Development and evaluation of trigger tools to identify pediatric blood management errors.

BACKGROUND: Pediatric patient blood management (PBM) programs require continuous surveillance of errors and near misses. However, most PBM programs rely on passive surveillance methods. Our objective was to develop and evaluate a set of automated trigger tools for active surveillance of pediatric PBM errors. MATERIALS AND METHODS: We used the Rand-UCLA method with an expert panel of pediatric transfusion medicine specialists to identify and prioritize candidate trigger tools for all transfused blood products. We then iteratively developed automated queries of electronic health record (EHR) data for the highest priority triggers. Two physicians manually reviewed a subset of cases meeting trigger tool criteria and estimated each trigger tool's positive predictive value (PPV). We then estimated the rate of PBM errors, whether they reached the patient, and adverse events for each trigger tool across four years in a single pediatric health system. RESULTS: We identified 28 potential triggers for pediatric PBM errors and developed 5 automated trigger tools (positive patient identification, missing irradiation, unwashed products despite prior anaphylaxis, transfusion lasting >4 hours, over-transfusion by volume). The PPV for ordering errors ranged from 38-100%. The most frequently detected near miss event reaching patients was first transfusions without positive patient identification (estimate 303, 95% CI: 288-318 per year). The only adverse events detected were from over-transfusions by volume, including 4 adverse events detected on manual review that had not been reported in passive surveillance systems. DISCUSSION: It is feasible to automatically detect pediatric PBM errors using existing data captured in the EHR that enable active surveillance systems. Over-transfusions may be one of the most frequent causes of harm in the pediatric environment.

Associations between Child Opportunity Index and Pediatric Cardiac Surgical Outcomes.

OBJECTIVE: To assess the relationship between the Child Opportunity Index (COI), a comprehensive measurement of social determinants of health, and specific COI domains on patient-specific outcomes following congenital cardiac surgery in the metropolitan region of Atlanta, Georgia. STUDY DESIGN: In this retrospective chart review, we included patients who underwent an index operation for congenital heart disease between 2010 and 2020 in a single pediatric health care system. Patients' addresses were geocoded and mapped to census tracts. Descriptive statistics, univariable analysis, and multivariable regression models were employed to assess associations between variables and outcomes. RESULTS: Of the 7460 index surgeries, 3798 (51%) met eligibility criteria. Presence of an adverse outcome, defined as either mortality or 1 of several other major postoperative morbidities, was significantly associated with COI in the univariable model (P = .008), but not the multivariable regression model (P = .39). Postoperative hospital length of stay was significantly associated with COI (P < .001) in univariable and multivariable regression models. There was no significant association between COI and readmission within 30 days of hospital discharge in univariable (P < .094) and multivariable (P = .49) models. CONCLUSION: COI is associated with postoperative hospital length of stay but not all outcomes in patients after congenital heart surgery. By understanding the role of COI in outcomes related to cardiac surgery, targeted interventions can be developed to improve health equity.

Accurate Dosing Weight: When the 10% Really Matters.

INTRODUCTION: Children are at increased risk of medication-associated adverse events, often due to weight-based dosing errors. We aimed to reduce the proportion of medications that were administered where the dosing weight was ≥ 10% different from the recorded weight. METHODS: We adopted in-situ usability testing to iteratively improve design of clinical decision support that would enable accurate dosing weight documentation by prompting clinicians to update weight if recorded weight was > 10% different and it had been at least 7 days since the last dosing weight update. RESULTS: The proportion of medication administrations with difference >10% between their recorded weight and dosing weight decreased from 13.1% (56,256/ 429,006) in the baseline period to 9.5% (35,560 / 372,443) in the intervention period (P < 0.001). DISCUSSION AND CONCLUSION: User-centered design of an interruptive alert improved the accuracy of dosing weights during medication administrations without substantial alert burden. In-situ usability testing is an effective approach to rapidly obtain feedback from frontline users and iterate on the design to effect desired behavior changes.

Current Inpatient Screening Practices for Sexual History and STIs: An Opportunity to Seize.

