Healthcare today continues to grow exponentially more complex, by virtue of remarkable advancements in medical science and technology over a relatively short time. The breadth of data related to each individual patient's care is often overwhelming, as is the critical task of efficiently and completely communicating these data to a colleague at the point of care transition. Transitions of care have been identified as one of the most dangerous events in a patient's hospitalization and they occur at least twice daily at most institutions. Errors in communication during these transitions of care (or handoffs) are a significant source of medical error and likely put patients at risk of harm.1 Errors in clinician communication have been identified as the cause of roughly one third of medical errors.2,3 A recent review of data from the Centers for Disease Control and Prevention and the medical literature shows that medical errors rank as the third leading cause of death in the United States.4,5 As a result, the Accreditation Council for Graduate Medical Education and the Joint Commission have made accurate and efficient transitions of care a top priority as a means of improving patient safety and medical education.6,7
To date, studies focused on care transition methodology have mostly been limited to observational and prospective cohort studies, rather than large, multicenter, randomized trials.8,9 However, the literature supports the fact that standardization of the use of evidence-based tools for patient handoff results in improvements in efficiency of care transitions, as well as reduction in communication errors and adverse events during patients' hospitalizations.10–13 In a study of pediatrics residency programs in nine tertiary care centers, Starmer et al12 (the I-PASS Study Group) observed a 23% decrease in medical errors and a 30% decrease in preventable adverse events after implementation of a handoff improvement program using the I-PASS mnemonic (illness severity, patient summary, action list, situational awareness, and synthesis by receiver). The I-PASS system was designed as a verbal and written framework by which to systematically communicate the most important information about a patient to the healthcare provider taking over during a transition of care. In addition, improvement in communication during handoff has been demonstrated by several other studies, most notably in a highly cited article by Abraham et al,13 who showed significantly fewer communication breakdowns using the Handoff Intervention Tool.
Despite these structured handoff tools that cue the clinician to focus on communicating important information, transitions of care can still suffer as a result of human error if flawed information is conveyed. Errors of this type are possible when written handoff documents supplement verbal communication during transitions of care because these could be easily overlooked by users engaged in patient handoff. One potential solution is partial automation during production of these handoff documents, which may decrease the cognitive load of the attention to detail required to manually enter reliably correct information. This feature can allow the clinician to focus more on elaborate contingency planning and accurate communication during transitions of care.14
The literature has repeatedly supported the fact that the use of a structured system for handoff, such as I-PASS, results in fewer communication errors and enhances patient safety by reducing adverse events.1,5 Some electronic medical record (EMR) systems actually include a partially automated tool for generating a list of patients on a given inpatient medical team that may be used to augment verbal transitions of care with up-to-date patient data. However, this feature is not universal. Most military treatment facilities use Essentris (CliniComp, Intl., San Diego, CA), an EMR solution licensed to the Department of Defense for use in the inpatient setting. Essentris does not include a feature for automatically generating a patient list that can be hand delivered to another healthcare provider during a transition of care. The accepted method for generating such a list at many institutions using an EMR without the capability of automatically generating one has been to create a document using a word processing program and to save this document to a shared drive on the local secure network. This method requires clinicians to manually enter data for every patient into the shared document before handoff. These data include patient name, date of birth, room number, identification number, allergies, resuscitation status, current medications, and current laboratory study results. The manually generated handoff documents also include higher-order information, such as contingency planning, encoded in the I-PASS format for clarity and completeness.
The objective of this quality improvement initiative was to determine whether the implementation of a partially automated, I-PASS–based electronic handoff tool would decrease errors in documented patient information communicated between providers during transitions of care for inpatient medical teams already using I-PASS.
From fall 2016 to spring 2017, our team worked closely with information technology staff to create a simple and streamlined electronic tool that would incorporate user-generated patient information in the I-PASS format (i.e., the higher-order information that can only be synthesized by a healthcare provider) with automatically compiled patient information that could easily be derived from data present in Essentris (i.e., patient name, date of birth, room number, identification number, allergies, resuscitation status, current medications, and current laboratory study results). The manually entered I-PASS information was to be entered in a physician note, updated daily for each patient on the inpatient team. After several design iterations, the team arrived at a finished product that securely stored these data in the local SQL Server (Microsoft, Redmond, WA) and could be accessed through a link in the EMR. This tool generates a printable handoff document that includes both automated data and user-generated I-PASS information for each patient on a given inpatient team. I-PASS is copyrighted by Boston Children's Hospital, but materials are freely available.
After the determination by the institutional review board that the need for informed consent was waived, the authors collected preintervention data for the first Plan-Do-Study-Act (PDSA) cycle.15 This first cycle involved replacing the existing system of completely manually generated handoff documents (created in a word processing program) with the new automated handoff tool for the six inpatient internal medicine teams at our institution. This occurred from May to August 2017. Preintervention and postintervention data collected included number of errors documented in the printed handoff document with respect to patient identification number, date of birth, room number, allergies, current medications, and recent laboratory study results. Errors were recorded by remotely accessing the medicine teams' handoff documents on the shared network drive around the time of evening transitions of care and comparing these data for each patient to the correct information in their electronic charts. Evening handoff was chosen for data collection over morning handoff for two reasons. First, the majority of data entry into the handoff documents typically occurs in the mid to late afternoon on the inpatient teams. Thus, measuring errors at this time of day was thought to yield data most indicative of true human error, as opposed to the presence of incorrect data on the handoff documents being a result of obsolete information that had simply not yet been updated. Second, collecting data around evening transitions of care was logistically easiest because doing so conflicted less with our own clinical duties.
Based on feedback received from users of the automated handoff tool during the first PDSA cycle, changes were made to its functionality and content. New data fields were added, including most recent weight and primary care manager, and improvements to the program's underlying code were made to decrease the amount of time required to open the handoff tool. The second PDSA cycle was then undertaken, which involved implementing the handoff tool with inpatient pediatric teams from January to April 2018.
During the first PDSA cycle, before intervention, transitions of care for 524 patients on the inpatient medicine teams were reviewed and 336 errors in written communication occurred. This means that each patient's individual risk of experiencing an error in written communication was 64.1% (95% confidence interval [CI] 59.9–68.1%). After implementation of the automated handoff tool, 57 errors occurred out of 307 patient handoffs observed (risk of error 18.6%; 95% CI 14.6–23.3%). This first cycle demonstrated an absolute risk reduction for errors of 45.6% (95% CI 39.2–51.2%) and a number needed to treat (NNT) of three patients (i.e., use of the automated handoff tool for three patients would prevent one error in written communication). During the second PDSA cycle, transitions of care for 93 patients on the inpatient pediatric teams were reviewed before intervention and 68 errors occurred, for a risk of 73.1% (95% CI 63.3–81.1%). Of these errors, 55 (59%) were errors in information related to allergies, medications, laboratory data, or code status. After implementation of the automated handoff tool, 18 errors occurred out of 91 handoffs observed (risk of error 19.8%; 95% CI 12.9–29.1%). The second PDSA cycle demonstrated an absolute risk reduction of 53.3% (95% CI 39.8–63.9%) and an NNT of two patients. Aggregate data from both PDSA cycles revealed a preimplementation risk of written communication error of 65.5% (95% CI 61.6–69.1%) and a postimplementation risk of 18.8% (95% CI 15.3–23.0%). This demonstrates an aggregate absolute risk reduction of 46.6% (95% CI 41.0–51.7%) and an NNT of three patients. All results are displayed in Table 1.
