Person-centred care is recognized as best practice in dementia care. The purpose of this study was to evaluate the effectiveness of a stakeholder engagement practice change initiative aimed at increasing the provision of person-centred mealtimes in a residential care home (RCH). A single-group, time series design was used to assess the impact of the practice change initiative on mealtime environment across four time periods (pre-intervention, 1-month, 3-month, and 6-month follow-up). Statistically significant improvements were noted in all mealtime environment scales by 6 months, including the physical environment (z = -3.06, p = 0.013), social environment (z = -3.69, p = 0.001), relationship and person-centred scale (z = -3.51, p = 0.003), and overall environment scale (z = -3.60, p = 0.002). This practice change initiative, which focused on enhancing stakeholder engagement, provided a feasible method for increasing the practice of person-centred care during mealtimes in an RCH through the application of supportive leadership, collaborative decision making, and staff engagement.
BackgroundPerson-centred care (PCC) is described as a care philosophy in which a positive relationship is established between a resident and staff member that respects the care recipient's preferences and life history, honours identity, and enables engagement in meaningful activity (Fazio et al., 2018). Research in long-term care (LTC) homes demonstrates that interventions aimed at increasing the provision of PCC, but not addressing contextual and system issues (e.g., deeply rooted care routines and regulatory standards that impede individuality, resident choice and staff flexibility), most often fail (Caspar et al., 2016). There is growing evidence demonstrating that the implementation of PCC in practice requires a multilevel, systems approach (Brooker, 2007;Evans, 2017). Review of the literature indicates that the following organizational factors may be especially salient in their ability to influence the extent to which PCC is really improved in practice:1. The presence of leaders and managers who embrace a leadership style of 'supporting and valuing staff' combined with being 'responsive to staff needs' and offering 'solution-focused approaches' to care decisions (Caspar et al., 2017a;Kirkley et al., 2011;McGilton, 2010;Sjogren et al., 2017). 2. The cultivation and implementation of empowered workforce practices that enable and encourage
Social Determinant of Health (SDOH) data are important targets for research and innovation in Health Information Systems (HIS). The ways we envision SDOH in “smart” information systems will play a considerable role in shaping future population health landscapes. Current methods for data collection can capture wide ranges of SDOH factors, in standardised and non-standardised formats, from both primary and secondary sources. Advances in automating data linkage and text classification show particular promise for enhancing SDOH in HIS. One challenge is that social communication processes embedded in data collection are directly related to the inequalities that HIS attempt to measure and redress. To advance equity, it is imperative thatcare-providers, researchers, technicians, and administrators attend to power dynamics in HIS standards and practices. We recommend: 1. Investing in interdisciplinary and intersectoral knowledge generation and translation. 2. Developing novel methods for data discovery, linkage and analysis through participatory research. 3. Channelling information into upstream evidence-informed policy.
Background: Electronic Health Records (EHRs) are key tools for integrating patient data into health
Session topic areaData science methods: machine learning in risk factor surveillance Overall objectives or goalBackgroundDecades of research have shown that factors such as living conditions, and not just medical treatments and lifestyles, are strongly associated with the health of individuals and populations. These distal factors (social, economic, cultural and environmental) are collectively called the social determinants of health (SDOH), and affect health inequities (i.e. differences in health outcomes that are avoidable, unfair and unjust). Gathering data on both risk factors (biomedical/clinical) and SDOH is of the utmost importance to quantify their contribution in disease causation at individual and population levels. Social determinants of health and biomedical/clinical risk factors surveillance (collectively termed as “risk factor surveillance”) refers to the monitoring of distal and proximal factors that impact the health of individuals and populations and health equity. It offers the opportunity to “forecast” population health, potential disease incidence, and guide intervention programs to prevent disease manifestation. However, current risk factor surveillance data is limited in geographical representation, completion, and content and time. Identifying novel methods of collecting risk factors and SDOH data can allow for opportunities for population health and disease forecasting using high quality, nationally-representative, real-time data. Recent breakthroughs in artificial intelligence (AI), such as speech and image recognition, offers new opportunities to develop novel methods to collect risk factor information at individual levels. Meanwhile, we can use intelligent computer systems to process vast amount of data and turn those data into actionable information and knowledge for improving population health. Collaborative Session ObjectiveThrough a CIHR-funded project, we are assembling a team of national and international experts including stakeholders, public health officers/physicians, and researchers, who will identify key gaps in risk factor surveillance and data collection technologies. Resulting projects will focus on using AI for risk factor surveillance, for the ultimate purpose of monitoring population health, guiding intervention programs, and preventing disease. Our projects will focus on discovering and refining innovative methods in data collection, management, as well as assessment of data quality (i.e. selection bias). We will engage scientists and knowledge users from the inception of the ideas to ensure the relevancy of the final projects. This project aims to link medical records, clinical information, and SDOH data, to alter the way we conduct surveillance and work with big data. Facilitators involved; home institutions Dr. Vineet Saini, University of Calgary; Alberta Health Services Dr. Mingkai Peng, University of Calgary Dr. Hude Quan, University of Calgary; World Health Organization Collaborating Centre for Classification, Measurement and Standardization Intended output or outcome Identify AI technologies for use in risk factor surveillance; innovative methods in data collection, management, as well as assessment of data quality (i.e. selection bias); uses for new data sources in improving health equity. Create partnerships between national and international experts in risk factor surveillance
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