Simulation of Clinical Visits as a Novel Approach to Evaluate Digital Health in Multiple Sclerosis: Simulation Study
Notice bibliographique
Résumé
Background: Efforts are being made to integrate digital health technologies into clinical care for multiple sclerosis (MS) to improve patient monitoring. Efficiently probing how they might impact clinical care could streamline digital tool development. The Floodlight digital tool, comprising 5 smartphone sensor-based tests, was used to generate health-related data on patient function and symptoms in a clinical simulation. Objective: The study had 3 objectives: (1) assess the utility of simulated clinical encounters as a research methodology for exploring the introduction of digital health technologies into clinical practice in MS, (2) confirm the fidelity of the simulated environment and patient cases developed and understand what metrics (eg, workflow, comprehensive evaluation) could be generated, and (3) generate insights into the utility of digitally collected data, including usability, clinical decision contribution, and impact on workflows, in clinical practice. Methods: A total of 2 patient cases consisting of clinical, radiological, and digital health data were developed with clinician input. US-based neurologists prepared for and conducted 2 simulated teleconsultations each, with an actor briefed on case profiles. Floodlight data were available, via the Floodlight MS™ Health Care Professional Portal, for 1 of the 2 consultations. Participant neurologists completed interviews and surveys assessing the fidelity of the cases presented, user experience and workflow metrics, patient concerns identified, care decisions made, and confidence in making decisions. Results: All 10 neurologists indicated that the simulations were high-fidelity representations of real consultations. Using the Floodlight technology for the first time, median time taken to prepare for and conduct the consultation was ~1.7-2 minutes longer, with slightly greater mental effort reported by participants, compared with not using the tool. The Floodlight MS Health Care Professional Portal scored an "above average" 79 on the System Usability Scale and an "acceptable" Net Promoter Score of 10. In total, 6 of the 10 neurologists "strongly agreed" that it was easier and quicker to identify patient concerns when they had access to the patient-generated Floodlight data to prepare for their encounters than when they did not. Overall, more care and management decisions were taken when the digital tool was used (37 vs 29). Of those 37 decisions, Floodlight data were reported as a trigger for 20 decisions, always in combination with other elements including patient history (20/20) and clinical exam findings (9/20). Conclusions: These findings advance our understanding of clinical simulation as a method for evaluating digital tools and other innovative technologies for MS care. High-fidelity patient cases could be provided for the mock teleconsultations, and the simulated clinical environment was useful for evaluating usability and utility of a new digital tool-yielding preliminary evidence on how digital data could be accessed and utilized by neurologists to support routine MS care.
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 | 0,009 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».