Assessing the Revised Safer Dx Instrument® in the understanding of ambulatory system design changes for type 1 diabetes and autism spectrum disorder in pediatrics.

OBJECTIVES: We sought within an ambulatory safety study to understand if the Revised Safer Dx instrument may be helpful in identification of diagnostic missed opportunities in care of children with type 1 diabetes (T1D) and autism spectrum disorder (ASD). METHODS: We reviewed two months of emergency department (ED) encounters for all patients at our tertiary care site with T1D and a sample of such encounters for patients with ASD over a 15-month period, and their pre-visit communication methods to better understand opportunities to improve diagnosis. We applied the Revised Safer Dx instrument to each diagnostic journey. We chose potentially preventable ED visits for hyperglycemia, diabetic ketoacidosis, and behavioral crises, and reviewed electronic health record data over the prior three months related to the illness that resulted in the ED visit. RESULTS: We identified 63 T1D and 27 ASD ED visits. Using the Revised Safer Dx instrument, we did not identify any potentially missed opportunities to improve diagnosis in T1D. We found two potential missed opportunities (Safer Dx overall score of 5) in ASD, related to potential for ambulatory medical management to be improved. Over this period, 40 % of T1D and 52 % of ASD patients used communication prior to the ED visit. CONCLUSIONS: Using the Revised Safer Dx instrument, we uncommonly identified missed opportunities to improve diagnosis in patients who presented to the ED with potentially preventable complications of their chronic diseases. Future researchers should consider prospectively collected data as well as development or adaptation of tools like the Safer Dx.

Incidence, severity and detection of blood pressure and heart rate perturbations in postoperative ward patients after noncardiac surgery.

STUDY OBJECTIVE: We sought to determine changes in continuous mean and systolic blood pressure and heart rate in a cohort of non-cardiac surgical patients recovering on the postoperative ward. Furthermore, we estimated the proportion of vital signs changes that would remain undetected with intermittent vital signs checks. DESIGN: Retrospective cohort. SETTING: Post-operative general ward. PATIENTS: 14,623 adults recovering from non-cardiac surgical procedures. INTERVENTIONS & MEASUREMENTS: Using a wireless, noninvasive monitor, we recorded postoperative blood pressure and heart rate at 15-s intervals and encouraged nursing intervention as clinically indicated. MAIN RESULTS: 7% of our cohort of 14,623 patients spent >15 sustained minutes with a MAP <65 mmHg, and 23% had MAP <75 mmHg for 15 sustained minutes. Hypertension was more common, with 67% of patients spending at least 60 sustained minutes with MAP >110 mmHg. Systolic pressures <90 mmHg were present for 15 sustained minutes in about a fifth of all patients, and 40% of patients had pressures >160 mmHg sustained for 30 min. 40% of patients were tachycardic with heart rates >100 beats/min for at least continuous 15 min and 15% of patients were bradycardic at a threshold of <50 beats/min for 5 sustained minutes. Conventional vital sign assessments at 4-h intervals would have missed 54% of mean pressure episodes <65 mmHg sustained >15 min, 20% of episodes of mean pressures >130 mmHg sustained >30 min, 36% of episodes of heart rate > 120 beats/min sustained <10 min, and 68% of episodes of heart rate sustained <40 beats per minute for >3 min. CONCLUSIONS: Substantial hemodynamic disturbances persisted despite implementing continuous portable ward monitoring coupled with nursing alarms and interventions. A significant proportion of these changes would have gone undetected using traditional intermittent monitoring. Better understanding of effective responses to alarms and appropriate interventions on hospital wards remains necessary.

Safer Type 1 Diabetes Care at Home: SEIPS-based Process Mapping with Parents and Clinicians.

