FHIR-Native Master Data Management & Enterprise Master Patient Index

MDMbox

Reduce duplicate records by 90% with a transparent and tunable MDM engine — built natively on FHIR.

Starts at $15,000 per year.

Book a Demo Try MDM Locally Documentation

Remove Duplicate Records

Bring together data from EHRs, LIS, telehealth, and claims systems, match and merge duplicates into a golden record to create a single source of truth.

Milliseconds

for match operations

100M+

records

20 minutes

bulk match throughput for 5M Patients

Instant Deduplication

identifies and resolves matching records in real time

Auto-Merge

configurable merge logic and controls

Data Steward UI

built-in interface for duplicate management

Steps to Create One Source of Truth

Upload your data

Use FHIR API for data import

Import patient records from any system using standard FHIR APIs.

Try matching

Use $match for Probabilistic Matching

Identify duplicate records using a probabilistic matching model. Works reliably for clean data and remains accurate when records are messy or incomplete.

Works with existing records stored in your systems
Checks new records for duplicates instantly as they appear

Available via standard FHIR APIs or a UI to configure matching rules and review results.

Bulk Match Dataset

Detect duplicate records across the entire dataset in one operation

Checks all existing records at once and returns a list of potential duplicates.

Merge Duplicates Into Single Record

Use customizable merge to work with the fields you need

Uses a hybrid merge strategy (golden + survivor record).
Works via FHIR API or UI.
Automatically relinks all related resources from duplicates to the survivor record.
Supports safe unmerge, with all actions recorded in a merge log.
Notifies external systems via webhooks about merge and unmerge events.

Setup Real-Time Deduplication Flow

Use MDMbox on resource creation step

Prevent new duplicates instead of regular full deduplication checks:

Send $match request to MDMbox
Get list of potential duplicates
Take the right decision

Fully Automate Your Workflow

Customizable Auto-Merge

Automate record merging based on rules you define and control exactly how matches are processed.

Implement custom merge policies via API.
Define confidence score thresholds.
Automatically merge high-confidence matches.
Send lower-confidence matches for manual review.

Built-in Data Steward UI for MDM Workflows

Match, compare, merge, unmerge — all in one interface, with full action history

Merge Merge Logs Unmerge

Build for Production Environments

Transparent architecture designed for scalability, reliability, and control.

MDMbox Pricing

MDMBox

$35,000 /year

Special offer for Aidbox users

$35,000

$15,000 /year

Additional services:

One-time model training (per resource): $10,000

Custom features and enhancements: $100/hour

Stand up your MDM/eMPI
in one Month

Explore how real-time matching, configurable MDM models, and FHIR-native MPI identity resolution work in practice.

Book a Demo Try MDM Locally Documentation

From the team behind MDMbox

Why duplicate patient records are hard to fix, what MPI vendors disagree about, and how merge actually works in production.

Master Patient Index (MPI): How It Works + Examples

Mar 9, 2024

11 min read

Master Patient Index (MPI): How It Works + Examples

Master Patient Index (MPI) explained: how record linkage works, common architectures, and a worked example using FHIR and Aidbox.

Several authors

2 authors

Beyond Patient/$merge: A Resource-Agnostic, Client-Driven Merge for FHIR

Apr 9, 2026

8 min read

Beyond Patient/$merge: A Resource-Agnostic, Client-Driven Merge for FHIR

FHIR R5's Patient/$merge is a start, but production MDM needs more. We built a resource-agnostic $merge with client-driven plans, atomic audit trails, and a generic $referencing operation.

Ivan Shukshin

Duplicate Patient Records in Healthcare Analytics

Apr 14, 2026

7 min read

Duplicate Patient Records in Healthcare Analytics

How duplicate patient records silently corrupt healthcare analytics — inflating population metrics, fragmenting clinical histories, skewing cohort construction, and biasing ML models — and why identity resolution upstream is the structural fix.

Several authors

2 authors

MPI Vendors Couldn't Agree. We Solved Patient Merge

Apr 20, 2026

8 min read

MPI Vendors Couldn't Agree. We Solved Patient Merge

FHIR's Patient/$merge assumes the server knows how to merge. Two decades of MPI vendor configs, EHR vendor divergence, and national registry policy show why one algorithm cannot serve every organization.

Ivan Shukshin