Evaluating the Impact of Patient Photographs for Preventing Wrong-Patient Errors

• ClinicalTrials.gov Identifier: NCT03626766
• Recruitment Status: Recruiting
• First Posted: August 13, 2018
• Last Update Posted: September 25, 2018
• Sponsor: Columbia University
• Collaborator: Agency for Healthcare Research and Quality (AHRQ)
• Information provided by (Responsible Party): Jason Adelman, Columbia University

Study Description

Brief Summary:

This is a multi-site, cluster-randomized controlled trial to test the effectiveness of patient photographs displayed in electronic health record (EHR) systems to prevent wrong-patient order errors. The study will be conducted at three academic medical centers that utilize two different EHR systems. Because EHR systems have different functionality for displaying patient photographs, two different study designs will be employed. In Allscripts EHR, a 2-arm randomized trial will be conducted in which providers are randomized to view order verification alerts with versus without patient photographs when placing electronic orders. In Epic EHR, a 2x2 factorial trial will be conducted in which providers are randomized to one of four conditions: 1) no photograph; 2) photograph displayed in the banner only; 3) photograph displayed in a verification alert only; or 4) photograph displayed in the banner and verification alert. The main hypothesis of this study is that displaying patient photographs in the EHR will significantly reduce the frequency of wrong-patient order errors, providing health systems with the evidence needed to adopt this safety practice.

We will use the Wrong-Patient Retract-and-Reorder (RAR) measure, a valid, reliable, and automated method for identifying wrong-patient orders, as the primary outcome measure. The RAR measure identifies orders placed for a patient that are retracted within 10 minutes, and then reordered by the same provider for a different patient within the next 10 minutes. These are near-miss errors, self-caught by the provider 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 errors per 100,000 orders. Real-time telephone interviews with clinicians determined that the RAR measure correctly identified near-miss errors in 76.2% of cases. Thus, the RAR measure provides sufficient valid and reliable outcome data for this study.

Condition or disease	Intervention/treatment	Phase
Medical Errors; Electronic Medical Records	Behavioral: Photo in Verification Alert; Behavioral: Photo in Banner; Behavioral: Photo in Banner and Verification Alert	Not Applicable

Detailed Description:

TThe main hypothesis of this study is that displaying patient photographs in electronic health record (EHR) systems will significantly reduce the frequency of wrong-patient electronic order errors.

SPECIFIC AIMS

Investigators will test this hypothesis using a multi-site randomized controlled trial 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 providers, patients, and orders that impact the effectiveness of patient photographs displayed in an EHR system to prevent wrong-patient orders.

METHODS Study Sites. The study will be conducted at NewYork-Presbyterian Hospital, Johns Hopkins Medicine, and Montefiore Medical Center in two different electronic health record (EHR) systems. Five NewYork-Presbyterian Hospital campuses that utilize Allscripts EHR will be included: Milstein Hospital, Weill Cornell Medical Center, Morgan Stanley Children's Hospital of New York, Lower Manhattan Hospital, and the Allen Hospital. In addition, affiliated Ambulatory Care Network outpatient sites will be included. The effectiveness of patient photographs in Epic will be tested at Johns Hopkins Hospital and three Montefiore Medical Center campuses (Moses Hospital, Weiler Hospital, Wakefield Hospital).

Research Design. The main hypothesis of this study is that displaying patient photographs in an EHR system at the time of placing electronic orders will significantly decrease the frequency of wrong-patient orders. Investigators will test this hypothesis with a randomized controlled trial using a 2-arm design in Allscripts and a 2 x 2 factorial design in Epic to determine the optimal configuration of patient photographs. The design at the different sites is based on the functionality of the EHR systems. In Allscripts, providers randomized to the intervention arm will be shown a verification alert with a patient photograph at the time of placing orders; providers randomized to the control arm will be shown the verification alert with an avatar indicating the patient's sex. Epic has the functionality to display a patient photograph in the banner at the top of the screen and in a verification alert at the time of placing orders. Therefore, in Epic at Johns Hopkins and Montefiore, providers will be randomized to one of four conditions: no photograph; photograph displayed in the banner only; photograph displayed in the verification alert only; or photograph displayed in the banner and alert.

Patient Inclusion Criteria. In this study, all patients for whom an order is placed during the study period at the study sites will be included in the analysis. At NewYork-Presbyterian, all patients age 5 years and older will be included; at Johns Hopkins Hospital and Montefiore Medical Center, all patients age 2 years and older will be included. Obtaining a photograph at registration is standard procedure at the participating study sites. Patients will not be 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.

