Implementing a sustainable medication reconciliation process in Australian hospitals: The World Health Organization High 5s project.

BACKGROUND: Medication reconciliation (medrec) is a mandated patient safety strategy by national, including Australian, accreditation bodies. Yet there are no validated performance measures. OBJECTIVE: To determine the feasibility of implementing the World Health Organization (WHO) Medrec Standard Operating Protocol (SOP) in a range of Australian acute care facilities to achieve measurable and sustainable reductions in medication discrepancies occurring at admission. METHODS: A multicentre, prospective national study was conducted in ten academic, urban and regional hospitals to implement the SOP using WHO High 5s project and quality improvement methodology. Sites collected data on the rate of medrec performed within 24 h of admission in a random selection of 50 patients aged ≥65 years admitted via the emergency department, monthly for four years. Medrec quality was reviewed in a subset of 30 patients using three performance measures. Barriers, enablers and benefits of SOP implementation were collected using qualitative surveys. RESULTS: Ten health services reviewed 42,003 patient records. Of these, 20,162 (49.5%) had medicines reconciled within 24 h of admission. Four services increased, two decreased, and in four, medrec completion rates remained static. Mean number of unintentional and undocumented intentional medication discrepancies per patient decreased: 0.21 to 0.16 (p = 0.001) and 0.34 to 0.08 (p = 0.003), respectively. Unintentional discrepancies decreased from 15.2% to 11.1% (p = 0.001). Barriers to full implementation included: medrec not seen as a priority, limited resources and lack of electronic systems integration. Enablers included: use of medrec measures for feedback, educational resources, and 7-day week clinical pharmacy services. Benefits included improvements in medication safety culture and multidisciplinary teamwork. CONCLUSIONS: The WHO SOP was feasible, although challenging, to implement in a range of acute health services, and produced measureable and sustainable improvements in medicines information accuracy on admission. Sustaining the quantum of quality and timely medrec requires investment in pharmacist resources and electronic systems integration.

Young-onset colorectal cancer in New South Wales: a population-based study.

OBJECTIVE: Australia has the highest incidence of colorectal cancer (CRC) in the world. The incidence of young-onset CRC (yCRC) is increasing in developed nations. Our aim was to determine the incidence of yCRC in New South Wales, the demographic and clinico-pathological characteristics of these patients, and their survival. DESIGN, SETTING, PARTICIPANTS: A population-based cohort study of all cases of CRC diagnosed in NSW during 2001-2008. Data on newly diagnosed cases of CRC were obtained from the NSW Central Cancer Registry; mortality data were obtained from the NSW Registry of Births Deaths and Marriages (to 2012). The characteristics and tumour-related factors of patients under 50 years of age (yCRC) were compared with those for patients aged 50 years or more. MAIN OUTCOME MEASURES: Current incidence of yCRC and trends in incidence; 5-year cancer-specific survival rates and risks of death compared with older patients. RESULTS: 32 178 patients were diagnosed with CRC, including 2001 (6.2%) with yCRC. The incidence of yCRC was unchanged across the study period (2001, 13.7 cases per 100 000 population; 2008, 11.8 per 100 000; P = 0.26). Rectal cancer was more frequent in yCRC than in older patients (34.4% v 26.0%), as was distant disease (21.2% v 15.3%). However, 5-year cancer-specific survival was greater for patients with yCRC (68.8%; 95% CI, 66.2-71.2%) than for older patients (66.3%; 95% CI, 65.6-67.0%; P < 0.001). CONCLUSIONS: The incidence of yCRC did not increase in NSW during 2001-2008. Despite more advanced disease at presentation, cancer-specific survival was better than for older patients with CRC.

Streamlined mean field variational Bayes for longitudinal and multilevel data analysis.

Streamlined mean field variational Bayes algorithms for efficient fitting and inference in large models for longitudinal and multilevel data analysis are obtained. The number of operations is linear in the number of groups at each level, which represents a two orders of magnitude improvement over the naïve approach. Storage requirements are also lessened considerably. We treat models for the Gaussian and binary response situations. Our algorithms allow the fastest ever approximate Bayesian analyses of arbitrarily large longitudinal and multilevel datasets, with little degradation in accuracy compared with Markov chain Monte Carlo. The modularity of mean field variational Bayes allows relatively simple extension to more complicated scenarios.

Variational methods for fitting complex Bayesian mixed effects models to health data.

We consider approximate inference methods for Bayesian inference to longitudinal and multilevel data within the context of health science studies. The complexity of these grouped data often necessitates the use of sophisticated statistical models. However, the large size of these data can pose significant challenges for model fitting in terms of computational speed and memory storage. Our methodology is motivated by a study that examines trends in cesarean section rates in the largest state of Australia, New South Wales, between 1994 and 2010. We propose a group-specific curve model that encapsulates the complex nonlinear features of the overall and hospital-specific trends in cesarean section rates while taking into account hospital variability over time. We use penalized spline-based smooth functions that represent trends and implement a fully mean field variational Bayes approach to model fitting. Our mean field variational Bayes algorithms allow a fast (up to the order of thousands) and streamlined analytical approximate inference for complex mixed effects models, with minor degradation in accuracy compared with the standard Markov chain Monte Carlo methods.