The results reported here are preliminary and only reflect two PDSA cycles of a project that is currently ongoing. As such, the sample size is lower than that of the previously published studies. We did not directly observe transitions of care, and so, the validity of information communicated verbally was not assessed; the primary outcome of our study (number of errors documented per patient handoff) served as a surrogate for errors in communication. Furthermore, incorrectly documented information in Essentris would populate to the automated handoff tool, but evaluating the accuracy of our institution's native EMR was outside of the scope of this study. Another limitation of this study is that it does not report data for adverse events directly related to patient safety; so, it was not possible to directly tie errors in communication to patient safety. However, previous works have demonstrated a connection between communication errors and adverse events.1,5,16 In addition, it may be noted that the second PDSA cycle included a significantly smaller sample than the first. This was due to a comparatively low census on the pediatric teams compared with the internal medicine teams at our institution. Given the relative consistency of data between the two cycles, we believe this sample size to be sufficient to demonstrate a difference in outcome. Finally, limitations in manpower for our working group prevented the inclusion of blinded chart reviewers for data collection; so, there is some risk of systematic error.
The results of this study show a significant reduction in inaccurate data communicated during transitions of care after adoption of a partially automated I-PASS–based tool for compiling patient information before handoff to oncoming care teams. The degree of risk reduction we observed was greater than expected based on the results of similar studies published in the literature, which showed improvements in outcomes related to patient safety. As such, it would be reasonable to extrapolate our outcome of decreased errors in written communication to the more patient-centered outcome of decreased adverse events. It should be noted that over half of the errors we observed were related to allergies, medications, laboratory data, and code status. If treatment decisions were made based on errors in higher stakes data like these, it is conceivable that a patient would be at risk of harm.
In addition, individual feedback from users of the automated I-PASS tool indicated significant improvements in administrative workload for house staff involved in performing transitions of care. This new process of creating documents for patient handoff seems to save time and decrease the cognitive load of preparing for handoff, as compared to the old model of manually updating these documents. Decreasing the time users spend performing menial tasks can allow for more time and energy to devote to higher-order thinking related to medical decision making. Specifically, clinicians unburdened from manual data entry by an automated handoff tool may be able to better conceptualize their patients' clinical pictures and more precisely plan for contingencies that the oncoming team might anticipate after transition of care. Surveying users of our automated I-PASS tool presents an opportunity for further investigation with respect to improvements in workflow.
Improving on the results observed here will require fine-tuning of the automated I-PASS tool. The majority of the residual errors observed after implementation were related to failure of the medication lists and most recent laboratory data to populate the handoff document. This was not surprising because the portion of code required to extract these data from the EMR is the most complex in the tool. Ensuring that these systems accurately extract information and reliably update it to the handoff document in a timely manner may allow for reduction of errors in communication to zero using our automated I-PASS tool. Sustainability will be the next challenge for this project. Training future users will be accomplished through the inclusion of a training module on the automated handoff tool as part of the hospital's required I-PASS training for all new house staff. Behind the scenes, the development team will train the information technology “help desk” staff on how best to troubleshoot any issues that come up and how to properly route more advanced issues to the developers.
The automated I-PASS handoff tool is still in the process of being expanded to more services at our institution, with the goal of standardization among all teams with hospital admitting privileges by spring 2019. Data are still being collected, both before and after implementation, for each new service that adopts the automated handoff tool. The working group for this project is modifying the handoff note that serves as the route for data entry so that consultant teams may also use this system for their own transitions of care. Addition of this feature will allow generalizability to all physician teams at our institution. Simultaneously, the nursing leadership at our hospital has implemented a process improvement initiative to use I-PASS during transitions of care between nurses as well. These parallel initiatives will allow for greater cross-talk between providers and nursing/allied health personnel. These projects exist within a larger institution-wide effort to improve transitions of care by way of standardizing the use of I-PASS. By conducting transitions of care using similar tools based on a common script, accuracy and efficiency of handoffs will be accomplished in a multidisciplinary fashion. Our automated tool is serving to streamline this process and allow for easy integration of I-PASS into everyday patient care.
As one of the first adopters of the I-PASS bundle, our institution set the example early on for transitions of care. However, until now, the limitations of our EMR system have held our institution back from the cutting edge of patient safety. This study demonstrates how improving one aspect of the routine task of patient handoff through the thoughtful application of technology may yield benefits in terms of decreasing the risk of medical error. The practice of modern medicine is staggeringly complex and as fraught with error as we are, it is challenging to devise and implement patient care plans without mistakes, especially when continuity of care requires transition of responsibility from one healthcare team to another. Thankfully, through automation of certain aspects of the healthcare machine, we may be able to reduce these errors and improve safety for our patients.
