Publications by authors named "Eric Kirkendall"

43 Publications

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

Learn Health Syst 2022 Jan 11;6(1):e10259. Epub 2021 Mar 11.

Wake Forest School of Medicine Center for Biomedical Informatics (WFBMI) Winston-Salem North Carolina USA.

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.
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http://dx.doi.org/10.1002/lrh2.10259DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8753307PMC
January 2022

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

J Am Med Inform Assoc 2021 11;28(12):2654-2660

Center for Healthcare Innovation, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.

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.
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http://dx.doi.org/10.1093/jamia/ocab179DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633657PMC
November 2021

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

Appl Clin Inform 2021 05 2;12(3):697-707. Epub 2021 Aug 2.

Clinical Informatics Center, UT Southwestern Medical Center, Dallas, Texas, United States.

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 ( = 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.
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http://dx.doi.org/10.1055/s-0041-1732402DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8328745PMC
May 2021

The Generalizability of a Medication Administration Discrepancy Detection System: Quantitative Comparative Analysis.

JMIR Med Inform 2020 Dec 2;8(12):e22031. Epub 2020 Dec 2.

Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.

Background: As a result of the overwhelming proportion of medication errors occurring each year, there has been an increased focus on developing medication error prevention strategies. Recent advances in electronic health record (EHR) technologies allow institutions the opportunity to identify medication administration error events in real time through computerized algorithms. MED.Safe, a software package comprising medication discrepancy detection algorithms, was developed to meet this need by performing an automated comparison of medication orders to medication administration records (MARs). In order to demonstrate generalizability in other care settings, software such as this must be tested and validated in settings distinct from the development site.

Objective: The purpose of this study is to determine the portability and generalizability of the MED.Safe software at a second site by assessing the performance and fit of the algorithms through comparison of discrepancy rates and other metrics across institutions.

Methods: The MED.Safe software package was executed on medication use data from the implementation site to generate prescribing ratios and discrepancy rates. A retrospective analysis of medication prescribing and documentation patterns was then performed on the results and compared to those from the development site to determine the algorithmic performance and fit. Variance in performance from the development site was further explored and characterized.

Results: Compared to the development site, the implementation site had lower audit/order ratios and higher MAR/(order + audit) ratios. The discrepancy rates on the implementation site were consistently higher than those from the development site. Three drivers for the higher discrepancy rates were alternative clinical workflow using orders with dosing ranges; a data extract, transfer, and load issue causing modified order data to overwrite original order values in the EHRs; and delayed EHR documentation of verbal orders. Opportunities for improvement were identified and applied using a software update, which decreased false-positive discrepancies and improved overall fit.

Conclusions: The execution of MED.Safe at a second site was feasible and effective in the detection of medication administration discrepancies. A comparison of medication ordering, administration, and discrepancy rates identified areas where MED.Safe could be improved through customization. One modification of MED.Safe through deployment of a software update improved the overall algorithmic fit at the implementation site. More flexible customizations to accommodate different clinical practice patterns could improve MED.Safe's fit at new sites.
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http://dx.doi.org/10.2196/22031DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744260PMC
December 2020

Integrating and Evaluating the Data Quality and Utility of Smart Pump Information in Detecting Medication Administration Errors: Evaluation Study.

JMIR Med Inform 2020 Sep 2;8(9):e19774. Epub 2020 Sep 2.

Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.

Background: At present, electronic health records (EHRs) are the central focus of clinical informatics given their role as the primary source of clinical data. Despite their granularity, the EHR data heavily rely on manual input and are prone to human errors. Many other sources of data exist in the clinical setting, including digital medical devices such as smart infusion pumps. When incorporated with prescribing data from EHRs, smart pump records (SPRs) are capable of shedding light on actions that take place during the medication use process. However, harmoniz-ing the 2 sources is hindered by multiple technical challenges, and the data quality and utility of SPRs have not been fully realized.

