Use of Video Consultation Between 2017 and 2020 in Outpatient Medical Care in Germany and Characteristics of Their User Groups: Analysis of Claims Data
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".