Green 2015.

Study characteristics
Methods	ITS study. Monthly measurements of wrong‐patient order rate were obtained before and after the implementation of the computerised provider order entry (CPOE)–based patient verification process. Five emergency department (EDs) were included: 2 adult EDs, 2 pediatric EDs, and 1 combined ED. The EDs serve a socioeconomically, racially, and ethnically diverse population in New York City and have a combined annual visit volume of 250,000 patients. The EDs support pediatrics and emergency medicine residency and pediatric emergency medicine fellowship programs. Unit of analysis: prescriptions
Participants	Adult and paediatric ED patients (N not available) IP/OP adults (ED)
Interventions	Intervention: Technology Prescribing and order communication systems. Computerised physician order entry (CPOE). As part of a quality improvement initiative, a custom patient verification module was integrated into the computerised provider order entry system with the intent of helping practitioners intercept wrong‐patient selection errors before order entry. Three patient identifiers were prominently displayed: full name, birth date, and medical record number. Additional information that could facilitate patient identification was also included, such as ED length of stay, chief complaint, bed location, and recent medication orders.
Outcomes	The primary outcome was intercepted wrong‐patient orders (expressed as a rate per 1000 orders), which was calculated with the retract‐and‐reorder method. The electronic health record system was fully implemented by January 2011 and all order entry was performed electronically in the study sites. A record of each order entry was obtained from electronic health record system logs. Additionally, the actions taken by providers within the patient verification module were also electronically recorded. We used the data from the electronic health record logs to perform our analysis.
Notes	This study was supported in part by National Library of Medicine grants 5 T15 LM007079 and LM006910. No trial number
Risk of bias
Bias	Authors' judgement	Support for judgement
Conflict of interest	Low risk	No conflict of interest
Other bias	Low risk	No other biases detected
Reliable primary outcome measure(s)	Low risk	The primary outcome was intercepted wrong‐patient orders (expressed as a rate per 1000 orders), which was calculated with the retract‐and‐reorder method described by Adelman et al. This method identifies orders placed for a patient but then rapidly discontinued by the same practitioner (i.e. the retract event); it then checks to determine whether an identical order was subsequently entered by the same provider for a different patient (i.e. the reorder event) within a short period after the retract event. Adelman et al. evaluated the accuracy of the retract‐and‐reorder method by interviewing the provider after a retract‐and‐reorder event occurred. The authors defined the method positive predictive value as the percentage of retract‐and‐reorder events that were reported because of a wrong‐patient order by the interviewed providers and estimated a positive predictive value of 76.2% (95% confidence interval (CI) 70.6% to 81.9%).
Blinded assessment of primary outcome(s)	Low risk	Not blinded but objective method. Medication orders are placed via computerised provider order entry (CPOE)
Data were analysed appropriately	Low risk	Primary data analysis: the authors assessed the potential effect of different confounding variables, using a logistic regression model. Confounding variables included in the model consisted of patient level variables (sex, age, and race), provider role (attending physician, resident, medical student, or other), and whether the order was placed during a day or a night shift. Furthermore, they compared the effect of intervention across the 5 sites included in this study. In a secondary analysis, they used the rate of wrong‐patient orders in the 5 facilities' 2019 inpatient settings to standardise the rate of such orders in the ED data. Standardisation was accomplished by dividing the rate of wrong‐patient orders in the ED setting for each study period by dividing the rate of such orders in the inpatient setting within the same period. This was done to eliminate the potential effect of secular trends, assuming that the influence of these trends was proportionally the same in inpatient and ED settings. The adjusted rate was then compared across study periods with the X2 test. They used change‐point analysis to study the longitudinal trends of wrong‐patient orders to identify whether the effect of intervention was sustained over time.
Protection against detection bias (same pre‐post data collection)	Unclear risk	"Wrong‐patient orders that remain unnoticed or are intercepted by a different clinician are not identified with this method, which may lead to an underestimation of the wrong‐patient order rate." "The retract‐and‐reorder method can identify only wrong‐patient orders that were identified and corrected by the same provider" Comment: same method applied pre & post. Potential detection bias could have had similar effect in pre & post measurement. Adjustment by provider role was performed (to account for better practices in more experienced doctors). No description of doctors provided in article
Completeness of data set	Low risk	"A record of each order entry was obtained from electronic health record system logs. Additionally, the actions taken by providers within the patient verification module were also electronically recorded. We used the data from the electronic health record logs to perform our analysis."
Reason for the number of points pre‐ and post‐intervention given	Low risk	The study sample included all orders written at these sites from January 2011 through April 2013. The pre‐intervention phase included orders written from January to April 2011. They used 2 different periods for the post‐intervention phase of the study: to assess short‐term effect of intervention, they used orders written in the 4 months after the intervention (June 2011 to September 2011); they excluded May from this analysis because the module was being gradually rolled out then. To evaluate the long‐term effect of the intervention, they used orders written between January 2013 and April 2013.
Protection against secular changes	High risk	"Additionally, our study used a before‐after design, and the results can be potentially confounded by an unknown simultaneous intervention that was not measured in the analyses; the use of a parallel control group can reduce the effect of unknown confounders, but because our control group was not matched with the study group (i.e. inpatient versus ED), we are reporting the result of our controlled analysis only as a secondary outcome and encourage the readers to interpret it with caution."
Shape of the intervention effect was specified	Unclear risk	Not stated in the article