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Background
Personal support services enable many individuals to stay in their homes, but there are no standard ways to classify need for functional support in home and community care settings. The goal of this project was to develop an evidence-based clinical tool to inform service planning while allowing for flexibility in care coordinator judgmen...
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Background:
The falls literature focuses on individuals with previous falls, so little is known about individuals who have not experienced a fall in the past. Predicting falls in those without a prior event is critical for primary prevention of injuries. Identifying and intervening before the first fall may be an effective strategy for reducing th...
Citations
... These data are collected as part of routine clinical processes and support systematic evaluation of home care client needs, ranging from symptomology (e.g., pain, dyspnea) to functional independence (e.g., mobility with assistive devices) to social support (e.g., presence and level of caregiver support). These data have been previously used to evaluate quality of existing care models [31,32], identify opportunities for system integration and improvements [33,34] and support identification of populations requiring targeted interventions [19,35,36]. ...
Calls to leverage routinely collected data to inform health system improvements have been made. Misalignment between home care services and client needs can result in poor client, caregiver, and system outcomes. To inform development of an integrated model of community-based home care, grounded in a holistic definition of health, comprehensive clinical profiles were created using Ontario, Canada home care assessment data. Retrospective, cross-sectional analyses of 2017–2018 Resident Assessment Instrument Home Care (RAI-HC) assessments (n = 162,523) were completed to group home care clients by service needs and generate comprehensive profiles of each group’s dominant medical, functional, cognitive, and psychosocial care needs. Six unique groups were identified, with care profiles representing home care clients living with Geriatric Syndromes, Medical Complexity, Cognitive Impairment and Behaviours, Caregiver Distress and Social Frailty. Depending on group membership, between 51% and 81% of clients had identified care needs spanning four or more Positive Health dimensions, demonstrating both the heterogeneity and complexity of clients served by home care. Comprehensive clinical profiles, developed from routinely collected assessment data, support a future-focused, evidence-informed, and community-engaged approach to research and practice in integrated home-based health and social care.
... This can be considered low and suggests that use of the RUG as the basis of a future resource allocation model would represent a significant departure from how home support allocations were decided in the pilot. (Sinn et al., 2017). Derived using interRAI assessment data, the framework describes six distinct care groups that are ordered hierarchically, with a higher group indicating a higher need for support. ...
... > 60 days of nursing and personal support services) [14]. Information from the RAI-HC assessment is used to guide care planning [15], inform resource allocation at the client-level [16] and evaluate quality of care [17]. The inter-rater reliability of the items included on the RAI-HC [13], and the internal consistency of items used in summary measures for instrumental and basic activity of daily living dependence [18] are strong. ...
Background:
The Hospital Frailty Risk Score (HFRS) is scored using ICD-10 diagnostic codes in administrative hospital records. Home care clients in Canada are routinely assessed with Resident Assessment Instrument-Home Care (RAI-HC) which can calculate the Clinical Frailty Scale (CFS) and the Frailty Index (FI).
Objective:
Measure the correlation between the HFRS, CFS and FI and compare prognostic utility for frailty-related outcomes.
Design:
Retrospective cohort study.
Setting:
Alberta, British Columbia and Ontario, Canada.
Subjects:
Home care clients aged 65+ admitted to hospital within 180 days (median 65 days) of a RAI-HC assessment (n = 167,316).
Methods:
Correlation between the HFRS, CFS and FI was measured using the Spearman correlation coefficient. Prognostic utility of each measure was assessed by comparing measures of association, discrimination and calibration for mortality (30 days), prolonged hospital stay (10+ days), unplanned hospital readmission (30 days) and long-term care admission (1 year).
Results:
The HFRS was weakly correlated with the FI (ρ 0.21) and CFS (ρ 0.28). Unlike the FI and CFS, the HFRS was unable to discriminate for 30-day mortality (area under the receiver operator characteristic curve (AUC) 0.506; confidence interval (CI) 0.502-0.511). It was the only measure that could discriminate for prolonged hospital stay (AUC 0.666; CI 0.661-0.673). The HFRS operated like the FI and CFI when predicting unplanned readmission (AUC 0.530 CI 0.526-0.536) and long-term care admission (AUC 0.600; CI 0.593-0.606).
Conclusions:
The HFRS identifies a different subset of older adult home care clients as frail than the CFS and FI. It has prognostic utility for several frailty-related outcomes in this population, except short-term mortality.
... Long-term care and aging in place can be further combined with the latest information and communication technology to successfully allow the elderly to deal with aging efficiently and effectively [3,[9][10][11]. More importantly, the development of information and communication technology can overcome some of the limitations of aging in place, thereby helping the elderly to live at home [12][13][14]. Consequently, labor demands are reduced, and resources can be allotted to further promoting care activities. ...
