Wrong-Patient Orders in Obstetrics

Secure Messaging Use and Wrong-Patient Ordering Errors Among Inpatient Clinicians

Importance

Use of secure messaging for clinician-to-clinician communication has increased exponentially over the past decade, but its association with clinician work is poorly understood.

Objective

To investigate the association between secure messaging use and wrong-patient ordering errors.

Design, Setting, and Participants

This cohort study included inpatient attending physicians, trainee physicians, and advanced practice practitioners (APPs) from 14 academic and community hospitals. Secure messaging volume was assessed over a 3-month period (February 1 to April 30, 2023).

Exposure

Secure messaging volume per clinician-day, measured as the count of secure messages sent and received by a clinician on a given clinician-day.

Main Outcomes and Measures

Retract-and-reorder events were used to identify wrong-patient ordering errors, and the presence of any retract-and-reorder event on a clinician-day was the primary outcome. Multilevel logistic regression was used to examine the association between secure messaging volume and wrong-patient ordering errors after adjusting for clinician age, sex, patient load, order volume, and clinical service.

Results

A total of 3239 clinicians (median [IQR] age, 37 [32-46] years; 1791 female [55.3%]; 1680 attending physicians [51.2%], 560 trainee physicians [17.3%], and 999 APPs [30.8%]) with 75 546 clinician-days were included. Median secure messaging volume was 16 (IQR, 0-61) messages per day. Retract-and-reorder events were identified on 295 clinician-days (0.4%). Clinicians with secure messaging volume at the 75th percentile had a 10% higher odds of wrong-patient ordering errors compared with those at the 25th percentile (odds ratio [OR], 1.10; 95% CI, 1.01-1.20). After stratifying by clinician role, the association between secure messaging and wrong-patient ordering errors was observed only for attending physicians (OR, 1.20; 95% CI, 1.02-1.42) and APPs (OR, 1.18; 95% CI, 1.00-1.40).

Conclusions and Relevance

In this cohort study of inpatient clinicians, higher daily secure messaging was associated with increased odds of wrong-patient ordering errors. Although messaging may increase cognitive load and risk for wrong-patient ordering errors, these results do not provide conclusive evidence regarding the direct impact of secure messaging on errors, as increased messaging may also reflect greater care coordination, increased patient complexity, or communication of the presence of a wrong-patient ordering error.

Risk factors for wrong-patient medication orders in the emergency department

Objectives

This paper investigates the risk factors for wrong-patient medication orders in an emergency department (ED) by studying intercepted ordering errors identified by the "retract-and-reorder" (RaR) metric (orders that were retracted and reordered for a different patient by the same provider within 10 min).

Materials and Methods

Medication ordering data of an academic ED were analyzed to identify RaR events. The association of RaR events with similarity of patient names and birthdates, matching sex, age difference, the month, weekday, and hour of the RaR event, the elapsed hours since ED shift start, and the proximity of exam rooms in the electronic medical record (EMR) dashboard's layout was evaluated.

Results

Over 5 years (2017-2021), 1031 RaR events were identified among a total of 561 099 medication orders leading to a proportional incidence of 184 per 100 000 ED orders (95% CI: 172; 195). RaR orders were less likely to be performed by nurses compared to physicians (OR 0.54 [0.47; 0.61], P < .001). Furthermore, RaR pairs were more likely to have the same sex (OR 1.26 [95% CI 1.10; 1.43], P = .001) and the proximity of the exam rooms was closer (-0.62 [95% CI -0.77; -0.47], P = .001) compared to control pairs. Patients' names, birthdates, age, and the other factors showed no significant association (P > .005).

Discussion and Conclusion

This study found no significant influence from factors such as similarity of patient names, age, or birthdates. However, the proximity of exam rooms in the user interface of the EMR as well as patients' same sex emerged as risk factors.

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.