A Randomized Controlled Trial Evaluating the Effectiveness of Displaying Patient Photographs in an Electronic Health Record to Prevent Wrong-Patient Electronic Orders
Phase: NA
Conditions: Medical Errors, Electronic Medical Records
Sponsor: Columbia University

1 
 
 
 
 
 
 
 
A Randomized Controlled Trial Evaluating the Effectiveness  
of Displaying Patient Photographs in an Electronic Health  
Record to Prevent Wrong-Patient Electronic Orders 
 
 
 
 
 
Principal Investigator: Jason Adelman, MD, MS 
NCT: NCT03626766 
Date: 10/01/2024 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

2 
 
BACKGROUND 
 
The problem of wrong-patient orders. Although computerized clinician order entry (CPOE) systems 
are associated with a reduction in medical errors,1-4 when orders are placed electronically certain 
types of errors, including placing orders on the wrong patient, may occur more frequently.5-10 The 
danger of wrong-patient electronic orders was highlighted by one hospital’s report of over 5,000 
wrong-patient orders in 1 year.11 All types of orders can be placed on the wrong patient including 
medications, lab and imaging tests, and nursing orders. If an order is carried out on the wrong patient, 
it may lead to significant patient harm. 
 
How patient photographs in electronic health records (EHRs) can prevent wrong-patient 
electronic orders. Prior research has shown that cognitively processing a photograph (face 
recognition) requires significantly less effort than reading a patient's name.12 As such, prominently 
displaying a patient photograph has the potential to be a fast and accurate method for verifying a 
patient's identity when placing electronic orders. Limited evidence suggests that patient photographs 
may be a promising strategy for reducing wrong-patient orders.13 In a 2012 study conducted by 
Hyman et al, researchers asked clinicians to voluntarily report wrong-patient ordering errors before 
and after patient photographs were implemented in an EHR. Wrong-patient orders declined from the 
year pre- to the year post-implementation from 12 to 3. While encouraging, this study was limited by 
small sample size, quasi-experimental design, and reliance on voluntary reporting of errors. We 
hypothesize that a large randomized controlled trial using an automated and reliable measure of 
wrong-patient errors instead of voluntary reporting will demonstrate that patient photographs can 
significantly prevent wrong-patient orders. 
 
Current use of patient photographs in EHRs. In 2013, Adelman et al conducted a national survey 
of hospitals to assess the use of patient photographs in EHRs (unpublished data). This study found 
that although over 80% of the 131 responding hospitals reported having the capability to display 
patient photographs in their inpatient EHR, only 17.6% used this function. A study that establishes the 
effectiveness of prominently displayed patient photographs for preventing wrong-patient errors will 
provide hospitals with the evidence needed to overcome obstacles and adopt this safety practice. 
 
Retract-and-Reorder Measure for identifying wrong-patient errors. The Retract-and-Reorder 
(RAR) measure is a validated, reliable, and automated mechanism for identifying wrong-patient 
errors.11 The RAR measure identifies orders placed on a patient that are retracted within 10 minutes, 
and then placed by the same clinician on a different patient within the next 10 minutes. These are 
near-miss errors, self-caught by the clinician before they reach the patient and cause harm. In one 
study, the RAR measure identified more than 5,000 wrong-patient orders in 1 year, with a rate of 58 
wrong-patient orders per 100,000 orders.11 Real-time telephone interviews with clinicians who placed 
and retracted the orders determined that the RAR measure correctly identified near-miss errors in 
170 of 223 cases (positive predictive value 76.2%).11 Thus, the RAR measure can provide reliable 
outcome data for this study. 
 
Why are near-miss errors important in patient safety research? The RAR measure captures self- 
caught wrong-patient errors, which are near-miss errors. The use of near-miss errors to test safety 
improvements in healthcare is encouraged by every major patient safety organization including the 
Agency for Healthcare Research and Quality (AHRQ), Institute of Medicine (IOM), World Health 
Organization (WHO), Institute for Healthcare Improvement (IHI), and The Joint Commission (TJC) 
because they have been shown to share the same causal pathway as errors that cause harm.14-20 
 
 
 

3 
 
SPECIFIC AIMS 
 
The main hypothesis of this study is that an EHR system that prominently displays patient 
photographs will be more effective for preventing wrong-patient errors than a system that does not 
display photographs. We will test our hypothesis using randomized controlled trials and pursue the 
following specific aims: 
 
Aim 1: Test the effectiveness of displaying patient photographs in EHR systems for preventing 
wrong-patient orders, using the Retract-and-Reorder measure to identify the outcome. 
 
