How to Improve Interoperability in Healthcare

Learning how to improve interoperability in healthcare requires more than connecting two systems. Healthcare organizations must improve patient matching, create semantic consistency, align data exchange with daily workflows, test every trading partner, and govern data quality over time.

A successful interface may deliver a message to the correct destination, but delivery alone does not make the information useful. The receiving system must identify the patient, correctly interpret each data element, place the information in the appropriate workflow, and make it available to the right person.

The Office of the National Coordinator for Health Information Technology supports the standardized exchange and use of electronic health information to improve care coordination, patient care, and public health outcomes.

Healthcare organizations can support those goals by treating interoperability as an ongoing operational program rather than a one-time technical project.

What Are the Five Levels of Interoperability?

Healthcare interoperability is commonly described across five levels, each representing a deeper degree of data exchange between healthcare providers and systems. The first level, foundational interoperability, establishes the basic transport layer, where one system can send data to another, but the receiving system is not required to interpret it.

The second level, structural interoperability, defines the data's format and syntax so that the receiving system can parse it correctly, even if it does not yet understand its meaning.

The third level, semantic interoperability, ensures that both systems share a common understanding of the data's meaning, typically through standard clinical terminologies such as SNOMED CT, LOINC, and RxNorm.

The fourth level, organizational interoperability, addresses the policies, governance structures, and workflows that allow healthcare providers and organizations to collaborate effectively using shared data.

A fifth level, political or cross-jurisdictional interoperability, is increasingly recognized as essential for improving interoperability at a population and public health scale, accounting for regulatory and legal frameworks that govern data sharing across state and national boundaries.

Improving interoperability in a meaningful way requires progress across all five levels, because a technically successful message exchange means little if the receiving clinician cannot trust, interpret, or act on the information delivered.

Why Basic Connectivity Does Not Guarantee Interoperability

Connectivity answers a simple question. Can one system send information to another?

True interoperability goes several steps further. It asks whether the receiving organization can identify, understand, trust, and use that information without unnecessary manual work.

A laboratory system might successfully send a test result to an electronic health record. Problems still occur when the EHR cannot match the result to the correct patient, recognize the laboratory code, interpret the unit of measure, or route an abnormal result to the responsible clinician.

Healthcare organizations must address several connected areas to make exchanged data useful.

• Technical exchange
• Patient identity
• Semantic meaning
• Data quality
• Operational use
• Security and access

Fixing only the transport layer leaves major gaps in patient safety, reporting accuracy, and workflow performance.

1. Assess the Current Data Exchange Environment

Organizations should begin with a clear inventory of the systems, interfaces, formats, and external partners involved in information exchange. This assessment shows where data enters, how systems change it, and where errors or delays occur.

A complete interoperability inventory should account for every system that sends, receives, stores, or modifies healthcare information.

• Electronic health record systems
• Laboratory information systems
• Pharmacy platforms
• Public health reporting connections
• Patient portals and mobile applications
• Health information exchanges
• Payer connections
• Medical devices
• Third-party integration platforms
• Manual file transfers and spreadsheet processes

Teams should document the format, frequency, owner, destination, and business purpose of every data flow. They should also record whether each connection uses HL7 Version 2, Clinical Document Architecture, FHIR, flat files, custom APIs, or another method.

This process often exposes duplicate interfaces, undocumented transformations, and manual processes that employees have accepted as normal. Organizations can use those findings to build a practical improvement plan.

Iron Bridge explains how different standards support these connections in its guide to healthcare data integration standards.

2. Strengthen Patient Identity Matching

Interoperability cannot work reliably when systems attach information to the wrong patient or fail to connect records that belong to the same person.

According to the ONC patient identity and record matching resource, patient matching involves identifying and linking a person’s data within and across health systems. Organizations usually compare demographic fields such as names, birth dates, addresses, and phone numbers.

Minor differences can prevent a system from identifying a valid match. A missing middle name, a changed address, a nickname, a transposed birth date, or an inconsistent apartment number can cause one person to appear as several patients.

Establish Consistent Registration Rules

Registration practices directly affect matching accuracy. Organizations should create common rules for how employees and systems collect, format, validate, and update patient demographic information.

These rules should cover the data elements that matching engines use most often.

