Understanding Health Care Workers’ Attitudes and Preferences Toward Digital Patient Monitoring Platforms: Cross-Country Survey Study
Notice bibliographique
Résumé
Background: The integration of digital health tools into the routines of health care workers (HCWs) holds potential to enhance health care delivery. In particular, digital tools for patient data monitoring allow HCWs to quickly access patient health information and detect early warning signs of potential issues. However, while interest in these tools, such as telemedicine and mobile health, has rapidly grown in recent years, limited research has explored HCWs' attitudes toward digital innovations or their feature preferences. Objective: This study aims to (1) assess HCWs' attitudes toward digital health tools for patient data monitoring, (2) identify socioeconomic factors influencing these attitudes, (3) determine HCWs' preferences for features of a novel digital health platform for patient data monitoring, and (4) examine whether HCWs' baseline attitudes impact their feature preferences. Methods: This study uses an integrated approach combining item response theory (IRT) and discrete choice experiment to evaluate the attitudes and preferences of HCWs. Data come from a web conjoint survey distributed to an international cohort of HCWs across the following 4 European countries: Spain, Albania, Bulgaria, and Greece. Results: Survey respondents comprise 260 HCWs from the 4 countries. The findings indicate that HCWs generally hold a positive attitude toward technological devices (all the IRT coefficients are statistically significant with P<.05). Socioeconomic characteristics, including factors such as gender (P=0.05), professional role (P=0.01), and educational background (P=0.01), significantly influence these attitudes. Results show that highly educated female HCWs are those who are mostly inclined to use technologies. In addition, the specific features of a digital health platform for patient data monitoring highly impact HCWs' willingness to incorporate such a tool into their daily practice (all coefficients related to the attributes' effects in the models for the discrete choice experiment results are significant (all P=0.01 except the data looking attribute which has P=0.03) . The findings suggest that an ideal digital health platform for patient data monitoring should offer intuitive graphs, comparative statistics against standards, and include patients' family clinical history. In addition, health workers should receive instructor-led group training to effectively use the platform. Conclusions: This study shows that health workers generally support the use of digital health tools, which have the potential to improve health care efficiency and patient outcomes through enhanced monitoring and timely interventions. To facilitate adoption, policymakers should strengthen infrastructure, enact supportive legislation, and tailor interventions for groups less inclined to use these tools. Aligning digital health platform features with HCW preferences is crucial, as it directly impacts HCWs' willingness to integrate these tools into daily routines, ultimately benefiting patients. Future research should examine additional factors influencing HCW adoption and address organizational and infrastructural barriers to optimize implementation of digital health platform and improve patient care.
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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 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».