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Impact of Patient Identity Management on Hospital Revenues

Published on August 25, 2022 
by CoverMe Team
Graphical illustration of two doctors arranging medical insurance cards and identity cards.

Patient Identity Management – A Critical yet Underappreciated Healthcare Component

How sure are you if the individual at the registration desk is the patient scheduled for the appointment? Do you know if their insurance information is valid? How about their address? Are you sure they are still residing in the same county as last year? Oftentimes during patient registration, the front desk staff are left with numerous doubts regarding the patient data.

As life goes on, people experience different changes. It may be necessary for them to relocate. They may switch jobs, which will affect their coverage. Their financial situation may change. And, all these can happen anytime. It is therefore impossible to know whether the information you received from your patients in the past will remain valid in the future. Thus, it’s important for healthcare facilities to ensure accurate patient data and coverage information.

Unfortunately, however, hospitals and health systems across the USA are mired in poor data quality. While inaccurate data can seriously hamper patient safety, inefficient patient identity management can also have serious implications on your bottom line.

But, let’s take a pause and think. Why do these errors occur in the first place? How can they impact hospital revenues? How can you leverage technology-driven solutions to streamline your patient identity management? Let us explore!

Patient Identification Errors – Why Do They Happen?

A 2020 survey by EHI and Nextgate reported that approximately 38% of healthcare providers in the US had faced an adverse event affecting patient safety, due to discrepancies in patient data. According to an AHIMA survey from 2017, 10 percent of patient records at any health organization are duplicated. Why are these numbers alarming?

It means, as a healthcare organization, if you are unable to ensure patient data quality, you are setting yourself up for the loss of revenue.

But why do such patient identity errors crop up in the first place? Let’s analyze:

  • Inadvertent data entry errors cause misspelled patient demographic information.
  • An erroneous patient identification during registration links the patient to wrong records at every touch point in the patient journey.
  • Registration desks are high-pressure areas, with a good influx of patient walk-ins and inquiries, leaving the front desk staff stressed out. In an attempt to reduce patient wait times, such situations can result in inaccurate patient identification based on incomplete or false inputs.
  • Inadequate training and lack of foolproof processes result in staff not following protocols or missing a crucial process step.
  • Inability to identify the correct patient record when queries return multiple or duplicate reports.
  • Inefficient workflows, where the data flow and interoperability across systems are mismanaged.
  • Unfavorable patient behavior where the patient submits false information knowingly or unknowingly.

Also Read: Accelerating Revenue Recovery – Priorities for Hospitals in 2022

Patient Misidentification – How Big is the Problem?

Patient identification forms the backbone of the healthcare system. When patient identification fails, or if something goes wrong with the patient data, it can result in far-reaching consequences. On the one hand, it causes negative patient experiences and raises worries about patient safety. On the other, it results in totally avoidable expenses for the provider and builds up as an operational nightmare.

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Did you know?
“Inaccurate patient identification accounts for approximately 33% of all denied claims, costing hospitals an average of $2.5 million (2020) and the US healthcare system over $6 billion each year.

But the real costs of such errors can be much more. A study by Blackbook reveals that incorrect patient identification leads to additional expenditures of approximately $2,000 per patient per hospital stay and $800 per patient per emergency room visit.

And, what about the cost of correcting these errors? According to stats presented at the 2014 HIMSS Conference on “Patient Safety and Rising Healthcare Costs.”, it costs an average of $1,000 to reconcile a single pair of duplicate records.

Patient misidentification remains a topic that most healthcare executives would rather not talk about. However, the figures above illustrate that it cannot be ignored altogether either.

While there is no silver bullet to solve this crisis, a proactive patient identity verification strategy can help mitigate the risk of such errors.

CoverMe Patient Identity Verification

The first step to navigating the challenges in patient identity verification and management is to ensure that the patient information is captured accurately at the registration. By streamlining your intake process, you can effectively cut down on the potential for human errors.

CoverMe empowers hospitals and health systems with a comprehensive front-end RCM solution that harnesses the capabilities of AI to validate demographics and other critical patient information effectively. Thus the solution enables healthcare providers and their staff to eliminate mistakes in identity resolution and minimize denials due to inaccurate information.

Rather than relying on the patient to provide accurate coverage information, CoverMe does the heavy lifting and determines this information using basic patient data. The solution also verifies the existing coverage through its coverage discovery module, scours through commercial databases, discovers other eligible coverage options, determines the patient’s financial propensity, and identifies potential financial assistance programs – all this within a span of a 5-minute interview.

De-risk patient safety and improve your bottom line with CoverMe patient identity verification solutions. Schedule a demo today or visit to know more about our marketplace solutions.

Written by CoverMe Team
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