Using powerful data from the interRAI MDS to support care and a learning health system: A case study from long-term care

Dash, Darly; Heckman, George A.; Boscart, Véronique; Costa, Andrew P.; Killingbeck, Jaimie; d’Avernas, Josie

doi:10.1177/0840470417743989

Cited by 15 publications

(11 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A strength of the 1 st Fall algorithm is that it was developed using decision tree analysis, which takes a more granular look at data than the traditional regression based approaches, as it is able to identify very small groups with similar characteristics and risks. 15 Furthermore, the 1 st Fall can be calculated with the RAI-MDS 2.0 and its newer version, the interRAI Long-Term Care Facilities (LTCF), 16 which is a mandatory reporting system in LTC facilities across many provinces in Canada and in 30+ countries internationally. A major advantage of this is that the 1 st Fall algorithm can be easily integrated into routine assessment without any burden on the health care provider.…”

Section: Discussionmentioning

confidence: 99%

Predicting First Time Falls: Validating a Novel Algorithm in Long Term Care

Kuspinar

Hirdes

Berg

et al. 2021

Physical & Occupational Therapy In Geriatrics

View full text Add to dashboard Cite

Aim: To determine the predictive validity of the 1 st Fall algorithm in long-term care (LTC) residents across four Canadian provinces. Methods: This retrospective cohort study included all clients admitted to LTC between 2006-2017 with no history of falls in the past 30 days. The outcome was occurrence of a fall and logistic regression analysis was performed to assess predictive validity. Results: A total of 199,997 LTC residents were studied (71% were >80 years old, 66% women, and 17% had severe cognitive impairment). For the total sample, clients in the 2 nd , 3 rd , 4 th and 5 th risk categories had 1.15, 1.58, 2.66, and 3.76 times greater odds of falling than the 1 st category, respectively. Similar trends were observed across provinces. Conclusions: 1 st Fall was developed to predict the risk of a first-time fall event in individuals with no history of a recent fall. 1 st Fall identified LTC residents at risk of a first-time fall, supporting its use in routine care.

show abstract

Section: Discussionmentioning

confidence: 99%

Predicting First Time Falls: Validating a Novel Algorithm in Long Term Care

Kuspinar

Hirdes

Berg

et al. 2021

Physical & Occupational Therapy In Geriatrics

View full text Add to dashboard Cite

show abstract

“…The interRAI Home Care Assessment (interRAI-HC) and the Resident Assessment Instrument – Home Care (RAI-HC), are standardized assessment tools used to guide care and service planning for home care clients in community settings across the world [ 29 , 30 ]. The interRAI-HC or RAI-HC has several embedded scales and algorithms derived from the assessment items, which are externally validated and used for decision support on health service needs [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

Lessons learned implementing and managing the DIVERT-CARE trial: practice recommendations for a community-based chronic disease self-management model

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background Chronic disease management models of care provide an opportunity to assist home care clients to manage their disease burden. However, pragmatic trial management practices and lessons learned from such models are poorly illustrated in the literature. Methods We describe the processes of implementing a community-based cardiorespiratory self-management model, known as DIVERT-CARE, across the home care programs of three health regions in Canada. The DIVERT-CARE model is a multi-component complex intervention that identifies home care clients at the highest risk of deterioration and provides them with resources and capacity to manage their conditions. We conducted a retrospective analysis of baseline participant characteristics, needs assessments, reviewed findings from site visits and a national workshop with study partners, and examined other study documentation. Results Three home care regions in Canada participated in the study. A robust and data-driven review of each site was necessary to understand the local context, home care caseloads, structure of local systems, and intensity of resources, which influenced study processes. The creation of an intervention framework highlighted the need to adapt the intervention in a way that was sensitive to the local context while maintaining intervention outcomes. Conclusion Our detailed review showcases the relevant activities and on-the-ground steps needed to manage and conduct a multi-site pragmatic trial in home care. This example can help other researchers in implementing multi-disciplinary and multi-component care models for practice-based research.

show abstract

“…When using interRAI’s digital application in LTCs, nurses and other clinicians add residents’ clinical data, which then triggers a set of clinical-assessment protocols (CAPs) ( Adams-Wendling et al, 2008 ). These CAPs are the problem areas in residents’ health that require care interventions and each resident’s care plan is informed by a set of CAPs from interRAI ( Adams-Wendling et al, 2008 ; Dash et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Optimizing the InterRAI Assessment Tool in Care Planning Processes for Long-Term Residents: A Scoping Review

et al. 2021

View full text Add to dashboard Cite

The aim of this review was to chart and report on existing literature that discusses how the interRAI assessment tool drives care-planning processes for residents in long-term-care settings. This scoping review was informed by the Joanna Briggs Institute guidelines for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guideline. Relevant studies were obtained from databases search of CINAHL (EBSCO), MEDLINE (Ovid), PsycINFO (EBSCO), Academic Search Premier (EBSCO), Embase (Elsevier), ProQuest Nursing and Allied Health Database (ProQuest), Sociological Abstracts (ProQuest), and Social Services Abstracts (ProQuest). Of the 17 included studies, five (29.4%) addressed interRAI’s minimum dataset component as a clinical data-collection tool; five (29.4%) addressed interRAI’s assessment scales and its clinical-assessment protocols as viable health-assessment tools; four (23.5%) considered interRAI’s assessment scales in terms of whether this tool is capable of predicting residents’ health risks; one (5.9%) addressed the effects of interRAI’s care plans on residents’ health outcomes; and the remaining two studies (11.8%) used interRAI’s quality-indicator function for both the performance of and improvements in the quality of care. The scoping review finds that there is no substantial evidence that supports the implementation of interRAI care plans for consistent health outcomes.

show abstract

Using powerful data from the interRAI MDS to support care and a learning health system: A case study from long-term care

Cited by 15 publications

References 13 publications

Predicting First Time Falls: Validating a Novel Algorithm in Long Term Care

Predicting First Time Falls: Validating a Novel Algorithm in Long Term Care

Lessons learned implementing and managing the DIVERT-CARE trial: practice recommendations for a community-based chronic disease self-management model

Optimizing the InterRAI Assessment Tool in Care Planning Processes for Long-Term Residents: A Scoping Review

Contact Info

Product

Resources

About