Introduction: The limited data indicate that pediatric medical errors in the outpatient setting, including at home, are common. This study is the first step of our Ambulatory Pediatric Patient Safety Learning Lab to address medication errors and treatment delays among children with T1D in the outpatient setting. We aimed to identify failures and potential solutions associated with medication errors and treatment delays among outpatient children with T1D. Methods: A transdisciplinary team of parents, safety researchers, and clinicians used Systems Engineering Initiative for Patient Safety (SEIPS) based process mapping of data we collected through in-home medication review, observation of administration, chart reviews, parent surveys, and failure modes and effects analysis (FMEA). Results: Eight (57%) of the 14 children who had home visits experienced 18 errors (31 per 100 medications). Four errors in two children resulted in harm, and 13 had the potential for harm. Two injuries occurred when parents failed to treat severe hypoglycemia and lethargy, and two were due to repeated failures to administer insulin at home properly. In SEIPS-based process maps, high-risk errors occurred during communication between the clinic and home or in management at home. Two FMEAs identified interventions to better communicate with families and support home care, especially during evolving illness. Conclusion: Using SEIPS-based process maps informed by multimodal methods to identify medication errors and treatment delays, we found errors were common. Better support for managing acute illness at home and improved communication between the clinic and home are potentially high-yield interventions.

Clinical Decision Support Stewardship: Best Practices and Techniques to Monitor and Improve Interruptive Alerts.

Interruptive clinical decision support systems, both within and outside of electronic health records, are a resource that should be used sparingly and monitored closely. Excessive use of interruptive alerting can quickly lead to alert fatigue and decreased effectiveness and ignoring of alerts. In this review, we discuss the evidence for effective alert stewardship as well as practices and methods we have found useful to assess interruptive alert burden, reduce excessive firings, optimize alert effectiveness, and establish quality governance at our institutions. We also discuss the importance of a holistic view of the alerting ecosystem beyond the electronic health record.

Feasibility, Acceptability, and Performance of a Continuous Temperature Monitor in Older Adults and Staff in Congregate-Living Facilities.

OBJECTIVES: Residents of congregate-living facilities are susceptible to disability and mortality from infection given the presence of advanced age, multimorbidity, and frailty-as demonstrated in the recent COVID pandemic. This study assessed the feasibility, acceptability, and applicability of a continuous temperature monitoring device in a congregate-living facility with residents of independent living, assisted living, and their care-providing staff. We hypothesized that a wearable device compared with daily manual temperature assessment would be well tolerated and more effective at detecting temperature variances than current standard of care body temperature assessment. DESIGN: Feasibility study. SETTING AND PARTICIPANTS: Residents of assisted and independent living and staff of a retirement community. METHODS: Thirty-five participants, including residents in assisted- and independent-living facilities (25) and staff (10) were enrolled in a 90-day feasibility study and wore a continuous temperature sensor from March to July 2021. Primary outcomes included study completion, ability to reapply the sensor, temperature data acquisition, and data availability from the sensors. A secondary analysis of the temperature data involved comparing the method of obtaining temperature using the continuous monitoring device against standard of care using traditional manual thermometers. RESULTS: Overall, 91.3% of residents, who were in the study during the first reapplication, were able to apply the device without assistance (21 of 23), and 80% of resident participants completed the study (20 of 25). For staff participants, completion rates and reapplication rates were 100%. Data acquisition rates from the continuous temperature devices were much higher than manual temperatures. Four episodes of fever were detected by the devices; manual temperature checks did not identify these events. CONCLUSIONS AND IMPLICATIONS: Continuous temperature monitoring in an older adult population and the staff in congregate-living facilities is feasible and acceptable. This approach identified fever undetected by current standard of care indicating the capability of this device for earlier detection of fevers.

Creating learning health systems and the emerging role of biomedical informatics.

