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Reducing the Poor Practice of Wrong Blood in Tube Errors

Using Evidence-Based Practice

Wrong Blood in Tube Errors

Pre-analytical errors in laboratory medicine are an example of poor practice with critical implications and devastating outcomes to patient care. The World Health Organisation has identified mislabelled specimens as one of the top three issues in sample collection (Khetan et al., 2018) despite multiple interventions with human factors attributed as the root cause. Specimen labelling errors expose patients to risks of misdiagnosis, mismanagement, medication errors and even transfusion-related death (Bolton-Maggs et al., 2015). Furthermore, poor-quality health care imposes social and economic costs estimated to amount to trillions of dollars annually to both healthcare systems, patients and their families and erodes trust in healthcare services.

Townsville University Hospital is a 775-bed tertiary level hospital servicing the entire North Queensland region providing acute, community, and outreach services to over two thousand patients per day (Townsville Hospital and Health Service, 2021). An audit at the hospital revealed poor practices, including overriding the scanning function in the integrated electronic Medical Record that confirms patient identity, pre-printing labels, and having another patient’s chart open, were causing “Wrong Blood in Tube errors” (WBIT). WBIT is the term used to describe specimens from one patient incorrectly labelled with another patient’s identifiers (Rosenbaum & Baron, 2018). Costs associated with WBIT errors include unnecessary hospital admissions, wasted resources, unnecessary medications and therapies (Lee, 2019). In addition, these errors result in a loss of confidence in the system’s safety, and the organisation’s reputation becomes tarnished.

Evidence-based practice links available evidence, clinical expertise, patient values with patient and organisation management and health policy and decision making.

Patient Values

Achieving safe, high-quality healthcare environments requires good relationships between clinical teams and patients based on shared values of openness and trust. Healthcare is about people, and as such, we must work collaboratively with our patients and their families to understand their needs, concerns and create better solutions. A patient’s perception of quality care is based on values like respect, courtesy, compassion, and feeling they are the focus. Evidence-based medicine is an amalgamation of patient values, clinical expertise and evidence (Kelly et al., 2015). A patient’s cultural considerations, needs, and values must be incorporated. For example, venipuncture is the most frequently performed invasive hospital clinical procedure worldwide. It causes distress, anxiety, pain, and discomfort for many patients (Hassanein & Deif, 2020), resulting in serious long-term consequences like needle phobia. An unfavourable venipuncture experience can reduce patient satisfaction with a healthcare service by 80% (Kantartjis et al., 2017). Evidence-based practices can enhance adherence to practice protocols and improve patient safety, reducing the unpleasant aspects of venipuncture, undermining future compliance and subsequent medical treatment and improving patient satisfaction and trust. Bolton-Maggs et al. (2015) recommend that engaging the patient or a family member to verify their information and encouraging them to speak up when it is incorrect effectively reduces WBIT errors. In addition to increasing patient awareness of patient identification policies, it also emphasises how important it is for all aspects of their care. Patient satisfaction surveys are an effective way to monitor patient satisfaction and detect and monitor areas that require improvement (Kantartjis et al., 2017).

Professional Expertise

Clinical decision making and judgement require skills and knowledge and are vital components of evidence-based practice. A fundamental objective in laboratory medicine is correctly pairing the specimen to the person it was collected from as it impacts every aspect of care (Dongen-Lases et al., 2016). Despite evidence-based standardised risk-reducing collection procedures that confirm patient identity, WBIT errors still occur. Human factors, including a lack of compliance to protocols, shortcuts, and workarounds (Sandhu et al., 2017), are responsible for these errors.

Townsville Hospital is a digital hospital where patients are assigned a unique barcode on their wristbands for patient safety and accurate identification. In 2010, The Laboratory Medicine Best Practice Initiative Working Group recommended barcoding systems as best practice for sample labelling to reduce WBIT errors and increase laboratory testing accuracy (Sandhu et al., 2017). However, a review of the integrated Electronic Medical record training process revealed that clinicians were instructed to override the scanning function that confirms patient identification due to the lack of scanners while practising specimen collections. Unfortunately, this workaround became entrenched into routine practice and resulted in poor patient outcomes.