Aim 2: Identify characteristics of clinicians, patients, and order sessions that impact the effectiveness 
of patient photographs displayed in an EHR system to prevent wrong-patient orders. 
 
Aim 3: Conduct an evaluation of the implementation of patient photographs in the different EHR 
systems, including staff and patients. 
 
METHODS 
 
Research Personnel. The study will be led by principal investigator (PI) Jason Adelman, MD, MS, 
Chief Patient Safety Officer and Associate Chief Quality Officer for Columbia University Medical 
Center/NewYork-Presbyterian (NYP) Hospital. Along with Dr. Adelman, the research team consists of 
co-investigators from Columbia University, Weill Cornell Medicine, Johns Hopkins Medicine, Albert 
Einstein College of Medicine/Montefiore Medical Center, and Harvard University/Brigham and 
Women’s Hospital. The collaborating Site Leads include the following: 
• 
Donald Dietz, MD, Assistant Professor of Medicine, Weill Cornell Medicine. 
• 
Joseph Cooke, MD, Chairman, Department of Medicine, New York-Presbyterian/Queens. 
• 
Brenna Farmer, MD, Chief, Department of Emergency Medicine, New York-Presbyterian Brooklyn 
Methodist Hospital. 
• 
Allen Chen, MD, PhD, MHS, Health IT Patient Safety Officer, Armstrong Institute for Patient 
Safety and Quality, Associate Professor Department of Oncology and Department of Pediatrics, 
Johns Hopkins Medicine. 
• 
Clyde Schechter, MD, MA, Professor, Department of Family and Social Medicine, Albert Einstein 
College of Medicine. 
• 
Hojjat Salmasian, MD, PhD, Vice President for Operational Data Science, Brigham and Women’s 
Hospital. 
 
Study Sites. Study sites include Columbia University Irving Medical Center, Weill Cornell Medicine, 
Johns Hopkins Medicine, Albert Einstein College of Medicine (for data analysis only), and Brigham & 
Women’s Hospital. Data collection for the study will be conducted in two leading electronic health 
record (EHR) systems, Allscripts and Epic. For the study in Allscripts, two NYP/Columbia University 
Irving Medical Center campuses that utilize Allscripts EHR will be included: Milstein Hospital and 
Allen Hospital. Because of NYP’s transition to Epic at Columbia University Irving Medical Center in 
January 2020, Weill Cornell Medicine in October 2020, and remaining NYP hospitals by December 
2021, we will evaluate the effectiveness of patient photographs in Epic at Columbia (Milstein Hospital, 
Allen Hospital, Hudson Valley Hospital, Morgan Stanley Children’s Hospital, and Lawrence Hospital) 
and Weill Cornell (Weill Cornell Medical Center, Lower Manhattan Hospital, NYP/Queens Hospital, 
NYP/Brooklyn Methodist Hospital, and Westchester Behavioral Health Center). Hudson Valley 
Hospital staff and employees will not be involved in research activities; all research activities at 
Hudson Valley Hospital will be conducted by Columbia employees. In addition, we plan to include 

4 
 
NYP’s affiliated Ambulatory Care Network. Data collection for the study will also be conducted in Epic 
at Johns Hopkins Medicine locations, including Johns Hopkins Hospital (Inpatient) and Johns Hopkins 
Community Physicians sites (Outpatient). Data analysis will be conducted at Albert Einstein College 
of Medicine. Limited data sets will be securely transferred to the study biostatistician, Dr. Schechter 
(see Human Subjects Protection). Dr. Salmasian at Brigham & Women’s Hospital will serve as 
consultant and technical advisor for the study. 
 
Institutional Review Board (IRB) Approval. As the Lead Institution, the PI at Columbia University 
Irving Medical Center will work with Site Leads at Weill Cornell Medicine, Johns Hopkins Medicine, 
Albert Einstein College of Medicine, and Brigham and Women’s Hospital to obtain IRB approval to 
implement study-related activities. Site Leads are responsible for obtaining approval from their 
respective IRB and are aware that human subjects research cannot commence until their institutional 
IRB is approved. The PI will work with Site Leads to ensure that all modifications and renewals are 
reviewed and approved appropriately. 
 
