Search

Introduction New Zealand general practice and primary care is currently facing significant challenges and opportunities following the impact of the coronavirus disease 2019 (COVID-19) pandemic and the introduction of health sector reform. For future sustainability, it is important to understand the workload associated with differing levels of patient case mix seen in general practice. Aim To assess levels of morbidity and concomitant levels of socio-economic deprivation among primary care practices within a large primary health organisation (PHO) and associated Māori provider network. Methods Routinely collected practice data from a PHO of 57 practices and a Māori provider (PHO) of five medical practices in the same geographical area were used to compare a number of population health indicators between practices that had a high proportion of high needs patients (HPHN) and practices with a low proportion of high needs patients (Non-HPHN). Results When practices in these PHOs are grouped in terms of ethnicity distribution and deprivation scores between the HPHN and Non-HPHN groups, there is significantly increased clustering of both long-term conditions and health outcome risk factors in the HPHN practices. Discussion In this study, population adverse health determinants and established co-morbidities are concentrated into the defined health provider grouping of HPHN practices. This ‘concentration of complexity’ raises questions about models of care and adequate resourcing for quality primary care in these settings. The findings also highlight the need to develop equitable and appropriate resourcing for all patients in primary care.

Background Māori are over-represented in Aotearoa New Zealand morbidity and mortality statistics. Other populations with high health needs include Pacific peoples and those living with material deprivation. General practice has evolved into different models of primary care. We describe nurse work in relation to these models of care; populations with high health need; and patient health outcomes. Methods Cross-sectional study (30 September 2018), data from national datasets and practices at patient level. Six primary outcome measures were selected because they could be improved by primary care: polypharmacy (≥65 years), HbA1c testing in adults with diabetes, immunisations (6 months), ambulatory sensitive hospitalisations (0-14, 45-64 years) and emergency department attendances. Analysis adjusted for patient and practice characteristics. Results Nurse FTE, and combined nurse, nurse practitioner and general practitioner FTE, were substantially higher in Trust/NGO, Māori, and Pacific practices than Traditional, Corporate, or Health Care Home practices. A progressive increase of clinically complex patients was associated with more clinical input and higher scores on all outcome measures. The highest rates of nurse consultations afterhours and with unenrolled patients, improving access, were in PHO/DHB, Pacific, Trust/NGO and Māori practices. Compared to general practitioners, nurses undertook more cardiovascular risk assessment in all models of care except PHO/DHB, and more cervical screening in Pacific, Trust/NGO and Māori practices. The highest rates of preventative care by nurses (cervical screening, cardiovascular risk assessment, PHQ9 assessment, HbA1c testing) were in Māori, Trust/NGO and Pacific practices. There was an 8-fold difference, across models of care, in percentage of PHQ9 undertaken by nurses and a 5-fold difference in cervical screening and HbA1c testing. Work not attributed to nurses in the practice records meant nurse work was underestimated to an unknown degree. Conclusions Transferring work to nurses in Traditional, Health Care Home, and Corporate practices, would release GP FTE to be utilised for other work. Worse patient health outcomes were associated with higher patient need and higher clinical input. It is plausible that there is insufficient clinical input to meet the degree of patient need. More practitioner FTE is required, especially in practices with high volumes of complex patients.

Background Primary care in Aotearoa New Zealand is largely delivered by general practices which are heavily subsidised by government. At least seven models of primary care have evolved: Traditional, Corporate, Health Care Home, Māori practices, Pacific practices, and practices owned by Primary Health Organisations/District Health Boards and Trust/Non-Governmental Organisations. Te Tiriti o Waitangi (1840) guarantees equal outcomes for Māori and non-Māori, but stark differences are longstanding and ongoing. Pacific peoples and those living with material deprivation also have unequal health outcomes. Methods Cross-sectional study (30 September 2018), data from national datasets and practices at patient level. We sought associations between practice characteristics and patient health outcomes, adjusted for patient characteristics. Practice characteristics included: model of care, size, funding model, rurality; number of consultations and time spent with nurses and doctors; practice and doctor continuity. Six primary outcomes measures were chosen: polypharmacy (≥ 65 years), HbA1c testing in adults with diabetes, immunisations (6 months), ambulatory sensitive hospitalisations (0–14, 45–64 years) and emergency department attendances. Results The study included 924 general practices with 4,491,964 enrolled patients. Traditional practices enrolled 73% of the population, but, on average, the proportion of Māori, Pacific and people living with material deprivation was low in any one Traditional practice. Patients with high needs disproportionately clustered into Māori, Pacific and Trust/NGO practices. There were multiple associations between models of care and patient health outcomes in fully-adjusted regressions. Patient health outcomes were most strongly associated with: age, Māori or Pacific ethnicity, deprivation (IMD), multi-morbidity (M3), clinical input, number of first specialist assessments, changing practice, and prescribing (SSRIs, tramadol, antibiotics). Being Māori or Pacific remained associated with poorer outcomes after full adjustment including measures of deprivation. Patients with high health need received more clinical input but this was insufficient to achieve equity of outcomes. Practice-level variance was highest for emergency department attendances. Conclusions Resource models of care with disproportionately high and complex patient health need (Māori, Pacific and Trust/NGO practices). Associations between patient and practice characteristics, and patient health outcomes, should be central to investment decisions.

