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Promoting Effective Patient Identification
Promoting Effective Patient Identification
Background
Patient identification is the process of appropriately matching the patient to the intended interventions and communicating the patient’s identity accurately in the continuum of care. Identifying patients involves both physical identification and the use of technology for accuracy in recognizing the patient. The mode of patient identification should be unique and consistent to optimize patient identification, which is required to meet the increased and diverse demands for the patients.
Several technological approaches can be used to collect data and identify patients but have varying levels of effectiveness. As the volume and variety of health information increases, it is increasingly important to match patients to the correct data to enhance safety and proper use of resources. The lack of accurate patient data impacts clinical decision making, treatment strategies, patient privacy, leads to duplicative testing, and raises the overall cost of healthcare.
When the patient is inaccurately matched to another patient’s data, patient safety and care are jeopardized as incorrect data will be used in internal and external networks such as radiology, laboratory, and health system networks. The errors potentially lead to wrong imaging and laboratory results, wrong-site surgeries, and medication errors which can be life-threatening. They may also result in several records for one patient. Healthcare workers may miss out on important information because it exists in the duplicate record. In these scenarios, the care decisions become incomplete or erroneous because the medical history is partial and the data is unreliable.
Innovation Strategies
Quality health information can be ensured by innovative approaches requiring solutions such as Unique Patient Identifiers (UPIs) and algorithms (Martin, 2017). Other technological innovations include biometrics, radio frequency identification devices, and referential matching, which further improve patient identification. The UPIs is a method that helps to standardize patient identification by assigning a unique code to identify and manage patient information. The code protects sensitive patient care information and facilitates the exchange of data between healthcare facilities.
On the other hand, algorithmic approaches can be utilized to match patients to their health data through demographic characteristics such as name, gender, birth date, or address (Teixeira et al., 2017). The complexity of the algorithm can vary widely depending on the installed electronic health record system. Matching the algorithms can help achieve the correct patient identification and other matching solutions (Teixeira et al., 2017). To make the process more accurate, accurate, and complete, patient demographic data must be logged in. The occurrences of incomplete or incorrect patient data can lead to a lack of best practices in the healthcare organization. Patients must also be encouraged to provide accurate information at the time of registration.
Referential matching software can also be used beyond the algorithm methodologies to increase the chances of identifying the patients correctly. The add-on technology uses software to augment data in which the third-party service provider adds another layer of patient demographic data outside of healthcare (Riplinger et al., 2020). They may include public utilities and credit reporting that are regularly maintained and updated to facilitate patient matching. The referential matching does not share patient’s information with other people outside of the healthcare institution; hence patient information is secure.
Biometric identification systems involve fingerprints, iris scanning, facial recognition, and palm vein scanning to identify the patients (Lippi et al., 2017). The advantage of the method is that it is hard to manipulate or exchange patient data and offers protection against fraud as they eliminate overlaid and duplicate medical records. They also reduce medical claim denials and improve financial outcomes. The method is less intrusive, and with touchless authentication, the patient is identified and correct drugs offered. Biometrics can be used in non-responsive patient identification, improving patient safety, and improving healthcare delivery (Lippi et al., 2017).
The radio frequency identification method is an additional technology used to identify and hold more data than bar codes and can be read automatically (Yamashita et al., 2018). They offer the re-writing function, which allows records to be modified. The radiofrequency systems provide advanced forms of data security because of the barcode technology, which allows encryption hence securing patient’s data. Therefore, the radiofrequency tags are attached to people to provide identification and capture data to help with the correct identification and treatment of patients.
Justification
The above creative patient identification methodologies can be combined as an innovative plan to identify patients in different departments of the healthcare system. The methods are expansive, from essential to complex, and can be implemented depending on the existing policy and legal impediments. They help with patient identification and prevent errors and near misses. The technologies improve patient safety as information is captured accurately and enhances the quality of care, from diagnostic testing to treatment strategies. It prevents scenarios such as admitting a patient with records of another patient and creating multiple patient records. They help solve the problem of duplicates which result from many factors, including not matching patient records accurately, lack of standardization of data, absence of policies and procedures, and frequent changing of demographic data.