Research Design. 
Aims 1 and 2—The hypothesis of the study is 
that displaying patient photographs in an EHR at 
the time of placing electronic orders will 
significantly decrease the frequency of wrong-
patient orders. We will test this hypothesis with a 
randomized controlled trial using a 2-arm design 
in Allscripts, and a randomized 2x2 factorial 
design in Epic to determine the optimal 
configuration of patient photographs (Figure 1). 
The design at the different sites is based on the 
functionality of the EHR systems. In Allscripts, 
clinicians randomized to the intervention arm will 
be shown a verification alert with a patient 
photograph at the time of placing orders; 
clinicians randomized to the control arm will be shown the alert with an avatar indicating the patient’s 
sex (Figure 2). Epic has the functionality to display a patient photograph in the banner at the side of 
the screen (called Storyboard in Epic) and in a verification alert at the time of placing orders (Figure 
3). Therefore, in Epic clinicians will be randomized to one of four conditions: 1) no photo; 2) photo 
displayed in Storyboard only; 3) photo displayed in the alert only; or 4) photo displayed in both 
Storyboard and in the alert.  
 
 
 
Figure 1. Photo Study Designs 
Figure 2. Verification Alert With vs Without Patient  
Photo. (not an actual patient) 
Figure 3. Patient Photo in Storyboard and 
Alert. (not an actual patient) 

5 
 
Patient Inclusion Criteria. In this study, we will include all patients for whom an electronic order is 
placed during the study period at the study sites. At all study sites, all patients aged 1 year and older 
will be included. Obtaining a photograph at registration is standard procedure at the participating 
study sites. Patients are not required to provide written or oral consent to have their photograph 
taken, as the photographs are used as part of routine care. However, patients or their legal guardians 
may refuse. 
 
Clinician Inclusion Criteria. Any clinician who can place an electronic order can potentially place an 
order on the wrong patient. We will therefore include in the study all clinicians who place an electronic 
order at the study sites during the course of the study period. 
 
Randomization. For the Patient Photo study in Allscripts at NYP Hospital, a simple random allocation 
scheme to intervention or control (1:1 per the 2-arm design) will be utilized. A computerized random 
number generator allocates a unique identifier to each EHR user when the user’s account is created. 
Numbers are divided into two groups (even and odd), which are expected to have an equal 
frequency. For the Patient Photo study in Epic, a simple random allocation scheme will be utilized to 
assign clinicians to one of four study groups (1:1:1:1 per the 2x2 design). Each study site will 
generate a list of ordering clinicians to be randomized, replace identifiers with coded pseudo-
identifiers, then transfer the de-identified list to the study biostatistician at Columbia for randomization. 
Randomization will be performed centrally using a statistical software program. De-identified lists for 
the four study groups will be transferred to each study site to be re-identified and uploaded into study 
groups in Epic using the Grouper function. Custom functionality has been built within Epic to enable 
randomization to the four study groups and the corresponding configuration of photos for each group. 
Randomization procedures will be repeated periodically throughout the study period to assign new 
clinicians to study groups.  
 
Clinician types whose prior ordering volumes are not representative of their future ordering volume 
(e.g., low ordering clinicians anticipated to be high ordering clinicians) will be placed in one of two 
strata (anticipated high orderers or anticipated low orderers) and similarly randomized. Clinician type 
is associated with anticipated ordering pattern, i.e., attending physicians place few orders, advanced 
practice clinicians place the most orders, and trainees place a relatively high volume of orders. New 
clinicians will be randomized by clinician type, within site, without differentiating anticipated high or 
low order volume.  
 
For the duration of the study, once randomized, clinicians will not be able to switch study groups or 
withdraw from the study. 
 
Primary Outcome. The primary outcome is the frequency of Retract-and-Reorder (RAR) events, 
identified using the RAR measure, defined as an order that is placed for a patient, then retracted 
within 10 minutes, and then placed by the same clinician for a different patient within 10 minutes of 
the retraction. 
 
Unit of Analysis. The unit of analysis will be the order session. 
 
Clustering of Orders within Order Sessions. If a clinician begins placing orders in the wrong 
patient’s record, there is the possibility that several such orders will be placed consecutively and then 
all retracted together. Therefore, individual orders do not represent independent opportunities for 
RAR events to occur. Rather, orders are clustered within order sessions. An order session is defined 
as a series of orders placed by a clinician for a single patient that begins with opening that patient’s 
order file and terminates when an order is placed for another patient or after 60 minutes, whichever 
comes first. 

