Cohort Profile: Effectiveness of a 12-Month Patient-Centred Medical Home Model Versus Standard Care for Chronic Disease Management among Primary Care Patients in Sydney, Australia.

Evidence suggests that patient-centred medical home (PCMH) is more effective than standard general practitioner care in improving patient outcomes in primary care. This paper reports on the design, early implementation experiences, and early findings of the 12-month PCMH model called 'WellNet' delivered across six primary care practices in Sydney, Australia. The WellNet study sample comprises 589 consented participants in the intervention group receiving enhanced primary care in the form of patient-tailored chronic disease management plan, improved self-management support, and regular monitoring by general practitioners (GPs) and trained clinical coordinators. The comparison group consisted of 7750 patients who were matched based on age, gender, type and number of chronic diseases who received standard GP care. Data collected include sociodemographic characteristics, clinical measures, and self-reported health assessments at baseline and 12 months. Early study findings show the mean age of the study participants was 70 years with nearly even gender distribution of males (49.7%) and females (50.3%). The most prevalent chronic diseases in descending order were circulatory system disorders (69.8%), diabetes (47.4%), musculoskeletal disorders (43.5%), respiratory diseases (28.7%), mental illness (18.8%), and cancer (13.6%). To our knowledge, the WellNet study is the first study in Australia to generate evidence on the feasibility of design, recruitment, and implementation of a comprehensive PCMH model. Lessons learned from WellNet study may inform other medical home models in Australian primary care settings.

The effectiveness of patient-centred medical home model versus standard primary care in chronic disease management: protocol for a systematic review and meta-analysis of randomised and non-randomised controlled trials.

BACKGROUND: Studies suggest that the Patient-Centred Medical Home (PCMH) model of primary care is more effective than standard care for improving clinical outcomes in patients with chronic diseases (non-communicable diseases), but the strength of the evidence base is unclear. The aim of the proposed systematic review is to generate a current synthesis of relevant studies on the effectiveness of PCMH model of primary care versus standard care in chronic disease management. METHODS: Electronic databases such as MEDLINE, CINAHL, Embase, Cochrane Library, and Scopus will be searched using predefined search terms for PCMH, primary care, and chronic diseases for articles published up to November 2018. Reference lists of included articles and relevant reviews will also be hand searched. This review will consider eligible randomised controlled trials and controlled trials against predetermined criteria including two or more principles of PCMH model endorsed by Australian Medical Association. Data extraction will be performed independently by two reviewers, and retrieved papers will be assessed for quality using JBI Critical Appraisal Tools. Where possible, quantitative data will be pooled in statistical meta-analysis using the R packages 'Meta' and 'metafor'. Effect sizes will be expressed as odds ratio (for categorical data) and weighted mean differences (for continuous data) and their 95% confidence intervals will be calculated for meta-analysis; robustness will be explored using sensitivity analysis. Heterogeneity will be assessed narratively and statistically using the Q statistics and visualised using Baujat plots including subgroup or sensitivity analyses techniques where possible. Where statistical pooling is not possible, the findings will be presented narratively. DISCUSSION: The findings of the proposed systematic review will provide the highest level of evidence to date on the effectiveness of the PCMH model versus standard primary care in chronic disease management. We believe that our findings will inform patients, primary care providers, and public health administrators and policy-makers on the benefits and risks of PCMH model of care. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018085378.

Clustering multivariate time series using Hidden Markov Models.

In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs), where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers.