Optimize your medical database for patient matching

by Experian Health 4 min read November 8, 2018

In a recent healthcare information technology survey, more than 40 percent of chief information officers identified patient matching as healthcare’s top IT concern. And though a quarter of the respondents admitted it wasn’t a current priority for their organizations, they did say that it very much should be.

There’s no shortage of reasons why, but the most pressing is the need to reduce medical errors, which account for over 250,000 deaths in the United States every single year. Case in point: Seventeen percent of CIOs acknowledged that errors in matching data with the right medical identities have led directly to adverse outcomes for patients.

The numbers speak for themselves: Healthcare organizations must find more effective ways to manage the data within their networks. That begins with building a robust medical database that not only hoses data, but also knows how to match it with the proper patients.

How robust EMPIs streamline workflows

An enterprise master patient index (EMPI) is a database that can help you clean up your data and eliminate duplicate and inaccurate records. It uses algorithms to match exact data elements among disparate records, as well as elements that fall within an acceptable range of possible compatibility.

Using technology that can apply an algorithm of probabilistic and referential matching methodologies will allow healthcare organizations to expand beyond the limitations of conventional single methodology matching, as both probabilistic and referential matching techniques provide a higher degree of likeliness.

The system assigns these data points to unique identities that follow patients throughout the organization. Any new data generated within the network is also attached to this identity, meaning physicians, specialists, pharmacists, and other members of the patient’s care team can access and update it as needed.

EMPI support tools and unique patient identities are building blocks toward creating a healthcare ecosystem that’s truly interoperable. According to an April 2018 survey by Black Book, hospitals with an EMPI report “consistently correct patient identification at an overall average 93 percent of registrations and 85 percent of externally shared records among non-networked providers.”

Unfortunately, not all healthcare systems possess the IT infrastructure to support these programs. And as long as some organizations fail to integrate similar platforms, providers won’t reap the benefits of industry-wide interoperability — and patients will continue to suffer. Whether it’s a frustrating billing mix-up, privacy breach, or a detrimental (or even fatal) misdiagnosis, many errors can be successfully prevented with an EMPI.

Filling in the holes

The goal of such a system should be to standardize data entry and access within each healthcare organization, as well as across the entire industry. Such a network could protect, govern, and match unique patient identities across every discipline and every aspect of their care continuum. But in order for the system to achieve these goals, you need to be sure you’re feeding it relevant, recent patient information. To ensure you have enough patient data to build an EMPI that accurately matches profiles, ask yourself these questions:

1. What kind of medical care have my patients received before this visit?

When patients enter a new hospital, they’re given a brand-new identity, or patient number, that’s only relevant to that healthcare system. The identity you assign them within your own organization doesn’t provide any insight about what they’ve experienced before their current visit — and that’s the crux of the matter. When patient information is siloed within a specific system, you have no view of the patient’s medical history. But when it’s shared across systems and fed into a more dynamic and interoperable data management system, patients will ultimately receive better care.

2. Who are my patients when they’re not “patients”?

It’s important to understand who patients are when they’re not in the hospital. Yes, they’re husbands and wives, mothers and fathers, brothers and sisters. But some could be physically fit, while others haven’t seen the inside of a gym in years. Some might get regular checkups, but others cannot afford to see a physician regularly. All of these traits factor into your patients’ identities. With a comprehensive EMPI, you can tie them together to understand the environmental and socioeconomic factors that influence your patients’ health. You can then identify what social determinants of health need to be addressed or could potentially influence the efficacy of certain treatments.

3. Can we identify patients without a picture ID?

Biometrics such as fingerprints and iris scans are more secure forms of identification than a photo ID. They’ll not only make it easier to identify patients, but will also offer heightened security against fraud. That being said, even biometric identification isn’t 100 percent secure unless it’s part of a database, such as the EMPI, that accurately matches patient identities with relevant medical data.

Accepting that the healthcare industry needs better data management and patient-matching strategies is the first step to realizing those goals. EMPIs have shown organizations the value in universal patient identities. Now, they simply need comprehensive databases that are robust enough to keep patient identities consistent across the entire healthcare ecosystem.

