Survey: Patient Matching Problems Routine in Healthcare

By Julie Dooling, MSHI, RHIA, CHDA; Lorraine Fernandes, RHIA; Annessa Kirby; Grant Landsbach, RHIA; Katherine Lusk, RHIA; Megan Munns, RHIA; Neysa Noreen, RHIA; Michele O’Connor, RHIA, FAHIMA; and Melinda Patten, RHIA, CHPS, CDIP

A recent survey with AHIMA members revealed that over half of HIM professionals routinely work on mitigating possible patient record duplicates at their facility, and of those 72 percent work on mitigating duplicate records weekly. Contributing to the issue, less than half (47 percent) of respondents state they have a quality assurance step in their registration or post registration process, and face a lack of resources to adequately correct duplicates.

In order to learn more about AHIMA members’ experience with patient matching as it relates to linking patient records, an AHIMA membership survey answered by 815 participants using 12 different EHR systems was conducted this summer. AHIMA plans to use the information generated from the survey to help shape its future goals and advocacy efforts in accurate patient matching—an area of HIM it feels can have a significant impact on the care of patients.

Accurate patient matching “underpins and enables the success of all strategic initiatives in healthcare,” according to an AHIMA press release on the survey, including:

  • Patient-centric care: Identifiers serve to “link” all patient data. Compromising the “linking” ability compromises care delivery.
  • Health information exchange: Correlating patient data across enterprises, regions, or states requires accurate matching of patient data.
  • Population health: While population health has many facets, the one common thread is the need to match consumer information at an individual level in order to address the goals.
  • Analytics: Identifying best outcomes for patient study groups, identifying consumers across a continuum of care for engagement strategies, and effective research requires accurate patient matching.
  • Finance: Value-based purchasing, risk sharing reimbursement models, and accountable care organizations all rely on accurate patient matching across a care continuum.

Information governance encompassing patient matching is essential to successfully executing disruptive and transformational healthcare activities, according to AHIMA officials.

Five Key Survey Findings

The five key finding from the patient matching survey, as well as AHIMA’s analysis on the findings, are:

  • Q: Do you measure data quality as it relates to patient matching? A total of 43 percent of respondents are measuring data quality as it relates to patient matching. Routine quality check exercises are an important component of data quality for patient matching. These routine quality checks include examples such as daily, weekly, and monthly reporting on demographic changes made to patients, duplicates created, and feedback mechanisms. The process may also include a reconciliation process for temporary values as indicated with trauma, unknown, and newborn patients.
  • Q: Do you have a quality assurance step in your registration or post registration? At total of 47 percent of respondent’s state they have a quality assurance step in their registration or post registration process. A robust quality process that provides regular feedback to registration staff improves data quality and one’s ability to match patients internally and externally.
  • Q: What is your duplicate medical record number rate in your EHR? When calculating your duplicate rate, what value do you use in the numerator? What value do you use for the denominator? A total of 55 percent of survey respondents were able to communicate the duplicate medical record rate within their organization, but additional questions relating to how the duplicate rate was calculated indicate a lack of a standard definition for duplicate rate calculation. For example, only 42 percent knew the numerator and 42 percent knew the denominator that factored into their organizations duplicate rate.
  • Q: Do you work your possible duplicates regularly? If yes, how often? A total of 57 percent of respondents work possible duplicates regularly. Of those respondents, 73 percent work duplicates at a minimum of weekly. Routine, consistent management of identified data integrity issues with clean up in a timely manner are recognized as a necessity by the majority of the respondents
  • Q: What kind of challenges do you face on a daily basis with managing your master patient index (MPI) or enterprise master patient index (EMPI)? The top five challenges identified by survey respondents in managing the MPI/EMPI are: Registration staff turnover; record matching/patient search terminology and/or algorithms; lack of resources to correct duplicates; inadequate information governance policy support and lack of executive support. These challenges showcase the diversity and complexities confounding resolution of this key component for sharing information.

How to Improve Patient Matching Initiatives

The authors of the survey said it shows the need to measure, monitor, and inform the marketplace of the need to better match patients to their specific health information. The survey responses illustrate the importance of information governance encompassing patient matching. Accurate patient matching is essential to patient-centric initiatives, and implementing quality assurance measures are critical steps to improving performance, the authors said.

“Reliable and accurate calculation of the duplicate rate is foundational to developing trusted data, reducing potential patient safety risks and measuring return on investments for strategic healthcare initiatives,” the survey authors note.

The Office of the National Coordinator for Health IT’s Report on Accurate Patient Matching, released in February 2014, recommended that organizations move to “prevention” of duplicate record creation versus the current method of back-end data stewardship.

“We cannot sit around and wait for others to correct this problem,” the survey authors said. “As healthcare professionals, we need to embrace the challenge and collaborate to develop scalable solutions to assure patient information is available when and where it is needed.”

3 Comments

  1. Thank you for sharing this information! I am looking forward to further study regarding MPI cleanup as we have facilities that need a better process and/or do not have the staff to do it. Through our Enterprise Program Management Office (EPMO), we will pursue an RFP and create a list of preferred vendors that the hospitals may choose from to maintain their MPI. Any information you can provide to assist with this will be awesome!

    Thanks again!

    Post a Reply
    • Thank you for this informative study and article that will hopefully encourage the HIM community to seriously address the challenge of duplicate patient records. One significant point that also needs to be raised is the absence of accurate measurement by which organizations are identifying duplicate patient records within their respective healthcare systems. If an independent organization such as AHIMA does not champion a method to measure patient matching accuracy of existing HIS, EHR, and even best-of-breed EMPI solutions, then we will continue to run the risk that a large number of duplicate patient records are going undetected while we believe the problem is solved.

      Please see the following LinkedIn post to get a better understanding of how duplicate patient records are being identified and what is being missed.

      https://www.linkedin.com/pulse/assessing-patient-matching-accuracy-steve-kotyk?trk=prof-post

      P.S. To the point the ONC raised in 2014 – “An ounce of prevention is worth a pound of cure” – Benjamin Franklin

      Post a Reply
    • Arnita –

      Good luck and so pleased you are on this journey. My advice would be to put a reconciliation process in place with daily feedback and monthly trending. If you start now and continue after the clean up – you will be able to change the culture and stop this from continuing.

      Post a Reply

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