INTRODUCTION: The nature of information used in medicine has changed. In the past, we were limited to routine clinical data and published clinical trials. Today, we deal with massive, multiple data streams and easy access to new tests, ideas, and capabilities to process them. Whereas in the past getting information for decision-making was a challenge, now, it is how to analyze, evaluate and prioritize all that is readily available through the multitude of data-collecting devices. Clinicians must become adept with the tools needed to deal with the era of big data, requiring a major change in how we learn to make decisions. Major change is often met with resistance and questions about value. A Learning Health System is an enabler to encourage the development of such tools and demonstrate value in improved decision-making. METHODS: We describe how we are developing a Biomedical Informatics program to help our medical institution's evolution as an academic Learning Health System, including strategy, training for house staff and examples of the role of informatics from operations to research. RESULTS: We described an array of learning health system implementations and educational programs to improve healthcare and prepare a cadre of physicians with basic information technology skills. The programs have been well accepted with, for example, increasing interest and enrollment in the educational programs. CONCLUSIONS: We are now in an era when large volumes of a wide variety of data are readily available. The challenge is not so much in the acquisition of data, but in assessing the quality, relevance and value of the data. The data we can get may not be the data we need. In the past, sources of data were limited, and trial results published in journals were the major source of evidence for decision making. The advent of powerful analytics systems has changed the concept of evidence. Clinicians will have to develop the skills necessary to work in the era of big data. It is not reasonable to expect that all clinicians will also be data scientists. However, understanding the role of AI and predictive analytics, and how to apply them, will become progressively more important. Programs such as the one being implemented at Wake Forest fill that need.

Alert burden in pediatric hospitals: a cross-sectional analysis of six academic pediatric health systems using novel metrics.

BACKGROUND: Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden. OBJECTIVE: (1) Analyze alert burden by alert type, care setting, provider type, and individual provider across 6 pediatric health systems. (2) Compare alert burden using different metrics. MATERIALS AND METHODS: We analyzed interruptive alert firings logged in EHR databases at 6 pediatric health systems from 2016-2019 using 4 metrics: (1) alerts per patient encounter, (2) alerts per inpatient-day, (3) alerts per 100 orders, and (4) alerts per unique clinician days (calendar days with at least 1 EHR log in the system). We assessed intra- and interinstitutional variation and how alert burden rankings differed based on the chosen metric. RESULTS: Alert burden varied widely across institutions, ranging from 0.06 to 0.76 firings per encounter, 0.22 to 1.06 firings per inpatient-day, 0.98 to 17.42 per 100 orders, and 0.08 to 3.34 firings per clinician day logged in the EHR. Custom alerts accounted for the greatest burden at all 6 sites. The rank order of institutions by alert burden was similar regardless of which alert burden metric was chosen. Within institutions, the alert burden metric choice substantially affected which provider types and care settings appeared to experience the highest alert burden. CONCLUSION: Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.

The Effect of Electronic Health Record Burden on Pediatricians' Work-Life Balance and Career Satisfaction.

OBJECTIVES: To examine pediatricians' perspectives on administrative tasks including electronic health record (EHR) documentation burden and their effect on work-life balance and life and career satisfaction. METHODS: We analyzed 2018 survey data from the American Academy of Pediatrics (AAP) Pediatrician Life and Career Experience Study (PLACES), a longitudinal cohort study of early and midcareer pediatricians. Cohorts graduated from residency between 2002 and 2004 or 2009 and 2011. Participants were randomly selected from an AAP database (included all pediatricians who completed U.S. pediatric residency programs). Four in 10 pediatricians (1,796 out of 4,677) were enrolled in PLACES in 2012 and considered participants in 2018. Data were weighted to adjust for differences between study participants and the overall population of pediatricians. Chi-square and multivariable logistic regression examined the association of EHR burden on work-life balance (three measures) and satisfaction with work, career, and life (three measures). Responses to an open-ended question on experiences with administrative tasks were reviewed. RESULTS: A total of 66% of pediatrician participants completed the 2018 surveys (1,192 of 1,796; analytic sample = 1,069). Three-fourths reported EHR documentation as a major or moderate burden. Half reported such burden for billing and insurance and 42.7% for quality and performance measurement. Most pediatricians reported satisfaction with their jobs (86.7%), careers (84.5%), and lives (66.2%). Many reported work-life balance challenges (52.5% reported stress balancing work and personal responsibilities). In multivariable analysis, higher reported EHR burden was associated with lower scores on career and life satisfaction measures and on all three measures of work-life balance. Open-ended responses (n = 467) revealed several themes. Two predominant themes especially supported the quantitative findings-poor EHR functionality and lack of support for administrative burdens. CONCLUSION: Most early to midcareer pediatricians experience administrative burdens with EHRs. These experiences are associated with worse work-life balance including more stress in balancing responsibilities and less career and life satisfaction.

