Background: With the increase in life expectancy around the globe, the incidence of postoperative delirium (POD) among older people (≥65 years) is growing. Previous studies showed a wide variation in the incidence of POD, from 4% to 53%, with a lack of specific evidence about the incidence of POD by specific surgery type among older people. The aim of this systematic review and meta‐analysis was to determine the incidence of POD by surgery type within populations 65 years and over.Methods: Databases including PubMed, Cochrane library, Embase, and CINAHL were searched until October 2020. Due to the relatively higher number of meta‐analyses undertaken in this area of research, a streamlined systematic meta‐analysis was proposed.Results: A total of 28 meta‐analyses (comprising 284 individual studies) were reviewed. Data from relevant individual studies (n = 90) were extracted and included in the current study. Studies were grouped into eight surgery types and the incidence of POD for orthopedic, vascular, spinal, cardiac, colorectal, abdominal, urologic, and mixed surgeries was 20%, 14%, 13%, 32%, 14%, 30%, 10%, and 26%, respectively. POD detection instruments were different across the studies, with Confusion Assessment Method (CAM & CAM‐ICU) being the most frequently adopted.Linking Evidence to Action: This study showed that POD incidence in older people undergoing surgery varied widely across surgery type. The more complex surgeries like cardiac and abdominal surgeries were associated with a higher risk of POD. This highlights the need to include the level of surgery complexity as a risk factor in preoperative assessments.

Background: Artificial intelligence (AI) for use in health care and social services is rapidly developing, but this has significant ethical, legal, and social implications. Theoretical and conceptual research in AI ethics needs to be complemented with empirical research to understand the values and judgments of members of the public, who will be the ultimate recipients of AI-enabled services. Objective: The aim of the Australian Values and Attitudes on AI (AVA-AI) study was to assess and compare Australians’ general and particular judgments regarding the use of AI, compare Australians’ judgments regarding different health care and social service applications of AI, and determine the attributes of health care and social service AI systems that Australians consider most important. Methods: We conducted a survey of the Australian population using an innovative sampling and weighting methodology involving 2 sample components: one from an omnibus survey using a sample selected using scientific probability sampling methods and one from a nonprobability-sampled web-based panel. The web-based panel sample was calibrated to the omnibus survey sample using behavioral, lifestyle, and sociodemographic variables. Univariate and bivariate analyses were performed. Results: We included weighted responses from 1950 Australians in the web-based panel along with a further 2498 responses from the omnibus survey for a subset of questions. Both weighted samples were sociodemographically well spread. An estimated 60% of Australians support the development of AI in general but, in specific health care scenarios, this diminishes to between 27% and 43% and, for social service scenarios, between 31% and 39%. Although all ethical and social dimensions of AI presented were rated as important, accuracy was consistently the most important and reducing costs the least important. Speed was also consistently lower in importance. In total, 4 in 5 Australians valued continued human contact and discretion in service provision more than any speed, accuracy, or convenience that AI systems might provide. Conclusions: The ethical and social dimensions of AI systems matter to Australians. Most think AI systems should augment rather than replace humans in the provision of both health care and social services. Although expressing broad support for AI, people made finely tuned judgments about the acceptability of particular AI applications with different potential benefits and downsides. Further qualitative research is needed to understand the reasons underpinning these judgments. The participation of ethicists, social scientists, and the public can help guide AI development and implementation, particularly in sensitive and value-laden domains such as health care and social services.

Background: The use of government health data for secondary purposes, such as monitoring the quality of hospital services, researching the health needs of populations, and testing how well new treatments work, is increasing. This increase in the secondary uses of health data has led to increased interest in what the public thinks about data sharing, in particular, the possibilities of sharing with the private sector for research and development. Although international evidence demonstrates broad public support for the secondary use of health data, this support does not extend to sharing health data with the private sector. If governments intend to share health data with the private sector, knowing what the public thinks will be important. This paper reports a national survey to explore public attitudes in Australia toward sharing health data with private companies for research on and development of therapeutic drugs and medical devices. Objective: This study aims to explore public attitudes in Australia toward sharing government health data with the private sector. Methods: A web-based survey tool was developed to assess attitudes about sharing government health data with the private sector. A market research company was employed to administer the web-based survey in June 2019. Results: The survey was completed by 2537 individuals residing in Australia. Between 51.8% and 57.98% of all participants were willing to share their data, with slightly fewer in favor of sharing to improve health services (51.99%) and a slightly higher proportion in favor of sharing for research and development (57.98%). There was a preference for opt-in consent (53.44%) and broad support for placing conditions on sharing health information with private companies (62% to 91.99%). Wide variability was also observed in participants’ views about the extent to which the private sector could be trusted and how well they would behave if entrusted with people’s health information. In their qualitative responses, the participants noted concerns about private sector corporate interests, corruption, and profit making and expressed doubt about the Australian government’s capacity to manage data sharing safely. The percentages presented are adjusted against the Australian population. Conclusions: This nationally representative survey provides preliminary evidence that Australians are uncertain about sharing their health data with the private sector. Although just over half of all the respondents supported sharing health data with the private sector, there was also strong support for strict conditions on sharing data and for opt-in consent and significant concerns about how well the private sector would manage government health data. Addressing public concern about sharing government health data with the private sector will require more and better engagement to build community understanding about how agencies can collect, share, protect, and use their personal data.

In the age of data, data mining provides feasible tools with which to handle large datasets consisting of data from multiple sources. However, there is limited research on retrieving statistical information from data when data are confidential and cannot be shared directly. In this paper, we address this problem and propose a framework for performing data analysis using data from multiple sources without revealing true values for privacy purposes. The proposed framework includes three steps. First, data custodians individually mask data before publishing; then, the masked data collection is used to reconstruct the density function of the original dataset, from which resampled values are generated; last, existing data mining techniques are applied directly to the resampled data. This framework utilises the technique of reconstructing an original density function from noise-masked data using the moment-based density estimation method, which plays an essential role. Simulation studies show that the proposed framework performs well; analysis results from the resampled data are comparable to those of the original data when the density of the original data is estimated well. The proposed framework is demonstrated in data clustering analysis using the example of a real-life Australian soybean dataset. Results from the k-means algorithms with two and three fitted clusters are presented to show that cluster analysis using resampled data can well replicate that of the original data.