Childhood respiratory illness presentation and service utilisation in primary care: a six-year cohort study in Wellington, New Zealand, using natural language processing (NLP) software

OBJECTIVES To identify childhood respiratory tract-related illness presentation rates and service utilisation in primary care by interrogating free text and coded data from electronic medical records. DESIGN Retrospective cohort study. Data interrogation used a natural language processing software inference algorithm. SETTING 36 primary care practices in New Zealand. Data analysed from January 2008 to December 2013. PARTICIPANTS The records from 77 582 children enrolled were reviewed over a 6-year period to estimate the presentation of childhood respiratory illness and service utilisation. This cohort represents 268 919 person-years of data and over 650 000 unique consultations. MAIN OUTCOME MEASURE Childhood respiratory illness presentation rate to primary care practice, with description of seasonal and yearly variation. RESULTS Respiratory conditions constituted 46% of all child-general practitioner consultations with a stable year-on-year pattern of seasonal peaks. Upper respiratory tract infection was the most common respiratory category accounting for 21.0% of all childhood consultations, followed by otitis media (12.2%), wheeze-related illness (9.7%), throat infection (7.4%) and lower respiratory tract infection (4.4%). Almost 70% of children presented to their general practitioner with at least one respiratory condition in their first year of life; this reduced to approximately 25% for children aged 10-17. CONCLUSION This is the first study to assess the primary care incidence and service utilisation of childhood respiratory illness in a large primary care cohort by interrogating electronic medical record free text. The study identified the very high primary care workload related to childhood respiratory illness, especially during the first 2 years of life. These data can enable more effective planning of health service delivery. The findings and methodology have relevance to many countries, and the use of primary care 'big data' in this way can be applied to other health conditions.