6 
 
Clinician Level, Patient Level, Order-Session Level, and Order Level Covariates. The data for 
this study will have a nested, hierarchical structure with orders clustered within order sessions, and 
order sessions clustered within clinicians. The analysis will account for this hierarchical structure. We 
will also extract from the electronic medical record attributes of the clinician, patient, order session, 
and order. 
Clinician level covariates: type of ordering clinician (attending, resident, medical student, physician 
assistant, nurse practitioner, or other), department, and total number of orders placed during the 
study period (a measure of the frequency with which the clinician uses the system). 
Patient level covariates: date of birth, age, race, ethnicity, sex, insurance status, unit, date and time 
of admission, and date and time of discharge. 
Order-session level covariates: location of the order session (medical-surgical unit, intensive care 
unit, labor and delivery, pediatrics, other specialty floors), date and time of the start of the order 
session and date and time of the first retracted order, and duration of the order session. 
Order level covariates: type of order placed (medication, radiology, lab, nursing, other). 
 
Primary Analysis. The dependent variable is the proportion of order sessions that contain at least 
one order that was retracted and reordered (an RAR event). Inference about effectiveness of the 
intervention will be based on a Wald test of the coefficient of an interaction term between study-arm 
assignment and an indicator of pre- or post-intervention time period. The estimate of effectiveness will 
be the odds ratio (exponentiated coefficient) reported with its 95% confidence interval. With 
randomization at the clinician level, patient and order session level attributes may not be evenly 
distributed across groups. To reduce confounding bias, covariates at these levels that are both 
differentially distributed in the study groups and associated with the outcome will be included in this 
primary analysis. Clinician-level covariates will likely be balanced across study groups by 
randomization and will be included in this primary analysis only if descriptive statistics show 
otherwise. 
 
Missing Data. Due to the automatic functioning of the EHR, we expect there will be no missing data 
concerning Retract-and-Reorder events, the mode of intervention group vs. control group, or the 
clinician-level covariates. There may be sporadic missing information regarding the patient-level 
covariates. In a prior RAR data set consisting of 11.6 million order sessions, there were no missing 
observations for patient age, sex, or race. An indicator for Hispanic ethnicity was missing in just 4 
records. With similar levels of missing data, we believe that restricting regression models to only 
include cases with complete data will not introduce appreciable levels of bias. If we find that any 
variables are missing for more than 1 record per 1,000 (after backfilling based on other records 
involving the same patient), we will extend our analyses to address this. We will presume the data are 
missing at random and will apply multiple imputation with chained equations. 
 
Methods for Aim 1 and 2: Photograph Study 
 
2-Arm RCT: Statistical Power/Sample Size. The overall design is a randomized trial, with order-
session observations nested within randomized clinicians. The number of clinicians is a fixed attribute 
of the study site and is not subject to investigator control. Based on preliminary data, we assume that 
we will obtain data on 12,000 clinicians, with half randomized to each study arm. 
 
Over 2.5 years of observation, we will accrue an average of 5,000 order sessions per clinician, with a 
coefficient of variation of 1.27. We anticipate that the intra-clinician correlation for the outcome will be 
about 0.001. The most recently available analyses from NYP suggest that the wrong-patient order 
session rate (a weighted average of inpatient and outpatient rates) is about 130 per 100,000 order 

7 
 
sessions. Using a two-tailed test at the 0.05 significance level, this will provide >90% power to detect 
a 25% reduction in the wrong-patient order-session rate in the intervention group in the primary 
analysis. 
 
2x2 Factorial RCT: Statistical Power/Sample Size. For our 2x2 factorial study, we did a statistical 
power simulation. We assumed, based on prior information from our studies and information reported 
by our collaborators elsewhere that approximately 8,268 clinicians would be randomized in 1:1:1:1 
ratio to the four study arms (no photo, photo in Storyboard only, alert with photo only, photo in both 
Storyboard and alert). We assumed that clinicians would generate an average of 74 order sessions 
per month, with a Poisson distribution, and that data collection would continue for 1 year. We 
assumed based on our previous studies that the variance component (in the log-odds metric) at the 
clinician level would be 1.9 (corresponding to an intra-clinician correlation of 0.52). We assumed that 
the base rate of RAR events is 9 per 10,000 order sessions. We ran 400 simulations of our study 
under these assumptions to determine that we would have 91% power (95% CI 88%-94%) to detect 
an odds ratio of 0.75 (i.e., 25% reduction in RAR events) for either photo intervention (in Storyboard 
or in alert) compared to no intervention. Our power to detect an additional 15% reduction by the 
combination of photo interventions (both in Storyboard and in alert) compared to either alone is 
99.8% (95% CI 98.6%-99.9%). 
 