Objective: This study aims to evaluate the quality and utility of SPRs incorporated with EHR data in detecting medication administration errors. Our overarching hypothesis is that SPRs would contribute unique information in the med-ication use process, enabling more comprehensive detection of discrepancies and potential errors in medication administration.

Methods: We evaluated the medication use process of 9 high-risk medications for patients admitted to the neonatal inten-sive care unit during a 1-year period. An automated algorithm was developed to align SPRs with their medica-tion orders in the EHRs using patient ID, medication name, and timestamp. The aligned data were manually re-viewed by a clinical research coordinator and 2 pediatric physicians to identify discrepancies in medication ad-ministration. The data quality of SPRs was assessed with the proportion of information that was linked to valid EHR orders. To evaluate their utility, we compared the frequency and severity of discrepancies captured by the SPR and EHR data, respectively. A novel concordance assessment was also developed to understand the detec-tion power and capabilities of SPR and EHR data.

Results: Approximately 70% of the SPRs contained valid patient IDs and medication names, making them feasible for data integration. After combining the 2 sources, the investigative team reviewed 2307 medication orders with 10,575 medication administration records (MARs) and 23,397 SPRs. A total of 321 MAR and 682 SPR dis-crepancies were identified, with vasopressors showing the highest discrepancy rates, followed by narcotics and total parenteral nutrition. Compared with EHR MARs, substantial dosing discrepancies were more commonly detectable using the SPRs. The concordance analysis showed little overlap between MAR and SPR discrepan-cies, with most discrepancies captured by the SPR data.

Conclusions: We integrated smart infusion pump information with EHR data to analyze the most error-prone phases of the medication lifecycle. The findings suggested that SPRs could be a more reliable data source for medication error detection. Ultimately, it is imperative to integrate SPR information with EHR data to fully detect and mitigate medication administration errors in the clinical setting.
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http://dx.doi.org/10.2196/19774DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7495258PMC
September 2020

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

Drug Saf 2020 11;43(11):1073-1087

Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.

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.
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http://dx.doi.org/10.1007/s40264-020-00986-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7750013PMC
November 2020

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

Kidney Int 2020 03 1;97(3):580-588. Epub 2019 Nov 1.

Division of Nephrology, Mattel Children's Hospital, Los Angeles, California, USA.

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.
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http://dx.doi.org/10.1016/j.kint.2019.10.015DOI Listing
March 2020

Smart pumps improve medication safety but increase alert burden in neonatal care.

BMC Med Inform Decis Mak 2019 11 7;19(1):213. Epub 2019 Nov 7.

Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA.

Background: Smart pumps have been widely adopted but there is limited evidence to understand and support their use in pediatric populations. Our objective was to assess whether smart pumps are effective at reducing medication errors in the neonatal population and determine whether they are a source of alert burden and alert fatigue in an intensive care environment.

Methods: Using smart pump records, over 370,000 infusion starts for continuously infused medications used in neonates and infants hospitalized in a level IV NICU from 2014 to 2016 were evaluated. Attempts to exceed preset soft and hard maximum limits, percent variance from those limits, and pump alert frequency, patterns and salience were evaluated.

Results: Smart pumps prevented 160 attempts to exceed the hard maximum limit for doses that were as high as 7-29 times the maximum dose and resulted in the reprogramming or cancellation of 2093 infusions after soft maximum alerts. While the overall alert burden from smart pumps for continuous infusions was not high, alerts clustered around specific patients and medications, and a small portion (17%) of infusions generated the majority of alerts. Soft maximum alerts were often overridden (79%), consistent with low alert salience.

Conclusions: Smart pumps have the ability to improve neonatal medication safety when compliance with dose error reducing software is high. Numerous attempts to administer high doses were intercepted by dosing alerts. Clustered alerts may generate a high alert burden and limit safety benefit by desensitizing providers to alerts. Future efforts should address ways to improve alert salience.
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http://dx.doi.org/10.1186/s12911-019-0945-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6836424PMC
November 2019

Electronic Health Record-Embedded Decision Support Platform for Morphine Precision Dosing in Neonates.

