A Local COVID-19 Support Platform for Nursing Homes: Feedback and Perspectives

Coulongeat, Matthieu; Aïdoud, Amal; Poupin, Pierre; Dubnitskiy-Robin, Sophie; Fougère, Bertrand

doi:10.1016/j.jamda.2021.06.001

Cited by 5 publications

(11 citation statements)

References 7 publications

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“…In studies involving care or nursing homes residents, the largest study population was 6161 residents [ 21 ], and the smallest study population was 52 residents in the study by Walker et al [ 24 ]. In studies involving health care professionals, the largest number of participants was 27 staff members in the study by Coulongeat et al [ 10 ], and the smallest number of participants was 14 registered nurses in the study by Johansson-Pajala et al [ 16 ].…”

Section: Resultsmentioning

confidence: 99%

“…In total, 65% (11/17) of the studies defined the professional user group using the decision support tool, whereas 35% (6/17) did not indicate who the intended users of the tools were [ 11 , 13 , 17 , 21 , 24 , 25 ]. Where stated (5/11, 45%), the most commonly specified professionals were either care home staff or nursing home staff [ 10 , 14 , 18 , 22 , 23 ]. Of these 5 studies, 4 (80%) specified that the clinical decision support tool that was evaluated was used by nurses ; 50% (2/4) of these studies provided further detail on the types of nursing staff, including registered nurses , special needs educator , and nurse aides [ 14 ] or directors, physicians, and nurses [ 10 ].…”

Section: Resultsmentioning

confidence: 99%

“…Where stated (5/11, 45%), the most commonly specified professionals were either care home staff or nursing home staff [ 10 , 14 , 18 , 22 , 23 ]. Of these 5 studies, 4 (80%) specified that the clinical decision support tool that was evaluated was used by nurses ; 50% (2/4) of these studies provided further detail on the types of nursing staff, including registered nurses , special needs educator , and nurse aides [ 14 ] or directors, physicians, and nurses [ 10 ]. Of the 11 studies that defined the professional user group using the decision support tool, 1 (9%) specified that the users of the decision support tool were pharmacists [ 12 ], and 1 (9%) stated that the tool was used by health professionals [ 16 ].…”

Section: Resultsmentioning

confidence: 99%

“…Coulongeat et al [ 10 ] investigated a local support platform that aimed to help nursing homes manage their cases of COVID-19 infection. Although the other decision support tools included in the review consisted mostly of 2 actors (a human and a computer [or paper in some cases]), the COVID-19–infection management tool used multiple human actors as the decision support.…”

Section: Resultsmentioning

confidence: 99%

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Decision Support Tools in Adult Long-term Care Facilities: Scoping Review

Lapp¹,

Egan²,

McCann³

et al. 2022

J Med Internet Res

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Background Digital innovations are yet to make real impacts in the care home sector despite the considerable potential of digital health approaches to help with continued staff shortages and to improve quality of care. To understand the current landscape of digital innovation in long-term care facilities such as nursing and care homes, it is important to find out which clinical decision support tools are currently used in long-term care facilities, what their purpose is, how they were developed, and what types of data they use. Objective The aim of this review was to analyze studies that evaluated clinical decision support tools in long-term care facilities based on the purpose and intended users of the tools, the evidence base used to develop the tools, how the tools are used and their effectiveness, and the types of data the tools use to contribute to the existing scientific evidence to inform a roadmap for digital innovation, specifically for clinical decision support tools, in long-term care facilities. Methods A review of the literature published between January 1, 2010, and July 21, 2021, was conducted, using key search terms in 3 scientific journal databases: PubMed, Cochrane Library, and the British Nursing Index. Only studies evaluating clinical decision support tools in long-term care facilities were included in the review. Results In total, 17 papers were included in the final review. The clinical decision support tools described in these papers were evaluated for medication management, pressure ulcer prevention, dementia management, falls prevention, hospitalization, malnutrition prevention, urinary tract infection, and COVID-19 infection. In general, the included studies show that decision support tools can show improvements in delivery of care and in health outcomes. Conclusions Although the studies demonstrate the potential of positive impact of clinical decision support tools, there is variability in results, in part because of the diversity of types of decision support tools, users, and contexts as well as limited validation of the tools in use and in part because of the lack of clarity in defining the whole intervention.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Decision Support Tools in Adult Long-term Care Facilities: Scoping Review

