Modeling the Implementation Context of a Telemedicine Service: Work Domain Analysis in a Surgical Setting
Notice bibliographique
Résumé
BACKGROUND: A telemedicine service enabling remote surgical consultation had shown promising results. When the service was to be scaled up, it was unclear how contextual variations among different clinical sites could affect the clinical outcomes and implementation of the service. It is generally recognized that contextual factors and work system complexities affect the implementation and outcomes of telemedicine. However, it is methodologically challenging to account for context in complex health care settings. We conducted a work domain analysis (WDA), an engineering method for modeling and analyzing complex work environments, to investigate and represent contextual influences when a telemedicine service was to be scaled up to multiple hospitals. OBJECTIVE: We wanted to systematically characterize the implementation contexts at the clinics participating in the scale-up process. Conducting a WDA would allow us to identify, in a systematic manner, the functional constraints that shape clinical work at the implementation sites and set the sites apart. The findings could then be valuable for informed implementation and assessment of the telemedicine service. METHODS: We conducted observations and semistructured interviews with a variety of stakeholders. Thematic analysis was guided by concepts derived from the WDA framework. We identified objects, functions, priorities, and values that shape clinical procedures. An iterative "discovery and modeling" approach allowed us to first focus on one clinic and then readjust the scope as our understanding of the work systems deepened. RESULTS: We characterized three sets of constraints (ie, facets) in the domain: the treatment facet, administrative facet (providing resources for procedures), and development facet (training, quality improvement, and research). The constraints included medical equipment affecting treatment options; administrative processes affecting access to staff and facilities; values and priorities affecting assessments during endoscopic retrograde cholangiopancreatography; and resources for conducting the procedure. CONCLUSIONS: The surgical work system is embedded in multiple sets of constraints that can be modeled as facets of the system. We found variations between the implementation sites that might interact negatively with the telemedicine service. However, there may be enough motivation and resources to overcome these initial disruptions given that values and priorities are shared across the sites. Contrasting the development facets at different sites highlighted the differences in resources for training and research. In some cases, this could indicate a risk that organizational demands for efficiency and effectiveness might be prioritized over the long-term outcomes provided by the telemedicine service, or a reduced willingness or ability to accept a service that is not yet fully developed or adapted. WDA proved effective in representing and analyzing these complex clinical contexts in the face of technological change. The models serve as examples of how to analyze and represent a complex sociotechnical context during telemedicine design, implementation, and assessment.
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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 | 0,005 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».