Provider Inclusion Criteria. Any provider who places an electronic order can potentially place an order on the wrong patient. Therefore all randomized providers who place an electronic order at the study sites during the course of the study period will be included.

Randomization. In Allscripts at NewYork-Presbyterian, the EHR system has been configured such that the first time a provider opens the Order Entry screen s/he will be automatically randomized to either the control or intervention group (1:1 per the 2-arm design) using a computerized randomization algorithm. In Epic, randomization will be performed using the same randomization algorithm; however, providers will be manually assigned to the control or intervention groups (1:1:1:1 per the 2 x 2 design) using the Grouper function in Epic.

Primary Outcome. The primary outcome is the frequency of Wrong-Patient Retract-and-Reorder (RAR) events, identified using the RAR measure and defined as an order that is placed for a patient, retracted within 10 minutes of placing the order, and then reordered by the same provider for a different patient within 10 minutes of the retraction.

Clustering of Orders within Order Sessions. If a provider 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 provider on a single patient that begins with opening that patient's order file and terminates when an order is placed on another patient or after 60 minutes, whichever comes first.

Unit of Analysis. The unit of analysis will be the order session. Provider 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 providers. The analysis will account for this hierarchical structure. Additional variables will be extracted from the electronic medical record, including attributes of the provider (attending, resident, physician assistant, nurse practitioner, or other), patient (age, race, ethnicity, sex, insurance status, unit), order session (location, duration, number of orders), and order (medication, radiology, lab, other). The presence or absence of a patient photograph will be tracked as an order level covariate.

Primary Analysis. The unit of analysis is the order session. The dependent variable is the proportion of order sessions that contain at least one order that was retracted and re-ordered (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 cluster randomization at the provider 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. Provider-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.

2-Arm RCT: Statistical Power/Sample Size. The overall design is a cluster randomized trial, with order-session observations nested within randomized providers. The number of providers (clusters) is a fixed attribute of the study site and will not be subject to investigator control. Based on preliminary data, the assumption is that data on 12,000 providers will be collected, with half randomized to each study arm. Over 2.5 years of observation, it is estimated that an average of 5,000 order sessions per provider will be accrued, with a coefficient of variation of 1.27. An intra-provider correlation of 0.001 for the outcome is anticipated. The most recent 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 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. A statistical power simulation was conducted for the 2x2 factorial study of banner photographs (photo/no photo in banner) and alert photographs (alert with photo/no alert). Based on prior studies and guidance from study investigators, it is estimated that approximately 8,268 providers will be randomized in a 1:1:1:1 ratio to the four study arms (no photograph, photograph in the banner only, photograph in the verification alert only, or photograph in the banner and verification alert). Providers were assumed to generate an average of 74 order sessions per month, with a Poisson distribution, and that data collection would continue for 1 year. Based on previous studies, the variance component (in the log-odds metric) at the provider level would be 1.9 (corresponding to an intra-provider correlation of 0.52). The base rate of RAR events was assumed to be 90 per 100,000 order sessions. Based on 400 simulations conducted under these assumptions, it was determined that the study 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 single-photograph intervention compared to no intervention. The power to detect an additional 15% reduction by the combination of both photographs (compared to either alone) is 99.8% (95% CI 98.6%-99.9%).

Missing Data. Due to the automatic functioning of the EHR, no missing data concerning Retract-and-Reorder events, the mode of intervention group versus control group, or the provider-level covariates is expected. There may be sporadic missing information regarding the patient-level covariates. If any variables are missing for more than 1 record per 1,000 (after backfilling based on other records involving the same patient), analyses will be extended to address this. The data will be presumed to be missing at random and will apply multiple imputation with chained equations.

Interim Analysis. To safeguard against the possibility that the 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, 50% of the data is expected 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. The associated nominal P value is .0024. Combined with a final analysis using a critical z-value of 1.9669 (nominal P = .0492), the overall alpha of 0.05 will be spent by 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.

Intention-to-Treat vs As-Treated Analyses. Some patients will likely decline to be photographed or for whom photographs are not taken for various reasons. The photo capture rate (the number of patients with a photograph divided by the number of patients with a visit during the study period) will be monitored throughout the study period. In the primary analyses for all trials, an intention-to-treat principle will be used, and these patients' and sites' records will be included in the analysis as if they had been photographed and participated. A second "as-treated" analysis will be performed limited to compliant patients and sites, recognizing that such analyses may be subject to confounding by patient and site factors.