Although our study was not designed to observe patient safety events, the literature supports that errors in communication are related to adverse outcomes. Adverse events are often multifactorial, but communication errors may play a significant role. Further investigation is necessary to show a truly causal relationship, but our study demonstrates a solution to a problem that has yet to be completely elucidated. Multicenter, longitudinal investigation of automated tools for use during transitions of care is needed to corroborate the exciting preliminary results reported here.
None of the material contained in this article was published previously. The views expressed are those of the authors and do not necessarily reflect the official policy or position of the Department of the Navy, the Department of the Army, the Department of Defense, or the U.S. Government.
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Michael M. Skaret, MD, is a staff internist and hospitalist at the Walter Reed National Military Medical Center, Bethesda, MD.
Travis D. Weaver, DO, is a fellow in endocrinology at the Walter Reed National Military Medical Center, Bethesda, MD.
Ross J. Humes, MD, is a fellow in gastroenterology at the Walter Reed National Military Medical Center, Bethesda, MD.
Thomas V. Carbone, DO, is a resident physician in internal medicine at the Walter Reed National Military Medical Center, Bethesda, MD.
Ian A. Grasso, MD, is a fellow in pulmonary and critical care medicine at the Walter Reed National Military Medical Center, Bethesda, MD.
Harjinder Kumar, MD, is a staff internist, geriatrician, and hospitalist at the Walter Reed National Military Medical Center, Bethesda, MD.
Journal for Healthcare Quality is pleased to offer the opportunity to earn continuing education (CE) credit to those who read this article and take the online posttest at www.nahq.org/journal/ce. This continuing education offering, JHQ 281 (41.6 November/December 2019), will provide 1 hour to those who complete it appropriately.
Core CPHQ Examination Content Area
IV. [Domain—Domain-2. Information Management and Domain 4. Patient Safety]
Automation of the I-PASS Tool to Improve Transitions of Care
- Define the I-PASS mnemonic for conducting transitions of care.
- Describe the potential benefits of automation during preparation for patient handoff.
- Describe the process of using PDSA cycles in quality improvement.
- 1. According to the Centers for Disease Control and Prevention (CDC), _____ is the third leading cause of death in the United States.
2. Improving transitions of care is a top priority of which organization(s)?
- Cardiovascular disease
- Medical error
3. “I-PASS” is a mnemonic for a script to use during patient handoffs. It stands for _____.
- The Joint Commission and the Accreditation Council for Graduate Medical Education (ACGME)
- The Joint Commission only
- The Accreditation Council for Graduate Medical Education (ACGME) only
- The Liaison Committee on Medical Education (LCME)
4. Which of the following is NOT a handoff tool mentioned in the article?
- Information, Patient name, Awareness items, Signs, and Symptoms
- Illness severity, Patient summary, Action list, Situational awareness, and Synthesis by receiver
- Illness severity, Patient name, Awareness items, Situation, and Synthesis by receiver
- Information, Patient name, Action list, Situational awareness, and Synthesis by receiver
5. The “I-PASS” system for transitions of care involves two components: _____ and _____.
- SBAR (Situation-Background-Assessment-Recommendation)
- Handoff Intervention Tool
- Patient Handoff Tool
6. _____ can improve the accuracy of information communicated during handoffs while simultaneously decreasing the cognitive load required by this process.
- Physician; nurse
- Patient; provider
- Illness; severity
- Verbal; written.
7. The ______ system is a widely used process for quality improvement, by which changes are rolled out to groups of ever-increasing size, and the effects of these changes are measured after each cycle.
- Attention to detail
8. During process improvement initiatives, it is helpful to ______ after each cycle of implementing new processes.
9. PDSA is an acronym for ______.
- Completely redesign the project
- Discard previous data
- Focus on another unrelated project
- Incorporate helpful feedback
10. The aggregate data from this study (both cycles) showed a number-needed-to-treat of ______ patient(s) to prevent one error in communication.