Evaluating Serial Strategies for Preventing Wrong-Patient Orders in the NICU

Retrospective cohort study of wrong-patient imaging order errors: how many reach the patient?

Studying near-miss errors is essential to preventing errors from reaching patients. When an error is committed, it may be intercepted (near-miss) or it will reach the patient; estimates of the proportion that reach the patient vary widely. To better understand this relationship, we conducted a retrospective cohort study using two objective measures to identify wrong-patient imaging order errors involving radiation, estimating the proportion of errors that are intercepted and those that reach the patient. This study was conducted at a large integrated healthcare system using data from 1 January to 31 December 2019. The study used two outcome measures of wrong-patient orders: (1) wrong-patient orders that led to misadministration of radiation reported to the New York Patient Occurrence Reporting and Tracking System (NYPORTS) (misadministration events); and (2) wrong-patient orders identified by the Wrong-Patient Retract-and-Reorder (RAR) measure, a measure identifying orders placed for a patient, retracted and rapidly reordered by the same clinician on a different patient (near-miss events). All imaging orders that involved radiation were extracted retrospectively from the healthcare system data warehouse. Among 293 039 total eligible orders, 151 were wrong-patient orders (3 misadministration events, 148 near-miss events), for an overall rate of 51.5 per 100 000 imaging orders involving radiation placed on the wrong patient. Of all wrong-patient imaging order errors, 2% reached the patient, translating to 50 near-miss events for every 1 error that reached the patient. This proportion provides a more accurate and reliable estimate and reinforces the utility of systematic measure of near-miss errors as an outcome for preventative interventions.

Automated search methods for identifying wrong patient order entry-a scoping review

Objective

To investigate: (1) what automated search methods are used to identify wrong-patient order entry (WPOE), (2) what data are being captured and how they are being used, (3) the causes of WPOE, and (4) how providers identify their own errors.

Materials and Methods

A systematic scoping review of the empirical literature was performed using the databases CINAHL, Embase, and MEDLINE, covering the period from database inception until 2021. Search terms were related to the use of automated searches for WPOE when using an electronic prescribing system. Data were extracted and thematic analysis was performed to identify patterns or themes within the data.

Results

Fifteen papers were included in the review. Several automated search methods were identified, with the retract-and-reorder (RAR) method and the Void Alert Tool (VAT) the most prevalent. Included studies used automated search methods to identify background error rates in isolation, or in the context of an intervention. Risk factors for WPOE were identified, with technological factors and interruptions deemed the biggest risks. Minimal data on how providers identify their own errors were identified.

Discussion

RAR is the most widely used method to identify WPOE, with a good positive predictive value (PPV) of 76.2%. However, it will not currently identify other error types. The VAT is nonspecific for WPOE, with a mean PPV of 78%-93.1%, but the voiding reason accuracy varies considerably.

Conclusion

Automated search methods are powerful tools to identify WPOE that would otherwise go unnoticed. Further research is required around self-identification of errors.

Using electronic health record audit log data for research: insights from early efforts

Electronic health record audit logs capture a time-sequenced record of clinician activities while using the system. Audit log data therefore facilitate unobtrusive measurement at scale of clinical work activities and workflow as well as derivative, behavioral proxies (eg, teamwork). Given its considerable research potential, studies leveraging these data have burgeoned. As the field has matured, the challenges of using the data to answer significant research questions have come into focus. In this Perspective, we draw on our research experiences and insights from the broader audit log literature to advance audit log research. Specifically, we make 2 complementary recommendations that would facilitate substantial progress toward audit log-based measures that are: (1) transparent and validated, (2) standardized to allow for multisite studies, (3) sensitive to meaningful variability, (4) broader in scope to capture key aspects of clinical work including teamwork and coordination, and (5) linked to patient and clinical outcomes.

