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The Impact of Patient Identification Errors

The average hospital loses $17.4 million annually in claim denials based on misidentification, according to the 2016 Ponemon Misidentification Report (Ponemon Institute). The study states, “On average, hospitals have 30% of all claims denied and an average of 35% of these denied claims are attributed to inaccurate patient identification or inaccurate/incomplete patient information.” Denials adversely affect both cash flow and AR days.

Duplicate Patient Records Are a Big Problem

According to Patricia Consolver, CHAM (Certified Healthcare Access Manager), “Incorrect identification of patients by registering a nickname, for example, instead of the legal name used on the health insurance policy, may result in denials, subsequent appeals, and delayed payments. Duplicate records may also lead to the repetition of lab or diagnostic tests already performed but documented in a different record. If the costs of these tests have already been paid by the insurance company, they will not be covered a second time, resulting in unreimbursed care for which the hospital cannot collect.”

Providers also incur the cost of correcting these errors. According to the American Health Information Management Association (AHIMA) Foundation, duplicate medical records for a single patient, called overlaps, cost up to $1,000 each to correct. Overlays, when two patients’ records are mistakenly merged, cost up to $5,000 to correct because of the administrative time required to search and sort data.

Best Practices for Preventing and Catching Misidentification

Implementation of an EHR with an enterprise master patient index (EMPI) helps to solve these problems going forward, but a retrospective scrub of the EMPI is necessary to find and correct past overlaps and overlays. This is typically performed as a step in the implementation of an EHR.

In its “SAFER Self-Assessment Guide,” CMS offers several examples of best practices to maintain clean records:

  • During the creation of a new patient record, a phonetic algorithm, such as Soundex, is used to display an alert or warning if the patient, or a patient with similar demographic data, exists in the system.
  • When looking up a patient, if the results list returns multiple patients with similar demographic data, the names are displayed in a visually distinct manner.
  • The system monitors for similar names (i.e., nicknames), or changed last names (e.g., marriage, divorce, adoption), when other demographics match.
  • An alert provides additional demographic information context for the existing patient to help the user confirm or rule out that it is the same patient.

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References:

CMS (2016). Self-Assessment Patient Identification General Instructions for the SAFER Self-Assessment Guides. Retrieved from https://www.healthit.gov/sites/safer/files/guides/safer_patient_identification.pdf

Ponemon Institute. 2016 National Patient Identification Report. Retrieved from https://pages.imprivata.com/rs/imprivata/images/Ponemon-Report_121416.pdf

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