• Legal and previous names
• Middle names and initials
• Name suffixes
• Dates of birth
• Current and previous addresses
• Apartment and unit numbers
• Phone numbers
• Email addresses
• Sex and other required demographic fields

Systems should distinguish between missing information and information that does not apply. They should also preserve previous demographic values when those values could help match older records.

Monitor Duplicate and Overlay Rates

Organizations should measure matching performance rather than assume the matching system works correctly. A duplicate record occurs when one person receives more than one patient record. An overlay occurs when staff members combine information from different people under one identity. Overlays can place allergies, medications, laboratory results, or diagnoses in the wrong medical record.

Healthcare organizations should monitor several indicators to identify these problems early.

• Duplicate record creation rates
• Confirmed overlay incidents
• Unresolved matching queues
• Manual review volume
• Automatically matched record rates
• False positive and false negative matches
• Inbound records that systems cannot assign

Teams should investigate sudden changes in these measures. A software update, registration policy change, or new data partner can quickly affect matching performance.

3. Create Semantic Consistency Across Systems

Systems must interpret exchanged information in the same way. A receiving application should not need to guess what a laboratory test, diagnosis, medication, immunization, or clinical observation means.

HL7 FHIR provides a standard for exchanging healthcare information electronically. It organizes data into reusable resources such as Patient, Observation, Condition, Medication, and Immunization.

FHIR can improve the structure of an exchange, but it does not automatically resolve every difference in terminology, local coding, or clinical context.

Use Standard Clinical Terminologies

Recognized clinical terminologies help trading partners assign the same meaning to the same concept. Organizations should select each terminology according to the data and use case involved.

Common healthcare code systems include the following.

• LOINC for laboratory tests and clinical observations
• SNOMED CT for clinical findings and concepts
• RxNorm for normalized medication names
• ICD-10-CM for diagnosis classification
• CVX for vaccine product identification
• UCUM for units of measure

Local codes may still appear in source systems. When they do, teams should create, validate, and maintain mappings to the appropriate standard.

A mapping should not rely only on similar labels. Analysts must consider the specimen, method, timing, scale, result type, and unit. Two tests with similar names may represent different procedures or produce results that clinicians cannot safely compare.

Control Data Transformations

Integration teams often modify data when converting one message format to another. Every transformation should follow documented rules and preserve the original clinical meaning.

For example, a source system may send a temperature in degrees Fahrenheit, while the receiving workflow expects it in Celsius. The interface must convert both the value and the unit. Changing only the number or only the unit produces incorrect clinical information.

Teams should version transformation rules, review mapping changes, and retain enough source information to trace how each final value reached the receiving system.

Iron Bridge addresses these challenges through its Pub Hub Data Integration platform, which supports standards-based healthcare data exchange across systems and trading partners.

4. Align Interoperability With Clinical Workflows

Even accurate data provides little value when it arrives too late, ends up in the wrong queue, or requires staff members to search for it.

Organizations should design each exchange around a real clinical or administrative workflow. Technical teams need direct input from clinicians, registration staff, pharmacists, laboratory professionals, infection prevention teams, and public health personnel.

Every interoperability use case should answer several operational questions before development begins.

1. Who needs the information
2. When does that person need it
3. Where should the system display it
4. What action should follow
5. Who handles errors and exceptions
6. How does the sender confirm completion

Consider electronic laboratory reporting. Sending a report does not complete the workflow. The organization must determine whether the public health agency accepted the message, rejected it, or requested a correction.

The same principle applies to immunization reporting, electronic case reporting, syndromic surveillance, transitions of care, and payer data exchange. Each connection needs a business owner, an escalation process, and a clear definition of successful completion.

5. Test With Every Trading Partner

An interface that works in an internal environment may fail when a partner sends unexpected values, optional fields, older code sets, or unusual message structures.

Partner testing should cover the content and use of the information, not only message delivery. Teams should test how systems respond to valid data, invalid data, corrections, duplicates, missing fields, and partner downtime.

A complete testing program should include several stages.

• Connectivity testing
• Message structure validation
• Terminology validation
• Patient matching tests
• Positive and negative test cases
• Workflow testing
• Error and retry testing
• Security and access testing
• User acceptance testing
• Production monitoring after launch

Tests should include realistic edge cases. Useful examples include twins, patients with similar names, changed addresses, missing middle names, corrected laboratory results, canceled orders, duplicate submissions, and updated vaccination histories.

Partners should also agree on how to handle acknowledgments. The sending organization needs to know what each acceptance, warning, or rejection response means and who must act on it.

