Grants

Lead Site

Developing and Validating Health IT Safety Measures to Capture Violations of the Five Rights of Medication Safety (AHRQ R01-HS024538)

The 2011 Institute of Medicine report, Health IT and Patient Safety, raised awareness of risks introduced by Health IT systems and called for the development of "new measures for reliably assessing the current state of Health IT safety and monitoring for improvements." Adelman and colleagues developed and validated the first Health IT Safety measure—the Wrong-Patient Retract-and-Reorder (RAR) Measure—endorsed by the National Quality Forum. The Wrong-Patient 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. The measure identified over 5,000 near-miss, wrong-patient electronic orders at one hospital in a single year, which is more than 500 times the average number of errors previously identified by voluntary reporting. In this project, we will use the Retract-and-Reorder automated detection method to develop a set of valid and reliable measures to capture violations of the Five Rights of Medication Safety: right patient, right drug, right dose, right route, and right frequency. In Aim 1, we will develop and validate measures for detecting wrong-drug, wrong-dose, wrong-route, and wrong-frequency electronic orders using a novel methodology that enables us to conduct near real-time interviews with providers after an RAR event occurs. In Aim 2, we will implement the automated measures at a second hospital using a different EHR to evaluate the reliability of the measures. In Aim 3, we will conduct a multi-site observational study describing the overall frequency of these types of errors in different settings and systems. Developing and validating a full set of medication safety measures will provide a greater understanding of the epidemiology of these critical errors, enable regulatory bodies to conduct ongoing surveillance of health system performance, and allow researchers to test interventions aimed at preventing these errors and improving patient safety.

Using the Retract-and-Reorder automated detection method, we will develop a set of measures to capture five types of medication safety errors: wrong patient, wrong medication, wrong dose, wrong route, and wrong frequency.

Role: Lead Site; Jason Adelman PI

Study Period: 9/1/2016–6/30/2020

Providing Evidence and Developing a Toolkit to Accelerate the Adoption of Patient Photographs in Electronic Health Records (AHRQ R01-HS024713)

We are studying whether patient photographs prevent wrong-patient errors in electronic health record systems. Providers are randomly assigned to either see a patient photo or not see patient photos when placing electronic orders.

To prevent wrong-patient errors, the Office of the National Coordinator for Health Information Technology (ONC) Patient Identification SAFER Guide recommends displaying patient photographs in EHRs, but the vast majority of healthcare systems have not adopted this safety practice. In a national survey of hospitals, respondents identified lack of evidence that photographs improve safety and workflow challenges as major barriers to adoption. Preliminary data suggest that displaying patient photographs in an EHR significantly decreases the frequency of wrong-patient orders. In this project, we are pursuing the following aims: Aim 1) provide rigorous evidence that patient photographs prevent wrong-patient order errors, using the Wrong-Patient Retract-and-Reorder Measure to identify the outcome; Aim 2) demonstrate generalizability by evaluating the effectiveness of patient photographs across three large health systems using three different EHR systems; and Aim 3) develop a Health IT Toolkit to guide healthcare organizations through the implementation process. Because implementation of patient photographs will likely vary across systems, this project leverages a collaboration among Columbia University, NewYork-Presbyterian (NYP) Hospital, NYP/Brooklyn Methodist Hospital, Johns Hopkins Medicine, and Montefiore Medical Center/Albert Einstein College of Medicine. Based on the functionality of the EHRs, we are conducting a randomized controlled trial in Allscripts and Epic, in which providers are randomized to view EHR screens with versus without patient photos, and we will conduct a pre- versus post-implementation study in Cerner. We are working with the ECRI Institute's Partnership for Health IT Patient Safety to develop the first Health IT Safety Toolkit, Implementing Patient Photographs in EHR Systems, to share lessons learned.

