Early Identification of Childhood Asthma: The Role of Informatics in an Era of Electronic Health Records

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.

Keywords: EHR; asthma; children; early; identification; informatics.

Figure 1

A high-level diagram of NLP algorithms for asthma ascertainment (i.e., NLP-PAC and NLP-API). There are two components in NLP algorithms: the document-level processing component extracts asthma-related concepts from unstructured data (clinical free text) using pattern-based rules and structured data (Lab and PPI) in asthma ascertainment criteria in Table 2, and the patient-level classification component aggregates processed information to ascertain asthma at a patient level. EHR, electronic health record; NLP, natural language processing; PPI, patient provided information; PAC, Predetermined Asthma Criteria; API, Asthma Predictive Index.