It is not uncommon for clinicians to have incomplete or inaccurate pictures of patients’ medical histories and conditions because the record and the patient do not match. This exposes healthcare organizations to medical errors, increased costs, and negative patient experiences.
Patient Matching in the Era of EHRs
“On average, hospitals have 30 percent of all claims denied, and an average of 35 percent of these denied claims are attributed to inaccurate patient identification or inaccurate/incomplete patient information,” says a recent report.
It is essential that clinicians have accurate and complete medical records of their patients if they are to deliver high quality care. In days of yore—when people had one “GP” and seldom saw another physician—maintaining a medical record was relatively simple. But today’s medical practice is increasingly specialized and care is episodic. It is delivered by different providers, at discrete points in time, and recorded in different electronic health record (EHR) systems.
On the surface, it would seem a simple task for EHR systems to share information with each other—a process known as “interoperability”—but it turns out that that is easier said than done. Organizational policies or contract terms can prevent sharing. The EHR technologies may be nonstandard or incompatible—that is, they lack interoperability—or vendors or providers may actually take willful actions that impede the flow of health information. Such actions amount to “information blocking,” a practice prohibited by federal law and punishable by civil monetary penalties (42 U.S. Code § 300jj–52).
Other causes of mismatched patient records include the following:
- Incorrect patient identification at registration
- Time pressures during treatment
- Insufficient training of personnel
- Use of templates and copy/paste functions in the EHR
- Simple human errors
See related sidebar: Shared Birthdays Cause Patient Matching Errors
Whatever the cause, it is not uncommon for a clinician to have an incomplete or inaccurate picture of a patient’s medical history and condition because the record and the patient do not match. In addition, the failure to match patients to their proper records can result in medical errors, increased costs, and negative patient experiences.
Types of Errors in Patient Matching
There are two types of patient matching errors.
False negatives. A “false negative”—an error in which a record is not linked to the patient—can occur within a single facility or when multiple provider organizations are involved, according to a recent report by the Pew Charitable Trusts. It states that as many as “one out of every five patients may not be matched to all his or her records when seeking care at a location where [they have already been seen].”
Match rates between organizations can be much lower. A study conducted for the Office of the National Coordinator for Health IT (ONC) “found match rates as low as 50 percent even between organizations that share the same EHR vendor because of the variability in technology and processes.” In other words, a lack of “interoperability” of EHR systems.
Patient matching errors. Patient matching errors—“false positives”—involve records that contain information relating to another person entirely. This can result in the patient being treated for someone else’s diagnosis, a clear liability issue. A 2012 survey by the College of Healthcare Information Management Executives (CHIME) found that one in five hospital chief information officers indicated that patients had been harmed in the previous year because of mismatched records.
Eighty-six percent of the financial operations personnel who responded to the Ponemon Institute’s “2016 National Patient Misidentification Survey” stated that they have witnessed or know of a medical error that was the result of patient misidentification. In addition, two-thirds of the respondents reported that when searching for information about a patient, they find duplicate medical records “almost all the time.” These cases may lead to reportable “sentinel events” under Joint Commission policies, and they can also be expensive because they lead to duplicate tests and procedures and costly billing errors.
For example, in one case mentioned in the Pew report, the care provided to an infant was documented in her twin’s record, resulting in the hospital’s failure to recoup $43,000 from the health plan. The Ponemon report states, “On average, hospitals have 30 percent of all claims denied, and an average of 35 percent of these denied claims are attributed to inaccurate patient identification or inaccurate/incomplete patient information.”
The Pew report found that overall, healthcare institutions focus most on preventing false positives (incorrectly merged information), thus “failure to link patients to their own records is the more common problem.”
Interoperability of EHR Systems
Provisions within the 21st Century Cures Act were intended to optimize the use of technology and allow for the seamless flow of health information from one provider to another so the patient’s health history will be accurate and available at all times. Such interoperability would improve match rates between organizations.
To that end, one section of the Cures Act gave the Secretary of the Department of Health and Human Services the task of issuing regulations to prevent information blocking. To date, nearly two years after the law was enacted, no implementing regulations have been published, but ONC sent a proposed rule to the Office of Management and Budget (OMB) on Sept. 17.
According to OMB, the proposed regulation “would update the ONC Health IT Certification Program by implementing certain provisions of the 21st Century Cures Act, including conditions and maintenance of certification requirements for health information technology (IT) developers, the voluntary certification of health IT for use by pediatric healthcare providers, … adoption of a trusted exchange framework and common agreement in support of network-to-network exchange, and reasonable and necessary activities that do not constitute information blocking. The rulemaking would also modify the Program through other complementary means to advance health IT certification and interoperability.”
Recommendations and Future considerations
There are some steps that can be taken now to improve patient matching. Following are some recommendations culled from various sources:
- Improve patient identification at registration and thereafter through use of multiple identifiers, biometrics, and smartphone apps.
- Assess other vulnerabilities in the patient registration process and educate clinicians and administrative personnel to raise awareness and address errors.
- Educate staff to reconfirm patient ID during “hand-off conversations” between clinicians, when collecting samples, when giving meds, or other activities.
- Make the business case for replacing legacy master patient index systems with state-of-the-art technology.
- Eliminate or minimize “copy-and-paste” functions and similar shortcuts in EHRs.
These efforts will be helpful, but the dream of true EHR system interoperability remains unrealized and is probably years away. To achieve it, there is a need for a nationwide strategy to identify best practices, gain commitments from providers and IT developers, and involve patients in the solutions, the Pew report concluded. “Although some opportunities exist to make meaningful, incremental progress in the near term, more robust change will require the use of new approaches and technologies.”
If the report’s recommendations were to be implemented, EHRs would be more likely to contain complete, accurate, and up-to-date medical information. This would improve safety, reduce costs, and lead to better coordination of care.
- “ Managing the integrity of patient identity in health information exchange,” American Health Information Management Association, 2009.
- Patient Identification and Matching, Final Report Audacious Inquiry, LLC, 2014.
- “ Partnership for Health IT Patient Safety Issues Recommendations for the Safe Use of Health IT for Patient Identification,” ECRI Institute, 2017.
- “ Patient Identification and Matching—An Essential Element of Using an Enterprise Data Warehouse to Manage Population Health,” HealthCatalyst
- Heath, S., “ Understanding the Patient’s Role in Addressing Patient Matching,” Patient Engagement HIT, Oct. 2, 2018.
- Monica, K., “ How to Create a Standardized Nationwide Patient Matching Strategy,” EHR Intelligence, July 12, 2018.
- “ Patient Demographic Data Quality Framework,” Office of the National Coordinator for Health IT
- Rudin, R.S., Hillestad, R., Ridgely, M.S., et al., “ Defining and Evaluating Patient-Empowered Approaches to Improving Record Matching,” RAND Corporation, 2018.
- Snell, E., “ Refining Patient Matching Process for Stronger Health Data Exchange,” EHR Intelligence, May 30, 2018.
- St. Thomas, S., “ How Improving Patient Matching Can Improve Patient Satisfaction,” HealthData Answers, May 21, 2018.
J. Stuart Showalter, JD, MFS, is a contributing editor for HFMA.