Interim Analysis. To safeguard against the possibility that our intervention actually worsens 
(increases) the rate of RAR events, and to prevent unnecessary continuation of a study that is already 
conclusive, a data safety monitoring committee will conduct one interim review of the data. At the 
midpoint of the study, we expect 50% of the data to have been accrued. Using the Lan-Demets alpha 
spending procedure with symmetric O’Brien-Fleming boundaries, the stopping rule at the interim 
review will be a z-statistic of magnitude 3.0318 or greater.21 The associated nominal P value is 
0.0024. Combined with a final analysis using a critical z-value of 1.9669 (nominal P = 0.0492), we will 
have spent our overall alpha of 0.05 at the end of the study. The effects of this interim analysis 
procedure on nominal statistical power (see above) is negligible, less than 0.5 percentage points, so 
no adjustments to data collection are needed to account for this. 
 
Methods for Aim 3 
 
Observations/Surveys. We propose conducting an evaluation to assess barriers and facilitators that 
impact the implementation of patient photos in the EHR system. We will use anonymous surveys and 
observations; no individual identifiers will be collected. The purpose of this evaluation is to 1) elicit 
staff feedback about their experience during the photo implementation and registration process, 2) 
elicit patient feedback on their reasons for declining to be a part of the photo implementation process 
in the EHR, and 3) document observations of the photo implementation and registration process. Staff 
and patients will be assured that participating in the study is completely voluntary and will not affect 
staff’s professional standing in any way or the standard quality of care the patient receives. All 
responses will be shared only with research personnel and will have no identifying information. The 
surveys and observation tool are attached in the Documents section. 
 
Focus Groups/Interviews. We propose conducting a qualitative evaluation consisting of focus 
groups or interviews at participating study sites to elicit user experience of the photograph feature in 
the different EHRs, benefits of patient photographs, and barriers to adoption. Lena Mamykina, PhD, 
Assistant Professor of Biomedical Informatics at Columbia University, will lead the user experience 
and usability evaluation. The focus groups/interviews will be facilitated using semi-structured 
interview guides. Dr. Mamykina will conduct separate focus groups/interviews for clinicians and 
registrars at the three participating study sites. As a result of the activities proposed in this Aim, we 
will develop a rich understanding of user experiences with different patient photograph features and 

8 
 
local factors that impact adoption. 
Recruitment. For the staff survey, an electronic survey link will be sent to all registration staff by 
email providing information about the study and asking them to complete the anonymous survey. The 
Recruitment Email is attached in the Documents section. For the patient survey, patients who decline 
to have their photograph taken and uploaded into the EHR system will be approached by a Research 
Assistant and asked to participate in a brief survey about why they declined. For the observation 
component, a Research Assistant will use an observation tool to record a sample of registrations and 
document the process. 
 
For focus groups and interviews, we will recruit approximately 75 individuals (25 at each study site: 
Columbia, Weill Cornell, Johns Hopkins) exposed to patient photograph features or involved in 
implementation in two EHR systems (Allscripts and Epic). We will recruit the following individuals: 1) 
patients, 2) clinicians, and 3) registration staff who experienced patient photographs during the study 
period. For the three participating sites, we plan to recruit 2-5 individuals in each category. 
Recruitment will be conducted by email using information sheets to describe the study. Verbal 
consent will be obtained for those who agree to participate (see Protection of Human Subjects 
addendum). 
 
Data Collection, Management, and Analysis. All data for observations and surveys will be collected 
using Qualtrics, an online survey and research tool available at Columbia. The survey items will be 
analyzed using descriptive statistics. The data will use dummy subject ID numbers and will have no 
identifiers that can be traced back to an individual. 
 
Focus group/interview guides will be designed to address relevant questions for each group; a 
preliminary semi-structured interview guide for clinicians and registration staff is attached. For 
clinicians, the focus will be on experience using the photograph features, perceived benefits, 
limitations in their design, and impact on clinical workflows. For registrars, the main focus will be on 
the perceived impact of taking photographs on time, patient triage, and workflows. All interviews will 
be audio recorded and transcribed verbatim for analysis, with identifiers deleted. 
 
