Use of Video Consultation Between 2017 and 2020 in Outpatient Medical Care in Germany and Characteristics of Their User Groups: Analysis of Claims Data
Notice bibliographique
Résumé
Background Supplementing outpatient medical care with the use of video consultations could, among other benefits, improve access, especially in structurally disadvantaged areas. Objective This claims data analysis, carried out as part of the German research project “Preference-based use of video consultation in urban and rural regions,” aimed to analyze the use of video consultations and the characteristics of its user groups. Methods Claims data from 3 Statutory Health Insurance Funds (SHIFs) and 4 Associations of Statutory Health Insurance Physicians (ASHIPs) from the period April 2017 to the end of 2020 were used. Data from a sample of about 6.1 million insured and 33,100 physicians and psychotherapists were analyzed. In addition to data on the use of video consultations, patient data on sociodemographic characteristics, diagnoses, and place of residence were included. To analyze the physicians’ perspectives, specialty groups, demographic characteristics, and the type of practice location were also included. In consideration of the principles of data economy and the fact that data analysis represents merely a preliminary phase within the broader project, the SHIFs and ASHIPs transmitted aggregated data (cross-tabulations per subgroup analysis) to the evaluator. For this reason, the analyses were constrained to a comparison of video consultation users versus nonusers, differentiated according to the aforementioned subgroups. Furthermore, the association between place of residence or type of region of the practice location and the use of video consultation was examined. A significance level of P<.05 was set for chi-square tests. Results From 2017 to 2019, almost no video consultations were used in outpatient care in the German health care system. Although this changed considerably in relative terms with the start of the COVID-19 pandemic (but still at a very low absolute level), there was also a clear decline in the use of video consultations as the number of infections flattened out. Physicians working in psychotherapy and psychological psychotherapists used video consultations with around 16% (44,808/282,530) of their treatment cases in the second quarter of 2020, followed by psychotherapists using video consultations for children (10,828/113,293, 10%). Although the absolute number of treatment cases with video consultations among general practitioners was very high compared with other specialist groups, their share of video consultations in all treatment cases was very low at 0.3% (29,600/9,837,118). Younger age groups and those located in urban areas used video consultations more frequently; this applies to both patients (age groups: χ27=9903.2, P<.001; region types: χ22=3746.2, P<.001) and service providers (age groups: χ23=11,338.2, P<.001; region types: χ22=8474.1, P<.001). Conclusions The current use of video consultations is below its potential in terms of scope and user groups. The widespread and lasting use of video consultations will only succeed if the potential user groups accept this form of service provision and recognize its advantages. Further analyses (both qualitative, such as focus group discussions, and quantitative, such as preference surveys) should therefore investigate the preferences of user groups for the use of video consultations. International Registered Report Identifier (IRRID) RR2-10.2196/50932
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».