MétaCan
Menu
Back to cohort
Record W2980222714 · doi:10.2196/14683

Using Patient and Family Engagement Strategies to Improve Outcomes of Health Information Technology Initiatives: Scoping Review

2019· article· en· W2980222714 on OpenAlexafffund
K. Leung, Drew Lu-McLean, Craig Kuziemsky, Richard Booth, Sarah Collins Rossetti, Elizabeth M. Borycki, Gillian Strudwick

Bibliographic record

VenueJournal of Medical Internet Research · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of OttawaMcMaster UniversityUniversity of VictoriaImpactWestern UniversityCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsGrey literatureHealth information technologyInclusion (mineral)Health careKnowledge managementMedicineMedical educationPsychologyNursingMEDLINEComputer sciencePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Many health care organizations around the world have implemented health information technologies (ITs) to enhance health service efficiency, effectiveness, and safety. Studies have demonstrated that promising outcomes of health IT initiatives can be obtained when patients and family members participate and engage in the adoption, use, and evaluation of these technologies. Despite knowing this, there is a lack of health care organizations using patient and family engagement strategies to enhance the use and adoption of health ITs, specifically. OBJECTIVE: This study aimed to answer the following three research questions (RQs): (1) what current frameworks or theories have been used to guide patient and family engagement in health IT adoption, use, implementation, selection, and evaluation?, (2) what studies have been done on patient and family engagement strategies in health IT adoption, use, implementation, selection, and evaluation?, and (3) what patient and family engagement frameworks, studies, or resources identified in the literature can be applied to health IT adoption, use, implementation, selection, and evaluation? METHODS: This scoping review used a five-step framework developed by Arksey and O'Malley and adapted by Levac et al. These steps include the following: (1) identifying the RQ, (2) identifying relevant studies, (3) selecting studies, (4) charting relevant data, and (5) summarizing and reporting the result. Retrieved academic and grey literature records were evaluated using a literature review software based on inclusion and exclusion criteria by two independent reviewers. If consensus was not achieved, two reviewers would resolve conflicts by discussion. Research findings and strategies were extracted from the studies and summarized in data tables. RESULTS: A total of 35 academic articles and 23 gray literature documents met the inclusion criteria. In total, 20 of the 35 included studies have been published since 2017. Frameworks found include the patient engagement framework developed by Healthcare Information and Management Systems Society and the patient and family engagement framework proposed by Carman et al. Effective strategies include providing patients with clear expectations and responsibilities and providing reimbursement for time and travel. The gray literature sources outlined key considerations for planning and supporting engagement initiatives such as providing patients with professional development opportunities, and embedding patients in existing governance structures. CONCLUSIONS: Several studies have reported their findings regarding successful strategies to engage patients and family members in health IT initiatives and the positive impact that can emerge when patients and family members are engaged in such initiatives in an effective manner. Currently, no framework has consolidated all of the key strategies and considerations that were found in this review to guide health care organizations when engaging patients and family members in a health IT-specific project or initiative. Further research to evaluate and validate the existing strategies would be of value.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.219
GPT teacher head0.592
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations48
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Medical Internet ResearchSame topicMobile Health and mHealth ApplicationsFrench-language works237,207