Towards Safe Patient Identification Practices: the Development of a Conceptual Framework from the Findings of a Ph.D. Project

Patient identification errors are considered the root cause of other patient safety incidents. Despite the development, recommendation, and application of several initiatives to reduce and prevent misidentification in hospital settings, errors continue to occur. They directly impact the quality of care provided, resulting in delays in care, added costs, unnecessary injuries, misdiagnosis or wrong treatment, and other serious and irreversible types of harm and death. Furthermore, the certainty of the evidence of the effectiveness of interventions to reduce patient identification errors is considered very low.

This paper reports on the development of a conceptual framework for safe practices in the area of patient identification. The proposed conceptual framework was developed based on presuppositions regarding learning health systems and the available evidence from the published systematic reviews of the effectiveness of interventions in reducing patient identification errors in hospital settings. The core circle of the framework represents the partnership between managers, healthcare professionals, patients, and families working toward integrative and collaborative efforts for safe patient identification practices. The inner dimension states the recommendations for practice sustained by applying technological resources and educational strategies to raise awareness of the importance of accurate patient identification and interdisciplinarity, which works as an axis that supports integrated and collective work between healthcare professionals aiming for safe care. The outer dimension represents recommendations for teaching and research to develop effective patient identification practices that can enhance patient safety and the quality of care provided in hospital settings.

This framework provides a valuable method for engaging interdisciplinary teams to improve the safety of patient identification systems.

"What's in a name?" Identification of newborn infants at birth using their given names

OBJECTIVE

To determine the proportion of pregnant women who selected names for their babies to be born and were willing to disclose them for use in hospital systems, thereby potentially reducing infant identification errors.

STUDY DESIGN

Survey of pregnant women admitted to postpartum or antepartum units at a large academic hospital. Descriptive analyses were conducted to determine the proportion who had chosen names prior to delivery. Chi-square tests and calculated odds ratios assessed the association with demographic and pregnancy factors.

RESULTS

Of postpartum participants, 79.0% had names for their newborns at birth. This proportion was significantly lower in self-identified non-Hispanic, white, and married women. Of antepartum participants, 65.7% had selected a name at the time of survey.

CONCLUSION

Most participants had names chosen for use at birth. This finding was consistent across demographic and pregnancy characteristics, supporting the feasibility of using given names for newborns in hospital systems at birth.

Marques PA, Barker TH. Interventions to Reduce Patient Identification Errors in the Hospital Setting: A Systematic Review

Background:

Patient identification is considered as a fundamental part of the care process and a relevant resource for safety practices in hospital settings.

Objective:

We aimed to review the literature on interventions to reduce patient identification errors in hospital settings.

Methods:

A systematic review of effectiveness using The Joanna Briggs Institute (JBI) methodology was conducted. A three-step search strategy was utilised to explore primary research published up to March 2020 in English, Spanish, and Portuguese across eight databases. Grey literature was also assessed. The titles and abstracts of the studies were screened for assessment of the inclusion criteria. Two reviewers independently appraised the full text of the selected studies and extracted data using standardised tools from JBI. Due to the heterogeneity of studies and insufficient data for statistical pooling, meta-analysis was not feasible. Therefore, the results were synthesised narratively.

Results:

Twelve studies met the review criteria; all were rated at a moderate risk of bias and four different groups of interventions were identified: educational staff interventions alone and those combined with a partnership with families and patients through education; and information technology interventions alone, and combined with an educational staff strategy. Although most studies showed a statistically significant reduction in patient identification errors, the overall quality of the evidence was considered very low.

Conclusion:

High-quality research is needed to understand the real impact of interventions to reduce patient identification errors. Nurses should recognise the importance of patient identification practices as a part of their overall commitment to improving patient safety.

PROSPERO Registration Number: CRD42018085236

Wrong-Patient Orders in Obstetrics

OBJECTIVE

To compare rates of wrong-patient orders among patients on obstetric units compared with reproductive-aged women admitted to medical-surgical units.

METHODS

This was an observational study conducted in a large health system in New York between January 1, 2016, and December 31, 2018. The primary outcome was near-miss wrong-patient orders identified using the National Quality Forum-endorsed Wrong-Patient Retract-and-Reorder measure. All electronic orders placed for eligible patients during the study period were extracted retrospectively from the health system data warehouse, and the unit of analysis was the order session (consecutive orders placed by a single clinician for a patient within 60 minutes). Multilevel logistic regression models were used to estimate odds ratios (ORs) and 95% CIs comparing the probability of retract-and-reorder events in obstetric and medical-surgical units, overall, and in subgroups defined by clinician type and order timing.

