Objectives:This article focuses on the results of evaluations of two business plans developed in response to a policy initiative which aimed to achieve greater integration between primary and secondary health providers in New Zealand. We employ the Consolidated Framework for Implementation Research to inform our analysis. The Better, Sooner, More Convenient policy programme involved the development of business plans and, within each business plan, a range of areas of focus and associated work-streams.Methods:The evaluations employed a mixed method multi-level case study design, involving qualitative face-to-face interviews with front-line staff, clinicians and management in two districts, one in the North Island and the other in the South Island, and an analysis of routine data tracked ambulatory sensitive hospitalisations and emergency department presentations. Two postal surveys were conducted, one focussing on the patient care experiences of integration and care co-ordination and the second focussing on the perspectives of health professionals in primary and secondary settings in both districts.Results:Both evaluations revealed non-significant changes in ambulatory sensitive hospitalisations and emergency department presentation rates and slow uneven progress with areas of focus and their associated work-streams. Our evaluations revealed a range of implementation issues, the barriers and facilitators to greater integration of healthcare services and the implications for those who were responsible for putting policy into practice.Conclusion:The business plans were shown to be overly ambitious and compromised by the size and scope of the business plans; dysfunctional governance arrangements and associated accountability issues; organisational inability to implement change quickly with appropriate and timely funding support; an absence of organisational structural change allowing parity with the policy objectives; barriers that were encountered because of inadequate attention to organisational culture; competing additional areas of focus within the same timeframe; and consequent overloading of front-line staff which led to workload stress, fatigue and disillusionment. Where success was achieved, this largely hinged on the enthusiasm of a small pool of front-line workers and their initial buy-into the idea of integrated care.
ObjectiveTo 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 algorithm's ability to estimate the prevalence and burden of childhood respiratory illness in primary care.DesignAlgorithm 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.SettingThirty-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 measuresSensitivity, specificity, positive predictive value and F-measure were calculated to illustrate the algorithm's ability to replicate judgements of expert clinicians within the 1200 record gold standard validation set.ResultsThe 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).ConclusionsA 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.
ObjectivesTo 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.DesignRetrospective cohort study. Data interrogation used a natural language processing software inference algorithm.Setting36 primary care practices in New Zealand. Data analysed from January 2008 to December 2013.ParticipantsThe 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 measureChildhood respiratory illness presentation rate to primary care practice, with description of seasonal and yearly variation.ResultsRespiratory 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.ConclusionThis 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.
ObjectiveTo 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.DesignA 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.SettingGeneral practice, using EMRs from two primary health organisations located in the lower North Island, New Zealand.ParticipantsThirty-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 measuresThe 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.ResultsOverall, 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.ConclusionsZoster consultations in general practice are rare, and the burden of these cases on overall general practice caseload is low.
BackgroundWe designed and validated a rule-based expert system to identify influenza like illness (ILI) from routinely recorded general practice clinical narrative to aid a larger retrospective research study into the impact of the 2009 influenza pandemic in New Zealand.MethodsRules were assessed using pattern matching heuristics on routine clinical narrative. The system was trained using data from 623 clinical encounters and validated using a clinical expert as a gold standard against a mutually exclusive set of 901 records.ResultsWe calculated a 98.2 % specificity and 90.2 % sensitivity across an ILI incidence of 12.4 % measured against clinical expert classification. Peak problem list identification of ILI by clinical coding in any month was 9.2 % of all detected ILI presentations. Our system addressed an unusual problem domain for clinical narrative classification; using notational, unstructured, clinician entered information in a community care setting. It performed well compared with other approaches and domains. It has potential applications in real-time surveillance of disease, and in assisted problem list coding for clinicians.ConclusionsOur system identified ILI presentation with sufficient accuracy for use at a population level in the wider research study. The peak coding of 9.2 % illustrated the need for automated coding of unstructured narrative in our study.
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