These issues can be addressed at a national level by adopting evidence-based guidelines and at the organisation level through periodic audits (Khetan et al., 2018). Best practices are established by linking professional practice with the best available evidence and measuring care against audit results, established standards, and considering local circumstances, monitoring and making improvements as required (Khetan et al., 2018). Human errors are impossible to eliminate and require sound knowledge, expertise, and understanding to reduce their impact. An organisation-wide innovative patient-centred culture approach is required to develop interventions to improve patient care and safety (Sandhu et al., 2017). Establishing this culture requires a multidisciplined collaborative approach between stakeholders to improve communication, develop organisational protocols emphasising patient identification and a zero tolerance to non-standard practice.

Evidence-based practice recommends performance assessments, adequate numbers of qualified staff, electronic positive patient identification systems, performance tracking dashboards and reinforcing labelling protocols (Sandhu et al., 2017). Behavioural modification can be achieved through 360-degree feedback and praise to reduce complacency combined with continuous education and training sessions such as practical demonstrations, seminars, technical briefs, or online courses to maintain awareness. The healthcare leadership team must provide the necessary support and resources to support patient identification improvement initiatives and generate belief in the intervention to overcome resistance.

Evidenced-based Research

Evidence-based research systematically utilises prior research to solve problems and guarantees the research is both constructive and beneficial (Robinson et al., 2021). Medical errors are not only a cause of patient suffering and occasionally death but are also costly in terms of treatment delays, unnecessary hospital admissions and wasted resources (Lee, 2019). The World Health Organisation considers reducing patient identification errors critical to improving patient safety for everyone involved in modern health care (Sandhu et al., 2017). The failure to correctly identify a patient and link it to any clinical intervention compromises patient safety, resulting in adverse outcomes (Australian Commission on Safety and Quality in Healthcare, 2021). However, as illustrated by the audit results at Townsville Hospital, and despite significant bodies of evidence, ongoing theory-practice gaps are placing patient’s lives at risk (Plebani et al., 2014). Therefore, the Australian Commission for Safety and Quality in Healthcare Standards (ASQHS) recommends standardised processes and strict adherence to safety routines to address patient identification (Australian Commission on Safety and Quality in Healthcare, 2021). The available evidence recommends that at least three patient identifies and available technology such as barcode scanners, electronic medical records, and computerised order entry systems, when properly used, will assist in preventing WBIT errors. Protocols must also include strategies for patient identification in the absence of identification bands such as a drivers licence or passport (Australian Commission on Safety and Quality in Healthcare, 2021). Bolton-Maggs et al. ( 2015) recommend using open-ended questions like asking the patient to provide their full name and date of birth or a family member in situations of impaired consciousness or language barriers. Also, have them check the request form and sample labels to verify the information is complete and correct and attach the labels at the bedside. Studies conducted internationally indicate that end-to-end electronic systems and patient wristband barcode scanning can reduce medical errors by 50% (Robinson et al., 2018); however, the use of wristbands is not immune to incorrect or incomplete information. Nevertheless, it leads to decreased costs, quality improvements, and improved patient satisfaction (Khammarnia et al., 2015).

Evidence-based research recommends that multi-pronged approaches are most effective, including standardised identification procedures, external quality control programs and easy-to-read protocols and convenient access to necessary equipment, all improve accuracy (Bolton-Maggs et al., 2015). In addition, addressing the human factors of lack of compliance to protocols, shortcuts and workarounds (Sandhu et al., 2017) and a zero-tolerance for labelling errors should be adopted for all laboratory samples. Finally, continuous monitoring, feedback from laboratories and patients’, and improvement of equipment design can reduce identification errors and improve patient safety.

Conclusion

WBIT errors and patient identification will continue to be an ongoing dilemma despite clinical staff having the necessary skills, knowledge and access to available evidence. Although human factors can not be irradicated completely, all healthcare professionals must be aware of new evidence, technological advances, and practice improvements so that patients receive the high quality, safe, evidence-based care they deserve.

