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Objective To investigate the incidence of primary care presentations for herpes zoster (zoster) in a representative New Zealand population and to evaluate the utilisation of primary healthcare services following zoster diagnosis. Design A cross-sectional retrospective cohort study used a natural language processing software inference algorithm to identify general practice consultations for zoster by interrogating 22 million electronic medical record (EMR) transactions routinely recorded from January 2005 to December 2015. Data linking enabled analysis of the demographics of each case. The frequency of doctor visits was assessed prior to and after the first consultation diagnosing zoster to determine health service utilisation. Setting General practice, using EMRs from two primary health organisations located in the lower North Island, New Zealand. Participants Thirty-nine general practices consented interrogation of their EMRs to access deidentified records for all enrolled patients. Out-of-hours and practice nurse consultations were excluded. Main outcome measures The incidence of first and repeated zoster-related visits to the doctor across all age groups and associated patient demographics. To determine whether zoster affects workload in general practice. Results Overall, for 6 189 019 doctor consultations, the incidence of zoster was 48.6 per 10 000 patient-years (95% CI 47.6 to 49.6). Incidence increased from the age of 50 years to a peak rate of 128 per 10 000 in the age group of 80–90 years and was significantly higher in females than males (p<0.001). Over this 11-year period, incidence increased gradually, notably in those aged 80–85 years. Only 19% of patients had one or more follow-up zoster consultations within 12 months of a zoster index consultation. The frequency of consultations, for any reason, did not change between periods before and after the diagnosis. Conclusions Zoster consultations in general practice are rare, and the burden of these cases on overall general practice caseload is low.

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.

Objective To develop a natural language processing software inference algorithm to classify the content of primary care consultations using electronic health record Big Data and subsequently test the algorithm9s ability to estimate the prevalence and burden of childhood respiratory illness in primary care. Design Algorithm development and validation study. To classify consultations, the algorithm is designed to interrogate clinical narrative entered as free text, diagnostic (Read) codes created and medications prescribed on the day of the consultation. Setting Thirty-six consenting primary care practices from a mixed urban and semirural region of New Zealand. Three independent sets of 1200 child consultation records were randomly extracted from a data set of all general practitioner consultations in participating practices between 1 January 2008–31 December 2013 for children under 18 years of age (n=754 242). Each consultation record within these sets was independently classified by two expert clinicians as respiratory or non-respiratory, and subclassified according to respiratory diagnostic categories to create three ‘gold standard’ sets of classified records. These three gold standard record sets were used to train, test and validate the algorithm. Outcome measures Sensitivity, specificity, positive predictive value and F -measure were calculated to illustrate the algorithm9s ability to replicate judgements of expert clinicians within the 1200 record gold standard validation set. Results The algorithm was able to identify respiratory consultations in the 1200 record validation set with a sensitivity of 0.72 (95% CI 0.67 to 0.78) and a specificity of 0.95 (95% CI 0.93 to 0.98). The positive predictive value of algorithm respiratory classification was 0.93 (95% CI 0.89 to 0.97). The positive predictive value of the algorithm classifying consultations as being related to specific respiratory diagnostic categories ranged from 0.68 (95% CI 0.40 to 1.00; other respiratory conditions) to 0.91 (95% CI 0.79 to 1.00; throat infections). Conclusions A software inference algorithm that uses primary care Big Data can accurately classify the content of clinical consultations. This algorithm will enable accurate estimation of the prevalence of childhood respiratory illness in primary care and resultant service utilisation. The methodology can also be applied to other areas of clinical care.