IdentiMatch™ Worklist Automation Resolves the “Gray Zone” Backlog. Accelerate Your Data Quality Goals. Meet Your Project Deadlines.
Whether you are a managing a hospital database or an HIE with data quality initiatives, the “Gray Zone” is likely slowing down progress.
Discover the real potential of your patient data with our complimentary Data Cleanup Test Drive. We’ll take a sample of your exception list and process it though the IdentiMatch™ advanced automation engine.
Experience firsthand how a cleaned, validated exception list can cut manual review by up to 90%, eliminating costly errors and freeing your data stewards to focus on high-value tasks. No commitment, just results.
During a major hospital merger, Boston Medical Center faced a critical deadline to reduce its Epic-generated work list from 9.8% to below 3%—or risk missed go-live dates and costly penalties. Using 4medica’s MPI and IdentiMatch™, the team reduced unmatched patient records to 2.6% in under one month, eliminating 177,000 duplicates and meeting Epic’s quality goals ahead of schedule.
Preparing for a MEDITECH upgrade, Grand River Health faced a massive backlog of duplicate records and no automated interface to merge them. Rather than hiring staff for manual cleanup, they utilized 4medica’s Worklist Automation as a Service to mimic human workflows. We autonomously cleared the entire queue, delivering a pristine patient roster for go-live without utilizing a single client resource.
To clear a 100,000-record backlog, a typical team of eight data stewards needs 10 months of manual review. With automation, that same team can finish the job in just one month. See the math behind the backlog.
While records sit in the "Gray Zone" waiting for manual review, clinicians are making decisions based on fragmented patient data. Discover why the delay between "probable match" and "merged record" is a critical patient safety risk.
Deterministic matching engines are great at the "first pass," but they dump the hardest 10-20% of work onto your staff. Learn why the "probable match" gap exists and how to close it without hiring a single new employee.
Ingests work lists from any MPI or EHR matching engine
Analyzes record pairs and groups them by match criteria
Applies your rules once—decisions carry across all similar cases
Automates the rest, shrinking the work list dramatically
Audits for consistency to improve stewardship and compliance
Merge populations quickly without sacrificing accuracy
Ensure clean, consistent data before go-live
Keep records clean and compliant over time
Maintain longitudinal views and avoid false matches
Works with or without 4medica’s matching engine
Software or full-service delivery
Rapid deployment in weeks, not months
Business rule–driven automation for match/no-match decisions
Optional data stewardship and auditing services