Clin Pharmacol Ther 2020 01 11;107(1):186-194. Epub 2019 Dec 11.

Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.

Morphine is the opioid most commonly used for neonatal pain management. In intravenous form, it is administered as continuous infusions and intermittent injections, mostly based on empirically established protocols. Inadequate pain control in neonates can cause long-term adverse consequences; however, providing appropriate individualized morphine dosing is particularly challenging due to the interplay of rapid natural physiological changes and multiple life-sustaining procedures in patients who cannot describe their symptoms. At most institutions, morphine dosing in neonates is largely carried out as an iterative process using a wide range of starting doses and then titrating to effect based on clinical response and side effects using pain scores and levels of sedation. Our background data show that neonates exhibit large variability in morphine clearance resulting in a wide range of exposures, which are poorly predicted by dose alone. Here, we describe the development and implementation of an electronic health record-integrated, model-informed decision support platform for the precision dosing of morphine in the management of neonatal pain. The platform supports pharmacokinetic model-informed dosing guidance and has functionality to incorporate real-time drug concentration information. The feedback is inserted directly into prescribers' workflows so that they can make data-informed decisions. The expected outcomes are better clinical efficacy and safety with fewer side effects in the neonatal population.
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http://dx.doi.org/10.1002/cpt.1684DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378965PMC
January 2020

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

Pediatr Crit Care Med 2020 02;21(2):129-135

Department of Anesthesiology and Critical Care Medicine, Division of Critical Care Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA.

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.
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http://dx.doi.org/10.1097/PCC.0000000000002106DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7007854PMC
February 2020

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

Appl Clin Inform 2019 05 26;10(3):471-478. Epub 2019 Jun 26.

Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States.

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.
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http://dx.doi.org/10.1055/s-0039-1692401DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6594834PMC
May 2019

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

Biomed Inform Insights 2019 28;11:1178222619829079. Epub 2019 May 28.

Department of Biomedical Informatics, University of Cincinnati, Cincinnati, OH, USA.

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.
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http://dx.doi.org/10.1177/1178222619829079DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539576PMC
May 2019

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

Pediatr Emerg Care 2020 Jul;36(7):e417-e422

Biomedical Informatics, Emergency Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH.

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.
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http://dx.doi.org/10.1097/PEC.0000000000001847DOI Listing
July 2020

Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support.

J Med Internet Res 2019 05 22;21(5):e13047. Epub 2019 May 22.

Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, United States.

Background: The continued digitization and maturation of health care information technology has made access to real-time data easier and feasible for more health care organizations. With this increased availability, the promise of using data to algorithmically detect health care-related events in real-time has become more of a reality. However, as more researchers and clinicians utilize real-time data delivery capabilities, it has become apparent that simply gaining access to the data is not a panacea, and some unique data challenges have emerged to the forefront in the process.

Objective: The aim of this viewpoint was to highlight some of the challenges that are germane to real-time processing of health care system-generated data and the accurate interpretation of the results.

Methods: Distinct challenges related to the use and processing of real-time data for safety event detection were compiled and reported by several informatics and clinical experts at a quaternary pediatric academic institution. The challenges were collated from the experiences of the researchers implementing real-time event detection on more than half a dozen distinct projects. The challenges have been presented in a challenge category-specific challenge-example format.

Results: In total, 8 major types of challenge categories were reported, with 13 specific challenges and 9 specific examples detailed to provide a context for the challenges. The examples reported are anchored to a specific project using medication order, medication administration record, and smart infusion pump data to detect discrepancies and errors between the 3 datasets.

Conclusions: The use of real-time data to drive safety event detection and clinical decision support is extremely powerful, but it presents its own set of challenges that include data quality and technical complexity. These challenges must be recognized and accommodated for if the full promise of accurate, real-time safety event clinical decision support is to be realized.
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http://dx.doi.org/10.2196/13047DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6549472PMC
May 2019

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

AMIA Annu Symp Proc 2018 5;2018:1103-1109. Epub 2018 Dec 5.

Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, OH.

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.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371361PMC
November 2019

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

J Pediatr 2019 03 5;206:164-171.e2. Epub 2018 Dec 5.

Department of Biomedical Informatics, Vanderbilt University, Nashville, TN; Department of Biomedical Informatics, Vanderbilt University, Nashville, TN. Electronic address:

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.
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http://dx.doi.org/10.1016/j.jpeds.2018.10.039DOI Listing
March 2019

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

J Am Med Inform Assoc 2018 05;25(5):555-563

Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.

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.
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http://dx.doi.org/10.1093/jamia/ocx156DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6018990PMC
May 2018

Predicting Unplanned Transfers to the Intensive Care Unit: A Machine Learning Approach Leveraging Diverse Clinical Elements.

JMIR Med Inform 2017 Nov 22;5(4):e45. Epub 2017 Nov 22.

The MITRE Corporation, Bedford, MA, United States.

Background: Early warning scores aid in the detection of pediatric clinical deteriorations but include limited data inputs, rarely include data trends over time, and have limited validation.

Objective: Machine learning methods that make use of large numbers of predictor variables are now commonplace. This work examines how different types of predictor variables derived from the electronic health record affect the performance of predicting unplanned transfers to the intensive care unit (ICU) at three large children's hospitals.

Methods: We trained separate models with data from three different institutions from 2011 through 2013 and evaluated models with 2014 data. Cases consisted of patients who transferred from the floor to the ICU and met one or more of 5 different priori defined criteria for suspected unplanned transfers. Controls were patients who were never transferred to the ICU. Predictor variables for the models were derived from vitals, labs, acuity scores, and nursing assessments. Classification models consisted of L1 and L2 regularized logistic regression and neural network models. We evaluated model performance over prediction horizons ranging from 1 to 16 hours.

Results: Across the three institutions, the c-statistic values for our best models were 0.892 (95% CI 0.875-0.904), 0.902 (95% CI 0.880-0.923), and 0.899 (95% CI 0.879-0.919) for the task of identifying unplanned ICU transfer 6 hours before its occurrence and achieved 0.871 (95% CI 0.855-0.888), 0.872 (95% CI 0.850-0.895), and 0.850 (95% CI 0.825-0.875) for a prediction horizon of 16 hours. For our first model at 80% sensitivity, this resulted in a specificity of 80.5% (95% CI 77.4-83.7) and a positive predictive value of 5.2% (95% CI 4.5-6.2).

Conclusions: Feature-rich models with many predictor variables allow for patient deterioration to be predicted accurately, even up to 16 hours in advance.
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http://dx.doi.org/10.2196/medinform.8680DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719228PMC
November 2017

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

Hosp Pediatr 2017 Nov 10;7(11):675-681. Epub 2017 Oct 10.

Divisions of Hospital Medicine and.

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.
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http://dx.doi.org/10.1542/hpeds.2017-0043DOI Listing
November 2017

Leveraging Food and Drug Administration Adverse Event Reports for the Automated Monitoring of Electronic Health Records in a Pediatric Hospital.

Biomed Inform Insights 2017 8;9:1178222617713018. Epub 2017 Jun 8.

Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, OH, USA.

The objective of this study was to determine whether the Food and Drug Administration's Adverse Event Reporting System (FAERS) data set could serve as the basis of automated electronic health record (EHR) monitoring for the adverse drug reaction (ADR) subset of adverse drug events. We retrospectively collected EHR entries for 71 909 pediatric inpatient visits at Cincinnati Children's Hospital Medical Center. Natural language processing (NLP) techniques were used to identify positive diseases/disorders and signs/symptoms (DDSSs) from the patients' clinical narratives. We downloaded all FAERS reports submitted by medical providers and extracted the reported drug-DDSS pairs. For each patient, we aligned the drug-DDSS pairs extracted from their clinical notes with the corresponding drug-DDSS pairs from the FAERS data set to identify Drug-Reaction Pair Sentences (DRPSs). The DRPSs were processed by NLP techniques to identify ADR-related DRPSs. We used clinician annotated, real-world EHR data as reference standard to evaluate the proposed algorithm. During evaluation, the algorithm achieved promising performance and showed great potential in identifying ADRs accurately for pediatric patients.
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http://dx.doi.org/10.1177/1178222617713018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467704PMC
June 2017

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

Appl Clin Inform 2017 05 10;8(2):491-501. Epub 2017 May 10.