The American Academy of Pediatrics recommends utilizing hospitalizations as an opportunity to provide sexual health screenings for adolescents. This study aimed to describe the current practice of sexual history documentation (SHxD) and sexually transmitted infection (STI) testing among adolescents admitted to a pediatric hospital medicine service. Retrospective cross-sectional study of adolescents (14-19 years old) admitted to the PHM service from 2017-2019 was performed at an academic children's health system. Patient (demographics, history of complex chronic condition, and insurance), hospitalization (length of stay, diagnosis, STI tests ordered/results), and physician (level of training and gender) characteristics were extracted for each encounter. A natural language processing algorithm identified the presence of SHxD. Univariate analysis and multivariable analysis were performed to detect factors associated with SHxD and STI screening. The prevalence of STIs was calculated for those who were tested. Out of 2242 encounters, SHxD and STI testing rates were 40.9% and 17.2%, respectively. Patient gender, race, lack of complex chronic condition, and resident involvement were predictive of SHxD and STI testing. SHxD increased the odds of STI testing significantly (OR 5.06, CI 3.90-6.58). Among those who were tested, the prevalence of STIs was highest for chlamydia (37/329, 11.2%). Overall, sexual health screening rates remain low in the hospital setting and future improvement initiatives are needed.

Pharmacist Metrics in the Pediatric Intensive Care Unit: an Exploration of the Medication Regimen Complexity-Intensive Care Unit (MRC-ICU) Score.

INTRODUCTION: The medication regimen complexity-intensive care unit (MRC-ICU) score has been developed and validated as an objective predictive metric for patient outcomes and pharmacist workload in the adult critically ill population. The purpose of this study was to explore the MRC-ICU and other workload metrics in the pediatric ICU (PICU). METHODS: This study was a retrospective cohort of pediatric ICU patients admitted to a single institution -between February 2, 2022 - August 2, 2022. Two scores were calculated, including the MRC-ICU and the pediatric Daily Monitoring System (pDMS). Data were extracted from the electronic health record. The primary outcome was the correlation of the MRC-ICU to mortality, as measured by Pearson -correlation -coefficient. Additionally, the correlation of MRC-ICU to number of orders was evaluated. Secondary -analyses explored the correlation of the MRC-ICU with pDMS and with hospital and ICU length of stay. RESULTS: A total of 2,232 patients were included comprising 2,405 encounters. The average age was 6.9 years (standard deviation [SD] 6.3 years). The average MRC-ICU score was 3.0 (SD 3.8). For the primary outcome, MRC-ICU was significantly positively correlated to mortality (0.22 95% confidence interval [CI 0.18 - 0.26]), p<0.05. Additionally, MRC-ICU was significantly positively correlated to ICU length of stay (0.38 [CI 0.34 - 0.41]), p<0.05. The correlation between the MRC-ICU and pDMS was (0.72 [CI 0.70 - 0.73]), p<0.05. CONCLUSION: In this pilot study, MRC-ICU demonstrated an association with existing prioritization metrics and with mortality and length of ICU stay in PICU population. Further, larger scale studies are required.

Effectiveness of a Vendor Predictive Model for the Risk of Pediatric Asthma Exacerbation: A Difference-in-Differences Analysis.

