Patient navigation programs for people with dementia, their caregivers, and members of their care team: a scoping review protocol

Doucet, Shelley; Luke, Alison; Anthonisen, Grailing; Kelly, Katherine; Goudreau, Alex; MacNeill, A. Luke; MacNeill, Lillian

doi:10.11124/jbies-21-00049

Cited by 9 publications

(18 citation statements)

References 23 publications

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“…Other knowledge syntheses on related topics were consulted to supplement identified terms, such as Doucet et al ’s scoping review protocol on dementia navigation programmes. 26 The text words contained in the titles and abstracts of relevant articles, and the index terms used to describe the articles, were used to develop a full search strategy. The search terms that were identified in this first step were then tested in CINAHL (EBSCO) to establish that the search results both completely reflected the scope of the research available and reduced the inclusion of irrelevant results.…”

Section: Methods and Analysismentioning

confidence: 99%

Hospital-based patient navigation programs for patients who experience injury-related trauma and their caregivers: a scoping review

2022

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ObjectiveThis review’s objective is to map the literature on the characteristics, impact, barriers and facilitators of hospital-based patient navigation programmes that support patients who experience injury-related trauma and their caregivers. Patients who experience injury-related trauma frequently require support from multiple care teams and face many challenges to care, both in hospital and when transitioning across settings and services. Patient navigation can improve their care.DesignThis review is conducted according to JBI methodology for scoping reviews. The initial database search took place on 6 June 2021 and the grey literature search took place between September and October 2021. The results are presented in a Preferred Reporting Items for Systematic Reviews and Meta-analyses for Scoping Reviews flow diagram.SettingThis review considered materials where the patient navigation programmes were delivered in hospital settings. There was no geographical limit to this study.ParticipantsThis review focused on hospital-based patient navigation programmes for patients who experience injury-related trauma and/or their caregivers.ResultsThis review captured 11 records that describe 10 programmes. All programmes were based in the USA. Most programmes provided education, care coordination, discharge planning, and referrals to resources, services, and programmes to assist patients and/or their families in the hospital or the community. Half the programmes were based in level 1 trauma centres. Common impacts included decreases in readmission rates and increases in satisfaction rates. Barriers included difficulty recruiting or enrolling patients with short hospital stays and hospital administrators’ and healthcare providers’ lack of understanding of the navigator role. Navigator background, either professional or experiential, was identified as a facilitator, as was flexibility in programme delivery and communication methods.ConclusionsEleven records show a small but distinct sample. Reported characteristics, impact, barriers and facilitators were consistent with findings from other patient navigation studies. The results can inform the development and implementation of similar programmes in trauma centres and support changes in policy to improve the delivery of care.

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Section: Methods and Analysismentioning

confidence: 99%

Hospital-based patient navigation programs for patients who experience injury-related trauma and their caregivers: a scoping review

2022

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“…There is some overlap between the roles of patient navigators and case managers, such as care coordination. However, navigators typically provide informational and emotional support, while case managers can provide clinical care 17 30. Patient navigators help individuals navigate through existing services and can advocate for missing services, whereas case managers fill this need by providing clinical care and acting as a care provider 17 30…”

Section: Methods and Analysismentioning

confidence: 99%

“…The librarian formulated a search strategy drawing from the words contained in the titles, abstracts and subject descriptors of these articles. Additionally, the search strategy drew from a number of knowledge syntheses on related topics, as well as the search strategy reported in Doucet et al (2022) 30. Once the search terms were identified, they were tested in CINAHL in a variety of combinations and using a variety of search fields until it was determined that the search results both completely reflected the scope of the research available on this topic and avoided unnecessary noise from irrelevant results.…”

Section: Methods and Analysismentioning

confidence: 99%

Hospital-based patient navigation programmes for patients who experience injury-related trauma and their caregivers: a scoping review protocol

et al. 2022

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IntroductionPatients who experience injury-related trauma tend to have complex care needs and often require support from many different care providers. Many patients experience gaps in care while in the hospital and during transitions in care. Providing access to integrated care can improve outcomes for these patients. Patient navigation is one approach to improving the integration of care and proactively supporting patients and their caregivers as they navigate the healthcare system. The objective of this scoping review is to map the literature on the characteristics and impact of hospital-based patient navigation programmes that support patients who experience injury-related trauma and their caregivers.Methods and analysisThis review will be conducted in accordance with Joanna Briggs Institute methodology for scoping reviews. The review will include primary research studies, unpublished studies and evaluation reports related to patient navigation programmes for injury-related trauma in hospital settings. The databases to be searched will include CINAHL (EBSCO), EMBASE (Elsevier), ProQuest Nursing & Allied Health, PsycINFO (EBSCO) and MEDLINE (Ovid). Two independent reviewers will screen articles for relevance against the inclusion criteria. Results will be presented in a Preferred Reporting Items for Systematic Reviews and Meta-analyses for Scoping Reviews (PRISMA-ScR) flow diagram and follow the PRISMA-ScR checklist. The extracted data will be presented both tabularly and narratively.Ethics and disseminationEthics approval is not required, as the scoping review will synthesise information from publicly available material. To disseminate the findings of this review, the authors will submit the results for publication in a medical or health sciences journal, present at relevant conferences and use other knowledge translation strategies to reach diverse stakeholders (eg, host webinars, share infographics).