RESULTS

Overall, 1,329,463 order sessions were placed during the study period, including 676,643 obstetric order sessions (from 45,436 patients) and 652,820 medical-surgical order sessions (from 12,915 patients). The rate of 79.5 retract-and-reorder events per 100,000 order sessions in obstetric units was significantly higher than the rate in the general medical-surgical population of 42.3 per 100,000 order sessions (OR 1.98, 95% CI 1.64-2.39). The obstetric retract-and-reorder event rate was significantly higher for attending physicians and house staff compared with advanced practice clinicians. There were no significant differences in error rates between day and night shifts.

CONCLUSION

Order errors occurred more frequently on obstetric units compared with medical-surgical units. Systems strategies shown to decrease these events in other high-risk specialties should be explored in obstetrics to render safer maternity care.

Application of Human Factors Methods to Ensure Appropriate Infant Identification and Abduction Prevention Within the Hospital Setting

Infant misidentification and abduction are recognized as "never" events for hospitals in the United States. As near misses are often unreported, root cause analysis of observed near misses may fail to uncover important contributors. We utilized failure mode and effects analysis to proactively identify and eliminate or reduce the risk of infant misidentification or abduction. We prioritized action plans based upon the highest risk priority failures and developed steps to eliminate the gaps in the infant identification process and the security within the Center for Women & Infants. The analysis identified 28 failure modes. Team discussion of the failure modes also yielded several collateral benefits of improvements in the unit climate. We present and discuss the action plans that were undertaken by the hospital to increase patient safety and reduce the risk of infant misidentification and abduction.

Interventions to Reduce Pediatric Prescribing Errors in Professional Healthcare Settings: A Systematic Review of the Last Decade

INTRODUCTION

Pediatric medication therapy is prone to errors due to the need for pharmacokinetic and pharmacodynamic individualization and the diverse settings in which pediatric patients are treated. Prescribing errors have been reported as the most common medication error.

OBJECTIVES

The aim of this review was to systematically identify interventions to reduce prescribing errors and corresponding patient harm in pediatric healthcare settings and to evaluate their impact.

METHODS

Four databases were systematically screened (time range November 2011 to December 2019), and experimental studies were included. Interventions to reduce prescribing errors were extracted and classified according to a 'hierarchy of controls' model.

RESULTS

Forty-five studies were included, and 70 individual interventions were identified. A bundle of interventions was more likely to reduce prescribing errors than a single intervention. Interventions classified as 'substitution or engineering controls' were more likely to reduce errors in comparison with 'administrative controls', as is expected from the hierarchy of controls model. Fourteen interventions were classified as substitution or engineering controls, including computerized physician order entry (CPOE) and clinical decision support (CDS) systems. Administrative controls, including education, expert consultations, and guidelines, were more commonly identified than higher level controls, although they may be less likely to reduce errors. Of the administrative controls, expert consultations were most likely to reduce errors.

CONCLUSIONS

Interventions to reduce pediatric prescribing errors are more likely to be successful when implemented as part of a bundle of interventions. Interventions including CPOE and CDS that substitute risks or provide engineering controls should be prioritized and implemented with appropriate administrative controls including expert consultation.

Incident Reports of Naming Errors among Two Sets of Infant Twins

Newborns are at high risk for identification errors due to their inability to speak and indistinguishable features. To reduce this risk, The Joint Commission requires hospitals to use a distinct identification method for newborns. Most hospitals create medical records for newborns at birth using temporary naming conventions, resulting in patients with similar identifiers. Typically, multiple-birth infants are distinguished from their siblings by a single character (1, 2, or A, B), placing them at higher risk for identification errors, which can delay care and compromise patient safety.