BACKGROUND: Asthma is a common cause of morbidity and mortality in children. Predictive models may help providers tailor asthma therapies to an individual's exacerbation risk. The effectiveness of asthma risk scores on provider behavior and pediatric asthma outcomes remains unknown. OBJECTIVE: Determine the impact of an electronic health record (EHR) vendor-released model on outcomes for children with asthma. METHODS: The Epic Systems Risk of Pediatric Asthma Exacerbation model was implemented on February 24, 2021, for volunteer pediatric allergy and pulmonology providers as a noninterruptive risk score visible in the patient schedule view. Asthma hospitalizations, emergency department (ED) visits, or oral steroid courses within 90 days of the index visit were compared from February 24, 2019, to February 23, 2022, using a difference-in-differences design with a control group of visits to providers in the same departments. Volunteer providers were interviewed to identify barriers and facilitators to model use. RESULTS: In the intervention group, asthma hospitalizations within 90 days decreased from 1.4% (54/3,842) to 0.7% (14/2,165) after implementation with no significant change in the control group (0.9% [171/19,865] preimplementation to 1.0% [105/10,743] post). ED visits in the intervention group decreased from 5.8% (222/3,842) to 5.5% (118/2,164) but increased from 5.5% (1,099/19,865) to 6.8% (727/10,743) in the control group. The adjusted difference-in-differences estimators for hospitalization, ED visit, and oral steroid outcomes were -0.9% (95% confidence interval [CI]: -1.6 to -0.3), -2.4% (-3.9 to -0.8), and -1.9% (-4.3 to 0.5). In qualitative analysis, providers understood the purpose of the model and felt it was useful to flag high exacerbation risk. Trust in the model was calibrated against providers' own clinical judgement. CONCLUSION: This EHR vendor model implementation was associated with a significant decrease in asthma hospitalization and ED visits within 90 days of pediatric allergy and pulmonology clinic visits, but not oral steroid courses.

User-Centered Design and Evaluation of Clinical Decision Support to Improve Early Peanut Introduction: Formative Study.

BACKGROUND: Peanut allergy has recently become more prevalent. Peanut introduction recommendations have evolved from suggesting peanut avoidance until the age of 3 years to more recent guidelines encouraging early peanut introduction after the Learning Early about Peanut Allergy (LEAP) study in 2015. Guideline adherence is poor, leading to missed care opportunities. OBJECTIVE: In this study, we aimed to develop a user-centered clinical decision support (CDS) tool to improve implementation of the most recent early peanut introduction guidelines in the primary care clinic setting. METHODS: We edited the note template of the well-child check (WCC) visits at ages 4 and 6 months with CDS prompts and point-of-care education. Formative and summative usability testing were completed with pediatric residents in a simulated electronic health record (EHR). We estimated task completion rates and perceived usefulness of the CDS in summative testing, comparing a test EHR with and without the CDS. RESULTS: Formative usability testing with the residents provided qualitative data that led to improvements in the build for both the 4-month and 6-month WCC note templates. During summative usability testing, the CDS tool significantly improved discussion of early peanut introduction at the 4-month WCC visit compared to scenarios without the CDS tool (9/15, 60% with CDS and 0/15, 0% without CDS). All providers except one at the 4-month WCC scenario gave at least an adequate score for the ease of use of the CDS tool for the history of present illness and assessment and plan sections. During the summative usability testing with the 6-month WCC new build note template, providers more commonly provided comprehensive care once obtaining a patient history concerning for an immunoglobulin E-mediated peanut reaction by placing a referral to allergy/immunology (P=.48), prescribing an epinephrine auto-injector (P=.07), instructing on how to avoid peanut products (P<.001), and providing an emergency treatment plan (P=.003) with CDS guidance. All providers gave at least an adequate score for ease of use of the CDS tool in the after-visit summary. CONCLUSIONS: User-centered CDS improved application of early peanut introduction recommendations and comprehensive care for patients who have symptoms concerning for peanut allergy in a simulation.

Quasi-experimental, Nonrandomized Initiative to Minimize Sleep Disruptions among Hospitalized Children.

