 to $0.68, a 55% reduction in indexing cost

Accuracy At Scale

98% document indexing accuracy achieved organization-wide

Staff Redeployed

Team members moved from repetitive indexing into analytical and quality-focused roles

Built To Scale

A foundation that absorbs growing document volumes without adding headcount

Results

Reduced administrative burden on healthcare staff

Staff redeployed into higher-value operational roles

Improved consistency across document processing workflows
Enhanced scalability to support future growth

View Full Case Study

Learn how this large health system partnered with DataBank to address a critical challenge and deliver measurable results. This case study provides a clear overview of the problem, the solution, and result.

Download PDF

Explore More Case Studies

View additional customer success stories from DataBank

0 Comments