Acute admission risk stratification of New Zealand primary care patients using demographic, multimorbidity, service usage and modifiable variables.

Introduction Risk stratification tools in primary care may help practices better identify high-risk patients and plan for their treatment. Patients of all ages can be at high risk of acute hospital admissions. Aim We aim to improve existing risk stratification tools by using larger datasets, and accounting for practice-level variations in hospitalisation rates and read-code quality. Methods This work has derived an acute admission risk stratification tool in the Wellington, Kapiti Coast and Wairarapa regions of New Zealand. An open cohort, starting 1 March 2017 and finishing 1 November 2021, contains 319 943 patients. An accelerated failure time survival regression model is used to model acute admission risk. Candidate models are tested on holdout data using six different test metrics. Results Patient risk is most affected by demographic, and the frequency of recent healthcare system usage. Morbidity categories have less predictive capability, but may still be useful from a practical perspective. The preferred model has an area under the receiver operating characteristic curve (AUROC) of 0.75 for a 6-month forecast period. Discussion The model is straightforward to apply to other datasets. Although most of the highest-risk patients will be well-known to their primary care practices already, the model helps to identify the patients who are high risk but not regularly attendees of the practice, and may benefit from proactive care planning.

The concentration of complexity: case mix in New Zealand general practice and the sustainability of primary care.

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 Maori provider network. Methods Routinely collected practice data from a PHO of 57 practices and a Maori 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.