Health Literacy-Informed Communication to Reduce Discharge Medication Errors in Hospitalized Children: A Randomized Clinical Trial.

Importance: Inadequate communication between caregivers and clinicians at hospital discharge contributes to medication dosing errors in children. Health literacy-informed communication strategies during medication counseling can reduce dosing errors but have not been tested in the pediatric hospital setting. Objective: To test a health literacy-informed communication intervention to decrease liquid medication dosing errors compared with standard counseling in hospitalized children. Design, Setting, and Participants: This parallel, randomized clinical trial was performed from June 22, 2021, to August 20, 2022, at a tertiary care, US children's hospital. English- and Spanish-speaking caregivers of hospitalized children 6 years or younger prescribed a new, scheduled liquid medication at discharge were included in the analysis. Interventions: Permuted block (n = 4) randomization (1:1) to a health literacy-informed discharge medication communication bundle (n = 99) compared with standard counseling (n = 99). A study team member delivered the intervention consisting of a written, pictogram-based medication instruction sheet, teach back (caregivers state information taught), and demonstration of dosing with show back (caregivers show how they would draw the liquid medication in the syringe). Main Outcome and Measures: Observed dosing errors, assessed using a caregiver-submitted photograph of their child's medication-filled syringe and expressed as the percentage difference from the prescribed dose. Secondary outcomes included caregiver-reported medication knowledge. Outcome measurements were blinded to participant group assignment. Results: Among 198 caregivers randomized (mean [SD] age, 31.4 [6.5] years; 186 women [93.9%]; 36 [18.2%] Hispanic or Latino and 158 [79.8%] White), the primary outcome was available for 151 (76.3%). The observed mean (SD) percentage dosing error was 1.0% (2.2 percentage points) among the intervention group and 3.3% (5.1 percentage points) among the standard counseling group (absolute difference, 2.3 [95% CI, 1.0-3.6] percentage points; P < .001). Twenty-four of 79 caregivers in the intervention group (30.4%) measured an incorrect dose compared with 39 of 72 (54.2%) in the standard counseling group (P = .003). The intervention enhanced caregiver-reported medication knowledge compared with the standard counseling group for medication dose (71 of 76 [93.4%] vs 55 of 69 [79.7%]; P = .03), duration of administration (65 of 76 [85.5%] vs 49 of 69 [71.0%]; P = .04), and correct reporting of 2 or more medication adverse effects (60 of 76 [78.9%] vs 13 of 69 [18.8%]; P < .001). There were no differences in knowledge of medication name, indication, frequency, or storage. Conclusions and Relevance: A health literacy-informed discharge medication communication bundle reduced home liquid medication administration errors and enhanced caregiver medication knowledge compared with standard counseling. Routine use of these standardized strategies can promote patient safety following hospital discharge. Trial Registration: ClinicalTrials.gov Identifier: NCT05143047.

Positive Predictive Value of ICD-10 Codes to Identify Acute Suicidal and Self Harm Behaviors.

OBJECTIVE: The accuracy of diagnosis codes to identify suicidal behaviors, including suicide ideation (SI) and self-harm (SH) events, is unknown. The objective of this study was to determine the positive predictive value (PPV) of International Classification of Disease, 10th Revision codes to identify SI/SH events that may be used in studies using administrative and claims data. METHODS: We performed a secondary analysis of a cross-sectional study of children 5 to 17 years of age hospitalized at 2 US children's hospitals with a discharge diagnosis of a neuropsychiatric event, including an SI or SH event. A true International Classification of Disease, 10th Revision SI or SH diagnosis was defined as SI or SH present on admission and directly related to hospitalization as compared with physician record review. PPV with 95% confidence intervals (CIs) were calculated overall and stratified by diagnosis order and age (5 to 11 years vs 12 to 17 years). RESULTS: There were 376 children or adolescents with a discharge diagnosis of an SI or SH event. The median age was 14 years, and the majority of individuals were female (58%), non-Hispanic White (69%), and privately insured (57%). A total of 332 confirmed SI/SH cases were identified with a PPV of 0.88 (95% CI 0.85-0.91). PPVs were similar when stratified by diagnosis order: primary 0.94 (95% 0.88-0.97) versus secondary 0.86 (95% CI 81-90). PPVs were also similar in adolescents (0.89, CI 0.85-0.92) compared with children (0.84, 95% CI 0.74-0.91). CONCLUSIONS: The use of these validated code sets to identify SI or SH events may minimize misclassification in future studies of suicidal and self-harm hospitalizations.