Role: Lead Site; Jason Adelman PI

Study Period: 09/30/2017–07/31/2021

Effectiveness of Pictographs to Prevent Wrong-Patient Errors in the NICU (NICHD R01-HD094793)

We developed an innovative identifier for newborns called a Pictograph that includes a distinctive object, the baby’s name, and a color-coded border indicating the baby’s sex. Three Pictographs—a Lion, a Spaceship, and a Clover—distinguish triplets whose names in the electronic health record are very similar (1Anitasboy, 2Anitasboy, 3Anitasboy).

Newborns in the neonatal intensive care unit (NICU) are at high risk for wrong-patient errors. A major contributing factor is the use of temporary, nondistinct first names (e.g., Babyboy/Babygirl) that are assigned to newborns at birth and remain unchanged throughout their hospital stay. Our research found that a distinct newborn naming convention that incorporates the mother’s first name (e.g., Wendysgirl) reduced the risk of wrong-patient orders in the NICU by 36%. However, the distinct naming convention conferred benefit only for singletons—multiples remained at high risk as a result of siblings sharing the same name distinguished by a single character (e.g., 1Wendysgirl, 2Wendysgirl). Displaying patient photographs in EHRs is a promising strategy to improve patient identification, but photographs are unlikely to be effective in the NICU because newborns lack distinguishing physical features. Instead, we are testing Pictographs as a

We are testing whether Pictographs will prevent wrong-patient errors among newborns, and specifically among multiple birth infants. Providers are randomly assigned to see a Pictograph or not see Pictographs when placing orders in the electronic health record.

“photo equivalents” for newborns in the NICU, displayed at the bedside and in the EHR to serve as a visual cue when providers place orders. Pictographs consist of three elements: an image of an easy-to-remember object; the infant’s given name (when available); and a color-coded border indicating the infant’s sex. Parents select a distinctive Pictograph for their infants for the duration of their hospital stay, with no two infants having the same Pictograph at the same time in the same NICU. We will pursue the following aims: Aim 1) conduct a multi-site, randomized controlled trial to compare the frequency of wrong-patient orders in the NICU between providers assigned to view EHR screens with versus without Pictographs, as identified by the Wrong-Patient Retract-and-Reorder Measure; Aim 2) conduct subgroup analyses of the effectiveness of Pictographs for reducing the frequency of wrong-patient orders among siblings of multiple births; and Aim 3) conduct a qualitative evaluation to examine the perceptions and experience of Pictographs among providers and parents. If proven effective, Pictographs could be a landmark innovation that safeguards newborns in the NICU.

Role: Lead Site; Jason Adelman PI

Study Period: 04/01/2018–03/31/2023

Assessing the Risk of Wrong-Patient Errors in an EHR that Allows Multiple Records Open
(AHRQ R21-HS023704)

Although patient safety experts recommend limiting providers to one patient record open at a time in EHRs, no evidence supports this recommendation. Therefore, we conducted the first randomized comparative effectiveness trial to assess the risk of wrong-patient orders in a "restricted” configuration that limited providers to open one patient record at a time compared to an "unrestricted” configuration that allowed providers to open up to four records at once. The study included more than 3,000 providers who placed more than 12,000,000 orders, for more than 500,000 patients. Using the Wrong-Patient Retract-and-Reorder Measure, we found that restricting providers to one record open did not decrease the risk of wrong-patient orders, overall and in the emergency department, inpatient, and outpatient settings. In a survey of randomized providers, satisfaction and usability overwhelmingly favored the unrestricted arm. Data collection for this study is complete; however, secondary analysis and manuscript development are ongoing.

In this study, providers were randomly assigned to open 1 patient record at a time (Restricted arm) or up to 4 records at a time (Unrestricted arm).