Every country in the world is facing serious demographic aging, since the average life expectancy is consistently increasing. Agencies involved in the implementation of caregiving through long-term care institutions can develop more convenient approaches using information and communication technology to enhance overall efficiency. Communication technology has enabled the strengthening of physiological instruments, improving the efficiency and quality of services, while integrating management systems for optimum efficiency. This work conducted empirical studies, collecting responses to questionnaires from residents and caregivers in five institutions located in the south of Taiwan. The PZB model, proposed by Parasuraman, Zeithaml, and Berry, was used to construct the questionnaire to analyze the service quality following the incorporation of information and communication technology. The results of the empirical study show that 34% and 63% of the relatives of the residents agreed and strongly agreed that the system was practical and convenient, respectively. As for the caregivers, 77% of them agreed or strongly agreed that the system was mobile, practical, and convenient, and they agreed that the system could significantly increase working efficiency, reduce waiting time, and improve administration for chronic diseases among care-home residents.
... Assessment data. This study used the following validated scales and algorithms from the interRAI Home Care assessment: Activities of Daily Living Hierarchy Scale (ADLH) ranges from 0 to 6 with higher levels indicating greater difficulty in performing activities of daily living [26]; Cognitive Performance Scale 2 (CPS2) ranges from 0 to 8 with higher levels indicating greater cognitive impairment [27]; Depression Rating Scale (DRS) ranges from 0 to 14 with higher levels indicating more and/or frequent depressive symptoms [28]; Communication Scale ranges from 0 to 8 with higher levels indicating greater difficulty with making self-understood and ability to understand others; Changes in Health, End-stage disease, Signs, and Symptoms Scale (CHESS) ranges from 0 to 5 with higher levels indicating greater health instability [29,30]; Personal Support (PS) Algorithm ranges from 1 to 6 with higher groups suggesting greater need for personal support services [31]. Additionally, items assessing recent changes in decision-making and functional status were used to code for cognitive and functional decline. ...
Objective
The objective was to compare home care episode, standardised assessment, and service patterns in Ontario’s publicly funded home care system during the first wave of the COVID-19 pandemic (i.e., March to September 2020) using the previous year as reference.
Study design and setting
We plotted monthly time series data from March 2019 to September 2020 for home care recipients in Ontario, Canada. Home care episodes were linked to interRAI Home Care assessments, interRAI Contact Assessments, and home care services. Health status measures from the patient’s most recent interRAI assessment were used to stratify the receipt of personal support, nursing, and occupational or physical therapy services. Significant level and slope changes were detected using Poisson, beta, and linear regression models.
Results
The March to September 2020 period was associated with significantly fewer home care admissions, discharges, and standardised assessments. Among those assessed with the interRAI Home Care assessment, significantly fewer patients received any personal support services. Among those assessed with either interRAI assessment and identified to have rehabilitation needs, significantly fewer patients received any therapy services. Among patients receiving services, patients received significantly fewer hours of personal support and fewer therapy visits per month. By September 2020, the rate of admissions and services had mostly returned to pre-pandemic levels, but completion of standardised assessments lagged behind.
Conclusion
The first wave of the COVID-19 pandemic was associated with substantial changes in Ontario’s publicly funded home care system. Although it may have been necessary to prioritise service delivery during a crisis situation, standardised assessments are needed to support individualised patient care and system-level monitoring. Given the potential disruptions to home care services, future studies should examine the impact of the pandemic on the health and well-being of home care recipients and their caregiving networks.
... In Ontario, these assessments are completed by trained home care coordinators and measure numerous domains of health and wellbeing including functional and cognitive performance, mood, behaviour, and pain (12). Information from these home care assessments is used to guide care planning and inform resource allocation at the clientlevel (13,14) while also providing quality indicators and data to support research and policy development at the system-level (15,16). To augment frailty assessment in the ICU, particularly for triage, information from assessments completed before hospitalization offer a direct measure of baseline frailty (17). ...
Background
The extent by which the degree of baseline frailty, as measured using standardized multidimensional health assessments before admission to hospital, predicts survival among older adults following admission to an intensive care unit (ICU) remains unclear.
Research Question
Is baseline frailty an independent predictor of survival among older adults receiving care in an ICU?
Study Design and Methods
Retrospective cohort study of community-dwelling older adults (age ≥65 years) receiving public home services that were admitted to any ICU in Ontario, Canada between April 1st, 2009 and March 31st, 2015. All individuals had an interRAI-Home Care (RAI-HC) assessment completed within 180 days of ICU admission; these assessments were linked to hospital discharge abstract records. Patients were categorized using frailty measures each calculated from the RAI-HC: a classification tree version of the Clinical Frailty Scale (CFS); the Frailty Index - Acute Care (FI-AC); and the Changes in Health, End-Stage Disease, Signs, and Symptoms Scale (CHESS). One-year survival models were used to compare their performance. Patients were stratified based on the receipt of mechanical ventilation in the ICU.