Hospitalized children experience frequent sleep disruptions. We aimed to reduce caregiver-reported sleep disruptions of children hospitalized on the pediatric hospital medicine service by 10% over 12 months. Methods: In family surveys, caregivers cited overnight vital signs (VS) as a primary contributor to sleep disruption. We created a new VS frequency order of "every 4 hours (unless asleep between 2300 and 0500)" as well as a patient list column in the electronic health record indicating patients with this active VS order. The outcome measure was caregiver-reported sleep disruptions. The process measure was adherence to the new VS frequency. The balancing measure was rapid responses called on patients with the new VS frequency. Results: Physician teams ordered the new VS frequency for 11% (1,633/14,772) of patient nights on the pediatric hospital medicine service. Recorded VS between 2300 and 0500 was 89% (1,447/1,633) of patient nights with the new frequency ordered compared to 91% (11,895/13,139) of patient nights without the new frequency ordered (P = 0.01). By contrast, recorded blood pressure between 2300 and 0500 was only 36% (588/1,633) of patient nights with the new frequency but 87% (11,478/13,139) of patient nights without the new frequency (P < 0.001). Overall, caregivers reported sleep disruptions on 24% (99/419) of reported nights preintervention, which decreased to 8% (195/2,313) postintervention (P < 0.001). Importantly, there were no adverse safety issues related to this initiative. Conclusion: This study safely implemented a new VS frequency with reduced overnight blood pressure readings and caregiver-reported sleep disruptions.

Reducing Therapeutic Duplication in Inpatient Medication Orders.

BACKGROUND: Therapeutic duplication, the presence of multiple agents prescribed for the same indication without clarification for when each should be used, can contribute to serious medical errors. Joint Commission standards require that orders contain clarifying information about when each order should be given. In our system, as needed (PRN) acetaminophen and ibuprofen orders are major contributors to therapeutic duplication. OBJECTIVE: The objective of this study is to design and evaluate effectiveness of clinical decision support (CDS) to reduce therapeutic duplication with acetaminophen and ibuprofen orders. METHODS: This study was done in a pediatric health system with three freestanding hospitals. We iteratively designed and implemented two CDS strategies aimed at reducing the therapeutic duplication with these agents: (1) interruptive alert prompting clinicians for clarifying PRN comments at order entry and (2) addition of discrete "first-line" and "second-line" PRN reasons to orders. Therapeutic duplications were measured by manual review of orders for 30-day periods before and after each intervention and 6 months later. RESULTS: Therapeutic duplications decreased from 1,485 in the 30 days prior to the first alert implementation to 818 in the 30 days after but rose back to 1,208 in the 30 days prior to the second intervention. After discrete reasons were added to the order, therapeutic duplication decreased to 336 in the immediate 30 days and 6 months later remained at 277. Alerts firing rates decreased from 76.0 per 1,000 PRN acetaminophen or ibuprofen orders to 42.9 after the second intervention. CONCLUSION: Interruptive alerts may reduce therapeutic duplication but are associated with high rates of user frustration and alert fatigue. Leveraging discrete PRN reasons for "first line" and "second line" produced a greater reduction in therapeutic duplication as well as fewer interruptive alerts and less manual entry for providers.

The Effect of Implementation of Guideline Order Bundles Into a General Admission Order Set on Clinical Practice Guideline Adoption: Quasi-Experimental Study.

BACKGROUND: Clinical practice guidelines (CPGs) and associated order sets can help standardize patient care and lead to higher-value patient care. However, difficult access and poor usability of these order sets can result in lower use rates and reduce the CPGs' impact on clinical outcomes. At our institution, we identified multiple CPGs for general pediatrics admissions where the appropriate order set was used in <50% of eligible encounters, leading to decreased adoption of CPG recommendations. OBJECTIVE: We aimed to determine how integrating disease-specific order groups into a common general admission order set influences adoption of CPG-specific order bundles for patients meeting CPG inclusion criteria admitted to the general pediatrics service. METHODS: We integrated order bundles for asthma, heavy menstrual bleeding, musculoskeletal infection, migraine, and pneumonia into a common general pediatrics order set. We compared pre- and postimplementation order bundle use rates for eligible encounters at both an intervention and nonintervention site for integrated CPGs. We also assessed order bundle adoption for nonintegrated CPGs, including bronchiolitis, acute gastroenteritis, and croup. In a post hoc analysis of encounters without order bundle use, we compared the pre- and postintervention frequency of diagnostic uncertainty at the time of admission. RESULTS: CPG order bundle use rates for incorporated CPGs increased by +9.8% (from 629/856, 73.5% to 405/486, 83.3%) at the intervention site and by +5.1% (896/1351, 66.3% to 509/713, 71.4%) at the nonintervention site. Order bundle adoption for nonintegrated CPGs decreased from 84% (536/638) to 68.5% (148/216), driven primarily by decreases in bronchiolitis order bundle adoption in the setting of the COVID-19 pandemic. Diagnostic uncertainty was more common in admissions without CPG order bundle use after implementation (28/227, 12.3% vs 19/81, 23.4%). CONCLUSIONS: The integration of CPG-specific order bundles into a general admission order set improved overall CPG adoption. However, integrating only some CPGs may reduce adoption of order bundles for excluded CPGs. Diagnostic uncertainty at the time of admission is likely an underrecognized barrier to guideline adherence that is not addressed by an integrated admission order set.