The method of patient identification helps to conserve finances and resources. The repetitive cycle perspective reduces monotonous tests and treatments, which are likely to add costs and affect the timeliness for delivery of care. They reduce insurance denials because of wrong patient identification. It is time-consuming and very costly to correct medical records hence the need to use effective methods for patient identification.
Interoperability and data sharing may also be impacted by patient misidentification. The inability to match patients correctly in the health care systems limits the exchange of critical information. The limited health information exchange results in poor clinical decision-making because it is based on incomplete information and increases the chances for misdiagnosis, inadequate treatment choices, and unnecessary testing procedures. Accurate patient identification is also essential for secondary use of information for population health, quality improvement, and research.
Evaluation
Additional research can be conducted to facilitate the standardization of the technological elements to promote correct patient identification. The data would help evaluate how the methods can be incorporated for different patients without interfering with one another and how data can be used to optimize the accuracy of matching various algorithms. Additional research would also be needed to evaluate the improvement rates when using this software to identify patients and detect any potential gaps in the use of the technology to patient outcomes.
Nursing leadership can be incorporated to offer guidance on preventing any adverse events that are associated with patient identification errors (Nurmeksela et al., 2021). The first step will regard existent practices and processes to address any present patient identification errors. The activities will enhance patient matching and include requiring the patients to be taken photos during registration and including them in the database so their information can be assessed across the board through different patient identification techniques. Standard processes can also be implemented on how staff should record particular patient demographic details and other data attributes such as their names and addresses.
Nurse managers play a significant role in preventing errors by using technology through leadership skills, including ethical leadership. Programs can be designed to increase error reporting, ensure any mistakes are caught before care is provided, and avoid fatal consequences (Nurmeksela et al., 2021). Most of the wrong patient identification errors are preventable, and nurse managers can ensure the use of multiple procedures and processes for patient identification to be accurate. Correct patient identification is paramount to healthcare, and the use of innovative technological means should be able to solve the issue.
Conclusion
The use of unique and reliable patient identification methods addresses patient care, cost, and resource issues and enhances interoperability and data sharing across the healthcare system. The techniques of patient identification range from UPIs to biometric identifications and can be combined to reduce the challenges that one technique may possess. The workload of data is expected to increase hence the demand for new techniques to identify patients correctly. The new data streams from the various techniques can merge to help healthcare workers be increasingly competent in patient identification. The delivery of safe care is a team effort. Therefore, nurse managers can provide appropriate guidelines and support to ensure that the patients receive the best care the health institution can offer. Transparent ways of dealing with patient identification errors are needed to create opportunities for growth and improvement (Nurmeksela et al., 2021).
References
Lippi, G., Mattiuzzi, C., Bovo, C., & Favaloro, E. J. (2017). Managing the patient identification crisis in healthcare and laboratory medicine. Clinical biochemistry, 50(10-11), 562-567.
Martin, C. (2017). A Review of Current Patient Matching Techniques. Informatics Empowers Healthcare Transformation, 238, 205.
Nurmeksela, A., Mikkonen, S., Kinnunen, J., & Kvist, T. (2021). Relationships between nurse managers’ work activities, nurses’ job satisfaction, patient satisfaction, and medicationerrors at the unit level: a correlational study. BMC Health Services Research, 21(1),1–13. https://doi-org.auth.lib.niu.edu/10.1186/s12913-021-06288-5
Riplinger, L., Piera-Jiménez, J., & Dooling, J. P. (2020). Patient Identification Techniques–Approaches, Implications, and Findings. Yearbook of Medical Informatics, 29(01), 081-086.
Teixeira, P. L., Wei, W. Q., Cronin, R. M., Mo, H., VanHouten, J. P., Carroll, R. J., … & Denny, J. C. (2017). Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals. Journal of the American Medical Informatics Association, 24(1), 162-171.
Yamashita, K., Kusuda, K., Ito, Y., Komino, M., Tanaka, K., Kurokawa, S., … & Sawa, Y. (2018). Evaluation of surgical instruments with radiofrequency identification tags in the operating room. Surgical innovation, 25(4), 374-379.
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