Designing an Electronic Patient Management System for Multiple Sclerosis: Building a Next Generation Multiple Sclerosis Documentation System
Notice bibliographique
Résumé
BACKGROUND: Technologies like electronic health records or telemedicine devices support the rapid mediation of health information and clinical data independent of time and location between patients and their physicians as well as among health care professionals. Today, every part of the treatment process from diagnosis, treatment selection, and application to patient education and long-term care may be enhanced by a quality-assured implementation of health information technology (HIT) that also takes data security standards and concerns into account. In order to increase the level of effectively realized benefits of eHealth services, a user-driven needs assessment should ensure the inclusion of health care professional perspectives into the process of technology development as we did in the development process of the Multiple Sclerosis Documentation System 3D. After analyzing the use of information technology by patients suffering from multiple sclerosis, we focused on the needs of neurological health care professionals and their handling of health information technology. OBJECTIVE: Therefore, we researched the status quo of eHealth adoption in neurological practices and clinics as well as health care professional opinions about potential benefits and requirements of eHealth services in the field of multiple sclerosis. METHODS: We conducted a paper-and-pencil-based mail survey in 2013 by sending our questionnaire to 600 randomly chosen neurological practices in Germany. The questionnaire consisted of 24 items covering characteristics of participating neurological practices (4 items), the current use of network technology and the Internet in such neurological practices (5 items), physicians' attitudes toward the general and MS-related usefulness of eHealth systems (8 items) and toward the clinical documentation via electronic health records (4 items), and physicians' knowledge about the Multiple Sclerosis Documentation System (3 items). RESULTS: From 600 mailed surveys, 74 completed surveys were returned. As much as 9 of the 10 practices were already connected to the Internet (67/74), but only 49% preferred a permanent access. The most common type of HIT infrastructure was a complete practice network with several access points. Considering data sharing with research registers, 43% opted for an online interface, whereas 58% decided on an offline method of data transmission. eHealth services were perceived as generally useful for physicians and nurses in neurological practices with highest capabilities for improvements in clinical documentation, data acquisition, diagnosis of specific MS symptoms, physician-patient communication, and patient education. Practices specialized in MS in comparison with other neurological practices presented an increased interest in online documentation. Among the participating centers, 91% welcomed the opportunity of a specific clinical documentation for MS and 87% showed great interest in an extended and more interconnected electronic documentation of MS patients. Clinical parameters (59/74) were most important in documentation, followed by symptomatic parameters like measures of fatigue or depression (53/74) and quality of life (47/74). CONCLUSIONS: Physicians and nurses may significantly benefit from an electronically assisted documentation and patient management. Many aspects of patient documentation and education will be enhanced by eHealth services if the most informative measures are integrated in an easy-to-use and easily connectable approach. MS-specific eHealth services were highly appreciated, but the current level of adoption is still behind the level of interest in an extended and more interconnected electronic documentation of MS patients.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».