The source of the problem
Accurate patient matching is foundational to your EHR but all EHRs struggle with patient matching. Accurate patient matching is 10x more challenging today due to an explosion of data and data sources. Mergers/acquisitions within hospital systems, patient engagements, advanced analytics and information exchanges have all added to the patient matching challenges.
EHRs’ patient matching woes are evidenced by rising duplicate rates. In 2008, an AHIMA study identified average duplicate record rates of between 8-12%. Black Book Market Research Survey shows that in 2018, this number has reached 18%. By 2020, these rates are expected to rise to 20%. This is in sharp contrast to the ONC mandate of a .5% match rate.
The cost of the problem
No matter what EHR system you use, inaccurate patient matching has huge costs. According to the 2018, Mid-Year EHR Consumer Satisfaction Survey, Black Book Market Research, every duplicate record costs health systems $1,950 per inpatient stay, costs health systems $800 per Emergency Room visit and increases duplicate tests by 30%. The 2016, National Patient Misidentification Report by the Ponemon Institute reported that inaccurate patient matching causes $17.4M in denied claims annually for the average hospital.
Healthcare systems spend a lot of time “fixing” duplicate and inaccurate patient records. Traditionally, this is done by data matching. Data matching can be either deterministic or probabilistic. In probabilistic, or referential matching, several field values from a variety of sources are compared between two records and each field is assigned a weight that indicates how closely the two-field values match. The sum of the individual field weights indicates the likelihood of a match between two records.
In deterministic matching, a unique identifier for each record is compared to determine a match or an exact comparison is used between fields. Unique identifiers can include national IDs, system IDs, or Patient Biometric Verification IDs. Deterministic matching is completely reliable when a single field can provide an absolute match between two records.
The fundamental problem is that patient demographic data is notoriously error-prone and constantly falling out of date. Names, addresses and phone numbers change over time. Spelling and transcription errors create invalid or missing data segments and confusion over twins, Sr and Jr or culturally different names add to the problem. EHR matching technologies are only as accurate as the underlying patient demographic data. EHR matching technologies cannot automatically make 15-30% of matches, resulting in EHRs flagging thousands or millions of “potential matches” to be resolved manually. The only way to stop potential matches is to prevent them in the first place.
The final permanent fix
Moving forward, the only permanent fix is to adopt a biometric patient identification system. Biometric identification provides an absolute (or deterministic) match as opposed to less reliable probabilistic or referential matches on name, address, or phone numbers.
Whether you use an EHR from Epic®, Cerner®, McKesson®, eClinicalWorks®, or another vendor, it has one fatal flaw that is costing your organization money, diminishing care quality, and preventing strategic initiatives. Every EHR struggles with patient matching, and this struggle is becoming insurmountable as organizations integrate new data sources, migrate to single EHR instances, and onboard patient data from newly acquired hospitals and clinics. Organizations are forced to either (1) suffer rising duplicate rates, which drastically impact care quality and patient safety, or (2) throw more money at an already substantial EHR investment by hiring a team to manually resolve a growing queue of potential duplicates.
Privasent, which offers a uniquely patented combination of a smart card and a biometric identifier, can protect the patient’s identity at each point of care within in Healthcare system regardless of the Electronic Health Record system used and provides a Unique, Absolute and Interoperable Identity. It is the next generation of patient identity matching, using two-factor authentication to provide absolute certainty as opposed to probabilistic or referential matching. Privasent eliminates identification errors at the point of registration by capturing a unique biometric identity using palm vein scanning technology coupled with a smart card. An encrypted identity is associated with the patient’s medical record number each time the patient authenticates.
If you would like to learn more about how you can save money and provide absolute healthcare identification with biometrics, request a consultation.