Integrating structured and unstructured data for timely prediction of bloodstream infection among children.

BACKGROUND: Hospitalized children with central venous lines (CVLs) are at higher risk of hospital-acquired infections. Information in electronic health records (EHRs) can be employed in training deep learning models to predict the onset of these infections. We incorporated clinical notes in addition to structured EHR data to predict serious bloodstream infections, defined as positive blood culture followed by at least 4 days of new antimicrobial agent administration, among hospitalized children with CVLs. METHODS: Structured EHR information and clinical notes were extracted for a retrospective cohort including all hospitalized patients with CVLs at a single tertiary care pediatric health system from 2013 to 2018. Deep learning models were trained to determine the added benefit of incorporating the information embedded in clinical notes in predicting serious bloodstream infection. RESULTS: A total of 24,351 patient encounters met inclusion criteria. The best-performing model restricted to structured EHR data had a specificity of 0.951 and positive predictive value (PPV) of 0.056 when the sensitivity was set to 0.85. The addition of contextualized word embeddings improved the specificity to 0.981 and PPV to 0.113. CONCLUSIONS: Integrating clinical notes with structured EHR data improved the prediction of serious bloodstream infections among pediatric patients with CVLs. IMPACT: Developed an advanced infection prediction model in pediatrics that integrates the structured and unstructured EHRs. Extracted information from clinical notes to do timely prediction in a clinical setting. Developed a deep learning model framework that can be employed in predicting rare events in a complex and dynamic environment.

Influence of user-centered clinical decision support on pediatric blood product ordering errors.

BACKGROUND: Children are at increased risk from transfusion-related medical errors. Clinical decision support (CDS) can enhance pediatric providers' decision-making regarding transfusion practices including indications, volume, rate, and special processing instructions. Our objective was to use CDS in a pediatric health system to reduce:blood product-related safety events from ordering errors;special processing ordering errors for patients with T-cell dysfunction, sickle cell disease (SCD), or thalassemia;transfusions administered faster than 5 mL/kg/h. MATERIALS AND METHODS: In this single-center before and after quality improvement study, we evaluated how user-centered design of pediatric blood product orders influenced pediatric transfusion practices and outcomes. Safety events were identified through active and passive surveillance. Other clinically relevant outcomes were identified through electronic health record queries. RESULTS: Blood product-related safety events from ordering errors did not change significantly from the baseline period (6 events, 0.4 per month, from 1/1/2018-3/27/2019) to the intervention period (1 event, 0.1 per month, from 3/28/2019-12/31/2019; rate ratio: 0.27 [0.01-2.25]). Packed red blood cell (PRBC) and platelet orders for patients with T-cell dysfunction that did not specify irradiation decreased significantly from 488/12,359 (3.9%) to 204/6,711 (3.0%, risk ratio: 0.77 [0.66-0.90]). PRBC orders for patients with SCD or thalassemia that did not specify phenotypically similar units fell from 386/2,876 (13.4%) to 57/1,755 (3.2%, risk ratio: 0.24 [0.18-0.32]). Transfusions administered faster than 5 mL/kg/h decreased from 4,112/14,641 (28.1%) to 2,125/9,263 (22.9%, risk ratio: 0.82 [0.78-0.85]). DISCUSSION: User-centered design of CDS for pediatric blood product orders significantly reduced special processing ordering errors and inappropriate transfusion rates. Larger studies are needed to evaluate the impact on safety events.

