The Cases

Multiple electrocardiograms (EKGs) were incorrectly documented at a large urban tertiary care hospital over three months. All of the cases involved the nurse or EKG technician either entering the wrong medical record number (MRN), or not clearing the previous patient’s MRN from in the machine, while entering a new patient’s name. The incorrect patient documentation on the EKG caused EKG results to be uploaded to the wrong patients’ charts.

One case involved a female infant whose treating physician received an electronic medical record (EMR) message shortly after her discharge from the hospital with the electrocardiogram result of sinus bradycardia and/or sinus arrest. Upon this physician’s review, an EKG had never been ordered on this patient throughout her hospital stay, nor did the EKG diagnosis fit with the clinical history of the patient. Upon further review it was determined that the EKG recorded was from a patient in the medical intensive care unit (MICU) but had been recorded in the wrong patient chart.

The Commentary

By Christopher Chen, MD and Sandhya Venugopal, MD, MS-HPEd

Background and Significance

Wrong patient orders, incorrect documentation, and faulty result reporting may incur significant patient harm. A private health analytics firm, which has logged over 15,000 electronic health record (EHR) related safety events, noted that 3% (450) of them resulted in patient harm; the firm also noted that this number was “drastically underreported”.¹ Charting on Wrong Patient in EHR (COWPIE) is prevalent; for example, an average of 14 wrong orders were placed per day at one major urban academic center.² The prevalence of wrong chart documentation is estimated to be about 50 per 100,000 electronic notes.³ One in five patients who participated in a Kaiser Family Foundation survey had found an error in their electronic health record documentation.⁴ In an extreme example, the wrong patient was given a sedative and paralytic agent resulting in respiratory failure and death.⁵

The electrocardiogram is an essential tool in the assessment of heart disease which can be very useful in the evaluation of several conditions including but not limited to acute chest pain, dyspnea, palpitations, as well as arrhythmias.⁶ Incorrect EKG documentation can lead to significant harm.  For example, documentation of an EKG showing atrial fibrillation in the wrong patient chart could result in an inappropriate diagnosis and initiation of unnecessary or even harmful medications with potential to carry risk of significant morbidity or mortality.  Documentation of a normal EKG in the wrong chart may have unintended consequences as well. For example, if a normal EKG is documented on a patient intended to receive an EKG for chest pain, it may result in a serious error such as a missed myocardial infarction or arrhythmia.

Fortunately, there were no adverse effects on any of the patients involved in the cases of mentioned above.  Despite this, incorrect EKG documentation can create unnecessary distress to all parties involved, while additionally risking an increase in unnecessary healthcare expenditures. 

Systems Changes Needed and Quality Improvement Approach

In reviewing the cases, the team sought to identify potential root causes, describe potential corrective actions, and document these with the goal of future prevention.  Root causes of the problem included issues identified at both system and workflow levels.

The majority of cases came from the emergency department (ED), consistent with previously published literature regarding misidentified order entry. For example, Levin et al. concluded that misidentified order entry was associated with the clinical context, and less with similarities in patient names, with the biggest contributing factors being fatigue and distraction of medical personnel.⁷ High patient volume and the level of patient acuity are among the multiple factors that contribute to higher rates of error in the ED.

The process for acquiring EKGs starts with order entry from the ordering provider. An EKG technician or nurse performs patient identification (using a downloaded order, barcoding system, or manual entry) and performs the EKG. Manual entry is used either for machines lacking wireless order downloading or barcoding technology, or when pre-existing orders are not available, as in the case of verbal orders. The physical EKG results are then usually given to the healthcare provider for review, and the electronic EKG report may be subsequently routed to a centralized system for final review.

The nine cases of misidentified EKG results comprising this WebM&M issue all involved errors in patient identification entry, which resulted in mismatches between the recorded EKG and the patient label. These errors were secondary to users either failing to correctly enter new patient information or failing to clear prior patient information from the EKG machine. Manual entry was required for all of these EKGs, presumably because verbal orders had been given or the EKG system used lacked wireless order downloading and/or patient barcoding technology. Over half of these misidentified cases were sent to the centralized system for electronic review prior to error recognition (near miss).

With most ED protocols, these near miss cases should have failed two checkpoints: initial patient identification prior to EKG completion and final identification check prior to provider review. In the majority of the cases, the error was detected by the nurse or clerk prior to EKG transmission to the centralized system. However, for one case, initial review by the provider had been completed, and the EKG results had then been sent to the centralized EKG system for electronic review. Cardiologist review had been completed for that case, and still those EKG results went to the wrong patient chart (error).

