How many patient identifiers

An example of such circumstances may include an injured, unresponsive patient presenting to the emergency department. Although not addressed in the requirements, a temporary "name" e. These identifiers could then be used to identify the patient and match against specimen labels, medication orders, blood product labels, etc.

In this process, formal identification of the patient should occur as soon as possible and once confirmed this identifying information should be used instead of the temporary identification. Ultimately, the organization determines how such scenarios should be managed. Use of an alias to protect anonymity There are no Joint Commission standards that address this practice.

Organizations should evaluate risks associated with this practice. However, if an organization implements use of an alias, two patient identifiers must still be used, as defined by policy. Last updated on October 19, Manual: Home Care. Was this response helpful? Yes No. Comments If you have additional standards-related questions regarding this topic, please use the Standards Online Submission Form.

Additional hospital resources. Hospital Breakfast Briefings. Get the standards. To inform the paper, the authors conducted a literature review for relevant peer reviewed and grey literature articles focusing on: 1 patient identification techniques adopted worldwide and 2 consequences and implications of unresolved patient identification issues published from January to October in Scopus, PubMed, Web of Knowledge, Web of Science, the Association for Computing Machinery ACM Digital Library, and the American Health Information Management Association AHIMA Body of Knowledge.

Searches for grey literature i. Articles for the review were drawn from peer-reviewed journals, conference papers, consumer studies, health professional studies, research performed by independent research institutions, as well as systematic and narrative reviews of the various topics.

A snowball approach was undertaken to identify additional sources of information. Ensuring quality health information has and will increasingly become complicated as new data streams are utilized and as organizations share records electronically using different information systems and standards 13 Worldwide, there are several different patient identification approaches, techniques, and solutions including UPIs and algorithms.

At the same time, technological and methodological innovations have introduced new approaches such as referential matching, biometrics, and radio frequency identification device RFID technologies as ways to further improve techniques for patient identification 4.

Below we examine a variety of techniques utilized to address patient identification. However, even though UPIs are widely implemented and constitute a preferred method due to reduced reliance on patient attributes for patient matching, the challenges of generating and implementing UPIs often lead to limited implementations within institutions compared to other care settings 4 14 25 Privacy concerns are also often cited as a major concern in implementing UPIs In Canada, because healthcare is funded and governed at the province-level and every province has different regulatory frameworks, the deployment of a nationwide UPI proved difficult As a result, patient registries, which support the centralized storage and retrieval of patient identification data, are used to identify patients in conjunction with an enterprise-wide master patient index MPI , data quality remediation, and governance policies In the US, long-standing policy barriers have hindered adoption of a unique patient identifier.

In , the Congress passed legislation calling for the adoption of a UPI However, due to privacy and security concerns, the Congress included statutory language in an annual appropriations bill limiting the promulgation or adoption of a UPI Despite this ongoing limitation, the Congress recently included report language in the spending agreement encouraging HHS to provide technical assistance to private sector-led patient identification initiatives and directed the Office of the National Coordinator for Health Information Technology ONC to 1 submit a report to the Congress on the technological and operational methods that improve identification of patients 29 , and 2 recommend actions increasing the likelihood of accurately matching patients to their health data.

Inclusion of the report language may continue to help foster private sector-led initiatives focused on patient identification and help identify and implement important policy levers to further advance a nationwide patient identification strategy in the US. Algorithms are another common approach to matching patients to their health information using demographic characteristics including: first name, last name, gender, date of birth, social security number in the US , and address Algorithms range from basic, i.

The complexity of such algorithms varies widely, may be vendor-specific and dependent on the customization added to the base installation of an electronic health record EHR system Inaccurate or incomplete patient demographic information can hinder the enhancement of algorithmic accuracy Occurrences of inaccurate or incomplete demographic data can be the result of: lack of best practices in collecting demographic data at registration, variations in organizational and health information technology IT vendor policies and processes in how demographic data are collected, failure by patients to provide the correct information at registration e.

Transcription and free text errors can also limit algorithm accuracy including spelling variations, phonetic variations, double last names, double first names, and alternate first names 37 Standardized data elements are also generally needed to optimize matching algorithm accuracy e.

Evidence suggests that standardizing certain demographic data elements could improve match rates Beyond algorithmic methodologies, a growing number of organizations are implementing add-on technologies including referential matching software to increase odds of identifying patients correctly.

Referential matching software is a data augmentation where a third-party service provider adds an additional layer of demographic data typically from outside of healthcare including datasets from credit reporting and public utilities that are routinely updated and maintained to enhance patient matching Concerns have been raised that referential matching could lead to clinicians and payers having access to personal and financial information, including credit information.

However, existing referential matching approaches do not appear to share patient health information outside of the healthcare institution Accuracy of non-health information such as data from the US Postal Service and the Social Security Administration used by matching software is also a concern for clinicians and patients Referential matching also has limitations related to certain patient populations including children, homeless individuals, and undocumented immigrants because data sources used for referential matching do not contain or have limited information on these populations Limitations with UPIs and matching algorithms have led to the exploration of other add-on technologies including biometric identification technologies.

Such approaches include fingerprints, palm vein scanning, iris scanning, and facial recognition Biometric technologies are increasingly being used to identify patients in the US 2. However, they are not without limitations.

Research suggests that biometric methods to date for infant identification including eye scanning, ear and face recognition, and finger and palm-based methods solutions are not as effective as with adults since such features are difficult to capture and subject to change during child development 42 In Europe, biometric technologies also raise ethical and legal considerations Additional emerging technologies, like radio frequency identification RFID are under exploration by hospitals to enhance patient identification.

Unlike existing barcode technologies, RFID labels can hold more data than barcodes and be read automatically without user intervention At the same time, application of RFID technology is not widespread as it can be cost-prohibitive 46 , and lacks standards or guidelines for implementation within healthcare. The variety of patient identification methodologies is expansive and may include hybrid models that combine different methods described above.

From the basic to the most complex methods, researchers agree that no perfect patient linkage solution exists and all the approaches present challenges that must be addressed 14 25 In the US, these challenges are further hampered by existing legal and policy impediments.

Lack of a perfect patient identity solution raises significant concerns. The literature review revealed three distinct themes associated with unresolved patient identification issues. The second theme identifies financial, payment, and resource implications associated with patient misidentification.

The third theme identifies the limitations patient misidentification places on data sharing and interoperability. Each theme is examined below. Failure to accurately identify patients raises patient safety and quality of care concerns across the care continuum from diagnostic testing to treatment Duplicates are the result of several factors including varying methods of matching patient records, lack of data standardization, lack of policies and procedures, and frequently changing demographic data The existence of duplicate records can also lead to duplicative testing and treatments because of inaccurate or unavailable data.

Challenges associated with accurately matching patients to their health information raise financial and resource concerns. Repetitive tests and treatments are likely to add costs and impact timeliness of care delivery. From a revenue cycle perspective, there may be claims denials 52 53 and implementation of time consuming and costly processes to correct medical records Patient misidentification raises data sharing and interoperability concerns as well.

Historically, patient identification practices in the US have been fragmented and inconsistent Furthermore, hospitals report that difficulties in accurately matching patients to their health information across health IT systems limit health information exchange The use of two identifiers also helps ensure that a correct match is made between the service or treatment and the individual.

This process will help eliminate errors and enhance patient care. The two identifiers must be directly associated with the individual and the same two identifiers associated with the medication, blood product, specimen container attached label , treatment, or procedure. Patients may wonder why their identity is confirmed so often. Staff members should always explain that it is done to ensure the right care is provided to the right patient all the time.