A Risk Stratification Algorithm for Asthma Identification and Prioritization in a Low-Income Urban School.

BACKGROUND: Asthma can interfere with school attendance and engagement. School health programs are central to asthma management. Case identification is limited by reliance on parent-completed forms, which are often missing. This project tested a low-burden screening algorithm to stratify students based on priority for nurse outreach at 2 large, urban schools with high asthma prevalence. METHODS: Students in grades 1-8 completed a 4-item asthma screener. Two-stage stratification incorporated screener responses, school nurse records, and absenteeism. Students were assigned low, medium, or high priority for follow up. Asthma prevalence in the high priority group was calculated for substantiated asthma. Whether stratification was more likely than chance to identify new cases of asthma in the high-priority group was evaluated using chi-square tests. RESULTS: Of 1397 students, 69.7% were screened. Secondary stratification decreased the number of students in the high and medium priority groups. New asthma cases were identified in 46.4% of high-priority families reached for follow up. High-priority students were more likely to be identified as having asthma than chance alone (p < .001). CONCLUSIONS: A low-burden screening algorithm appropriately placed students with asthma in the high priority group. This approach may allow efficient, targeted follow up of the highest need students in high prevalence populations.

Bolstering diagnostic reasoning skills with adaptive learning.

PURPOSE: Adaptive learning emerges when precise assessment informs delivery of educational materials. This study will demonstrate how data from Human Dx, a case-based e-learning platform, can characterize an individual's diagnostic reasoning skills, and deliver tailored content to improve accuracy. METHODS: Pearson Chi-square analysis was used to assess variability in accuracy across three groups of participants (attendings, residents, and medical students) and three categories of cases (core medical, surgical, and other). Logistic regression analyses were conducted to explore the relationship between solve duration and accuracy. Mean accuracy and duration were calculated for 370 individuals. Repeated measures analysis of variance (ANOVA) were used to assess variability for an individual solver across the three categories. RESULTS: There were significant differences in accuracy across the three groups and the three categories (p < 0.001). Individual solvers have significant variance in accuracy across the three categories. Shorter solve duration predicted higher accuracy. Patterns of performance were identified; four profiles are highlighted to demonstrate potential adaptive learning interventions. CONCLUSIONS: Human Dx can assess diagnostic reasoning skills. When weaknesses are identified, adaptive learning strategies can push content to promote skill development. This has implications for customizing curricular elements to improve the diagnostic skills of healthcare professionals.