Judith Dexheimer, PhD, Division of Emergency Medicine, Cincinnati Children's Hospital Medical Center, MLC 2008, 3333 Burnet Avenue, Cincinnati, OH 45229-3039, Email: Phone: 513-803-2962, Fax: 513-803-2581.

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.
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http://dx.doi.org/10.4338/ACI-2016-10-RA-0168DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6241745PMC
May 2017

Casting a Wider Safety Net: The Promise of Electronic Safety Event Detection Systems.

Jt Comm J Qual Patient Saf 2017 04 16;43(4):153-154. Epub 2017 Feb 16.

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http://dx.doi.org/10.1016/j.jcjq.2017.01.002DOI Listing
April 2017

Assessing Frequency and Risk of Weight Entry Errors in Pediatrics.

JAMA Pediatr 2017 04;171(4):392-393

Department of Information Services, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio2Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio3Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.

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http://dx.doi.org/10.1001/jamapediatrics.2016.3865DOI Listing
April 2017

Development of a Pediatric Adverse Events Terminology.

Pediatrics 2017 01;139(1)

Uniformed Services University of the Health Sciences, Bethesda, Maryland.

In 2009, the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) established the Pediatric Terminology Harmonization Initiative to establish a core library of terms to facilitate the acquisition and sharing of knowledge between pediatric clinical research, practice, and safety reporting. A coalition of partners established a Pediatric Terminology Adverse Event Working Group in 2013 to develop a specific terminology relevant to international pediatric adverse event (AE) reporting. Pediatric specialists with backgrounds in clinical care, research, safety reporting, or informatics, supported by biomedical terminology experts from the National Cancer Institute's Enterprise Vocabulary Services participated. The multinational group developed a working definition of AEs and reviewed concepts (terms, synonyms, and definitions) from 16 pediatric clinical domains. The resulting AE terminology contains >1000 pediatric diseases, disorders, or clinical findings. The terms were tested for proof of concept use in 2 different settings: hospital readmissions and the NICU. The advantages of the AE terminology include ease of adoption due to integration with well-established and internationally accepted biomedical terminologies, a uniquely temporal focus on pediatric health and disease from conception through adolescence, and terms that could be used in both well- and underresourced environments. The AE terminology is available for use without restriction through the National Cancer Institute's Enterprise Vocabulary Services and is fully compatible with, and represented in, the Medical Dictionary for Regulatory Activities. The terminology is intended to mature with use, user feedback, and optimization.
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http://dx.doi.org/10.1542/peds.2016-0985DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5292241PMC
January 2017

Automated identification of antibiotic overdoses and adverse drug events via analysis of prescribing alerts and medication administration records.

J Am Med Inform Assoc 2017 Mar;24(2):295-302

Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.

Objectives: Electronic trigger detection tools hold promise to reduce Adverse drug event (ADEs) through efficiencies of scale and real-time reporting. We hypothesized that such a tool could automatically detect medication dosing errors as well as manage and evaluate dosing rule modifications.

Materials And Methods: We created an order and alert analysis system that identified antibiotic medication orders and evaluated user response to dosing alerts. Orders associated with overridden alerts were examined for evidence of administration and the delivered dose was compared to pharmacy-derived dosing rules to confirm true overdoses. True overdose cases were reviewed for association with known ADEs.