Lead Site; Jason Adelman PI

Period: Completed, data analysis ongoing

Columbia University Patient Safety Research Fellowship in Hospital Medicine (AHRQ T32-HS026121)

The aim of this postdoctoral research fellowship is to support promising Medicine and Pediatric clinician-researchers to pursue academic careers and become future leaders in hospital-based patient safety and health services research. The program is designed to prepare fellows to conduct innovative interdisciplinary research, employ rigorous and varied research methodologies, present and publish study findings, and successfully compete for extramural peer-reviewed research funding. The fellowship offers a unique combination of formal research education, mentored research projects, and exposure to patient safety operations at a large academic medical center. Fellows will have the opportunity to work with a diverse and accomplished group of Columbia faculty with a proven track record of grant funding, interdisciplinary research collaboration, publication, and mentorship. Our Faculty Mentors represent a broad range of clinical and academic disciplines, and have expertise and active research support in focus areas including medical errors, medication safety, healthcare-associated infections, health informatics, quality measurement and outcomes, cost and cost-effectiveness, chronic disease epidemiology, and health disparities. This 2-year training program consists of five core components: 1) Formal Research Education; 2) Mentored Research Projects; 3) Patient Safety Immersion; 4) Bi-Weekly Research Seminars; and 5) Grant Proposal Development. All fellows will earn a master’s degree in Epidemiology or Patient Oriented Research at the Mailman School of Public Health. In addition, a distinctive core component of the program is the Patient Safety Immersion, consisting of a patient safety curriculum leading to the Certified Professional in Patient Safety (CPPS) credential and a fellow-developed research project based on real-world patient safety hazards.

Role: Multiple PIs/Co-Directors; Jason Adelman, R. Graham Barr

Study Period: 07/01/2018–06/30/2023

For more information about the Patient Safety Research Fellowship, visit the Patient Safety Research Fellowship page.

Collaborating Site

Preventing Wrong-Drug and Wrong-Patient Errors with Indication Alerts in CPOE Systems
(AHRQ R01-HS024945)

A core principle of medication safety is making sure the right patient gets the right drug, yet wrong-drug and wrong-patient errors occur at a rate of about 1 per 1000 orders in inpatient and outpatient settings, resulting in millions of errors annually in the US. Indication-based prescribing has the potential to reduce these errors, and improve the completeness of patients’ problem lists. This project develops, validates, and replicates indication alerts in two health systems that utilize two different EHR systems. Indication alerts are triggered when a provider orders a medication indicated for a condition that is not in the patient’s problem list. Medications targeted for alerts are ones that are 1) prone to look-alike and sound-alike errors; 2) commonly prescribed; and 3) have a narrow indication (e.g., metformin for diabetes). We will implement a set of 30–50 indication alerts developed by the University of Illinois at Chicago and NewYork-Presbyterian. Using a time series design (pre- versus post-implementation), we will study the effect of indication alerts on the rate of near-miss, wrong-drug and wrong-patient order errors. The study will also assess the impact of indication alerts on the likelihood of adding new diagnoses to the problem list.

Role: Collaborating Site; Bruce Lambert PI; Jason Adelman Site Lead

Study Period: 10/01/2016–9/30/2021

Generalizability and Spread of an Evidenced-based Fall Prevention Toolkit: Fall TIPS (AHRQ R18-HS024945)

In the U.S., 1 million hospitalized patients fall annually and approximately 30% of falls result in injury. The Fall TIPS (Tailoring Interventions for Patient Safety) toolkit is an evidence-based fall prevention program integrated into hospital-based EHR systems. Fall TIPS significantly reduced falls in a randomized trial, particularly among older patients who are at greatest risk. In this project, the Fall TIPS program is being implemented in 15 hospitals across three large healthcare systems using different EHR systems in diverse patient populations: Partners, Montefiore, and Columbia/NewYork-Presbyterian. We are using the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, Maintenance) to conduct an initiation, process, and outcome evaluation. Outcomes from this dissemination study will increase understanding of the overall impact of the Fall TIPS program on providers and hospitalized patients. The study will both advance the development of Fall TIPS as a high-quality, turnkey evidence-based program and obtain data on factors that facilitate the adoption, implementation, and maintenance of hospital-based fall prevention programs. The ultimate objective is to spread best practices related to implementing and sustaining an evidence-based fall prevention program that prevents unintentional injuries and reduces their consequences.

