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Record W4382645012 · doi:10.54941/ahfe1003216

Research Hotspots and Trend Analysis of User Experience Design for Healthcare Service System

2023· article· en· W4382645012 on OpenAlexaboutno aff
Xinyue Zhang

Bibliographic record

VenueAHFE international · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceService (business)BibliometricsHealth careMultidisciplinary approachWorld Wide WebCitationStandardizationUser experience designInformation systemData scienceInformation retrievalKnowledge managementEngineeringHuman–computer interaction

Abstract

fetched live from OpenAlex

International Organization for Standardization(ISO) defines user experience as the perceptions and responses of users about the usage or anticipated use of a system, product, or service. The notion of "patient-centred" service has steadily evolved over the last several years, focusing on user experience in the healthcare service system. Current research on user experience in healthcare service systems integrates topics such as psychology, design, and engineering. It is impossible to conduct an impartial analysis of this multidisciplinary topic based on a survey of the traditional literature due to the complexity and volume of the reference material. This study utilises bibliometrics to visualise the retrieved data's knowledge structure and structure of the retrieved data and to offer a foundation for future research in the area of user experience design for healthcare service systems.The information for this research comes from the Web of Science. The search strategy was TS=((User Experience)AND(Medical Services OR Medical Products OR Medical Diagnostic Equipment)), and the search sources were the five primary citation indexes typically utilised in the WOS database: SSCI, SCI-Expanded, A&HCI, CPCI-S and CPCI-SSH. During the search process, the sources had to be modified or eliminated to prevent the loss of interdisciplinary literature. The search results were produced as "complete records and cited references" text files. Manual screening is used to screen out publications that diverge from the subject of the study, lack on-site information (e.g., time, keywords, authors, and other crucial information), include duplicate data, or are otherwise distracting. For additional quantitative analysis, a total of 2030 articles were retrieved.This work employs a mix of bibliometrics, content analysis, and information visualisation, as proposed by Pritchard in 1969: bibliometrics may assist in identifying patterns and information in vast volumes of literature via quantitative analysis of all sorts of literature. The study also used a combination of two bibliometric tools, CiteSpace and VOSviewer, to examine keyword co-occurrence analysis and literature co-citation in the cited literature and to map the associated scientific information to visualise research paths and frontier regions.The results of the study indicate that: 1. From a macro perspective, the number of documents in the search area is increasing and will remain a key research direction in the academic community; 2. From the perspective of the number of articles published, the UK, the US, China, and Canada are leading the research in this field; kings coll London, Mcmaster univ, Boston univ and other institutions are more active, but there are few high-producing institutions, and eastern Europe is the least productive region. The need for more collaboration between research institutions and between institutions and writers and the shortage of prolific authors represent the most significant research limitations. 3.The disciplines of "healthcare," "experience," "mental health," "services," "telemedicine," "patient satisfaction," "impact," and "schizophrenia" are varied and strongly interrelated. Nonetheless, this topic's fundamental study has generated many great works.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.194
GPT teacher head0.399
Teacher spread0.205 · 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 designObservational
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

Citations2
Published2023
Admission routes1
Has abstractyes

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