Results: Of 55 546 orders reviewed, 539 were true overdose orders, which lead to 1965 known overdose administrations. Documentation of loose stools and diarrhea was significantly increased following drug administration in the overdose group. Dosing rule thresholds were altered to reflect clinically accurate dosing. These rule changes decreased overall alert burden and improved the salience of alerts.

Discussion: Electronic algorithm-based detection systems can identify antibiotic overdoses that are clinically relevant and are associated with known ADEs. The system also serves as a platform for evaluating the effects of modifying electronic dosing rules. These modifications lead to decreased alert burden and improvements in response to decision support alerts.

Conclusion: The success of this test case suggests that gains are possible in reducing medication errors and improving patient safety with automated algorithm-based detection systems. Follow-up studies will determine if the positive effects of the system persist and if these changes lead to improved safety outcomes.
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http://dx.doi.org/10.1093/jamia/ocw086DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6259663PMC
March 2017

A sustained quality improvement program reduces nephrotoxic medication-associated acute kidney injury.

Kidney Int 2016 07 21;90(1):212-21. Epub 2016 May 21.

Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.

Exposure to nephrotoxic medication is among the most common causes of acute kidney injury (AKI) in hospitalized patients. Here we conducted a prospective quality improvement project implementing a systematic Electronic Health Record screening and decision support process (trigger) in our quaternary pediatric inpatient hospital. Eligible patients were noncritically ill hospitalized children receiving an intravenous aminoglycoside for more than 3 days or more than 3 nephrotoxins simultaneously (exposure) from September 2011 through March 2015. Pharmacists recommended daily serum creatinine monitoring in exposed patients after appearance on the trigger report and AKI was defined by the Kidney Disease Improving Global Outcomes AKI criteria. A total of 1749 patients accounted for 2358 separate hospital admissions during which a total of 3243 episodes of nephrotoxin exposure were identified with 170 patients (9.7%) experiencing 2 or more exposures. A total of 575 individual AKI episodes occurred over the 43-month study period. Overall, the exposure rate decreased by 38% (11.63-7.24 exposures/1000 patient days), and the AKI rate decreased by 64% (2.96-1.06 episodes/1000 patient days). Assuming initial baseline exposure rates would have persisted without our project implementation, we estimate 633 exposures and 398 AKI episodes were avoided. Thus, systematic surveillance for nephrotoxic medication exposure and near real-time AKI risk can lead to sustained reductions in avoidable harm. These interventions and outcomes are translatable to other pediatric and nonpediatric hospitalized settings.
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http://dx.doi.org/10.1016/j.kint.2016.03.031DOI Listing
July 2016

Following the (Clinical Decision) Rules: Opportunities for Improving Safety and Resource Utilization With the Bacterial Meningitis Score.

Hosp Pediatr 2016 May 1;6(5):305-9. Epub 2016 Jan 1.

Division of Hospital Medicine, Cincinnati Children's Hospital and Medical Center Cincinnati, Ohio.

Background: The Bacterial Meningitis Score accurately classifies children with cerebrospinal fluid (CSF) pleocytosis at very low risk (VLR) versus not very low risk (non-VLR) for bacterial meningitis. Most children with CSF pleocytosis detected during emergency department evaluation are hospitalized despite the high accuracy of this prediction rule and the decreasing incidence of bacterial meningitis. The lack of widespread use of this rule may contribute to unnecessary risk exposure and costs.

Methods: This cross-sectional study included 1049 patients who, between January 2010 and May 2013, had suspicion for meningitis and underwent both a complete blood cell count and CSF studies during their emergency department evaluation. We then examined their hospitalizations to characterize exposure to drugs, radiologic studies, and the costs associated with their care to determine the safety and value repercussions of these VLR admissions. Primary outcomes include duration of antibiotics, exposure to drugs and radiology studies, safety events, and costs incurred during these VLR admissions.

Results: Twenty patients classified as VLR were admitted to the hospital. On average they received 35 hours of antibiotic therapy. There was 1 adverse drug event and 1 safety event.

