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Posted by / 30-Jul-2017 05:31

Validating indentity

More recent ONC activities such as an environmental scan of vendors, providers, and data exchange organizations are exploring current approaches, practices, and processes related to patient identification.

The output from this activity will be presented to the Health IT Policy and Health IT Standards Committees, and could potentially influence requirements for stage 3 of the “meaningful use” EHR Incentive Program.

Today’s health transformation initiatives push this function to the front of the health information exchange effort.

Incorrect or incomplete data capture within the healthcare setting can create critical patient care issues and risk privacy breaches, thus degrading consumer and user trust.

Health information organizations (HIOs) support, oversee, or govern the exchange of health-related information among organizations according to nationally recognized standards.

HIOs are the recipients of the stewardship and governance applied to patient identification processes, thus HIOs are today highlighting many of the weaknesses in the historical systems and practices.

Strong information governance that addresses patient identity integrity and accurate patient matching is key to a patient-centric health system and patient-centric processes.

We received your PA personal income tax return and refund request; however, to ensure refunds are issued only to their rightful owners, the department requires that you confirm your identity before refund payments are issued.It urges industry stakeholders to recognize that now is a critical time to address accuracy in patient identification systems.Various components of the healthcare ecosystem will address these goals and execute patient identification integrity activities to: Patient identification integrity is a complex concept, and one that is not well understood throughout the healthcare industry.Government-issued identification should be the standard. There is a lack of data standards addressing accurate and complete data capture and data matching for patient identification.And the standards that do exist are limited in scope and adherence is suboptimal.

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For more information on data quality attributes, refer to the 2012 AHIMA Data Quality Management Model practice brief here.