Patterns of Safety Incidents in a Neonatal Intensive Care Unit

Introduction: Safety incidents preceding manifest adverse events are barely evaluated in neonatal intensive care units (NICUs). This study aimed at identifying frequency and patterns of safety incidents in our NICU. Methods: A 6-month prospective clinical study was performed from May to October 2019 in a German 10-bed level III NICU. A voluntary, anonymous reporting system was introduced, and all neonatal team members were invited to complete paper-based questionnaires following each particular safety incident. Safety incidents were defined as safety-related events that were considered by the reporting team member as a "threat to the patient's well-being" which "should ideally not occur again." Results: In total, 198 safety incidents were analyzed. With 179 patients admitted, the incident/admission ratio was 1.11. Medication errors (n = 94, 47%) and equipment problems (n = 54, 27%) were most commonly reported. Diagnostic errors (n = 19, 10%), communication problems (n = 12, 6%), errors in documentation (n = 9, 5%) and hygiene problems (n = 10, 5%) were less frequent. Most safety incidents were noticed after 4-12 (n = 52, 26%) and 12-24 h (n = 47, 24%), respectively. Actual harm to the patient was reported in 17 cases (9%) but no life-threatening or serious events occurred. Of all safety incidents, 184 (93%) were considered to have been preventable or likely preventable. Suggestions for improvement were made in 132 cases (67%). Most often, implementation of computer-assisted tools and processes were proposed. Conclusion: This study confirms the occurrence of various safety incidents in the NICU. To improve quality of care, a graduated approach tailored to the specific problems appears to be prudent.

Health Information Technology–Related Wrong-Patient Errors: Context is Critical

Health information technology (HIT) provides many benefits, but also facilitates certain types of errors, such as wrong-patient errors in which one patient is mistaken for another. These errors can have serious patient safety consequences and there has been significant effort to mitigate the risk of these errors through national patient safety goals, in-depth research, and the development of safety toolkits. Nonetheless, these errors persist. We analyzed 1,189 patient safety event reports using a safety science and resilience engineering approach, which focuses on identifying processes to discover errors before they reach the patient so these processes can be expanded. We analyzed the general care processes in which wrong-patient errors occurred, the clinical process step during which the error occurred and was discovered, and whether the error reached the patient. For those errors that reached the patient, we analyzed the impact on the patient, and for those that did not reach the patient, we analyzed how the error was caught. Our results demonstrate that errors occurred across multiple general care process areas, with 24.4% of wrong-patient error events reaching the patient. Analysis of clinical process steps indicated that most errors occurred during ordering/prescribing (n=498; 41.9%) and most errors were discovered during review of information (n=286; 24.1%). Patients were primarily impacted by inappropriate medication administration (n=110; 37.9%) and the wrong test or procedure being performed (n=65; 22.4%). When errors were caught before reaching the patient, this was primarily because of nurses, technicians, or other healthcare staff (n=303; 60.5%). The differences between the general care processes can inform wrong-patient error risk mitigation strategies. Based on these analyses and the broader literature, this study offers recommendations for addressing wrong-patient errors using safety science and resilience engineering, and it provides a unique lens for evaluating HIT wrong-patient errors.

Wrong-Patient Ordering Errors in Peripartum Mother-Newborn Pairs: A Unique Patient-Safety Challenge in Obstetrics

Because maternal morbidity and mortality remain persistent challenges to the U.S. health care system, efforts to improve inpatient patient safety are critical. One important aspect of ensuring patient safety is reducing medical errors. However, obstetrics presents a uniquely challenging environment for safe ordering practices. When mother-newborn pairs are admitted in the postpartum setting with nearly identical names in the medical record (for example, Jane Doe and Janegirl Doe), there is a potential for wrong-patient medication ordering errors. This can lead to harm from the wrong patient receiving a medication or diagnostic test, especially a newborn receiving an adult dose of medication, as well as delaying treatment for the appropriate patient. We describe two clinical scenarios of wrong-patient ordering errors between mother-newborn pairs. The first involves an intravenous labetalol order that was placed for a postpartum patient but was released from the automated dispensing cabinet under the newborn's name. The medication was administered correctly, but an automatic order for labetalol was generated in the neonate's chart. Another scenario involves a woman presenting in labor with acute psychotic symptoms. The psychiatry service placed a note and orders for antipsychotic medications in the neonate's chart. These orders were cancelled shortly thereafter and replaced for the mother. These scenarios illustrate this specific patient-safety concern inherent in the treatment of mother-newborn pairs and highlight that perinatal units should evaluate threats to patient safety embedded in the unique mother-newborn relationship and develop strategies to reduce risk.