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

We use the method of coarsened exact matching (CEM) and general practice patient-record data from New Zealand to estimate the impact of mood disorders on medical care costs. The CEM model exploits a discretisation of the data to identify for each patient with a mood disorder a perfect statistical twin, which allows us to control for comorbidity-related variations in medical care costs. CEM with perfect balancing of covariates yields lower annual cost estimates of mood disorders per patient (NZ$366) than regression or conventional matching methods (between $NZ372 and $413). National government expenditures on managing mood disorders are estimated to lie between 0.06 per cent and 0.10 per cent of GDP.

The closure of the Manawatu Gorge in New Zealand in August 2011 caused a change in the travel time for patients living in the east of the MidCentral Health District to their health services located in Palmerston North. This presented an opportunity to study the effect a change in spatial access had on a population before and after such an event. We used a retrospective cohort design with routinely collected data from general practice and hospital services. Realised accessibility was calculated for 101,456 patients over 3.5 years. General practice utilization appeared to be the only service affected negatively during the gorge closure (rate ratio 1.106). Outpatient attendances had an increase in use by those with increased travel time (rate ratio 0.922). There was evidence of other unidentified factors that impacted the use of services across both intervention and control groups between the gorge open and closed periods. These results were more conservative than those produced by a traditional uncontrolled travel time category analysis which suggested a correlation in non-urgent ED attendance and general practice and boundary effects in all ED attendances and hospital admissions.

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.

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.

Background We 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. Methods Rules 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. Results We 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. Conclusions Our 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.

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.

This article canvasses the literature exploring issues related to the commercialisation of health data from the public health system. It examines whether commercialisation is a viable proposition in New Zealand, socially and ethically. In doing so, it provides a methodological approach to the development of an ethics and privacy policy framework for any potential commercialisation of public health data in New Zealand.

This paper is the first to use the method of coarsened exact matching (CEM) to estimate the impact of mood disorders on medical care costs in order to address the endogeneity of mood disorders. Models are estimated using restricted-use, general practice patient records data from New Zealand for 2009-2012. The CEM model, which exploits a discretization of the data to identify for each patient with a mood disorder a perfect statistical twin, yields estimates of the impact of mood disorders on medical costs that are lower than the estimates obtained from random effects models or conventional matching methods. For example, mood disorders lead to NZ$366 higher annual medical costs (in 2012 dollars) when perfect balancing of covariates is achieved, while minimal and conventional balancing yield estimated costs of over NZ$465 and NZ$400, respectively. The national government expenditures on managing mood disorders is estimated to be 13.4% of total general practice funding (NZ$123 Million) based on CEM.

Cardiovascular disease (CVD) represents a major cause of death around the world. Predicting incidence of CVD allows interventions in order to change lifestyle or prescribe medication. Current approaches to evaluating CVD risk use regression equations based on large data sets, but such data may not accurately reflect risks based on the individual, or specific groups. In addition, the regression equations require complete recording of clinical data which may be missing or inaccurate. This paper outlines an approach that uses a fuzzified ontology to attempt to both improve prediction of CVD and provide personalized predictive capacity.

To consider two definitions for utilisation reporting in primary care in New Zealand and to assess the affect of two reporting methods on volumes of utilisation in four primary health organisations (PHOs).Utilisation data was analysed for a 6-month period from 60 practices across four PHOs. Analysis was based on comparing the expected volumes from two alternative collection and reporting methods, named "matched" and "unmatched" reporting. The "unmatched" method is potentially sensitive to patients consulting outside of the practice in which they are funded. Volumes were grouped into categories based on those used for reporting.There was up to 25% difference in volumes in some reporting groups depending on the matching method used. Several of these were for high deprivation, New Zealand Maori, Pacific Islanders, and Community Service Card (CSC) holders--all potentially target populations within PHOs. Two PHOs were more affected having a total of 7.6% and 6.4% fewer reported encounters using the "unmatched" method. Data implies that some groups of patients may not be receiving continuity of care.There were differences in reporting volumes between the two methods. The Ministry of Health (MOH), district health boards (DHBs), and PHOs should be aware of how these results may potentially apply to them, especially where they have after-hours services or target groups as minorities.