Lapp¹,

Egan²,

McCann³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…136e138 Support platforms have included access to multidisciplinary decision support, specialist phone hotlines, mobile geriatric medicine teams, and videoconferences on SARS-CoV-2 outbreak management. 139,140 Such platforms were developed in several countries and provided crucial support to nursing homes experiencing outbreaks.…”

Section: On-site Medicalization Outreach Teams and Telemedicinementioning

confidence: 99%

Managing the Impact of COVID-19 in Nursing Homes and Long-Term Care Facilities: An Update

Dyer

Fallon

Noonan

et al. 2022

Journal of the American Medical Directors Association

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Factors influencing long-term care facility performance during the COVID-19 pandemic: a scoping review

Tchouaket,

Kruglova,

Létourneau

et al. 2024

BMC Health Serv Res

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Background The COVID-19 pandemic wreaked havoc on long-term care facilities (LTCFs). Some LTCFs performed better than others at slowing COVID-19 transmission. Emerging literature has mostly described infection prevention and control strategies implemented by LTCFs during the pandemic. However, there is a need for a comprehensive review of factors that influenced the performance of LTCFs in containing COVID-19 spread to inform public health policy. Objective To build on the existing literature, we conducted a scoping review of factors that influenced LTCF performance during the COVID-19 pandemic using a multidimensional conceptual framework of performance. Methods We followed the Joanna Briggs Institute’s methodology for scoping reviews. We queried CINAHL, MEDLINE (Ovid), CAIRN, Science Direct, Scopus, and Web of Science for peer-reviewed literature in English or French published between January 1 st , 2020 and December 31 st , 2021. Retrieved records were screened for context (COVID-19 pandemic), population (LTCFs), interest (internal and external factors that influenced LTCF performance), and outcomes (dimensions of performance: equity, accessibility, reactivity, safety, continuity, efficacy, viability, efficiency). Descriptive characteristics of included articles were summarized. Dimensions of performance as well as internal (e.g., facility characteristics) and external (e.g., visitors) factors identified to have influenced LTCF performance were presented. Results We retained 140 articles of which 68% were classified as research articles, 47% originated in North America, and most covered a period between March and July 2020. The most frequent dimensions of performance were “efficacy” (75.7%) and “safety” (75.7%). The most common internal factors were “organizational context” (72.9%) and “human resources” (62.1%), and the most common external factors were “visitors” (27.1%) and “public health guidelines” (25.7%). Conclusions Our review contributes to a global interest in understanding the impact of the COVID-19 pandemic on vulnerable populations residing and working in LTCFs. Though a myriad of factors were reported, a lack of randomized controlled trials makes it impossible to establish causality between the identified factors and LTCF performance. The use of a multidimensional framework can be recommended to evaluate healthcare system performance not merely in terms of efficacy and safety, but alongside other critical dimensions such as efficiency and equity. Trial registration Research Registry ID: researchregistry7026 Supplementary Information The online version contains supplementary material available at 10.1186/s12913-024-11331-2.

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A Local COVID-19 Support Platform for Nursing Homes: Feedback and Perspectives

Cited by 5 publications

References 7 publications

Decision Support Tools in Adult Long-term Care Facilities: Scoping Review

Decision Support Tools in Adult Long-term Care Facilities: Scoping Review

Managing the Impact of COVID-19 in Nursing Homes and Long-Term Care Facilities: An Update

Factors influencing long-term care facility performance during the COVID-19 pandemic: a scoping review

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