As the majority of these cases involved failure of initial patient identification prior to EKG completion, suggested workflow modifications to decrease associated failures (such as incorrect entry of new patient information or failure to clear prior patient information) include:

• Modify the ED protocol to decrease the need for manual entry of patient information.
• Require the patient sticker to be placed on the physical EKG for initial physician/nurse review to highlight potential mismatches (Figure 1).
• As not all EKG machines are capable of barcode scanning, develop a separate protocol for EKG machines without wireless and barcoding technology, adding additional checkpoints for the technician or nurse.
• Provide contact information on the protocol to streamline error reporting and correction if needed.
• After completing protocol revision(s), train staff and gather their feedback to gain cultural acceptance of the new processes and avoid workarounds.⁸

Figure 1

Systems-related causes should also be addressed. A review of EKG recording systems should be undertaken to identify any significant system-to-system variance for patient and order input. Some systems only offer manual entry of patient and order information, while others include automatic order downloading and barcoding technology to reduce error. Barcoding technology significantly reduces patient identification errors such as medication administration errors⁹ and point-of-care testing.¹⁰ It must be noted that the possibility for error may still exist secondary to human factors such as adherence to cumbersome protocols¹¹ and technical factors such as scanning difficulty due to curvature around the wrist.¹² Reasons for variance in EKG systems within hospital systems include lack of standardization and lack of centralized purchasing given that many EKG systems are purchased at the department level. The systems issues can also be addressed by updating/replacing outdated EKG machines without wireless and barcoding capability, and centralizing purchasing of EKG systems through the central EKG laboratory.

Take-Home Points

• Wrong patient orders, documentation, and result reporting are prevalent problems despite EHR use and may cause significant patient harm.
• Causes of wrong patient documentation are often multifactorial, including environmental (e.g. high frequency of distractions), workflow-related, and systems-related.
• Root cause analysis is helpful for identifying areas for corrective actions in complex systems.
• Technological innovations such as barcoding technology and wireless order downloading can help decrease errors but should be combined with training and workflow modifications to realize further benefit.
• Corrective actions should be based on prior incident reports with the goal of preventing recurrence.

Christopher Chen, MD
Clinical Cardiology Fellow
Department of Internal Medicine, Division of Cardiovascular Medicine
UC Davis Health

Sandhya Venugopal, MD, MS-HPEd
Associate Dean, Continuing Medical Education
Director of Heart Station/ECG laboratory
Department of Internal Medicine, Division of Cardiovascular Medicine
UC Davis Health

References

1. Schulte F, Fry E. Death by 1,000 clicks: Where electronic health records went wrong. Kaiser Health News. 2019;18.
2. Adelman JS, Kalkut GE, Schechter CB, Weiss JM, Berger MA, Reissman SH, et al. Understanding and preventing wrong-patient electronic orders: a randomized controlled trial. Journal of the American Medical Informatics Association. 2013;20(2):305-10.
3. Wilcox AB, Chen Y-H, Hripcsak G. Minimizing electronic health record patient-note mismatches. Journal of the American Medical Informatics Association. 2011;18(4):511-4.
4. Munana C KA, Brodie M. Data Note: Public’s Experiences With Electronic Health Records2019 February 19, 2020. Available from: https://www.kff.org/other/poll-finding/data-note-publics-experiences-with-electronic-health-records/.
5. Grissinger M. Oops, Sorry, Wrong Patient!: A Patient Verification Process is Needed Everywhere, Not Just at the Bedside. Pharmacy and Therapeutics. 2014;39(8):535.
6. Bayés de Luna A GD, Fiol M, Bayés-Genis A. SURFACE ELECTROCARDIOGRAPHY.  [cited February 17, 2020]. In: Hurst's The Heart [Internet]. New York, NY: McGraw-Hill. 14. [cited February 17, 2020]. Available from: http://accessmedicine.mhmedical.com/content.aspx?bookid=2046&sectionid=176550544.
7. Levin HI, Levin JE, Docimo SG, editors. “I meant that med for Baylee not Bailey!”: a mixed method study to identify incidence and risk factors for CPOE patient misidentification. AMIA Annual Symposium Proceedings; 2012: American Medical Informatics Association.
8. Patient Identification Errors. Health Technology Assessment Information Service Special Report [Internet]. 2016. Available from: https://www.ecri.org/Resources/HIT/Patient%20ID/Patient_Identification_Evidence_Based_Literature_final.pdf.
9. Poon EG, Keohane CA, Yoon CS, Ditmore M, Bane A, Levtzion-Korach O, et al. Effect of Bar-Code Technology on the Safety of Medication Administration. New England Journal of Medicine. 2010;362(18):1698-707.
10. Snyder SR, Favoretto AM, Derzon JH, Christenson RH, Kahn SE, Shaw CS, et al. Effectiveness of barcoding for reducing patient specimen and laboratory testing identification errors: a Laboratory Medicine Best Practices systematic review and meta-analysis. Clinical biochemistry. 2012;45(13-14):988-98.
11. Härkänen M, Kervinen M, Ahonen J, Turunen H, Vehviläinen-Julkunen K. An observational study of how patients are identified before medication administrations in medical and surgical wards. Nursing & Health Sciences. 2015;17(2):188-94.
12. Snyder ML, Carter A, Jenkins K, Fantz CR. Patient Misidentifications Caused by Errors in Standard Bar Code Technology. Clinical Chemistry. 2020;56(10):1554-60.