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Record W4322493296 · doi:10.2196/43966

Exploring Patient Journey Mapping and the Learning Health System: Scoping Review

2023· article· en· W4322493296 on OpenAlexaffvenue
Amanda L. Joseph, Helen Monkman, André Kushniruk, Yuri Quintana

Bibliographic record

VenueJMIR Human Factors · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsHomewood Research InstituteUniversity of Victoria
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Journey maps are visualization tools that can facilitate the diagrammatical representation of stakeholder groups by interest or function for comparative visual analysis. Therefore, journey maps can illustrate intersections and relationships between organizations and consumers using products or services. We propose that some synergies may exist between journey maps and the concept of a learning health system (LHS). The overarching goal of an LHS is to use health care data to inform clinical practice and improve service delivery processes and patient outcomes. OBJECTIVE: The purpose of this review was to assess the literature and establish a relationship between journey mapping techniques and LHSs. Specifically, in this study, we explored the current state of the literature to answer the following research questions: (1) Is there a relationship between journey mapping techniques and an LHS in the literature? (2) Is there a way to integrate the data from journey mapping activities into an LHS? (3) How can the data gleaned from journey map activities be used to inform an LHS? METHODS: A scoping review was conducted by querying the following electronic databases: Cochrane Database of Systematic Reviews (Ovid), IEEE Xplore, PubMed, Web of Science, Academic Search Complete (EBSCOhost), APA PsycInfo (EBSCOhost), CINAHL (EBSCOhost), and MEDLINE (EBSCOhost). Two researchers applied the inclusion criteria and assessed all articles by title and abstract in the first screen, using Covidence. Following this, a full-text review of included articles was done, with relevant data extracted, tabulated, and assessed thematically. RESULTS: The initial search yielded 694 studies. Of those, 179 duplicates were removed. Following this, 515 articles were assessed during the first screening phase, and 412 were excluded, as they did not meet the inclusion criteria. Next, 103 articles were read in full, and 95 were excluded, resulting in a final sample of 8 articles that satisfied the inclusion criteria. The article sample can be subsumed into 2 overarching themes: (1) the need to evolve service delivery models in health care, and (2) the potential value of using patient journey data in an LHS. CONCLUSIONS: This scoping review demonstrated the gap in knowledge regarding integrating the data from journey mapping activities into an LHS. Our findings highlighted the importance of using the data from patient experiences to enrich an LHS and provide holistic care. To satisfy this gap, the authors intend to continue this investigation to establish the relationship between journey mapping and the concept of LHSs. This scoping review will serve as phase 1 of an investigative series. Phase 2 will entail the creation of a holistic framework to guide and streamline data integration from journey mapping activities into an LHS. Lastly, phase 3 will provide a proof of concept to demonstrate how patient journey mapping activities could be integrated into an LHS.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.659
GPT teacher head0.497
Teacher spread0.161 · 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.

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

Citations55
Published2023
Admission routes2
Has abstractyes

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