Why patient matching is key to interoperability and patient safety
While technological advances in healthcare continue to be mainly positive, increased risk as part of digitization continues to burden hospitals entangled in troves of data, and definitive steps to confront the biggest barrier to interoperability¬—patient identification—remains a critical challenge.
Without consistently and correctly matching individuals to their data to enable a complete record of a one’s health history, patients will continue to suffer the consequences (and in the worst cases, die) due to preventable medical errors and patient misidentification.
Two recent reports highlight this problem. In October 2018 the Joint Commission issued a national safety advisory on avoiding patient identification and matching errors. The alert cautions that while health IT can provide ready access to and facilitate the sharing of data, it can also cause patient identification problems, such as providing treatment to the wrong patient and mistakenly putting a test into another patient’s electronic health record (EHR). Recommended safety actions to avoid inaccurate patient identification include standardized processes for identifying patients and data capture, improved EHR configurations, and implementing systems to detect duplicate medical records.
A new report released by Pew Charitable Trusts echoes the Joint Commission’s concern that better patient matching is “critical” to effective data exchange and improved patient care. Pew found that match rates can be as low as 80 percent - meaning one out of five patients may not be matched to all of his or her records – in a location where the patient has been seen before. The match rates can be much lower when providers are attempting to match records shared between different entities and can be as low as 50 percent even when the providers use the same EHR vendor. These error rates are hurting patients, resulting in incorrect diagnoses, missing information, and the inability to find test results.
The problem of inadequate patient matching of electronic data is not new. In 2012 a CHIME report found that 20 percent of CIOs surveyed had patients harmed in the prior year due to identification mismatches. AHIMA warned in 2013 that inadequate patient matching in EHRs and health information exchange was a serious safety issue, calling for a national approach to resolve.
In 2016 the nonprofit ECRI Institute conducted a “deep dive” into the issue, reviewing more than 7,600 cases of wrong-patient errors at more than 181 facilities. It found that patient identification errors in the EHR were common, leading to injury and death. ECRI also noted that these errors likely represented only a fraction of the wrong-patient errors that had occurred. And this study only reviewed patient matching errors within a facility; it did not examine error rates when healthcare providers shared data outside of their own systems.
The government deserves some credit for recognizing that patient matching needs to be addressed. For instance, in January 2018 the Office for the National Coordinator for Health IT (ONC) released its draft trusted exchange framework and common agreement (TEFCA) for health information to support interoperability of health records. The draft, which is a significant step in advancing data sharing and required by the 21st Century Cures Act, specifically states that electronic health information is to be exchanged and used in a manner that promotes patient safety, including consistently and accurately matching the information to the correct individual.
However, there lies concern that the issue of patient matching may get lost in the myriad of other issues affecting interoperability. Notably, HHS’ draft strategy to reduce health IT burdens and improve interoperability, released November 28 and also required by the Cures Act, talks about improving the functionality and usability of EHRs but fails to make mention of patient matching. Inadequate record matching not only impedes interoperability; it also undoubtedly increases providers’ administrative and clinical burdens, including the burden to keep patients safe.
At its core, healthcare is an industry rooted in helping individuals live better, longer, healthier lives. Each year, as many as 440,000 Americans die from preventable errors in hospitals, and the status quo for sharing mission critical data is no longer acceptable.
Patient data matching functionalities within EHRs often lack the complexities to unify information from disparate and external systems. Poorly designed systems that fail to integrate or communicate with one another exacerbate inefficiencies, causing needless redundancies and generating millions of duplicate and incomplete records that lead to patient safety errors, skewed reporting and analytics, administrative burdens, and lost revenue. Most data entry errors are preventable but without a centralized data matching system in place to automate record de-duplication and data integrity, hospitals are increasingly placing patients at risk and barring physicians from making informed, life-saving decisions.
Patient matching will become even more difficult and more important in healthcare’s digitized future, as merger and acquisition activity continue to soar, and organizations strive to create a more integrated clinical environment. It’s difficult enough to match patient information and share data when two hospitals use the same EHR vendor or health information exchange. Just imagine the bigger challenge when a hospital using hospital-oriented health IT acquires a physician group using a physician-oriented EHR, or when two widely divergent organizations join forces, as in the mergers of Cigna with Express Scripts and CVS with Aetna.
As we move forward to make interoperability more of a reality, let’s ensure that proper patient matching and identification is an integral part of the process. It will not only improve data quality; it will result in better patient care and outcomes and work to bend the cost of healthcare downward.
Also consider raising this issue during HHS’ open comment period for its Strategy on Reducing Burden Relating to the Use of Health IT and EHRs. Public comments on the draft strategy are due January 28, 2019. Patient matching and its effect on interoperability and patient safety should always be part of the conversation.