Digital Self-monitoring of Multiple Sclerosis: Interview Study With Dutch Health Care Providers on the Expected New Configuration of Roles and Responsibilities
Notice bibliographique
Résumé
BACKGROUND: Digital self-monitoring allows patients to produce and share personal health data collected at home. This creates a novel situation in which health care providers and patients must engage in a reconfiguration of roles and responsibilities. Although existing research pays considerable attention to the perceptions of patients regarding digital self-monitoring, less attention has been paid to the needs, wishes, and concerns of health care providers. As several companies and public institutions are developing and testing digital self-monitoring at the time of writing, it is timely and relevant to explore how health care providers envision using these technologies in their daily work practices. Our findings can be considered in decision-making processes concerning the further development and implementation of digital self-monitoring. OBJECTIVE: This study aims to explore how health care providers envisage using smartphone apps for digital self-monitoring of multiple sclerosis (MS) in their daily work practices, with a particular focus on physician-patient communication and on how health care providers respond to self-monitoring data and delegate tasks and responsibilities to patients. METHODS: We conducted semistructured in-depth interviews with 14 MS health care providers: 4 neurologists, 7 MS specialist nurses, and 3 rehabilitation professionals. They are affiliated with 3 different hospitals in the Netherlands that will participate in a pilot study to assess the efficiency and effectiveness of a specific smartphone app for self-monitoring. RESULTS: The interviewed health care providers seemed willing to use these smartphone apps and valued the quantitative data they produce that can complement the narratives that patients provide during medical appointments. The health care providers primarily want to use digital self-monitoring via prescription, meaning that they want a standardized smartphone app and want to act as its gatekeepers. Furthermore, they envisioned delegating particular tasks and responsibilities to patients via digital self-monitoring, such as sharing data with the health care providers or acting on the data, if necessary. The health care providers expected patients to become more proactive in the management of their disease. However, they also acknowledged that not all patients are willing or able to use digital self-monitoring apps and were concerned about the potential psychological and emotional burden on patients caused by this technology. CONCLUSIONS: Our findings show that health care providers envisage a particular type of patient empowerment and personalized health care in which tensions arise between health care providers acting as gatekeepers and patient autonomy, between patient empowerment and patient disempowerment, and between the weight given to quantitative objective data and that given to patients' subjective experiences. In future research, it would be very interesting to investigate the actual experiences of health care providers with regard to digital self-monitoring to ascertain how the tensions mentioned in this paper play out in practice.
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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 | 0,008 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| 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 ».