Dashboards in Health Care Settings: Protocol for a Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Health care organizations increasingly depend on business intelligence tools, including "dashboards," to capture, analyze, and present data on performance metrics. Ideally, dashboards allow users to quickly visualize actionable data to inform and optimize clinical and organizational performance. In reality, dashboards are typically embedded in complex health care organizations with massive data streams and end users with distinct needs. Thus, designing effective dashboards is a challenging task and theoretical underpinnings of health care dashboards are poorly characterized; even the concept of the dashboard remains ill-defined. Researchers, informaticists, clinical managers, and health care administrators will benefit from a clearer understanding of how dashboards have been developed, implemented, and evaluated, and how the design, end user, and context influence their uptake and effectiveness. OBJECTIVE: This scoping review first aims to survey the vast published literature of "dashboards" to describe where, why, and for whom they are used in health care settings, as well as how they are developed, implemented, and evaluated. Further, we will examine how dashboard design and content is informed by intended purpose and end users. METHODS: In July 2020, we searched MEDLINE, Embase, Web of Science, and the Cochrane Library for peer-reviewed literature using a targeted strategy developed with a research librarian and retrieved 5188 results. Following deduplication, 3306 studies were screened in duplicate for title and abstract. Any abstracts mentioning a health care dashboard were retrieved in full text and are undergoing duplicate review for eligibility. Articles will be included for data extraction and analysis if they describe the development, implementation, or evaluation of a dashboard that was successfully used in routine workflow. Articles will be excluded if they were published before 2015, the full text is unavailable, they are in a non-English language, or they describe dashboards used for public health tracking, in settings where direct patient care is not provided, or in undergraduate medical education. Any discrepancies in eligibility determination will be adjudicated by a third reviewer. We chose to focus on articles published after 2015 and those that describe dashboards that were successfully used in routine practice to identify the most recent and relevant literature to support future dashboard development in the rapidly evolving field of health care informatics. RESULTS: All articles have undergone dual review for title and abstract, with a total of 2019 articles mentioning use of a health care dashboard retrieved in full text for further review. We are currently reviewing all full-text articles in duplicate. We aim to publish findings by mid-2022. Findings will be reported following guidance from the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. CONCLUSIONS: This scoping review will provide stakeholders with an overview of existing dashboard tools, highlighting the ways in which dashboards have been developed, implemented, and evaluated in different settings and for different end user groups, and identify potential research gaps. Findings will guide efforts to design and use dashboards in the health care sector more effectively. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34894.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,083 | 0,110 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,006 |
| Méta-épidémiologie (sens large) | 0,017 | 0,018 |
| Bibliométrie | 0,027 | 0,023 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,010 | 0,009 |
| Science ouverte | 0,006 | 0,007 |
| Intégrité de la recherche | 0,008 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,075 | 0,012 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».