Association of Display of Patient Photographs in the Electronic Health Record With Wrong-Patient Order Entry Errors

IMPORTANCE

Wrong-patient order entry (WPOE) errors have a high potential for harm; these errors are particularly frequent wherever workflows are complex and multitasking and interruptions are common, such as in the emergency department (ED). Previous research shows that interruptive solutions, such as electronic patient verification forms or alerts, can reduce these types of errors but may be time-consuming and cause alert fatigue.

OBJECTIVE

To evaluate whether the use of noninterruptive display of patient photographs in the banner of the electronic health record (EHR) is associated with a decreased rate of WPOE errors.

DESIGN, SETTING, AND PARTICIPANTS

In this cohort study, data collected as part of care for patients visiting the ED of a large tertiary academic urban hospital in Boston, Massachusetts, between July 1, 2017, and June 31, 2019, were analyzed.

EXPOSURES

In a quality improvement initiative, the ED staff encouraged patients to have their photographs taken by informing them of the intended safety impact.

MAIN OUTCOMES AND MEASURES

The rate of WPOE errors (measured using the retract-and-reorder method) for orders placed when the patient's photograph was displayed in the banner of the EHR vs the rate for patients without a photograph displayed. The primary analysis focused on orders placed in the ED; a secondary analysis included orders placed in any care setting.

RESULTS

A total of 2 558 746 orders were placed for 71 851 unique patients (mean [SD] age, 49.2 [19.1] years; 42 677 (59.4%) female; 55 109 (76.7%) non-Hispanic). The risk of WPOE errors was significantly lower when the patient's photograph was displayed in the EHR (odds ratio, 0.72; 95% CI, 0.57-0.89). After this risk was adjusted for potential confounders using multivariable logistic regression, the effect size remained essentially the same (odds ratio, 0.57; 95% CI, 0.52-0.61). Risk of error was significantly lower in patients with higher acuity levels and among patients whose race was documented as White.

CONCLUSIONS AND RELEVANCE

This cohort study suggests that displaying patient photographs in the EHR provides decision support functionality for enhancing patient identification and reducing WPOE errors while being noninterruptive with minimal risk of alert fatigue. Successful implementation of such a program in an ED setting involves a modest financial investment and requires appropriate engagement of patients and staff.

Effect of an Alternative Newborn Naming Strategy on Wrong-Patient Errors: A Quasi-Experimental Study

OBJECTIVES

Newborns are often assigned temporary names at birth. Temporary newborn names-often a combination of the mother's last name and the newborn's gender-are vulnerable to patient misidentification due to similarities with other newborns or between a mother and her newborn. We developed and implemented an alternative distinct naming strategy, and then compared its effectiveness on reducing the number of wrong-patient orders with the standard distinct naming strategy.

METHODS

This study was conducted over a 14-month period in the newborn nursery and neonatal intensive care units of three hospitals that were part of the same health care system. We used a quasi-experimental study design using interrupted time series analysis to compare the differences in wrong-patient orders (an indicator of patient misidentification) before and after the implementation of the alternative distinct naming strategy.

RESULTS

Overall, there were 25 wrong-patient errors per 10,000 orders during entire study period (36.8 per 10,000 before and 19.6 per 10,000 after). However, there was no statistically significant change in the rate of wrong-patient ordering errors after the transition from the distinct to the alternative distinct naming strategy (β = 0.832, 95% confidence interval [CI] = -0.83 to 2.49, p = 0.326). We also found that, overall, 1.7% of the clinicians contributed to 62% of the wrong-patient errors.