A method to advance adolescent sexual health research: Automated algorithm finds sexual history documentation.

Background: We aimed to develop and validate a rule-based Natural Language Processing (NLP) algorithm to detect sexual history documentation and its five key components [partners, practices, past history of sexually transmitted infections (STIs), protection from STIs, and prevention of pregnancy] among adolescent encounters in the pediatric emergency and inpatient settings. Methods: We iteratively designed a NLP algorithm using pediatric emergency department (ED) provider notes from adolescent ED visits with specific abdominal or genitourinary (GU) chief complaints. The algorithm is composed of regular expressions identifying commonly used phrases in sexual history documentation. We validated this algorithm with inpatient admission notes for adolescents. We calculated the sensitivity, specificity, negative predictive value, positive predictive value, and F1 score of the tool in each environment using manual chart review as the gold standard. Results: In the ED test cohort with abdominal or GU complaints, 97/179 (54%) provider notes had a sexual history documented, and the NLP algorithm correctly classified each note. In the inpatient validation cohort, 97/321 (30%) admission notes included a sexual history, and the NLP algorithm had 100% sensitivity and 98.2% specificity. The algorithm demonstrated >97% sensitivity and specificity in both settings for detection of elements of a high quality sexual history including protection used and contraception. Type of sexual practice and STI testing offered were also detected with >97% sensitivity and specificity in the ED test cohort with slightly lower performance in the inpatient validation cohort. Conclusion: This NLP algorithm automatically detects the presence of sexual history documentation and its key components in ED and inpatient settings.

Free-Text Computerized Provider Order Entry Orders Used as Workaround for Communicating Medication Information.

OBJECTIVES: Medication information is frequently communicated via free-text computerized provider order entry (CPOE) orders in electronic health records. When such information is transmitted separately from a structured CPOE medication order, there is a significant risk of medication error. Although prior studies have described the frequency of using free-text CPOE orders for communicating medication information, there is a gap in understanding the nature of the medication information contained in the free-text CPOE orders. The aims of this study are to (1) identify the most common medication names communicated in free-text CPOE orders and their risk levels and (2) identify what actions physicians expect that nurses will complete when they place free-text CPOE orders, and (3) describe differences in these patterns across hospitals. METHODS: This study was a retrospective analysis of a sample of 26,524 free-text CPOE orders from 6 hospitals in the mid-Atlantic U.S. region. RESULTS: Free-text CPOE orders contained in the sample mentioned 193 medication names. Free-text CPOE orders were used frequently to communicate information about naloxone, heparin, flumazenil, and dextrose. Twenty-two percent of the free-text CPOE orders related to discontinuing medication(s), whereas 7% of the free-text CPOE orders relate to giving medication(s). There was high variation across hospitals both in the percentage of free-text CPOE orders mentioning medication information and in the proportion of those that referred to high-risk medications. CONCLUSIONS: The prevalence of medication information in free-text CPOE orders may suggest specific communication challenges in respect to urgency, uncertainty, planning, and other aspects of communication and clinical needs. Understanding and addressing communication challenges around commonly mentioned medication names and actions, especially those that are high risk, can help reduce the risk of medication errors.

Barriers to Seasonal Influenza Vaccine Uptake in a Pediatric Inpatient Healthcare Setting After Implementation of Clinical Decision Support.

Every heatlhcare encounter is an opportunity to provide both acute care and health maintenance to children. A Clinical Decision Support (CDS) intervention was instituted in a tertiary pediatric health system to improve influenza vaccination rates during the 2019-2020 season among eligible children receiving care in an acute care inpatient healthcare setting. This study explores reasons for low vaccine uptake following implementation of a CDS aimed at improving vaccine administration as well as identifying possible solutions to improve flu vaccine coverage.

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.

Automated Identification of Immunocompromised Status in Critically Ill Children.

