Related Experiment Video
Updated: May 2, 2026
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
A novel method for detecting inpatient pediatric asthma encounters using administrative data
Andrew J Knighton1, Andrew Flood, Brian Harmon
11 Research and Sponsored Programs, Children's Hospitals and Clinics of Minnesota , Minneapolis, Minnesota.
Insights
Accurate asthma encounter detection is crucial for public health surveillance and research. A new algorithm using diagnosis codes improves detection sensitivity and reporting accuracy, enhancing health impact measurement.
Area of Science:
- Health Services Research
- Epidemiology
- Medical Informatics
Background:
- Accurate detection of asthma encounters is vital for public health surveillance, quality reporting, and clinical research.
- Current methods may fail to capture the full scope of asthma, impacting the evaluation of interventions and healthcare.
- Asthma is often associated with other respiratory conditions, complicating detection based on principal diagnosis alone.
Purpose of the Study:
- To develop and validate an algorithm for detecting asthma encounters using principal discharge diagnosis and asthma diagnosis code position.
- To improve the sensitivity and specificity of asthma encounter identification for public health surveillance and research.
Main Methods:
- A medical record review (n=191) served as the gold standard for identifying asthma encounters.
- An algorithm was developed and tested, considering asthma diagnosis code position in relation to principal respiratory diagnoses.
- Statistical analysis, including odds ratios and covariate adjustment, was used to evaluate the algorithm's performance.
Main Results:
- Asthma coded in the second or third position showed no significant difference in odds compared to the principal diagnosis for certain respiratory conditions.
- Coding asthma in the fourth or fifth position significantly differed from the principal diagnosis (P<.001).
- The proposed algorithm, including asthma in the first three code positions, increased sensitivity to 0.84 and reporting accuracy to 0.83, outperforming principal diagnosis alone (0.55) and all asthma codes (0.66).
Conclusions:
- An algorithm incorporating specific respiratory principal diagnoses and asthma diagnosis code position reliably enhances asthma encounter detection.
- This improved detection is critical for accurate population-based health impact measurement and comparative effectiveness research.
- The algorithm significantly increased the reporting accuracy and identified a 64% increase in asthma-related bed days compared to using the principal diagnosis alone.
Abstract:
Multiple methods for detecting asthma encounters are used today in public surveillance, quality reporting, and clinical research. Failure to detect asthma encounters can make it difficult to measure the scope and effectiveness of hospital or community-based interventions important in comparative effectiveness research and accountable care. Given the pairing of asthma with certain respiratory conditions, the objective of this study was to develop and test an asthma detection algorithm with specificity and sensitivity using 2 criteria: (1) principal discharge diagnosis and (2) asthma diagnosis code position. A medical record review was conducted (n=191) as the gold standard for identifying asthma encounters given objective criteria. The study team observed that for certain principal respiratory diagnoses (n=110), the observed odds ratio that encounters were for asthma when asthma was coded in the second or third code position was not significantly different than when asthma was coded as the principal diagnosis, 0.36 (P=0.42) and 0.18 (P=0.14), respectively. In contrast, the observed odds ratio was significantly different when asthma was coded in the fourth or fifth positions (P<.001). This difference remained after adjusting for covariates. Including encounters with asthma in 1 of the 3 first positions increased the detection sensitivity to 0.84 [95% confidence interval (CI): 0.76-0.92] while increasing the false positive rate to 0.19 [95% CI: 0.07-0.31]. Use of the proposed algorithm significantly improved the reporting accuracy [0.83 95%CI:0.76-0.90] over use of (1) the principal diagnosis alone [0.55 95% CI:0.46-0.64] or (2) all encounters with asthma 0.66 [95% CI:0.57-0.75]. Bed days resulting from asthma encounters increased 64% over use of the principal diagnosis alone. Given these findings, an algorithm using certain respiratory principal diagnoses and asthma diagnosis code position can reliably improve asthma encounter detection for population-based health impact measurement.
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