Role: Collaborating Site; Patricia Dykes PI; Jason Adelman Site Lead

Study Period: 4/01/2017–01/31/2020

Ensuring Safe Performance of Electronic Health Records (AHRQ R01-HS023696)

EHRs are being deployed rapidly across the U.S., but it is uncertain to what extent they will improve safety and quality of patient care. To address this issue, Brigham and Women’s Hospital built the Leapfrog tool, which evaluates the safety performance of EHRs after deployment, particularly focusing on high-impact patient safety and medication safety issues in the inpatient setting. The Leapfrog tool is essentially a "flight simulator" for EHRs with computerized provider order entry (CPOE). Hospitals download simulated patients, attempt to enter simulated predefined groups of orders, and record whether critical decision support appears in response to these scenarios. After taking the test, hospitals get immediate feedback on their performance in high-impact high-prevalence safety areas, including a subset of potentially fatal orders in the test. In this project, the Leapfrog CPOE/EHR will be tested and further refined to cover additional high-impact clinical safety domains based on the experience of four hospitals using four different EHR systems, including Columbia/NewYork-Presbyterian. The updated test will be released for national use through the Leapfrog Group.

Role: Collaborating Site; David Bates PI; Jason Adelman Site Lead

Study Period: 09/01/2018–08/31/2019

Completed Grants

Making Acute Care More Patient Centered (AHRQ P30-HS023535)

The Patient Safety Learning Laboratory (PSLL) is a collaboration led by David Bates, MD, MSc at the Center for Patient Safety, Research and Practice at Brigham and Women's Hospital and James Benneyan, PhD at the Healthcare Systems Engineering Institute at Northeastern University. The PSLL develops tools to engage patients, family, and professional care team members in reliable identification, assessment, and reduction of patient safety threats in real-time, before they cause harm. The PSLL developed systems approaches to integrating health information technology (IT), stakeholder engagement mechanisms, and process design/engineering methods focused on three core projects: Project 1) Patient-centered Fall Prevention Toolkit; Project 2) Patient Safety Checklist Tool; and Project 3) MySafeCare Patient Safety Reporting System. As a result of increasing implementation and use of health IT and patient/family engagement in their plan of care, the PSLL will provide information, strategies, and tools for utilizing health IT to facilitate patient activation in eliminating harm in hospital settings. At NYP, we implemented the Fall TIPS program in selected pilot units and evaluated the perceptions of patients and staff about the program and the impact of the intervention on reducing falls.

Role: Collaborating Site; David Bates PI

Study Period: 09/30/2014–09/29/2018

Project RedDE: Reducing Diagnostic Errors in Primary Care Pediatrics (AHRQ R01-HS023608)

The limited research on pediatric diagnostic errors highlights the significance of the problem: 54% of pediatricians report making a diagnostic error at least monthly and 45% report making a diagnostic error that harms patients at least annually. This project was a multisite, prospective, cluster randomized trial testing a quality improvement collaborative intervention within the American Academy of Pediatrics Quality Improvement Innovation Networks (QuIIN). The goal was to reduce the incidence of three pediatric primary care diagnostic errors: missed diagnosis of adolescent depression, missed diagnosis of pediatric elevated blood pressure, and delayed diagnosis of actionable laboratory results. The study tested whether a quality improvement collaborative, consisting of evidence-based best-practice methodologies, mini-root cause analyses, data sharing, and behavior change techniques, reduced diagnostic error rates in a national group of pediatric primary care practices. This project will enhance the understanding of ambulatory pediatric diagnostic errors and serve as a foundation for projects aimed at reductions of pediatric diagnostic errors across settings and diagnoses.

Role: Collaborating Site; Michael Rinke PI

Study Period: 09/30/2014–09/29/2018