Transfusion errors can lead to serious patient harm. In this retrospective analysis of transfusion medical records and related documentation, researchers examined transfusion incident characteristics and logistical errors associated with incidents. Common logistical errors included elevated hemoglobin, inadequate bedside tests, inadequate patient identification, and laboratory errors.

Surgical never events are rare but devastating for patients. Using machine learning, this study identified 24 contributing factors to two types of surgical never events - wrong site surgery and retained items. Communication, the number and type of staff present, and the type and length of surgery were identified contributing factors.

Wrong blood in tube (WBIT) errors are rare but can lead to complications. One hospital implemented a quality improvement project to reduce WBIT errors with electronic patient identification, manual independent dual verification, and staff education. WBIT errors were significantly reduced and sustained over six years.

Wrong blood in tube (WBIT) errors can result in serious diagnostic and treatment errors, but may go unrecognized by clinical staff. In this study, machine learning was used to identify potential WBIT errors which were then compared to manual review by laboratory staff. The machine learning models showed higher accuracy, sensitivity, and specificity compared to manual review. 

Patient misidentification errors are common and potentially catastrophic. Patient identification incidents reported in one hospital were examined to identify errors and contributory factors. Of the 293 reported incidents, the most common errors were missing wristbands, wrong charts or notes in files, administrative issues, and wrong labeling. The most frequent contributory factors include absence of patient identity control, patient transfer, and emergency context.

Patient misidentification can lead to serious patient safety risks. Researchers used patient safety reports and root cause analyses (RCA) to characterize patient misidentification events in the Veterans Health Administration (VHA). The incidence of patient misidentification in inpatient and outpatient settings was similar and most commonly attributed to the absence of two unique patient identifiers. The authors identified three strategies to mitigate misidentification based on high-reliability principles: (1) develop policies for patient identification throughout the continuum of care, (2) develop policies to report and monitor patient misidentification measures, and (3) apply quality and process improvement tools to patient identification emphasizing use by front line staff.  

Physicians from different health systems using two computerized provider order entry (CPOE) systems participated in simulated patient scenarios using eye movement recordings to determine whether the physician looked at patient-identifying information when placing orders. The rate of patient identification overall was 62%, but the rate varied by CPOE system. An expert panel identified three potential reasons for this variation – visual clutter and information density, the number of charts open at any given time, and the importance placed on patient identification verification by institutions.  

A classic study found that the replacing the usual naming convention for newborns ("Babygirl" or "Babyboy") with one incorporating the mother's first name (e.g., "Marysgirl" or "Marysboy") reduced wrong-patient errors. Based on this finding, The Joint Commission issued a National Patient Safety Goal (NPSG) requiring the use of distinct naming systems for newborns. The authors of this study noted that the new standard would still leave multiple-birth infants vulnerable to wrong-patient errors, as most hospitals adopted naming standards that left room for confusion between infants (e.g., twin infants might be named "Marysgirl1" and "Marysgirl2"). Researchers examined the rate of wrong-patient errors in six neonatal intensive care units of two health systems that used the NPSG recommended naming conventions, comparing multiple-birth infants to singleton infants. They measured wrong-patient errors by tracking the rate of orders that were retracted and then immediately reordered for a different patient. The rate of wrong-patient errors was significantly higher among multiple-birth infants, most of which could be explained by intrafamilial errors (e.g., a medication was ordered for one twin when intended for another). The accompanying editorial points out that this study is an important example of carefully assessing the real-world impact of novel policies; in this case, the NPSG likely does protect against wrong-patient errors for singleton infants, but not for multiple-birth infants.

Having multiple patient records open in the electronic health record increases the potential risk of wrong-patient actions. This randomized trial tested two different electronic health record configurations: one allowed up to four patient records to be open at a time, and the other allowed only one to be open. Among the 3356 clinicians with nearly 4.5 million order sessions, there were no significant differences in wrong-patient orders. However, the investigators noted that clinicians in the multiple records group placed most orders with just one record open. A post hoc analysis determined that the rate of errors increased when orders were placed with multiple records open. A related editorial highlights the tradeoffs between safety and efficiency and argues for examining the context of the two configurations, including throughput and clinician satisfaction. A previous PSNet perspective discussed assessing and improving the safety of electronic health records.

Patient misidentification can result in incorrect diagnosis, treatment, and medication administration. This commentary discusses the practice of auditing patient identification wristbands to assess compliance and accuracy. The authors suggest that technological interventions such as smartphone facial recognition and barcode technologies be considered as strategies to avoid patient misidentification.

Inaccurate patient registration can result in information gaps that contribute to delay, misunderstandings, and harm. This review discusses registration errors in the blood transfusion process. The authors discuss how problems can occur during various stages in the transfusion process and result in blood-type discrepancies. They suggest improved reporting of identification mistakes and use of photo identification tools as strategies to prevent patient harm associated with registration errors.

Following a sentinel wrong-patient event, a multidisciplinary quality improvement team worked to enhance the safety of blood transfusion. The authors report significant improvement in protocol adherence following institution of barcoding and auditing via the electronic health record.