Patient Identification Insights

Duplicate medical records and overlays 101

What are duplicate medical records and overlays?

Duplicate medical records and overlays are created as a result of patient identification errors. A duplicate medical record occurs when a single patient is associated with more than one medical record. Oftentimes, duplicate medical records are partial duplicates that only capture a portion of a patient's medical history. An overlay occurs when one patient's record is overwritten with data from another patient's record, creating a combined, inaccurate record. 

Why are duplicate medical records and overlays critical issues?

Patient matching problems pose significant dangers for patients because, if patients are treated based on incomplete or inaccurate knowledge about their medical history or profile, serious errors and complications can ensue. For example, a duplicate medical record may not include the correct information about a patient's blood type, allergies, or their diagnostic, medication, or family histories. Similarly, overlays include inaccurate information about patients’ medical histories because they merge information from separate individuals into a single patient record. The imprecision of these medical records can cause unnecessary and costly duplicate testing, ineffective treatments, unintended medication interactions, and inappropriate care that can harm patients. Duplicate medical records can also negatively impact communications between healthcare providers and their patients: duplicate medical records are associated with a higher risk of missing important laboratory results and a higher likelihood that patients are subjected to unnecessary testing.1

How are duplicate medical records and overlays created? 

According to the ONC’s Patient Identification and Matching Final Report from 2014, patient matching errors are an inevitable byproduct of healthcare’s increasingly complicated technology environment.2 Many healthcare organisations use multiple systems for clinical, administrative, and specialty services, which increase the likelihood of patient matching errors, duplicate medical records, and overlays. In addition to the complexities of healthcare’s IT infrastructures, many duplicate medical record errors can be traced back to small errors and inconsistencies introduced in patient registration processes. A study conducted at Johns Hopkins Hospital revealed that 92 percent of the errors resulting in duplicate medical records were caused by inpatient registration mistakes.3 Human error is the most common culprit and cause of these mistakes. Simple mistakes, like misspelling a patient's name, mistyping their Social Security Number, or using inconsistent acronyms or abbreviations (such as Mrs. vs. Ms., or Street vs. St.) can easily create duplicate medical records for a single patient, or merge multiple records together, resulting in impartial and inconsistent medical histories and information. Another major cause of patient matching problems is inconsistent naming practices. For example, a single patient could be recorded as Maria Garcia, Maria L. Garcia, Maria de Lopez Garcia, and Maria Garcia Williams. Pinpointing individual patients becomes even more difficult when multiple patients share the same name and other identifying information. In such cases, the risk of overlays can be particularly high. 

How prevalent are patient matching problems?

Although duplicate medical records are inherently difficult to measure, the AHIMA has reported that the average duplication rate in is between 8 and 12 percent.4 Because many patient matching algorithms rely on matching multiple identifying factors (such as name, date of birth, gender, and Social Security Number) instead of a single unique patient identifier (UPI), the likelihood of false positive and false negative matches increase exponentially in larger patient information datasets, such as those used by Integrated Delivery Networks (IDNs) and Health Information Exchanges (HIEs). In addition to the statistical errors that IDN and HIE datasets suffer, large networks experience interoperability problems that make it even more difficult to maintain Master Patient Index (MPI) integrity across multiple interconnecting IT systems.

How can patient matching problems be addressed?

Costly and time-consuming data-cleansing processes can correct duplicate medical record and overlay errors. To avoid future patient matching problems, more hospitals and healthcare networks are opting for unique patient identification technology solutions that eliminate the need to type patient information, thus minimising the risk of human-error related matching problems. The ONC’s 2014 Patient Identification and Matching Final Report identified emerging technologies (such as advanced biometric systems) as promising solutions to patient matching problems. A number of key stakeholders in hospitals have identified biometric solutions’ potential to reduce healthcare organisations’ reliance on complicated, weighted statistical matching methods for demographics data sets. To address the inaccuracies of these matching methods, healthcare providers need a fully scalable solution that creates 1:1 link between the patient and their medical record and doesn’t rely on demographic matching techniques. Imprivata PatientSecure is that solution. Imprivata PatientSecure reduces the risks of duplicate medical records, increases patient safety, and improves patient experiences for healthcare networks of all sizes. To learn more about the solution, explore the Imprivata PatientSecure product page. 

1. Joffe E, Bearden CF, Byrne MJ, Bernstam EV. Duplicate Patient Records – Implication for Missed Laboratory Results. AMIA Annual Symposium Proceedings. 2012;2012:1269-1275.
2. Office of the National Coordinator, Patient Identification And Matching Final Report, 2014,
3. Bittle MJ, Charache P, Wassilchalk DM. Registration-associated patient misidentification in an academic medical center: causes and corrections. Joint Commission Journal on Quality and Patient Safety/Joint Commission Resources. 2007;33:25–33
4. AHIMA MPI Task Force. “Building an Enterprise Master Person Index.”Journal of AHIMA 75, no. 1 (Jan. 2004): 56A–D