Effects of the Covid-19 Pandemic on the use of online video consultation of general practitioners in Germany – a secondary data analysis of German Health Insurance Data
Bibliographic record
Abstract
Abstract Background:In order to manage the Covid-19 pandemic in Germany in March 2020, the government implemented non-pharmaceutical interventions including school closures, case-based measures, social distancing, and a ban of all public events. Given the circumstances, professional societies and associations in the healthcare sector recommended the use of video consultations.The objective of this analysis was to generate statistically relevant insights into the use of video consultations before and during the Covid-19 pandemic.Methods:This study is based on the analysis of a comprehensive claims data set comprising anonymized data of 5.6 million beneficiaries of a large German health insurance fund. The dataset encompassed healthcare claims from 1 January 2019 to 30 June 2020 covering 29,464,913 patient contacts. The claims were divided into video consultations and face-to-face consultations. This paper presents descriptive statistics and analyses associations between variables. Results:In 8,077 out of all contacts, a video consultation had taken place. This translates into 2.75 of 10,000 contacts. There was no significant difference in the utilization of video consultations by gender. However, the age differed significantly (Chi-square=2219,11 > Critical Value= 19,68) between the contacts with or without use of video consultation. There is an increase from 0.0000525% to 0.0002529% in the first Quarter and 0.0000461% to 0.0014307% in the second Quarter which is not significant. Conclusions:Apparent higher usage of telemedicine in the age group 50 to 64 years and the very low usage in the following groups of 65-74 year old patients shows that the use of video consultation is age sensitive and should be subject to goal oriented implementation of telemedical offers. Although there are many claims, that the video consultation has risen dramatically through the Covid-19 pandemic. There is no such evidence in this large dataset, because the increase is on a small level relatively to the patient contacts and not significant. Qualitative research to determine the factors for patients and general practitioners that lead to or hinder the use of video consultation should help in addressing the slow digitalization in the outpatient sector in Germany.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".