Study Design

• Study Type: Interventional (Clinical Trial)
• Estimated Enrollment: 20268 participants
• Allocation: Randomized
• Intervention Model: Parallel Assignment
• Masking: None (Open Label)
• Primary Purpose: Health Services Research
• Official Title: A Randomized Controlled Trial Evaluating the Effectiveness of Displaying Patient Photographs in an Electronic Health Record to Prevent Wrong-Patient Electronic Orders
• Actual Study Start Date: September 1, 2018
• Estimated Primary Completion Date: December 31, 2020
• Estimated Study Completion Date: December 31, 2021

Arms and Interventions

Arm	Intervention/treatment
Active Comparator: Photo in Verification Alert; Patient photo displayed in a patient ID verification alert when placing electronic orders in the electronic health record.	Behavioral: Photo in Verification Alert; Patient photograph displayed in a patient ID verification alert when placing electronic orders in the electronic health record.
Active Comparator: Photo in Banner; Patient photo displayed in the banner (at the top of the screen).	Behavioral: Photo in Banner; Patient photograph will be displayed in the banner at the top of the screen in the electronic health record.
Active Comparator: Photo in Banner and Verification Alert; Patient photograph displayed in the banner (at the top of the screen) AND patient photo displayed in a verification alert when placing electronic orders.	Behavioral: Photo in Banner and Verification Alert; Patient photograph will be displayed in the banner at the top of the screen in the electronic health record AND patient photograph displayed in a patient ID verification alert when placing electronic orders in the electronic health record.
No Intervention: No Photo; No patient photographs displayed in the electronic health record.

Outcome Measures

Primary Outcome Measures :

1. Frequency of order sessions with at least one Retract-and-Reorder (RAR) event as identified by the Wrong-Patient Retract-and-Reorder (RAR) measure. [ Time Frame: 2.5 years ]
   The Wrong-Patient Retract-and-Reorder (RAR) measure is an automated, validated, and reliable measure endorsed by the National Quality Forum (NQF #2723). The RAR measure identifies orders placed for a patient that are retracted within 10 minutes, and then placed by the same provider for a different patient within the next 10 minutes. These are near-miss errors, self-caught by the provider 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 errors per 100,000 orders. Real-time telephone interviews with providers determined that the RAR measure correctly identified near-miss errors in 76.2% of cases). Thus, the RAR measure provides sufficient valid and reliable outcome data for this study.

Eligibility Criteria

• Ages Eligible for Study: Child, Adult, Older Adult
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

Criteria

Inclusion Criteria:

• All patients for whom an order was placed in the study period.
• All providers with the authority to place electronic orders and who placed electronic orders during the study period.

Exclusion Criteria:

• None

Contacts and Locations

Contacts

Contact: Jason Adelman, MD,MS; 646-317-4803; adelman.jason@columbia.edu

Contact: Jo Applebaum, MPH; 212-304-5540; joa9145@nyp.org

Locations

United States, Maryland

Johns Hopkins Medicine; Not yet recruiting

Baltimore, Maryland, United States, 21287

Contact: Allen Chen, MD, PhD

United States, New York

Montefiore Medical Center; Not yet recruiting

Bronx, New York, United States, 10461

Contact: William Southern, MD, MS

New York-Presbyterian Hospital; Recruiting

New York, New York, United States, 10032

Principal Investigator: Jason S Adelman, MD

Sponsors and Collaborators

Columbia University

Agency for Healthcare Research and Quality (AHRQ)

Investigators

• Principal Investigator: Jason Adelman, MD,MS; Columbia University

More Information

Publications:

Adelman JS, Kalkut GE, Schechter CB, Weiss JM, Berger MA, Reissman SH, Cohen HW, Lorenzen SJ, Burack DA, Southern WN. Understanding and preventing wrong-patient electronic orders: a randomized controlled trial. J Am Med Inform Assoc. 2013 Mar-Apr;20(2):305-10. doi: 10.1136/amiajnl-2012-001055. Epub 2012 Jun 29.

• Responsible Party: Jason Adelman, Assistant Professor of Medicine, Chief Patient Safety Officer, Columbia University
• ClinicalTrials.gov Identifier: NCT03626766
• Other Study ID Numbers: AAAR0080
• First Posted: August 13, 2018
• Last Update Posted: September 25, 2018
• Last Verified: September 2018

Individual Participant Data (IPD) Sharing Statement:

• Plan to Share IPD: Undecided

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No