Exploring Human-Data Interaction in Clinical Decision-making Using Scenarios: Co-design Study
Notice bibliographique
Résumé
BACKGROUND: When caring for patients with chronic conditions such as chronic obstructive pulmonary disease (COPD), health care professionals (HCPs) rely on multiple data sources to make decisions. Collating and visualizing these data, for example, on clinical dashboards, holds the potential to support timely and informed decision-making. Most studies on data-supported decision-making (DSDM) technologies for health care have focused on their technical feasibility or quantitative effectiveness. Although these studies are an important contribution to the literature, they do not further our limited understanding of how HCPs engage with these technologies and how they can be designed to support specific contexts of use. To advance our knowledge in this area, we must work with HCPs to explore this space and the real-world complexities of health care work and service structures. OBJECTIVE: This study aimed to qualitatively explore how DSDM technologies could support HCPs in their decision-making regarding COPD care. We created a scenario-based research tool called Respire, which visualizes HCPs' data needs about their patients with COPD and services. We used Respire with HCPs to uncover rich and nuanced findings about human-data interaction in this context, focusing on the real-world challenges that HCPs face when carrying out their work and making decisions. METHODS: We engaged 9 respiratory HCPs from 2 collaborating health care organizations to design Respire. We then used Respire as a tool to investigate human-data interaction in the context of decision-making about COPD care. The study followed a co-design approach that had 3 stages and spanned 2 years. The first stage involved 5 workshops with HCPs to identify data interaction scenarios that would support their work. The second stage involved creating Respire, an interactive scenario-based web app that visualizes HCPs' data needs, incorporating feedback from HCPs. The final stage involved 11 one-to-one sessions with HCPs to use Respire, focusing on how they envisaged that it could support their work and decisions about care. RESULTS: We found that HCPs trust data differently depending on where it came from and who recorded it, sporadic and subjective data generated by patients have value but create challenges for decision-making, and HCPs require support in interpreting and responding to new data and its use cases. CONCLUSIONS: Our study uncovered important lessons for the design of DSDM technologies to support health care contexts. We show that although DSDM technologies have the potential to support patient care and health care delivery, important sociotechnical and human-data interaction challenges influence the design and deployment of these technologies. Exploring these considerations during the design process can ensure that DSDM technologies are designed with a holistic view of how decision-making and engagement with data occur in health care contexts.
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 | 0,062 | 0,110 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».