BACKGROUND: Easy identification of immunocompromised hosts (ICHs) would allow for stratification of culture results based on host type. METHODS: We utilized antimicrobial stewardship program (ASP) team notes written during handshake stewardship rounds in the pediatric intensive care unit (PICU) as the gold standard for host status; clinical notes from the primary team, medication orders during the encounter, problem list, and billing diagnoses documented prior to the ASP documentation were extracted to develop models that predict host status. We calculated performance for three models based on diagnoses/medications, with and without natural language processing from clinical notes. The susceptibility of pathogens causing bacteremia to commonly used empiric antibiotic regimens was then stratified by host status. RESULTS: We identified 844 antimicrobial episodes from 666 unique patients; 160 (18.9%) were identified as ICHs. We randomly selected 675 initiations (80%) for model training and 169 initiations (20%) for testing. A rule-based model using diagnoses and medications alone yielded a sensitivity of 0.87 (08.6-0.88), specificity of 0.93 (0.92-0.93), and positive predictive value (PPV) of 0.74 (0.73-0.75). Adding clinical notes into XGBoost model led to improved specificity of 0.98 (0.98-0.98) and PPV of 0.9 (0.88-0.91), but with decreased sensitivity 0.77 (0.76-0.79). There were 77 bacteremia episodes during the study period identified and a host-specific visualization was created. CONCLUSIONS: An electronic health record-based phenotype based on notes, diagnoses, and medications identifies ICH in the PICU with high specificity.

User-centered design of central venous access device documentation.

Objective: Safe care of central venous access devices (CVAD) requires clinicians be able to identify key CVAD properties from insertion until safe removal. Our objective was to design and evaluate interfaces to improve CVAD documentation quality and information retrieval. Materials and Methods: We applied user-centered design (UCD) to CVAD property documentation interfaces. We measured expert agreement and front-line clinician accuracy in retrieving key properties in CVADs documented pre- and postimplementation. Results: The new approach (1) optimized searches for line types, (2) enabled discrete entry of key properties which propagated to the display name, and (3) facilitated error correction by experts. Expert agreement on key CVAD properties improved from 42% to 83% (P < 0.01). Frontline nurses' perception of key CVAD properties improved from 31% to 86% (P < 0.01). Ease of use scores improved from 15/100 to 80/100 (P < 0.01). Conclusions: UCD significantly improved data quality and nurse perception of CVAD properties to guide subsequent care.

Missed Opportunities for Sexual History Documentation and Sexually Transmitted Infection Testing in the Pediatric Emergency Department.

PURPOSE: Sexually transmitted infections (STIs) are disproportionally prevalent in adolescents, and adolescents often present to the pediatric emergency department (PED) for STI care. Prior studies have found low rates of sexual history documentation and STI testing in the PED. However, these studies have had limited sample sizes because of the burden of manual chart review. We aimed to estimate the rate of sexual history documentation and identify factors associated with STI testing in a large cohort of adolescents using natural language processing (NLP). METHODS: We applied a validated NLP algorithm to all adolescent visits over a three-year period to the PED at a single large children's health care organization with a chief complaint potentially related to an STI. We utilized NLP to determine the prevalence of sexual history documentation in these patients. We applied logistic regression models to determine associations between sexual history documentation, patient demographic factors, and STI testing. RESULTS: Of the 1,987 patient encounters included, only 56% had a sexual history documented, and only 40% of all patients were tested for STIs. Patients were more likely to have a sexual history documented and to be tested for STIs if they were of non-Hispanic black race/ethnicity, were >15 years of age, and had nonprivate insurance. Patients with a sexual history documented were seven times more likely to have STI testing ordered. Of patients tested (n = 728), 25% were positive for an STI. CONCLUSIONS: Despite presenting to the PED with symptoms potentially related to an STI, many adolescents are not receiving recommended sexual health care. Rates of sexual history documentation and STI testing varied by demographic factors including race, age, and insurance status. Utilizing NLP technology allowed us to examine a larger sample size than previously documented in the adolescent sexual history and PED literature. This study highlights critical opportunities to improve sexual health provision and equity of care provided in the PED.

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.