Human-Based Errors Involving Smart Infusion Pumps: A Catalog of Error Types and Prevention Strategies.

Over 4000 preventable injuries due to medication errors occur each year in any given hospital. Smart pumps have been widely introduced as one means to prevent these errors. Although smart pumps have been implemented to prevent errors, they fail to prevent specific types of errors in the medication administration process and may introduce new errors themselves. As a result, unique prevention strategies have been implemented by providers. No catalog of smart pump error types and prevention strategies currently exists. The aim of this study is to review and catalog the types of human-based errors related to smart pump use identified in the literature and to summarize the associated error-prevention strategies. We searched MEDLINE, PubMed, PubMed Central, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) for literature pertaining to human-based errors associated with smart pumps. Studies related to smart pump implementation, other types of pumps, and mechanical failures were excluded. Final selections were mapped for error types and associated prevention strategies. A total of 1177 articles were initially identified, and 105 articles were included in the final review. Extraction of error types and prevention strategies resulted in the identification of 18 error types and ten prevention strategies. Through a comprehensive literature review, we compiled a catalog of smart pump-related errors and associated prevention strategies. Strategies were mapped to error types to provide an initial framework for others to use as a resource in their error reviews and improvement work. Future research should assess the application of the resources provided by this review.

A prospective multi-center quality improvement initiative (NINJA) indicates a reduction in nephrotoxic acute kidney injury in hospitalized children.

Nephrotoxic medication (NTMx) exposure is a common cause of acute kidney injury (AKI) in hospitalized children. The Nephrotoxic Injury Negated by Just-in time Action (NINJA) program decreased NTMx associated AKI (NTMx-AKI) by 62% at one center. To further test the program, we incorporated NINJA across nine centers with the goal of reducing NTMx exposure and, consequently, AKI rates across these centers. NINJA screens all non-critically ill hospitalized patients for high NTMx exposure (over three medications on the same day or an intravenous aminoglycoside over three consecutive days), and then recommends obtaining a daily serum creatinine level in exposed patients for the duration of, and two days after, exposure ending. Additionally, substitution of equally efficacious but less nephrotoxic medications for exposed patients starting the day of exposure was recommended when possible. The main outcome was AKI as defined by the Kidney Disease Improving Global Outcomes (KDIGO) serum creatinine criteria (increase of 50% or 0.3 mg/dl over baseline). The primary outcome measure was AKI episodes per 1000 patient-days. Improvement was defined by statistical process control methodology and confirmed by Autoregressive Integrated Moving Average (ARIMA) modeling. Eight consecutive bi-weekly measure rates in the same direction from the established baseline qualified as special cause change for special process control. We observed a significant and sustained 23.8% decrease in NTMx-AKI rates by statistical process control analysis and by ARIMA modeling; similar to those of the pilot single center. Thus, we have successfully applied the NINJA program to multiple pediatric institutions yielding decreased AKI rates.

Feedback at the Point of Care to Decrease Medication Alert Rates in an Electronic Health Record.

Frequently overridden alerts in the electronic health record can highlight alerts that may need revision. This method is a way of fine-tuning clinical decision support. We evaluated the feasibility of a complementary, yet different method that directly involved pediatric emergency department (PED) providers in identifying additional medication alerts that were potentially incorrect or intrusive. We then evaluated the effect subsequent resulting modifications had on alert salience. METHODS: We performed a prospective, interventional study over 34 months (March 6, 2014, to December 31, 2016) in the PED. We implemented a passive alert feedback mechanism by enhancing the native electronic health record functionality on alert reviews. End-users flagged potentially incorrect/bothersome alerts for review by the study's team. The alerts were updated when clinically appropriate and trends of the impact were evaluated. RESULTS: More than 200 alerts were reported from both inside and outside the PED, suggesting an intuitive approach. On average, we processed 4 reviews per week from the PED, with attending physicians as major contributors. The general trend of the impact of these changes seems favorable. DISCUSSION: The implementation of the review mechanism for user-selected alerts was intuitive and sustainable and seems to be able to detect alerts that are bothersome to the end-users. The method should be run in parallel with the traditional data-driven approach to support capturing of inaccurate alerts. CONCLUSIONS: User-centered, context-specific alert feedback can be used for selecting suboptimal, interruptive medication alerts.