Results
Of 24,499 individuals admitted to an ICU within 180 days of a RAI-HC assessment, 264% (6,467) received mechanical ventilation. Overall, 43.0% (95% CI 42.4% - 43.6%) survived 365 days after ICU admission. In general, among the overall cohort and both mechanical ventilation sub-groups, mortality hazards increased with the severity of baseline frailty. Models predicting survival 30, 90 and 365 days after admission to an ICU that adjusted for one of the frailty measures were more discriminant than reference models that adjusted only for age, sex, major clinical category, and area income quintile.
Interpretation
Severity of baseline frailty is independently associated with survival after ICU admission and should be considered when determining goals-of-care and treatment plans for persons with critical illness.
... The lack of standardisation in the allocation of personal support workers, for instance, may contribute to delays in care coordination for hospital patients awaiting discharge to go home. 57 Other variables that affect delayed discharge status include MAPLe, which is a widely used algorithm that reflects complex needs associated with caregiver distress and risk of admission to LTC. Behaviour symptoms may also pose as barriers to discharge home and potentially discharge to LTC. ...
Background
Improved identification of patients with complex needs early during hospitalisation may help target individuals at risk of delayed discharge with interventions to prevent iatrogenic complications, reduce length of stay and increase the likelihood of a successful discharge home.
Methods
In this retrospective cohort study, we linked home care assessment records based on the Resident Assessment Instrument for Home Care (RAI-HC) of 210 931 hospitalised patients with their Discharge Abstract Database records. We then undertook multivariable logistic regression analyses to identify preadmission predictive factors for delayed discharge from hospital.
Results
Characteristics that predicted delayed discharge included advanced age (OR: 2.72, 95% CI 2.55 to 2.90), social vulnerability (OR: 1.27, 95% CI 1.08 to 1.49), Parkinsonism (OR: 1.34, 95% CI 1.28 to 1.41) Alzheimer’s disease and related dementias (OR: 1.27, 95% CI 1.23 to 1.31), need for long-term care facility services (OR: 2.08, 95% CI 1.96 to 2.21), difficulty in performing activities of daily living and instrumental activities of daily living, falls (OR: 1.16, 95% CI 1.12 to 1.19) and problematic behaviours such as wandering (OR: 1.29, 95% CI 1.22 to 1.38).
Conclusion
Predicting delayed discharge prior to or on admission is possible. Characteristics associated with delayed discharge and inability to return home are easily identified using existing interRAI home care assessments, which can then facilitate the targeting of pre-emptive interventions immediately on hospital admission.
... The correlates of UI among older adults having daily UI would likely be different from those older adults who have less than daily UI (5). As well, older adults with daily or multiple daily episodes of UI would have different and greater care and service needs as UI is a main driver of receipt of personal support services in Ontario (22). The incontinence item in the RAI-HC has good reliability, with a weighted kappa of 0.76 (17). ...
Objectives:
Urinary incontinence (UI) is a burdensome condition for older adults with diabetes receiving home-care services, yet little is known about the prevalence and correlates of UI in this population. The objective of this cross-sectional study, informed by a complexity model, was to determine the prevalence and correlates of UI in older adults with diabetes receiving home care in Ontario, Canada.
Methods:
In this study, we analyzed population-level data of the most recently completed Resident Assessment Instrument for Home Care from 2011 to 2016 for older (≥65 years) home-care clients with diabetes. Older adults with daily or multiple daily episodes of UI were compared with adults who were continent or had less than daily UI on sociodemographic, functional, psychosocial and clinical variables. Multiple logistic regression was used to determine correlates of UI in this population.
Results:
Of 118,519 older adults with diabetes, 39,945 (33.7%) had daily or multiple daily episodes of UI. Correlates of UI included: impaired function in activities of daily living (odds ratio [OR], 5.31; 95% confidence interval [CI], 5.14‒5.50), cognitive impairment (OR, 2.37; 95% CI, 2.28‒2.47), female sex (OR, 1.87; 95% CI, 1.82‒1.93), multiple (≥2) chronic conditions (OR, 1.83; 95% CI, 1.74‒1.93), presence of a distressed caregiver (OR, 1.31; 95% CI, 1.27‒1.35), making economic trade-offs (OR, 1.23; 95% CI, 1.11‒1.34) and falls (OR, 1.22; 95% CI, 1.19‒1.26).
Conclusions:
Urinary incontinence is common among older adults with diabetes using home-care services. Targeted interventions are required to address the social, functional and clinical factors associated with UI in this population.
Background: Artificial intelligence (AI) holds the promise of supporting nurses' clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios. Objective: This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care.
Background
Artificial intelligence (AI) holds the promise of supporting nurses’ clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios.
Objective
This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care.
Methods
Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria.
Results
The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail.
Conclusions
The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care–specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.