To analyze the interviews, we will use inductive thematic analysis. With supervision by Dr. Mamykina, 
members of the research team with experience in qualitative research will review and code all 
transcripts. The investigators will initially code data collectively, discussing emerging categories and 
resolving any disagreements, and develop a preliminary coding scheme. Then, investigators will code 
independently, and meet to review the transcripts and selected codes to resolve any inconsistencies. 
Once coding is complete, an independent investigator will review and code a subset of data (5%-
10%) to assess inter-rater reliability. We will use triangulation and member checks to increase validity 
and decrease bias. 
 
PROTECTION OF HUMAN SUBJECTS 
 
Potential Risk. This randomized controlled trial does not present more than minimal risk to either 
patients or clinicians and does not involve any additional procedures for which verbal or written 
consent is normally required. Patients will be receiving clinical care in accordance with best practices 
and their clinicians’ discretion. On admission, patients have the option of having their photograph 
taken as part of the hospital’s identification registration system. Patients who participate are not 
required to provide written or oral consent to have their photograph taken, as the photographs are 
used as part of routine care. To ensure confidentiality, the analytic data set will not include patient 
names or patient photographs, and patient medical record numbers and account numbers will be 
replaced with pseudo-identifiers. In addition, all clinician identifiers will be removed from data sets 
prior to analysis to protect clinicians’ identities; a pseudo-identifier will be used instead. 

9 
 
 
Consent. The proposed research project does not present more than minimal risk of harm to 
subjects, and the research does not involve any procedures for which written consent is normally 
required. As such, per the code of Federal Regulations Title 45, Part 46.116 (d), we ask the IRB to 
waive informed consent for Aims 1, 2, and 3, and have completed the Waiver of Consent form for 
clinicians and patients regarding the analysis of primary outcome data. 
 
For Aim 3, our approach to informed consent includes verbal consent from clinicians to participate in 
focus groups and written consent from patients for usage of photos from their EHR. 
 
Data Security. Identifying information, including patient medical record numbers and clinician ID 
numbers, will be replaced by pseudo-identifiers. Dates may be retained, including order dates and 
discharge dates, in Limited Data Sets. Data will be stored in password-protected computers in locked 
offices, and all computers are encrypted to meet the security standards set forth by Columbia 
University and NYP. Data files will be imported into the native format of the statistical program used 
for analysis. Only the PI and co-investigators will have access to the data. All data transfers between 
study sites will occur using secure file transfer protocols. Columbia University Medical Center Patient 
Safety Research, Division of General Medicine will receive the data. Data will be transferred for 
analysis to the study biostatistician, Dr. Clyde Schechter, at Albert Einstein College of Medicine. 
Information will not be released except as necessary for monitoring by the Institutional Review Board. 
As such, per the Code of Federal Regulations Title 45, Parts 160 and 164 (a), we seek an exemption 
to the requirement for use of the HIPAA Authorization form and have completed the HIPAA Waiver of 
Authorization (Form B). 
 
Adverse Events. Any events that are brought to the investigators’ attention during the course of the 
study will be investigated and reviewed by the data safety monitoring committee (DSMC) and brought 
to the attention of the IRB. 
 
Communication with Clinicians. At the start of the randomized controlled trial, investigators will 
explain the purpose of the study to clinical staff via email using a message crafted by the study team. 
The message will assure clinicians that data will be kept confidential and participation carries no risk 
to them. It will also note that for the duration of the study, clinicians will not be able to switch study 
groups. 
 
One of the study sites, Johns Hopkins Medicine’s IRB, recommended that the study team not send 
out a communication in advance informing clinicians of the study, and also recommended not 
allowing clinicians to withdraw from the study, so as not to introduce a significant bias to the study 
results.  
 
At Columbia University Irving Medical Center and Weill Cornell Medicine, an IRB-approved 
communication informing all ordering clinicians of the study was disseminated prior to the start of the 
intervention. However, because this is not a blinded study, and because the intervention is a visual 
cue aimed at preventing wrong patient errors, we do not believe Columbia and Cornell opting to 
disseminate a communication and Johns Hopkins opting not to send a communication will affect the 
results of the study. 
 