Performance of a Clinical Decision Support Tool to Identify PICU Patients at High Risk for Clinical Deterioration.

OBJECTIVES: To evaluate the translation of a paper high-risk checklist for PICU patients at risk of clinical deterioration to an automated clinical decision support tool. DESIGN: Retrospective, observational cohort study of an automated clinical decision support tool, the PICU Warning Tool, adapted from a paper checklist to predict clinical deterioration events in PICU patients within 24 hours. SETTING: Two quaternary care medical-surgical PICUs-The Children's Hospital of Philadelphia and Cincinnati Children's Hospital Medical Center. PATIENTS: The study included all patients admitted from July 1, 2014, to June 30, 2015, the year prior to the initiation of any focused situational awareness work at either institution. INTERVENTIONS: We replicated the predictions of the real-time PICU Warning Tool by retrospectively querying the institutional data warehouse to identify all patients that would have flagged as high-risk by the PICU Warning Tool for their index deterioration. MEASUREMENTS AND MAIN RESULTS: The primary exposure of interest was determination of high-risk status during PICU admission via the PICU Warning Tool. The primary outcome of interest was clinical deterioration event within 24 hours of a positive screen. The date and time of the deterioration event was used as the index time point. We evaluated the sensitivity, specificity, positive predictive value, and negative predictive value of the performance of the PICU Warning Tool. There were 6,233 patients evaluated with 233 clinical deterioration events experienced by 154 individual patients. The positive predictive value of the PICU Warning Tool was 7.1% with a number needed to screen of 14 patients for each index clinical deterioration event. The most predictive of the individual criteria were elevated lactic acidosis, high mean airway pressure, and profound acidosis. CONCLUSIONS: Performance of a clinical decision support translation of a paper-based tool showed inferior test characteristics. Improved feasibility of identification of high-risk patients using automated tools must be balanced with performance.

Inpatient Communication Networks: Leveraging Secure Text-Messaging Platforms to Gain Insight into Inpatient Communication Systems.

OBJECTIVE: This study attempts to characterize the inpatient communication network within a quaternary pediatric academic medical center by applying network analysis methods to secure text-messaging data. METHODS: We used network graphing and statistical software to create network models of an inpatient communication system with secure text-messaging data from physicians, nurses, and other ancillary staff in an academic medical center. Descriptive statistics about the network, users within the network, and visualizations informed the team's understanding of the network and its components. RESULTS: Analysis of messages exchanged over approximately 23 days revealed a large, scale-free network with 4,442 nodes and 59,913 edges. Quantitative description of user behavior (messages sent and received) and network metrics (i.e., importance of nodes within a network) revealed several operational and clinical roles both sending and receiving > 1,000 messages over this time period. While some of these nodes represented expected "dispatcher" roles in our inpatient system, others occupied important frontline clinical roles responsible for bedside clinical care. CONCLUSION: Quantitative and network analysis of secure text-messaging logs revealed several key operational and clinical roles at risk for alert fatigue and information overload. This analysis also revealed a communication network highly reliant on these key roles, meaning disruption to these individuals or their workflows could lead to dysfunction of the communication network. While secure text-messaging applications play increasingly important roles in facilitating inpatient communication, little is understood about the impact these systems have on health care providers. Developing methods to understand and optimize communication between inpatient providers might help operational and clinical leaders to proactively prevent poorly understood pitfalls associated with these systems and build resilient and effective communication structures.