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“…Attributes with less entropy are tested first. 3. The third machine learning algorithm is a support vector machine.…”

Section: From Navigator Notes To Attributesmentioning

confidence: 99%

“…Patient navigation (PN) programs have demonstrated efficacy in improving health outcomes in marginalized populations across a range of clinical contexts. Initially developed in 1990 to address breast cancer disparities [1], PN has since been tested in other medical fields, demonstrating improved outcomes for other cancers [2], dementia [3], depression [4], sickle cell disease [5], and complex multimorbidity in children [6] and older adult populations [7], among other conditions. PN programs in the United States have typically focused on serving marginalized populations who experience health care inequities, including low-income individuals [8], racial and ethnic minority groups, immigrants and refugees, inner-city residents [9][10][11][12], and rural residents [13,14].…”

Section: Introductionmentioning

confidence: 99%

Predicting Social Determinants of Health in Patient Navigation: Case Study

Iacobelli¹,

Yang²,

Tom³

et al. 2023

JMIR Form Res

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Background Patient navigation (PN) programs have demonstrated efficacy in improving health outcomes for marginalized populations across a range of clinical contexts by addressing barriers to health care, including social determinants of health (SDoHs). However, it can be challenging for navigators to identify SDoHs by asking patients directly because of many factors, including patients’ reluctance to disclose information, communication barriers, and the variable resources and experience levels of patient navigators. Navigators could benefit from strategies that augment their ability to gather SDoH data. Machine learning can be leveraged as one of these strategies to identify SDoH-related barriers. This could further improve health outcomes, particularly in underserved populations. Objective In this formative study, we explored novel machine learning–based approaches to predict SDoHs in 2 Chicago area PN studies. In the first approach, we applied machine learning to data that include comments and interaction details between patients and navigators, whereas the second approach augmented patients’ demographic information. This paper presents the results of these experiments and provides recommendations for data collection and the application of machine learning techniques more generally to the problem of predicting SDoHs. Methods We conducted 2 experiments to explore the feasibility of using machine learning to predict patients’ SDoHs using data collected from PN research. The machine learning algorithms were trained on data collected from 2 Chicago area PN studies. In the first experiment, we compared several machine learning algorithms (logistic regression, random forest, support vector machine, artificial neural network, and Gaussian naive Bayes) to predict SDoHs from both patient demographics and navigator’s encounter data over time. In the second experiment, we used multiclass classification with augmented information, such as transportation time to a hospital, to predict multiple SDoHs for each patient. Results In the first experiment, the random forest classifier achieved the highest accuracy among the classifiers tested. The overall accuracy to predict SDoHs was 71.3%. In the second experiment, multiclass classification effectively predicted a few patients’ SDoHs based purely on demographic and augmented data. The best accuracy of these predictions overall was 73%. However, both experiments yielded high variability in individual SDoH predictions and correlations that become salient among SDoHs. Conclusions To our knowledge, this study is the first approach to applying PN encounter data and multiclass learning algorithms to predict SDoHs. The experiments discussed yielded valuable lessons, including the awareness of model limitations and bias, planning for standardization of data sources and measurement, and the need to identify and anticipate the intersectionality and clustering of SDoHs. Although our focus was on predicting patients’ SDoHs, machine learning can have a broad range of applications in the field of PN, from tailoring intervention delivery (eg, supporting PN decision-making) to informing resource allocation for measurement, and PN supervision.

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Patient navigation programs for people with dementia, their caregivers, and members of their care team: a scoping review protocol

Cited by 9 publications

References 23 publications

Hospital-based patient navigation programs for patients who experience injury-related trauma and their caregivers: a scoping review

Hospital-based patient navigation programs for patients who experience injury-related trauma and their caregivers: a scoping review

Hospital-based patient navigation programmes for patients who experience injury-related trauma and their caregivers: a scoping review protocol

Predicting Social Determinants of Health in Patient Navigation: Case Study

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