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Abstract
Emerging literature suggests that delayed identification of childhood asthma results in an increased risk of long-term and various morbidities compared to those with timely diagnosis and intervention, and yet this risk is still overlooked. Even when children and adolescents have a history of recurrent asthma-like symptoms and risk factors embedded in their medical records, this information is sometimes overlooked by clinicians at the point of care. Given the rapid adoption of electronic health record (EHR) systems, early identification of childhood asthma can be achieved utilizing (1) asthma ascertainment criteria leveraging relevant clinical information embedded in EHR and (2) innovative informatics approaches such as natural language processing (NLP) algorithms for asthma ascertainment criteria to enable such a strategy. In this review, we discuss literature relevant to this topic and introduce recently published informatics algorithms (criteria-based NLP) as a potential solution to address the current challenge of early identification of childhood asthma.
Highlights
Asthma is the most common chronic illness of childhood, affecting up to 17% of children and representing one of most burdensome chronic diseases in the US [1,2,3,4,5,6]
The results of the initial prototype of our NLP algorithm for PAC demonstrated that NLP algorithms significantly outperformed ICD-code based asthma ascertainment in both validity and timely identification of asthma
These two NLP algorithms are being further tested before implementation in our asthma care practice. To ensure whether these NLP algorithms can be generalizable to other study settings with a different population, different clinical practice, and even different electronic health records (EHR) system, we tested the performance of our current NLP algorithm for PAC in a different study setting with a different EHR system and demonstrated portability (PPV: 89% and NPV97% at a hospital in a different state) [65, 66]. These results provide a new opportunity for large scale and automated identification of childhood asthma which may potentially address challenges associated with a delayed identification of asthma resulting in delayed therapeutic interventions and possible long-term morbidity
Summary
Asthma is the most common chronic illness of childhood, affecting up to 17% of children and representing one of most burdensome chronic diseases in the US [1,2,3,4,5,6]. The recent literature suggests that childhood asthma poses numerous health threats through asthma-associated infectious and inflammatory disease comorbidities (AIICs) [20,21,22,23,24,25,26,27,28,29,30,31] Both children and adults with asthma are at an increased risk of serious respiratory infections [e.g., pneumococcal pneumonia or invasive pneumococcal diseases [20,21,22], pertussis [23], and common upper respiratory infections (e.g., otitis media and strep infection) [24,25,26]. Asthma Predictive Index (API) (Table 2-2) which was developed to identify young children at risk of developing asthma can be considered as feasibility of application of API to TABLE 1 | Operational diagnostic criteria for asthma in children 1–5 years of age, a Canadian Thoracic Society and Canadian Pediatric Society
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