Mathematical Model for Computer-Assisted Modification of Medication Dosing Rules.

OBJECTIVE: Medication dosing in pediatrics is complex and prone to errors that may lead to patient harm. To improve computer-assisted dosing, a mathematical model and algorithm were developed to optimize clinical decision support dosing rules and reduce spurious alerts. The objective was to evaluate the feasibility of using this algorithm to adjust dosing rules. MATERIALS AND METHODS: Incorporating historical ordering data, a mathematical model and algorithm were developed to automatically determine optimal dosing rule parameters. The algorithm optimizes the dosing rules by balancing the number of alerts generated for a medication with a minimal length dose interval. In all, 5 candidate medications were tested. An analysis was performed to compare the number of alerts generated by the new model with the current dosing rules. RESULTS: For the 5 medications, the algorithm generated multiple clinically relevant rule possibilities and the rules returned performed as well as current dosing rule or matched historical prescriber behavior. The rules were comparable to or better than the existing system rules in reducing the total alert burden. DISCUSSION: The mathematical model and algorithm are an accurate and scalable solution to adjusting medication dosing rules. They can be implemented to change suboptimal rules more quickly than current manual methods and can be used to help identify and correct poor quality rules. CONCLUSIONS: Mathematical modeling using historic prescribing data can generate clinically appropriate electronic dosing rule parameters. This approach represents an automatable and scalable solution that could help reduce alert fatigue and decrease medication dosing errors.

Trends in Use of Electronic Health Records in Pediatric Office Settings.

OBJECTIVES: To determine the prevalence and functionalities of electronic health records (EHRs) and pediatricians' perceptions of EHRs. STUDY DESIGN: An 8-page self-administered questionnaire sent to 1619 randomly selected nonretired US American Academy of Pediatrics members in 2016 was completed by 709 (43.8%). Responses were compared with surveys in 2009 and 2012. RESULTS: The percent of pediatricians who were using EHRs increased from 58% in 2009 and 79% in 2012 to 94% in 2016. Those with fully functional EHRs, including pediatric functionality, more than doubled from 8.2% in 2012 to 16.9% in 2016 (P = .01). Fully functional EHRs lacking pediatric functionality increased slightly from 7.8% to 11.1% (P = .3), and the percentage of pediatricians with basic EHRs remained stable (30.4% to 31.0%; P < .3). The percentage of pediatricians who lacked basic EHR functionality or who reported no EHR decreased (from 53.6% to 41.0%; P < .001). On average, pediatricians spent 3.4 hours per day documenting care. CONCLUSIONS: Although the adoption of EHRs has increased, >80% of pediatricians are working with EHRs that lack optimal functionality and 41% of pediatricians are not using EHRs with even basic functionality. EHRs lacking pediatric functionality impact the health of children through increased medical errors, missed diagnoses, lack of adherence to guidelines, and reduced availability of child-specific information. The pediatric certification outlined in the 21st Century Cures Act may result in improved EHR products for pediatricians.

Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit.

Background: Timely identification of medication administration errors (MAEs) promises great benefits for mitigating medication errors and associated harm. Despite previous efforts utilizing computerized methods to monitor medication errors, sustaining effective and accurate detection of MAEs remains challenging. In this study, we developed a real-time MAE detection system and evaluated its performance prior to system integration into institutional workflows. Methods: Our prospective observational study included automated MAE detection of 10 high-risk medications and fluids for patients admitted to the neonatal intensive care unit at Cincinnati Children's Hospital Medical Center during a 4-month period. The automated system extracted real-time medication use information from the institutional electronic health records and identified MAEs using logic-based rules and natural language processing techniques. The MAE summary was delivered via a real-time messaging platform to promote reduction of patient exposure to potential harm. System performance was validated using a physician-generated gold standard of MAE events, and results were compared with those of current practice (incident reporting and trigger tools). Results: Physicians identified 116 MAEs from 10 104 medication administrations during the study period. Compared to current practice, the sensitivity with automated MAE detection was improved significantly from 4.3% to 85.3% (P = .009), with a positive predictive value of 78.0%. Furthermore, the system showed potential to reduce patient exposure to harm, from 256 min to 35 min (P < .001). Conclusions: The automated system demonstrated improved capacity for identifying MAEs while guarding against alert fatigue. It also showed promise for reducing patient exposure to potential harm following MAE events.