For public health reporting, Iron Bridge offers RapidReport to help laboratories manage reportable conditions, submit required data, and track reporting progress.

6. Establish Ongoing Data Governance

Healthcare data does not remain static. New codes appear, implementation guides change, software vendors release updates, and public health agencies revise reporting requirements.

A data governance program assigns responsibility for those changes. It also prevents departments from creating separate definitions and mappings for the same information.

An interoperability governance group should include representatives from clinical, operational, technical, security, compliance, and data management. The group should approve changes and communicate those decisions to affected teams and partners.

*Set Measurable Data Quality Standards

General goals such as improving data quality do not provide enough direction. Organizations need measurable expectations for every important exchange.

Teams can use the following indicators to evaluate interoperability performance.

• Message acceptance rate
• Patient match rate
• Required field completion rate
• Invalid code rate
• Duplicate message rate
• Delivery delay
• Manual correction volume
• Time required to resolve an error
• Percentage of records that reach the intended workflow

Organizations should review trends rather than isolated incidents. A gradual increase in missing values may indicate a registration change, an interface modification, or a new partner configuration.

Maintain Clear Documentation

Interoperability programs depend on information that often sits with a few technical employees. Detailed documentation reduces that dependency and helps organizations manage vendor, staffing, and system changes.

Documentation should identify the source, meaning, format, allowed values, transformation rules, and owner of each important data element.

Teams should also maintain interface specifications, partner contacts, testing records, release notes, mapping tables, security requirements, and recovery procedures.

7. Use Standards Without Ignoring Implementation Details

Standards provide organizations with a shared foundation, but every interoperability project still requires specific implementation decisions.

Two organizations may both claim support for FHIR while using different profiles, required fields, extensions, terminology bindings, and search parameters. Teams should follow an applicable implementation guide instead of interpreting the base standard independently.

Organizations should confirm the standard version and implementation guide version that each partner supports. They should also document optional fields that the workflow treats as required.

The United States Core Data for Interoperability defines standardized health data classes and elements for nationwide exchange. Using USCDI helps organizations establish a common data foundation, but teams still need to validate how each system captures and represents those elements.

8. Build Security Into Every Exchange

Healthcare organizations must protect information while making it available to authorized users and systems. Security teams should participate throughout the design, testing, deployment, and maintenance phases. A final security review immediately before launch cannot replace early planning.

Each exchange should include appropriate safeguards and operating procedures.

• Strong authentication
• Role-based access controls
• Encryption during transmission and storage
• Audit logging
• Certificate management
• Third-party risk reviews
• Incident response procedures
• Routine access reviews
• Backup and recovery testing

Teams must also prepare for certificate expiration, authentication failures, partner outages, and unusual traffic. An interface can appear active while security errors prevent actual data delivery.

How to Measure Interoperability Improvement

Organizations should measure whether interoperability produces better clinical and operational results, not simply whether interfaces remain online.

Technical uptime matters, but it does not indicate whether clinicians received complete information or whether employees manually corrected hundreds of records. A balanced measurement program should review three connected categories.

Technical Measures

Technical measures show whether the exchange infrastructure operates as expected. These measures include uptime, message volume, response time, failed transmissions, API errors, acknowledgment status, and retry rates.

Data Quality Measures

Data quality measures show whether systems exchange complete and accurate information. These measures include patient-matching accuracy, missing fields, invalid codes, duplicate records, incorrect units, and terminology-mapping errors.

Workflow Measures

Workflow measures indicate whether the information exchanged helps people complete their work. These measures include manual correction volume, delayed results, rejected public health reports, unresolved exceptions, and time required to complete a task.

Together, these categories show whether the exchange supports patient care and operational needs.

How To Improve Interoperability in Healthcare:​ One Use Case at a Time

Healthcare organizations do not need to replace every system to improve interoperability. They can begin with a high-value use case, document the full workflow, address identity and terminology problems, test with partners, and measure the results.

The most effective programs treat interoperability as a shared responsibility. Technical teams move the data, but clinical and operational teams define what that data means and how people should use it.

IronBridge Corp. helps hospitals, EHR vendors, pharmacies, laboratories, urgent care centers, and public health agencies manage complex healthcare data exchange.

Contact IronBridge Corp. to discuss how your organization can improve data quality, strengthen public health reporting, and build more dependable connections between healthcare systems.