Potential Benefit. There is no anticipated benefit of participation in this study for patients or 
clinicians. However, we hypothesize that this randomized controlled study will demonstrate that an 
EHR system displaying patient photographs significantly reduces the frequency of wrong-patient 
orders. Because many EHR systems have the capability to display patient photos, results that clearly 
establish their effectiveness to prevent wrong-patient errors will provide hospitals with the evidence 

10 
 
needed to adopt this safety practice. 
Bibliography and References Cited: 
 
1. 
Bates DW, Teich JM, Lee J, et al. The impact of computerized physician order entry on 
medication error prevention. J Am Med Inform Assoc. 1999;6:313-21. 
2. 
Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical 
decision support systems on medication safety: a systematic review. Arch Intern Med. 
2003;163:1409-16. 
3. 
Reckmann MH, Westbrook JI, Koh Y, Lo C, Day RO. Does computerized provider order entry 
reduce prescribing errors for hospital inpatients? A systematic review. J Am Med Inform Assoc. 
2009;16:613-623. 
4. 
Koppel R, Metlay JP, Cohen A, et al. Role of computerized physician order entry systems in 
facilitating medication errors. JAMA. 2005;293:1197-1203. 
5. 
Harrison MI, Kopel R, Bar-Lev S. Unintended consequences of information technologies in 
health care: an interactive sociotechnical analysis. J Am Med Inform Assoc. 2007;14:542-549. 
6. 
Ash JS, Sittig DF, Poon EG, Guappone K, Campbell E, Dykstra RH. The extent and importance 
of unintended consequences related to computerized provider order entry. J Am Med Inform 
Assoc. 2007;14:415-423. 
7. 
Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. Types of unintended 
consequences related to computerized provider order entry. J Am Med Inform Assoc. 
2006;13:547-555. 
8. 
Sittig DF, Ash JS. Clinical Information Systems: Overcoming Adverse Consequences. Boston, 
MA: Jones and Bartlett, 2010. 
9. 
Ash J, Berg M, Coiera E. Some unintended consequences of information technology in health 
care: the nature of patient care information system-related errors. J Am Med Inform Assoc. 
2003;11:104-112. 
10. Cohen M. Medication Errors (2nd ed.). Washington DC: American Pharmaceutical Association, 
2006. 
11. Adelman JS, Kalkut GE, Schechter CB, et al. Understanding and preventing wrong-patient 
electronic orders: a randomized controlled trial. J Am Med Inform Assoc. 2013;20:305-310. 
12. Kanwisher N, Moscovitch M. The cognitive neuroscience of face processing: an introduction. 
Cogn Neuropsychol. 2000;17:1–11. 
13. Hyman D, Laire M, Redmond D, Kaplan DW. The use of patient pictures and verification 
screens to reduce computerized provider order entry errors. Pediatrics. 2012;130:211-219. 
14. Department of Veterans Affairs. VHA National Patient Safety Improvement Handbook. 2011 
http://www.va.gov/vhapublications/ViewPublication.asp?pub_ID=2389 (accessed August 21, 
2016). 
15. National Association for Healthcare Quality. National Association for Healthcare Quality (NAHQ) 
Issues 'Call to Action' for Enhancing Health Care Quality and Patient Safety. 2012. 
http://www.prnewswire.com/news-releases/national-association-for-healthcare-quality-nahq- 
issues-call-to-action-for-enhancing-health-care-quality-and-patient-safety-174423331.html 
(accessed August 21, 2016). 
16. Reason J. Human error: models and management. BMJ. 2000;320(7237):768. 
17. Institute of Medicine (IOM). Patient Safety: Achieving a New Standard for Care. Washington, 
DC: National Academy Press, 2004. 
18. Agency for Healthcare Research and Quality: Doing What Counts for Patient Safety: Federal 
Actions to Reduce Medical Errors and Their Impact Report of the Quality Interagency 
Coordination Task Force (QuIC) To the President. 2000 
http://archive.ahrq.gov/quic/Report/errors6.pdf (accessed August 21, 2016). 
19. Leape L., Abookire S. WHO Draft Guidelines for Adverse Event Reporting and Learning 
Systems: From Information to Action. Geneva, Switzerland: World Health Organization, 2005. 

11 
 
http://www.who.int/patientsafety/events/05/Reporting_Guidelines.pdf. (accessed August 21, 
2016). 
20. Institute for Healthcare Improvement. "Create a Reporting System” Apr. 2011. 
http://www.ihi.org/knowledge/Pages/Changes/CreateaReportingSystem.aspx (accessed August 
21, 2016). 
21. Demets DL, Lan KK. Interim analysis: the alpha spending function approach. Stat Med. 
1994;13:15-30.