References

Australian Commission on Safety and Quality in Healthcare. (2021). Communicating for Safety Standard. (Standard Six). https://www.safetyandquality.gov.au/standards/nsqhs-standards/communicating-safety-standard

Bolton-Maggs, P. H. B., Wood, E. M., & Wiersum-Osselton, J. C. (2015). Wrong blood in tube – potential for serious outcomes: can it be prevented? British Journal of Haematology, 168(1), 3-13. https://doi.org/https://doi.org/10.1111/bjh.13137

Dongen-Lases, E. C. v., Cornes, M. P., Grankvist, K., Ibarz, M., Kristensen, G. B. B., Lippi, G., Nybo, M., Simundic, A.-M., on behalf of the Working Group for Preanalytical Phase, E. F. o. C. C., & Medicine, L. (2016). Patient identification and tube labelling – a call for harmonisation. Clinical Chemistry and Laboratory Medicine (CCLM), 54(7), 1141-1145. https://doi.org/doi:10.1515/cclm-2015-1089

Hassanein, S., & Deif, H. (2020, January 1, 2020). Effect of customised venipuncture nursing technique on selected responses and insertion difficulty among patients with blood disorders [Original Article]. Egyptian Nursing Journal, 17(1), 23-35. https://doi.org/10.4103/enj.Enj_17_20

Kantartjis, M., Melanson, S. E. F., Petrides, A. K., Landman, A. B., Bates, D. W., Rosner, B. A., Goonan, E., Bixho, I., & Tanasijevic, M. J. (2017). Increased patient satisfaction and a reduction in pre-analytical errors following implementation of an electronic specimen collection Module in outpatient phlebotomy. Laboratory Medicine, 48(3), 282-289. https://doi.org/10.1093/labmed/lmx024

Kelly, M. P., Heath, I., Howick, J., & Greenhalgh, T. (2015, 2015/10/12). The importance of values in evidence-based medicine. BMC Medical Ethics, 16(1), 69. https://doi.org/10.1186/s12910-015-0063-3

Khammarnia, M., Kassani, A., & Eslahi, M. (2015). The Efficacy of Patients’ Wristband Bar-code on Prevention of Medical Errors: A Meta-analysis Study. Applied Clinical Informatics, 6(4), 716-727. https://doi.org/10.4338/ACI-2015-06-R-0077

Khetan, D., Katharia, R., Pandey, H. C., Chaudhary, R., Harsvardhan, R., Pandey, H., & Sonkar, A. (2018, Jan-Jun). Assessment of bedside transfusion practices at a tertiary care centre: A step closer to controlling the chaos. Asian Journal of Transfusion Science, 12(1), 27-33. https://doi.org/10.4103/ajts.AJTS_29_17

Lee, N. Y. (2019). Reduction of pre-analytical errors in the clinical laboratory at the University Hospital of Korea through quality improvement activities. Clinical Biochemistry, 70, 24-29. https://doi.org/10.1016/j.clinbiochem.2019.05.016

Plebani, M., Sciacovelli, L., Aita, A., Padoan, A., & Chiozza, M. L. (2014). Quality indicators to detect pre-analytical errors in laboratory testing. Clinica Chimica Acta, 432, 44-48. https://doi.org/10.1016/j.cca.2013.07.033

Robinson, K. A., Brunnhuber, K., Ciliska, D., Juhl, C. B., Christensen, R., & Lund, H. (2021). Evidence-Based Research Series-Paper 1: What Evidence-Based Research is and why is it important? Journal of Clinical Epidemiology, 129, 151-157. https://doi.org/10.1016/j.jclinepi.2020.07.020

Robinson, S., Harris, A., Atkinson, S., Atterbury, C., Bolton-Maggs, P., Elliott, C., Hawkins, T., Hazra, E., Howell, C., New, H., Shackleton, T., Shreeve, K., & Taylor, C. (2018). The administration of blood components: a British Society for Haematology Guideline. Transfusion Medicine, 28(1), 3-21. https://doi.org/10.1111/tme.12481

Rosenbaum, M. W., & Baron, J. M. (2018). Using machine learning-based multianalyte delta checks to detect wrong blood in tube errors. American Journal of Clinical Pathology, 150(6), 555-566. https://doi.org/10.1093/ajcp/aqy085

Sandhu, P., Bandyopadhyay, K., Ernst, D. J., Hunt, W., Taylor, J. T. H., Birch, R., Krolak, J., & Geaghan, S. (2017). Effectiveness of laboratory practices to reducing patient misidentification due to specimen labelling errors at the time of specimen collection in healthcare settings: LMBP™ Systematic Review. The journal of Applied Laboratory Medicine, 2(2), 244-258. https://doi.org/10.1373/jalm.2017.023762