Related Posts

Andy’s New WP Workflow Test Article Using Quick Edit

Key takeaways: Revenue cycle teams can use automation to reduce repetitive work and apply AI where data-driven prediction, matching or prioritization can improve a workflow. Experian Health’s 2025 State of Claims survey found that 41% of providers now face denial rates of 10% or higher, while 68% say submitting clean claims is more challenging than a year ago. Patient Access Curator™ (PAC) uses AI to support front-end data validation and insurance discovery, while AI Advantage™ helps teams predict denial risk and prioritize denial follow-up. Artificial intelligence (AI) and automation can support administrative work in healthcare. In the revenue cycle, teams depend on accurate information, timely decisions and efficient follow-up to keep claims moving. In revenue cycle management, AI and automation can help organizations reduce manual checks, find data gaps, predict denial risk and prioritize work queues. These tools are most useful when they support staff judgment, payer expertise and compliance oversight. They can handle repetitive, data-heavy tasks so staff can focus on exceptions and decisions that need human review. In 2023, McKinsey & Company reported that research suggests effectively deploying automation and analytics could eliminate $200 billion to $360 billion of spending in U.S. healthcare. For revenue cycle leaders, the practical question is where to apply those capabilities first. The case for applying AI and automation in healthcare Revenue cycle teams juggle many daily tasks. Staff collect and verify patient information, confirm eligibility, identify the right payer, submit clean claims, monitor status, work denials and manage collections. Small data gaps at the beginning of the process can create downstream rework and delays. Rework also consumes staff time, adding to these operational pressures. As costs rise and revenue cycles tighten, there is increasing pressure to do more with less. Experian Health’s 2025 State of Claims survey found that 54% of providers say claim errors are increasing and 90% of claim denials are reworked with at least some human review before resubmission. Providers are also managing broader financial and administrative pressures. The American Hospital Association has reported that prior authorization requirements, claim audits, denials and other payer policies add administrative burden and cost for hospitals and health systems. These requirements also consume staff time to appeal denials and manage payer processes. AI and automation are different but complementary. Automation follows defined rules to complete repeatable work. AI models can identify patterns in data, predict risk and help teams decide where to focus attention. When used together, they can support more consistent revenue cycle workflows. How AI and automation can support revenue cycle workflows Revenue cycle management automation and AI are most useful when tied to a specific workflow and a measurable operational problem. The goal is to help teams act earlier, reduce avoidable rework and focus staff time where judgment is needed most. For example, automation can complete rule-based eligibility checks. AI can help identify claims with a higher likelihood of denial. In insurance discovery workflows, AI can also help identify coverage that wasn’t captured at registration. When these tools fit into existing workflows, they can support more consistent decisions and reduce manual work. Three practical applications include: 1. Improving front-end data quality with Patient Access Curator Patient and coverage information collected early in the revenue cycle can affect downstream claim outcomes. Incomplete or outdated demographic details, eligibility responses, coordination of benefits or Medicare Beneficiary Identifier information can create problems that lead to claim delays or denials later in the cycle. Experian Health’s Patient Access Curator helps prevent claim denials by validating demographics, eligibility, insurance discovery, coordination of benefits and Medicare Beneficiary Identifier data in seconds. PAC’s AI and machine learning capabilities help improve match accuracy, coverage sequencing and data confidence by writing the validated data back into the host system and sequencing payers before the claim is created. This automates work that would otherwise require manual coverage checks. 2. Using insurance discovery to find coverage not captured at registration When active coverage isn’t identified during registration, claims may be delayed or submitted with incomplete insurance information. Insurance discovery looks for coverage that may not have been captured during registration. Patient Access Curator includes insurance discovery as part of its front-end validation workflow. It can help identify and correct missing or incorrect insurance information so claims can be submitted with more complete coverage data. 3. Using AI to prevent and prioritize denials Even with strong front-end processes, some claims still require additional attention. AI can help claims teams decide which claims to review before submission and which denials to work first after payer response. Experian Health’s AI Advantage supports two denial management use cases:1. AI Advantage – Predictive Denials uses a client’s historical claims data and Experian’s knowledge of payer rules to identify claims with a high likelihood of denial before submission so teams can take corrective action.2. AI Advantage – Denial Triage uses AI to segment denials and identify those with the highest potential for reimbursement. This approach can help teams prioritize with more confidence. Rather than treating every claim or denial the same way, teams can use predictive models to focus on the work that needs the most attention. Potential benefits of AI and automation in the revenue cycle A high-performing revenue cycle depends on timely, accurate and consistent work. AI and automation can help providers modernize that work without losing the expertise of the people who manage complex payer and patient situations every day. When applied to the right workflows, these tools can help organizations: Reduce manual data searches that take staff away from higher-value work Improve front-end data quality before claims are created Identify missing or incorrect coverage information earlier Spot claims that may be at higher risk of denial Prioritize denied claims by potential reimbursement Reduce rework caused by inaccurate or incomplete information Give staff more consistent information for follow-up decisions A focused AI strategy starts with the workflow problem, uses data that is relevant to that problem and keeps staff in control of judgment-based decisions. A more proactive approach to revenue cycle management Revenue cycle teams can move from reactive work toward a more proactive approach: catch errors earlier, validate coverage before claims are created and prioritize the claims and denials that need the most attention. Experian Health offers revenue cycle solutions that use AI and automation in targeted ways to support front-end data quality, reduce rework and manage denials. Patient Access Curator supports registration and coverage validation, while AI Advantage supports denial prediction and triage. Learn more about Experian Health’s Patient Access Curator and AI Advantage.

October 2, 2026 by Andy.Monte@experian.com
Experian Health ranked #1 in Best in KLAS for 2025

Experian Health is very pleased to announce that we've ranked #1 in the 2025 Best in KLAS: Software & Services report, for our Contract Manager and Contract Analysis product, for the third consecutive year. Contract Manager, when paired with Contract Analysis, empowers healthcare providers by ensuring payers comply with contract terms, identifying and recovering underpayments, and arming them with real claims data to negotiate contracts. This enables providers to negotiate more favorable terms and maintain financial stability.  Clarissa Riggins, Chief Product Officer at Experian Health, says, “In the ever-evolving healthcare landscape, our Contract Manager solution has once again been recognized as the #1 Revenue Cycle Management tool by KLAS for the third consecutive year. This prestigious ranking underscores the significant value our solution delivers to our clients by identifying underpayments and facilitating revenue recovery. We are honored to continue supporting our clients with innovative solutions that drive financial success and operational efficiency.”  Learn more about how Contract Manager and Contract Analysis can help your healthcare organization validate reimbursement accuracy, recover underpayments and boost revenue.   Learn more Contact us

February 5, 2025 by kelly.nguyen
How to increase patient engagement

Learn how providers can increase patient engagement, why it matters and key strategies that deliver improved end-to-end patient experiences.

January 30, 2025 by Experian Health

Spotlight test

Spotlight Description

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Sticky Subscribe Title

Sticky Subscribe Description
Sticky Subscribe

Testing Spotlight Paragraph block

Testing the spotlight block header

Archive Testing

Categories