According to the Joint Commission, a non-profit organization that accredits over 21,000 healthcare organizations in the United States, “Patient identification is what happens when a healthcare professional speaks with a patient or when patient information is assessed or recorded. This process carries a burden of the risk that wrong-patient errors may occur- errors that can trigger delays or the provision of the wrong treatment to the wrong patient.”

The Challenge of Duplicate Patient Records

To deliver appropriate care and ensure the best opportunity for good patient outcomes, it is imperative that each patient is identified accurately and fully. Since the adoption of digital information technology in the medical industry, the problem of duplicate records has grown. According to Craig Richardville, CIO of Carolinas Healthcare System, the average rate of duplicate patient files in US hospitals is 8-10%, and the rate of patients misidentified during health record searches is 7-10%.

In response to this growing problem, the Joint Commission has made accurate patient identification their top priority. In an advisory on accurate patient identification, Gerard M. Castro, Ph.D., MPH, project director, Patient Safety Initiatives, writes, “We must consider not only the technology but also the people involved and their processes. It is essential for health care professionals to receive adequate training and conduct reliable procedures. Accurate patient identification involves shared responsibility and involvement of all stakeholders.”

Why Accurate Patient Matching Matters

Inaccurate patient records pose a serious risk to patients, as well as to the revenue streams of healthcare organizations. Physicians Review Navigators estimates that for every 10,000 patients, 8,529 will have duplicate records. It’s not difficult to imagine how duplicate records can prove a significant detriment to revenue cycles, especially when it comes to claims and collections. Incorrect names, nicknames, and duplicate names can make it difficult, or impossible, to collect on accounts receivable.

Worse still, is the risk of incurring legal claims. PRN writes, “If a health insurance company is involved, things can get worse for your revenue cycle. It’s crucial to always use the name appearing on a patient’s insurance card. Using a nickname or mixing duplicate accounts can cause you to enter claims incorrectly. It can also result in duplicate claims. This could trigger a HIPAA violation and open your organization up to fraudulent claims.”

Revenue streams are not the only thing that duplicate records place at risk. It has been shown that providing wrong treatments is a leading cause of death for Americans. No healthcare professional wants to provide incorrect care, but when this happens- it can hurt your brand and your reputation irrevocably.

If an instance of providing the wrong care to the wrong patient should receive media attention- it could be a public relations disaster. Large healthcare institutions can sometimes survive these unfortunate events, but small to medium-sized practices are often under greater risk.

Connecting Interoperability with Patient Matching

The Healthcare Information and Management Systems Society (HIMSS) defines interoperability as, “the ability of different information systems, devices, and applications to effect coordinated connections within and across organizational boundaries to access, exchange and cooperatively use data amongst stakeholders, with the goal of optimizing the health of individuals and populations.”

Achieving interoperability is a realistic goal. But as long as duplicate records continue to plague the healthcare system, the reality of interoperability will always fall short of the ideal. Without the accurate and consistent matching of patients with data, the people we serve will continue to suffer, and revenue streams will continue to be at risk.

For interoperability to fully render accurate patient catalogs, it must meet four criteria;

• Foundational interoperability – connectivity between the building blocks of an information system

• Structural interoperability – the uniform movement of healthcare data

• Semantic interoperability – uniformity of definitions and the codification of data

• Organizational interoperability – clarity in policy, clinical and business culture in the organizational components of the system

To put it simply, these four components mean, clarity, transparency, and consistency across cultural, management, policy, procedural, and technological aspects of your organization’s information sharing system. That may sound like a monumental task, but we do have the tools to address them. They are efficient Patient Registration, EHR, Enhanced Interoperability and API’s.

Patient Matching Solutions

We’ve established that duplicate patient records are a hazard to patients, and to the healthcare organizations that serve them. Fortunately, we have at our fingertips a robust set of tools to deal with the problem. They are;

• Patient Registration Best Practices

• Electronic Health Records

• Enhanced Interoperability

• Application Programming Interfaces

Patient Registration Best Practices

Patient registration best practices have been identified by healthcare professionals across the industry. They include;

  • Two-factor (minimum) identification
  • Initiating verbal patient confirmation
  • Asking to see a driver’s license
  • Asking patients to read their wristbands
  • Photographing patients, and attaching photos to files
  • Patient registration kiosks for active patient self-matching

Electronic Health Records

Advanced EHR tools offer automated patient file duplicate alert systems. These prompt healthcare professionals to verify that records are accurate and up to date before delivering care.

Enhanced Interoperability

Single task specialized applications, universal standards, and automated alignment between nodes in a system provide a technological layer of protection from duplicate records.

Application Programming Interfaces

These software tools manage the flow of information between connected systems. Forward-thinking healthcare stakeholders are investing in API as the next phase in the development of more accurate EHR.

 Pew Charitable Trust on Patient Matching Solutions

Pew research generated strategies and approaches that the private sector and government can implement to improve patient matching. These suggestions include:

  • Creation of a unique identifier for patients.
  • Establishment of patient-empowered solutions, enabling each individual to ensure that his or her records are matched.
  • Standardization of demographic data, such as addresses
  • Multiple information sources to verify a person’s identity, known as referential matching

To ensure Patient Matching and prevent Patient Data Duplication, request a free demo with PrognoCIS.


Sources:

  1. https://www.youtube.com/watch?v=Qod_rgRsK-M&ytbChannel=Imprivata
  2. https://www.jointcommission.org/new_joint_commission_advisory_on_accurate_patient_identification/
  3. http://www.physicianrevenuenav.com/blog/how-your-duplicate-patient-records-problem-is-crippling-your-revenue-cycle/
  4. https://www.himss.org/library/interoperability-standards/what-is-interoperability
  5. https://hitinfrastructure.com/features/why-application-programming-interfaces-are-key-for-healthcare
  6. https://www.healthleadersmedia.com/clinical-care/6-best-practices-patient-identification
  7. https://www.glue42.com/improving-revenue-through-enhanced-interoperability-key-takeways-from-the-recent-tabb-webinar/
  8. http://www.ahimajournal-digital.com/ahimajournal/september_2018?pg=26#pg26

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