A Comparison of Existing Methods to Detect Weight Data Errors in a Pediatric Academic Medical Center.

Dosing errors due to erroneous body weight entry can be mitigated through algorithms designed to detect anomalies in weight patterns. To prepare for the development of a new algorithm for weight-entry error detection, we compared methods for detecting weight anomalies to human annotation, including a regression-based method employed in a real-time web service. Using a random sample of 4,000 growth charts, annotators identified clinically important anomalies with good inter-rater reliability. Performance of the three detection algorithms was variable, with the best performance from the algorithm that takes into account weights collected after the anomaly was recorded. All methods were highly specific, but positive predictive value ranged from < 5% to over 82%. There were 203 records of missed errors, but all of these were either due to no prior data points or errors too small to be clinically significant. This analysis illustrates the need for better weight-entry error detection algorithms.

Educational Added Value Unit: Development and Testing of a Measure for Educational Activities.

OBJECTIVES: University-based hospitalists educate health care professionals as an expectation, often lacking time and support for these activities. The purpose of this study was to (1) develop a tracking tool to record educational activities, (2) demonstrate its applicability and ease of completion for faculty members in different divisions, and (3) compare educational efforts of individuals from different professional pathways and divisions by using the educational added value unit (EAVU). METHODS: Educational activities were selected and ranked according to preparation effort, presentation time, and impact to calculate the EAVU. Faculty participants from 5 divisions at 1 institution (hospital medicine, general and community pediatrics, emergency medicine, behavior medicine and clinical psychology, and biostatistics and epidemiology) completed the retrospective, self-report tracking tool. RESULTS: A total of 62% (74 of 119) of invited faculty members participated. All faculty earned some EAVUs; however, there was a wide distribution range. The median EAVU varied by division (hospital medicine [21.7], general and community pediatrics [20.6], emergency medicine [26.1], behavior medicine and clinical psychology [18.3], and biostatistics and epidemiology [8.2]). Faculty on the educator pathway had a higher median EAVU compared with clinical or research pathways. CONCLUSIONS: The EAVU tracking tool holds promise as a mechanism to track educational activities of different faculty pathways. EAVU collection could be of particular benefit to hospitalists, who often perform unsupported teaching activities. Additional studies are needed to determine how to apply a similar process in different institutions and to determine how EAVUs could be used for additional support for teaching, curriculum development, and educational scholarship.

The Effects of Medication Alerts on Prescriber Response in a Pediatric Hospital.

OBJECTIVE: More than 70% of hospitals in the United States have electronic health records (EHRs). Clinical decision support (CDS) presents clinicians with electronic alerts during the course of patient care; however, alert fatigue can influence a provider's response to any EHR alert. The primary goal was to evaluate the effects of alert burden on user response to the alerts. METHODS: We performed a retrospective study of medication alerts over a 24-month period (1/2013-12/2014) in a large pediatric academic medical center. The institutional review board approved this study. The primary outcome measure was alert salience, a measure of whether or not the prescriber took any corrective action on the order that generated an alert. We estimated the ideal number of alerts to maximize salience. Salience rates were examined for providers at each training level, by day of week, and time of day through logistic regressions. RESULTS: While salience never exceeded 38%, 49 alerts/day were associated with maximal salience in our dataset. The time of day an order was placed was associated with alert salience (maximal salience 2am). The day of the week was also associated with alert salience (maximal salience on Wednesday). Provider role did not have an impact on salience. CONCLUSION: Alert burden plays a role in influencing provider response to medication alerts. An increased number of alerts a provider saw during a one-day period did not directly lead to decreased response to alerts. Given the multiple factors influencing the response to alerts, efforts focused solely on burden are not likely to be effective.