Mapping Quality Indicators to Assess Older Adult Health and Care in Community-, Continuing-, and Acute-Care Settings: A Systematic Review of Reviews and Guidelines.

Quality indicators (QIs) play a vital role in enhancing the care of older adults. This study aimed to identify existing QIs relevant to the health and care of older adults in community-care, continuing-care, and acute-care settings, along with available information such as definitions and calculation methods. A systematic review of published review studies, grey literature, and guidelines was undertaken, utilizing six electronic databases searched for materials dated from 2010 to 2 June 2023. To be included in this study, the literature had to provide data on QIs in a setting involving older adults. This study included 27 reviews and 44 grey literature sources, identifying a total of 6391 QIs. The highest number of indicators (37%) were relevant to continuing care; 32% and 28% were pertinent to community- and acute-care settings, respectively. The process domain had the highest number of QIs (3932), while the structure domain had the fewest indicators (521). A total of 39 focus areas were identified, with the five most common areas being, in descending order, orthopedics/hip fractures, end-of-life/palliative care, appropriate prescribing, neurocognitive conditions, and cardiovascular conditions; these areas ranged between 10% and 6%. When mapped against the Quadruple Aim framework, most QIs (85%) were linked to improving health outcomes. This inclusive compilation of QIs serves as a resource for addressing various focus areas pertinent to the Quadruple Aims. However, few quality indicators have been designed to provide a comprehensive and thorough evaluation of a specific aspect, taking into account all three key domains: structure, process, and outcomes. Addressing the description and psychometric properties of QIs is foundational for ensuring their trustworthiness and effective application.

Drivers of COVID-19 Outcomes in Long-Term Care Facilities Using Multi-Level Analysis: A Systematic Review.

This study aimed to identify the individual, organizational, and environmental factors which contributed to COVID-19-related outcomes in long-term care facilities (LTCFs). A systematic review was conducted to summarize and synthesize empirical studies using a multi-level analysis approach to address the identified influential factors. Five databases were searched on 23 May 2023. To be included in the review, studies had to be published in peer-reviewed journals or as grey literature containing relevant statistical data. The Joanna Briggs Institute critical appraisal tool was employed to assess the methodological quality of each article included in this study. Of 2137 citations identified after exclusions, 99 records met the inclusion criteria. The predominant individual, organizational, and environmental factors that were most frequently found associated with the COVID-19 outbreak comprised older age, higher dependency level; lower staffing levels and lower star and subset domain ratings for the facility; and occupancy metrics and co-occurrences of outbreaks in counties and communities where the LTCFs were located, respectively. The primary individual, organizational, and environmental factors frequently linked to COVID-19-related deaths comprised age, and male sex; higher percentages of racial and ethnic minorities in LTCFs, as well as ownership types (including private, for-profit, and chain membership); and higher occupancy metrics and LTCF's size and bed capacity, respectively. Unfolding the risk factors collectively may mitigate the risk of outbreaks and pandemic-related mortality in LTCFs during future endemic and pandemics through developing and improving interventions that address those significant factors.

"We could have used a lot more of this before ": A qualitative study understanding barriers and facilitators to implementing a provincial PPE safety coach program during the COVID-19 pandemic.

BACKGROUND: A Provincial PPE Safety Coach Program was introduced to support appropriate use of personal protective equipment by health care workers. The objective was to understand barriers and facilitators to implementation. METHODS: A qualitative study was conducted mid-2021. Participants were recruited using a purposive sampling strategy. Interviews were conducted using a guide informed by the Theoretical Domains Framework and Consolidated Framework for Implementation Research. Analysis was conducted using the Theoretical Domains Framework. RESULTS: Prominent domains identified by staff were "social influences and skills", "environmental context and resources", "social/professional role and identity", "emotion", and "belief of consequences". Prominent domains identified by safety coaches were "knowledge", "social/professional role and identity", "environmental context and resources", and "memory". Only "environmental context and resources" and "social/professional role and identity" were similar. The main facilitators were fear of COVID-19 and leadership commitment, while the main barriers were lack of clarity and balancing the role. DISCUSSION: Understanding the local context of a health care environment influenced the success of safety coaches. The role allowed individuals to develop leadership skills and help staff improve their perceived competence in using personal protective equipment. CONCLUSIONS: Safety coaches were well received. Influencing factors provide a basis for strategies to embed this approach throughout a health care system.

Artificial intelligence predictive analytics in heart failure: results of the pilot phase of a pragmatic randomized clinical trial.

OBJECTIVES: We conducted an implementation planning process during the pilot phase of a pragmatic trial, which tests an intervention guided by artificial intelligence (AI) analytics sourced from noninvasive monitoring data in heart failure patients (LINK-HF2). MATERIALS AND METHODS: A mixed-method analysis was conducted at 2 pilot sites. Interviews were conducted with 12 of 27 enrolled patients and with 13 participating clinicians. iPARIHS constructs were used for interview construction to identify workflow, communication patterns, and clinician's beliefs. Interviews were transcribed and analyzed using inductive coding protocols to identify key themes. Behavioral response data from the AI-generated notifications were collected. RESULTS: Clinicians responded to notifications within 24 hours in 95% of instances, with 26.7% resulting in clinical action. Four implementation themes emerged: (1) High anticipatory expectations for reliable patient communications, reduced patient burden, and less proactive provider monitoring. (2) The AI notifications required a differential and tailored balance of trust and action advice related to role. (3) Clinic experience with other home-based programs influenced utilization. (4) Responding to notifications involved significant effort, including electronic health record (EHR) review, patient contact, and consultation with other clinicians. DISCUSSION: Clinician's use of AI data is a function of beliefs regarding the trustworthiness and usefulness of the data, the degree of autonomy in professional roles, and the cognitive effort involved. CONCLUSION: The implementation planning analysis guided development of strategies that addressed communication technology, patient education, and EHR integration to reduce clinician and patient burden in the subsequent main randomized phase of the trial. Our results provide important insights into the unique implications of implementing AI analytics into clinical workflow.