CONCLUSION

Although we did not find statistically significant differences in wrong-patient errors, the alternative distinct naming approach provides pragmatic advantages over its predecessors. In addition, the localization of wrong-patient errors within a small set of clinicians highlights the potential for developing strategies for delivering training to clinicians.

EHR audit logs: A new goldmine for health services research?

A byproduct of the transition to electronic health records (EHRs) is the associated observational data that capture EHR users' granular interactions with the medical record. Often referred to as audit log data or event log data, these datasets capture and timestamp user activity while they are logged in to the EHR. These data - alone and in combination with other datasets - offer a new source of insights, which cannot be gleaned from claims data or clinical data, to support health services research and those studying healthcare processes and outcomes. In this commentary, we seek to promote broader awareness of EHR audit log data and to stimulate their use in many contexts. We do so by describing EHR audit log data and offering a framework for their potential uses in quality domains (as defined by the National Academy of Medicine). The framework is illustrated with select examples in the safety and efficiency domains, along with their accompanying methodologies, which serve as a proof of concept. This article also discusses insights and challenges from working with EHR audit log data. Ensuring that researchers are aware of such data, and the new opportunities they offer, is one way to assure that our healthcare system benefits from the digital revolution.

Effect of number of open charts on intercepted wrong-patient medication orders in an emergency department

To reduce the risk of wrong-patient errors, safety experts recommend allowing only one patient chart to be open at a time. Due to the lack of empirical evidence, the number of allowable open charts is often based on anecdotal evidence or institutional preference, and hence varies across institutions. Using an interrupted time series analysis of intercepted wrong-patient medication orders in an emergency department during 2010-2016 (83.6 intercepted wrong-patient events per 100 000 orders), we found no significant decrease in the number of intercepted wrong-patient medication orders during the transition from a maximum of 4 open charts to a maximum of 2 (b = -0.19, P = .33) and no significant increase during the transition from a maximum of 2 open charts to a maximum of 4 (b = 0.08, P = .67). These results have implications regarding decisions about allowable open charts in the emergency department in relation to the impact on workflow and efficiency.

Medication errors and adverse drug events in a UK hospital during the optimisation of electronic prescriptions: a prospective observational study

BACKGROUND

WHO's Third Global Patient Safety Challenge, Medication Without Harm, focused on reducing the substantial burden of iatrogenic harm associated with medications by 50% in the next 5 years. We aimed to assess whether the number and type of medication errors changed as an electronic prescribing system was optimised over time in a UK hospital.

METHODS

We did a prospective observational study at a tertiary-care teaching hospital. Eight senior clinical pharmacists reviewed patients' records and collected data across four adult wards (renal, cardiology, general medical, and orthopaedic surgical) over a 2-year period (from Sept 29, 2014, to June 9, 2016). All medication errors and potential and actual adverse drug events were documented and the number of medication errors measured over the course of four time periods 7-10 weeks long. Pharmacists also recorded instances where the electronic prescribing system contributed to an error (system-related errors). A negative-binomial model and a Poisson model were used to identify factors related to medication error rates.

FINDINGS

5796 primary errors were recorded over the four time periods (period 1, 47 days [Sep 29-Dec 2, 2014]; period 2, 38 days [April 20-June 12, 2015, for the renal, medical, and surgical wards and April 20-June 15, 2015, for the cardiology ward]; period 3, 35 days [Sep 28-Nov 27, 2015] for the renal ward, 37 days [Sep 28-Nov 23, 2015] for the medical ward, and 40 days [Sep 28-Nov 20, 2015] for the cardiology and surgical wards; and period 4, 37 days [Feb 22-April 15, 2015] for the renal and medical wards and 39 days for the cardiology [April 13-June 7, 2015] and surgery [April 18-June 9, 2015] wards; unanticipated organisational factors prevented data collection on some days during each time period). There was no change in the rate of primary medication errors per admission over the observation periods: 1·53 medication errors in period 1, 1·44 medication errors in period 2, 1·70 medication errors in period 3, and 1·43 medication errors in period 4, per admission. By contrast, the overall rate of different types of medication errors decreased over the four periods. The most common types of error were medicine-reconciliation, dose, and avoidable delay-of-treatment errors. Some types of errors appeared to reduce over time (eg, dose errors [from 52 errors in period 1 to 19 errors in period 4, per 100 admissions]), whereas others increased (eg, inadequate follow-up of therapy [from 12 errors in period 1 to 24 errors in period 4, per 100 admissions]). We also found a reduction in the rates of potential adverse drug events between the first three periods and period 4. 436 system-related errors were recorded over the study period.

