Pediatric asthma hospitalizations among urban minority children and the continuity of primary care.

OBJECTIVE: To examine the effect of ambulatory health care processes on asthma hospitalizations. METHODS: A retrospective cohort study using electronic health records was completed. Patients aged 2-18 years receiving health care from 1 of 5 urban practices between Jan 1, 2004 and Dec 31, 2008 with asthma documented on their problem list were included. Independent variables were modifiable health care processes in the primary care setting: (1) use of asthma controller medications; (2) regular assessment of asthma symptoms; (3) use of spirometry; (4) provision of individualized asthma care plans; (5) timely influenza vaccination; (6) access to primary healthcare; and (7) use of pay for performance physician incentives. Occurrence of one or more asthma hospitalizations was the primary outcome of interest. We used a log linear model (Poisson regression) to model the association between the factors of interest and number of asthma hospitalizations. RESULTS: 5,712 children with asthma were available for analysis. 96% of the children were African American. The overall hospitalization rate was 64 per 1,000 children per year. None of the commonly used asthma-specific indicators of high quality care were associated with fewer asthma hospitalizations. Children with documented asthma who experienced a lack of primary health care (no more than one outpatient visit at their primary care location in the 2 years preceding hospitalization) were at higher risk of hospitalization compared to those children with a greater number of visits (incidence rate ratio 1.39; 95% CI 1.09-1.78). CONCLUSIONS: In children with asthma, more frequent primary care visits are associated with reduced asthma hospitalizations.

Identifying surgical site infections in electronic health data using predictive models.

Objective: The objective was to prospectively derive and validate a prediction rule for detecting cases warranting investigation for surgical site infections (SSI) after ambulatory surgery. Methods: We analysed electronic health record (EHR) data for children who underwent ambulatory surgery at one of 4 ambulatory surgical facilities. Using regularized logistic regression and random forests, we derived SSI prediction rules using 30 months of data (derivation set) and evaluated performance with data from the subsequent 10 months (validation set). Models were developed both with and without data extracted from free text. We also evaluated the presence of an antibiotic prescription within 60 days after surgery as an independent indicator of SSI evidence. Our goal was to exceed 80% sensitivity and 10% positive predictive value (PPV). Results: We identified 234 surgeries with evidence of SSI among the 7910 surgeries available for analysis. We derived and validated an optimal prediction rule that included free text data using a random forest model (sensitivity = 0.9, PPV = 0.28). Presence of an antibiotic prescription had poor sensitivity (0.65) when applied to the derivation data but performed better when applied to the validation data (sensitivity = 0.84, PPV = 0.28). Conclusions: EHR data can facilitate SSI surveillance with adequate sensitivity and PPV.

User Testing an Information Foraging Tool for Ambulatory Surgical Site Infection Surveillance.

BACKGROUND: Surveillance for surgical site infections (SSIs) after ambulatory surgery in children requires a detailed manual chart review to assess criteria defined by the National Health and Safety Network (NHSN). Electronic health records (EHRs) impose an inefficient search process where infection preventionists must manually review every postsurgical encounter (< 30 days). Using text mining and business intelligence software, we developed an information foraging application, the SSI Workbench, to visually present which postsurgical encounters included SSI-related terms and synonyms, antibiotic, and culture orders. OBJECTIVE: This article compares the Workbench and EHR on four dimensions: (1) effectiveness, (2) efficiency, (3) workload, and (4) usability. METHODS: Comparative usability test of Workbench and EHR. Objective test metrics are time per case, encounters reviewed per case, time per encounter, and retrieval of information meeting NHSN definitions. Subjective measures are cognitive load using the National Aeronautics and Space Administration (NASA) Task Load Index (NASA TLX), and a questionnaire on system usability and utility. RESULTS: Eight infection preventionists participated in the test. There was no difference in effectiveness as subjects retrieved information from all cases, using both systems, to meet the NHSN criteria. There was no difference in efficiency in time per case between the Workbench and EHR (8.58 vs. 7.39 minutes, p = 0.36). However, with the Workbench subjects opened fewer encounters per case (3.0 vs. 7.5, p = 0.002), spent more time per encounter (2.23 vs. 0.92 minutes, p = 0.002), rated the Workbench lower in cognitive load (NASA TLX, 24 vs. 33, p = 0.02), and significantly higher in measures of usability. CONCLUSION: Compared with the EHR, the Workbench was more usable, short, and reduced cognitive load. In overall efficiency, the Workbench did not save time, but demonstrated a shift from between-encounter foraging to within-encounter foraging and was rated as significantly more efficient. Our results suggest that infection surveillance can be better supported by systems applying information foraging theory.

Imputing Missing Race/Ethnicity in Pediatric Electronic Health Records: Reducing Bias with Use of U.S. Census Location and Surname Data.

OBJECTIVE: To assess the utility of imputing race/ethnicity using U.S. Census race/ethnicity, residential address, and surname information compared to standard missing data methods in a pediatric cohort. DATA SOURCES/STUDY SETTING: Electronic health record data from 30 pediatric practices with known race/ethnicity. STUDY DESIGN: In a simulation experiment, we constructed dichotomous and continuous outcomes with pre-specified associations with known race/ethnicity. Bias was introduced by nonrandomly setting race/ethnicity to missing. We compared typical methods for handling missing race/ethnicity (multiple imputation alone with clinical factors, complete case analysis, indicator variables) to multiple imputation incorporating surname and address information. PRINCIPAL FINDINGS: Imputation using U.S. Census information reduced bias for both continuous and dichotomous outcomes. CONCLUSIONS: The new method reduces bias when race/ethnicity is partially, nonrandomly missing.