Characteristics Associated With Serious Self-Harm Events in Children and Adolescents.

OBJECTIVES: To identify patterns of psychiatric comorbidity among children and adolescents with a serious self-harm event. METHODS: We studied children aged 5 to 18 years hospitalized with a neuropsychiatric event at 2 children's hospitals from April 2016 to March 2020. We used Bayesian profile regression to identify distinct clinical profiles of risk for self-harm events from 32 covariates: age, sex, and 30 mental health diagnostic groups. Odds ratios (ORs) and 95% credible intervals (CIs) were calculated compared with a reference profile with the overall baseline risk of the cohort. RESULTS: We included 1098 children hospitalized with a neuropsychiatric event (median age 14 years [interquartile range (IQR) 11-16]). Of these, 406 (37%) were diagnosed with a self-harm event. We identified 4 distinct profiles with varying risk for a self-harm diagnosis. The low-risk profile (median 0.035 [IQR 0.029-0.041]; OR 0.08, 95% CI 0.04-0.15) was composed primarily of children aged 5 to 9 years without a previous psychiatric diagnosis. The moderate-risk profile (median 0.30 [IQR 0.27-0.33]; reference profile) included psychiatric diagnoses without depressive disorders. Older female adolescents with a combination of anxiety, depression, substance, and trauma disorders characterized the high-risk profile (median 0.69 [IQR 0.67-0.70]; OR 5.09, 95% CI 3.11-8.38). Younger males with mood and developmental disorders represented the very high-risk profile (median 0.76 [IQR 0.73-0.79]; OR 7.21, 95% CI 3.69-15.20). CONCLUSIONS: We describe 4 separate profiles of psychiatric comorbidity that can help identify children at elevated risk for a self-harm event and subsequent opportunities for intervention.

Identifying Acute Neuropsychiatric Events in Children and Adolescents.

OBJECTIVES: The objective of this study was to develop and validate an approach to accurately identify incident pediatric neuropsychiatric events (NPEs) requiring hospitalization by using administrative data. METHODS: We performed a cross-sectional, multicenter study of children 5 to 18 years of age hospitalized at two US children's hospitals with an NPE. We developed and evaluated 3 NPE identification algorithms: (1) primary or secondary NPE International Classification of Diseases, 10th Revision diagnosis alone, (2) NPE diagnosis, the NPE was present on admission, and the primary diagnosis was not malignancy- or surgery-related, and (3) identical to algorithm 2 but without requiring the NPE be present on admission. The positive predictive value (PPV) of each algorithm was calculated overall and by diagnosis field (primary or secondary), clinical significance, and NPE subtype. RESULTS: There were 1098 NPE hospitalizations included in the study. A total of 857 confirmed NPEs were identified for algorithm 1, yielding a PPV of 0.78 (95% confidence interval [CI] 0.76-0.80). Algorithm 2 (n = 846) had an overall PPV of 0.89 (95% CI 0.87-0.91). For algorithm 3 (n = 938), the overall PPV was 0.86 (95% CI 0.83-0.88). PPVs varied by diagnosis order, NPE clinical significance, and subtype. The PPV for critical clinical significance was 0.99 (0.97-0.99) for all 3 algorithms. CONCLUSIONS: We identified a highly accurate method to identify neuropsychiatric adverse events in children and adolescents. The use of these approaches will improve the rigor of future studies of NPE, including the necessary evaluations of medication adverse events, infections, and chronic conditions.

Natural Language Processing and Machine Learning to Enable Clinical Decision Support for Treatment of Pediatric Pneumonia.

Pneumonia is the most frequent cause of infectious disease-related deaths in children worldwide. Clinical decision support (CDS) applications can guide appropriate treatment, but the system must first recognize the appropriate diagnosis. To enable CDS for pediatric pneumonia, we developed an algorithm integrating natural language processing (NLP) and random forest classifiers to identify potential pediatric pneumonia from radiology reports. We deployed the algorithm in the EHR of a large children's hospital using real-time NLP. We describe the development and deployment of the algorithm, and evaluate our approach using 9-months of data gathered while the system was in use. Our model, trained on individual radiology reports, had an AUC of 0.954. The intervention, evaluated on patient encounters that could include multiple radiology reports, achieved a sensitivity, specificity, and positive predictive value of0.899, 0.949, and 0.781, respectively.