Conclusions: The VLR patients admitted to the hospital were exposed to risk and costs despite their low risk stratification. Systematic application of the Bacterial Meningitis Score could prevent these exposures and costs.
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http://dx.doi.org/10.1542/hpeds.2015-0176DOI Listing
May 2016

Automated detection of medication administration errors in neonatal intensive care.

J Biomed Inform 2015 Oct 17;57:124-33. Epub 2015 Jul 17.

Division of Neonatology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States. Electronic address:

Objective: To improve neonatal patient safety through automated detection of medication administration errors (MAEs) in high alert medications including narcotics, vasoactive medication, intravenous fluids, parenteral nutrition, and insulin using the electronic health record (EHR); to evaluate rates of MAEs in neonatal care; and to compare the performance of computerized algorithms to traditional incident reporting for error detection.

Methods: We developed novel computerized algorithms to identify MAEs within the EHR of all neonatal patients treated in a level four neonatal intensive care unit (NICU) in 2011 and 2012. We evaluated the rates and types of MAEs identified by the automated algorithms and compared their performance to incident reporting. Performance was evaluated by physician chart review.

Results: In the combined 2011 and 2012 NICU data sets, the automated algorithms identified MAEs at the following rates: fentanyl, 0.4% (4 errors/1005 fentanyl administration records); morphine, 0.3% (11/4009); dobutamine, 0 (0/10); and milrinone, 0.3% (5/1925). We found higher MAE rates for other vasoactive medications including: dopamine, 11.6% (5/43); epinephrine, 10.0% (289/2890); and vasopressin, 12.8% (54/421). Fluid administration error rates were similar: intravenous fluids, 3.2% (273/8567); parenteral nutrition, 3.2% (649/20124); and lipid administration, 1.3% (203/15227). We also found 13 insulin administration errors with a resulting rate of 2.9% (13/456). MAE rates were higher for medications that were adjusted frequently and fluids administered concurrently. The algorithms identified many previously unidentified errors, demonstrating significantly better sensitivity (82% vs. 5%) and precision (70% vs. 50%) than incident reporting for error recognition.

Conclusions: Automated detection of medication administration errors through the EHR is feasible and performs better than currently used incident reporting systems. Automated algorithms may be useful for real-time error identification and mitigation.
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http://dx.doi.org/10.1016/j.jbi.2015.07.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4715992PMC
October 2015

A trigger tool to detect harm in pediatric inpatient settings.

Pediatrics 2015 Jun 18;135(6):1036-42. Epub 2015 May 18.

Division of Hospitalist Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California; and Center for Quality and Clinical Effectiveness, Lucile Packard Children's Hospital, Palo Alto, California.

Objectives: An efficient and reliable process for measuring harm due to medical care is needed to advance pediatric patient safety. Several pediatric studies have assessed the use of trigger tools in varying inpatient environments. Using the Institute for Healthcare Improvement's adult-focused Global Trigger Tool as a model, we developed and pilot tested a trigger tool that would identify the most common causes of harm in pediatric inpatient environments.

Methods: After formal training, 6 academic children's hospitals used this novel pediatric trigger tool to review 100 randomly selected inpatient records per site from patients discharged during the month of February 2012.

Results: From the 600 patient charts evaluated, 240 harmful events ("harms") were identified, resulting in a rate of 40 harms per 100 patients admitted and 54.9 harms per 1000 patient days across the 6 hospitals. At least 1 harm was identified in 146 patients (24.3% of patients). Of the 240 total events, 108 (45.0%) were assessed to have been potentially or definitely preventable. The most common patient harms were intravenous catheter infiltrations/burns, respiratory distress, constipation, pain, and surgical complications.

Conclusions: Consistent with earlier rates of all-cause harm in adult hospitals, harm occurs at high rates in hospitalized children. Availability and use of an all-cause harm identification tool will establish the epidemiology of harm and will provide a consistent approach to assessing the effect of interventions on harms in hospitalized children.
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http://dx.doi.org/10.1542/peds.2014-2152DOI Listing
June 2015
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