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Nearly 12 out of every 100 records at an average hospital are duplicates.

This can lead to incorrect medication prescriptions, misdiagnoses, and patient mistreatments. In addition to risks to patient safety, duplicate records and misidentification also have a financial impact. According to one survey, the cost of each duplicate record is $96, which could amount to over $1,000 for every 100 records in your database.1 Studies show that one in five hospital radiology tests are duplicates and add $20 billion in U. S. healthcare costs annually.2

As the use of electronic medical record becomes more pervasive, the challenge of patient identification and matching grows as well. The Office of National Coordinator for Health Information Technology (ONC) launched an initiative in an effort to help solve the problem. Their final report focused on “identifying steps to help ensure the accuracy of every patient’s identity, and the availability of the individual’s information whenever and wherever care is needed.”

The report concludes that accurately identifying patients and correctly matching their data is a key component to ensure meeting the goals of Triple Aim – improved patient care, enhanced patient experience, and reduced costs. While much more needs to be done on a global basis, the ONC report offers helpful guidance for organizations moving forward. Here are five suggestions you might consider now to address the patient identification and matching dilemma.

5 things you can do now:

1. Standardize data attributes 

Many patient matching issues arise when different data attributes are used to identify patients. Although this is a significant problem among healthcare organizations, it can be a problem within your own organization as well. The ONC report notes that even though several common data attributes are used by most organizations for matching, the format of these attributes can vary.

The three most common attributes used are the patient’s name, date of birth, and gender. Phone number, address (full or ZIP code only) are also often included in the basic set of attributes. The report stresses that the more attributes used, the better the matching results. And though it’s difficult to find attributes that are stable, you should standardize the format of the ones you do use. For example, whether you will use the patient’s full name, use of middle name or initial, how hyphens will be handled, and whether previous names or nicknames will be recognized.

One thing is sure. The first step to more accurate matching is standardizing your attribute use.

2. Capture and check data electronically 

One of the key root causes for duplicate records is errors made as a result of manual data entry. Health organizations usually have staff in their medical records or HIM departments specifically focused on reviewing duplicate reports generated by the EMR/EHR systems. These reviews require manual checking and merging of duplicate records. This process is fraught with potential errors when handled manually.

A manual review and correction process requires pulling records from the file, and these missing records can disrupt patient care. The ONC is also concerned about the consequences of poorly matched records being shared outside the organization through HIE, which can expand the impact of errors even further.

The ONC report suggests keeping manual review and correction to a minimum. They urge electronic capture of data as a key to preventing duplicates. An next generation Enterprise Master Patient Index (EMPI) solution provides sophisticated tools and matching algorithms that connect siloed databases, minimize manual intervention, and address the challenge of trying to gain a single, complete, accurate picture of your patients.

3. Establish registration best practices 

According to a study conducted by Johns Hopkins Hospital, more than nine out of ten patient identification errors resulting in duplicate health records are caused by inpatient registration mistakes.3 These errors can result from using a female patient’s maiden name instead of her married name, or other mistakes in other data attributes like date of birth or address.

ONC suggests encouraging the use of patient portals, waiting room kiosks, electronic tablets (iPads or Surface) to have patients enter their own demographic information electronically. This data can then be fed directly into your EMR/EHR to populate or update the patient record. Regardless of the method you are using to collect personal information, it’s critical to educate patients on the importance of accurate demographic information in preventing duplicate records.

Ensure that registration staff members are trained in policies that will maintain data integrity, including an emphasis on filling in demographic fields accurately and completely and explaining the implications to caregiving of incorrect information and duplicated records. Other best practices recommended in the report include restricting the number and type of hospital personnel who can create a patient record and encouraging registration staff to obtain appropriate certification.

One of the most effective methods of preventing errors is to use an next generation EMPI solution at the point of registration. This allows you to use IHE (Integrating the Healthcare Enterprise) standards to communicate between the EMR and the EMPI so that the search for patients can be done in a probabilistic manner within the EMPI database rather than within only the EMR database. In this way, you get information on patients that may not be in your EMR but may be in an HIE or other health system EMR. This will enable you to bring in the most up-to-date, accurate patient data based on what is stored in the EMPI as opposed to what is located solely in the local EMR.

4. Encourage patient involvement

The need for better patient engagement has been a consistent focus area of CMS, beginning with meaningful use and implementation of MACRA. The scoring for the Advancing Care Information component of MACRA is weighted heavily toward patient electronic access and coordination of care through patient engagement. CMS recently released the Person and Family Engagement Strategy (PFE) aimed at making patients partners in decisions about their health care.

If you have multiple systems housing patient data, it can be difficult to provide a single source for patients to access their data. The report notes that patients are the primary source of demographic information used in matching, so involving them is an integral component of ensuring data quality during the registration and admission process. Making it easy for patients to enter and update their demographic information is a key step in ensuring data integrity and minimizing duplicates.

5. Implement an next generation EMPI 

The ONC report points out the difficulties in patient matching without a common patient identifier across all IT systems. Given the many HIT systems commonly in use, ONC acknowledges that developing a common identifier is not likely to happen anytime soon. The report notes, however, that HIM professionals recognize that matching algorithms of varying types can be effective in improving patient matching.

That’s where an next generation EMPI solution can help. Individual vendor solutions aren’t capable of communicating with each other, so you need a platform that will break down the siloes and integrate the data from those disparate systems and form an overarching technology umbrella. An next generation EMPI resolves and synchronizes data issues and provides a single patient view that can be accessed across the enterprise.


The ONC report concludes that healthcare organizations across the country are developing best practices to solve the patient matching challenge. Sharing these ideas and processes and leveraging evolving technology is a key element in ultimately solving the problem.

To find out more about the challenges and opportunities involved in establishing a single view of the patient, watch our free webinar: Maury Regional Medical Center: A Case Study in Creating the 'Golden Record'.

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About VisionWare

VisionWare is a leading provider of Patient Identification and Matching Solution, Provider Directory and Master Data Management solutions to the healthcare, public sector and financial services markets. Our technology enables organizations to break down data silos by creating a single, complete view of any data entity across the enterprise. Enabling this ‘single view of the truth’ is an essential prerequisite to leverage the benefits of a data-driven business, including improved customer engagement and operational efficiency, as well as reduced reputational and regulatory risk.

For the last seven years, VisionWare has achieved industry recognition in the Gartner Magic Quadrant for MDM of Customer Data and in the Information Difference MDM Market Landscape Report. Founded in 1993, VisionWare has headquarters in Glasgow, Scotland and Newton, Massachusetts.

1 Why Patient Matching is a Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying Fields, by Beth Haenke Just MBA, RHIA, FAHIMA; David Marc, MBS, CHDA; Megan Munns, RHIA; and Ryan Sandefer, MA, CPHIT, Perspectives in Health Information Management, AHIMA Foundation, Spring 2016.

2 Doing Away With Duplicate Testing Can Cut Healthcare Costs, by Heather Demello, Healthy UNH, May 12, 2015.

3 Registration-associated patient misidentification in an academic medical center: causes and corrections. Joint Commission Journal on Quality and Patient Safety/Joint Commission Resources Bittle MJ, Charache P, Wassilchalk DM.. 2007; 33:25–33.

Posted by Shawn Frazier on 28 Mar 2017

About the author
Shawn Frazier

Shawn Frazier

Shawn Frazier is the Regional Vice President of Sales at VisionWare.


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