Data Analysis Model Design of Health Service Monitoring System for China's Elderly Population: The Proposal of the F-W Model Based on the Collaborative Governance Theory of Healthy Aging.

In the era of artificial intelligence, big data and 5G, health care for elderly people is facing an important digital transformation. The objective of this study is to design the data analysis module of the elderly health service monitoring system (HSMS) and attempt to put forward a new healthy aging (HA) model that is applicable not only to the individual HA, but also to the regional HA system. Based on the HA theory of collaborative governance, we divided the elderly HSMS into four modules, including physical health, mental health, ability of daily activity, and social participation. Then, factors that influence HA were assessed by stepwise logistic regression to build the analysis model, using the public micro-panel data of the China Health and Retirement Longitudinal Survey (CHARLS). Age (odds ratio (OR) = 1.55 (95% confidence interval (CI): 1.06-2.27)), living in urban areas (OR = 1.57 (95% CI: 1.03-2.39)), being literate (OR = 1.51 (95% CI: 1.01-2.23)), expecting to get long-term health care in the future from their grown children (OR = 1.69 (95% CI: 1.10-2.61)) and having literate grown children (OR = 2.01 (95% CI: 0.26-0.97)) had a significant positive impact on HA of elderly people. Therefore, the F-W (factors and weighs, also family and welfare) model is proposed in this paper. The outcomes can contribute with designing HSMS for different provinces and several different regions in China and leave a door open to improve the model and algorithm application for HSMS in the future studies.

Determining patient attendance, access to interventions and clinical outcomes in a publicly funded obesity programme: Results from the Canberra Obesity Management Service.

Multi-disciplinary specialist services have a crucial role in the management of patients with obesity. As demand for these services increases, so too does the need to monitor individual service performance and compare outcomes across multiple sites. This paper reports on results from the publicly funded Canberra Obesity Management Service. A descriptive observational study was conducted on new patients who attended an initial medical review from July 2016 to June 2017. Baseline characteristics, comorbidities, attendance, service utilization and outcomes were collated until June 2018. Of the 162 patients identified, 64% continued to attend beyond initial medical review. Dietetics was the most commonly accessed allied health service, followed by exercise physiology and psychology. Very low-energy diet was the most commonly trialled intensive intervention, followed by pharmacotherapy and bariatric surgery. Mean baseline weight for those who continued beyond initial medical review was 142.0 kg (SD 26.6 kg), with a mean weight change of -6.2 kg (SD 10.2 kg) and a mean change in percentage body weight of -5% (SD 7%). Clinically significant weight loss was achieved in 36% of these patients, with a further 47% achieving weight stabilization. Mean Depression, Anxiety and Stress Scale scores reduced from 8-6-8 to 7-5-5, and mean Epworth Sleepiness Scale scores decreased from 8/24 to 6/24. Polysomnography referrals were made for 37% of all new patients, 87% of whom were diagnosed with varying degrees of obstructive sleep apnoea. We present these findings in the hope that they may serve as an example for data collection, individual service monitoring and comparison across multiple obesity services.