INTERPRETATION

Although the overall rates of primary medication errors per admission did not change, we found a reduction in some error types and a significant decrease in the rates of potential adverse drug events over a 2-year period, during which system optimisation occurred. Targeting some error types could have the added benefit of reducing others, which suggests that system optimisation could ultimately help improve patient safety and outcomes.

FUNDING

No funding.

Risk of Wrong-Patient Orders Among Multiple vs Singleton Births in the Neonatal Intensive Care Units of 2 Integrated Health Care Systems

IMPORTANCE

Multiple-birth infants in neonatal intensive care units (NICUs) have nearly identical patient identifiers and may be at greater risk of wrong-patient order errors compared with singleton-birth infants.

OBJECTIVES

To assess the risk of wrong-patient orders among multiple-birth infants and singletons receiving care in the NICU and to examine the proportion of wrong-patient orders between multiple-birth infants and siblings (intrafamilial errors) and between multiple-birth infants and nonsiblings (extrafamilial errors).

DESIGN, SETTING, AND PARTICIPANTS

A retrospective cohort study was conducted in 6 NICUs of 2 large, integrated health care systems in New York City that used distinct temporary names for newborns per the requirements of The Joint Commission. Data were collected from 4 NICUs at New York-Presbyterian Hospital from January 1, 2012, to December 31, 2015, and 2 NICUs at Montefiore Health System from July 1, 2013, to June 30, 2015. Data were analyzed from May 1, 2017, to December 31, 2017. All infants in the 6 NICUs for whom electronic orders were placed during the study periods were included.

MAIN OUTCOMES AND MEASURES

Wrong-patient electronic orders were identified using the Wrong-Patient Retract-and-Reorder (RAR) Measure. This measure was used to detect RAR events, which are defined as 1 or more orders placed for a patient that are retracted (ie, canceled) by the same clinician within 10 minutes, then reordered by the same clinician for a different patient within the next 10 minutes.

RESULTS

A total of 10 819 infants were included: 85.5% were singleton-birth infants and 14.5% were multiple-birth infants (male, 55.8%; female, 44.2%). The overall wrong-patient order rate was significantly higher among multiple-birth infants than among singleton-birth infants (66.0 vs 41.7 RAR events per 100 000 orders, respectively; adjusted odds ratio, 1.75; 95% CI, 1.39-2.20; P < .001). The rate of extrafamilial RAR events among multiple-birth infants (36.1 per 100 000 orders) was similar to that of singleton-birth infants (41.7 per 100 000 orders). The excess risk among multiple-birth infants (29.9 per 100 000 orders) appears to be owing to intrafamilial RAR events. The risk increased as the number of siblings receiving care in the NICU increased; a wrong-patient order error occurred in 1 in 7 sets of twin births and in 1 in 3 sets of higher-order multiple births.

CONCLUSIONS AND RELEVANCE

This study suggests that multiple-birth status in the NICU is associated with significantly increased risk of wrong-patient orders compared with singleton-birth status. This excess risk appears to be owing to misidentification between siblings. These results suggest that a distinct naming convention as required by The Joint Commission may provide insufficient protection against identification errors among multiple-birth infants. Strategies to reduce this risk include using given names at birth, changing from temporary to given names when available, and encouraging parents to select names for multiple births before they are born when acceptable to families.

Effect of Restriction of the Number of Concurrently Open Records in an Electronic Health Record on Wrong-Patient Order Errors: A Randomized Clinical Trial

IMPORTANCE

Recommendations in the United States suggest limiting the number of patient records displayed in an electronic health record (EHR) to 1 at a time, although little evidence supports this recommendation.

OBJECTIVE

To assess the risk of wrong-patient orders in an EHR configuration limiting clinicians to 1 record vs allowing up to 4 records opened concurrently.

DESIGN, SETTING, AND PARTICIPANTS

This randomized clinical trial included 3356 clinicians at a large health system in New York and was conducted from October 2015 to April 2017 in emergency department, inpatient, and outpatient settings.

INTERVENTIONS

Clinicians were randomly assigned in a 1:1 ratio to an EHR configuration limiting to 1 patient record open at a time (restricted; n = 1669) or allowing up to 4 records open concurrently (unrestricted; n = 1687).

MAIN OUTCOMES AND MEASURES

The unit of analysis was the order session, a series of orders placed by a clinician for a single patient. The primary outcome was order sessions that included 1 or more wrong-patient orders identified by the Wrong-Patient Retract-and-Reorder measure (an electronic query that identifies orders placed for a patient, retracted, and then reordered shortly thereafter by the same clinician for a different patient).

RESULTS

Among the 3356 clinicians who were randomized (mean [SD] age, 43.1 [12.5] years; mean [SD] experience at study site, 6.5 [6.0] years; 1894 females [56.4%]), all provided order data and were included in the analysis. The study included 12 140 298 orders, in 4 486 631 order sessions, placed for 543 490 patients. There was no significant difference in wrong-patient order sessions per 100 000 in the restricted vs unrestricted group, respectively, overall (90.7 vs 88.0; odds ratio [OR], 1.03 [95% CI, 0.90-1.20]; P = .60) or in any setting (ED: 157.8 vs 161.3, OR, 1.00 [95% CI, 0.83-1.20], P = .96; inpatient: 185.6 vs 185.1, OR, 0.99 [95% CI, 0.89-1.11]; P = .86; or outpatient: 7.9 vs 8.2, OR, 0.94 [95% CI, 0.70-1.28], P = .71). The effect did not differ among settings (P for interaction = .99). In the unrestricted group overall, 66.2% of the order sessions were completed with 1 record open, including 34.5% of ED, 53.7% of inpatient, and 83.4% of outpatient order sessions.

CONCLUSIONS AND RELEVANCE

A strategy that limited clinicians to 1 EHR patient record open compared with a strategy that allowed up to 4 records open concurrently did not reduce the proportion of wrong-patient order errors. However, clinicians in the unrestricted group placed most orders with a single record open, limiting the power of the study to determine whether reducing the number of records open when placing orders reduces the risk of wrong-patient order errors.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT02876588.

Factors contributing to medication errors made when using computerized order entry in pediatrics: a systematic review

Objective

To identify and understand the factors that contribute to medication errors associated with the use of computerized provider order entry (CPOE) in pediatrics and provide recommendations on how CPOE systems could be improved.

Materials and Methods

We conducted a systematic literature review across 3 large databases: the Cumulative Index to Nursing and Allied Health Literature, Embase, and Medline. Three independent reviewers screened the titles, and 2 authors then independently reviewed all abstracts and full texts, with 1 author acting as a constant across all publications. Data were extracted onto a customized data extraction sheet, and a narrative synthesis of all eligible studies was undertaken.

Results

A total of 47 articles were included in this review. We identified 5 factors that contributed to errors with the use of a CPOE system: (1) lack of drug dosing alerts, which failed to detect calculation errors; (2) generation of inappropriate dosing alerts, such as warnings based on incorrect drug indications; (3) inappropriate drug duplication alerts, as a result of the system failing to consider factors such as the route of administration; (4) dropdown menu selection errors; and (5) system design issues, such as a lack of suitable dosing options for a particular drug.

Discussion and Conclusions

This review highlights 5 key factors that contributed to the occurrence of CPOE-related medication errors in pediatrics. Dosing support is the most important. More advanced clinical decision support that can suggest doses based on the drug indication is needed.