Analysis of second opinion programs provided by German statutory and private health insurance – a survey of statutory and private health insurers
Bibliographic record
Abstract
Abstract Background Second medical opinions can give patients confidence when choosing among treatment options and help them understand their diagnosis. Health insurers in several countries, including Germany, offer formal second opinion programs (SecOPs). We systematically collected and analyzed information on German health insurers’ approach to SecOPs, how the SecOPs are structured, and to what extent they are evaluated. Methods In April 2019, we sent a questionnaire by post to all German statutory (n = 109) and private health insurers (n = 52). In September 2019, we contacted the nonresponders by email. The results were analyzed descriptively. They are presented overall and grouped by type of insurance (statutory/private health insurer). Results Thirty one of One hundred sixty one health insurers (response rate 19%) agreed to participate. The participating insurers covered approximately 40% of the statutory and 34% of the private health insured people. A total of 44 SecOPs were identified with a median of 1 SecOP (interquartile range (IQR) 1–2) offered by a health insurer. SecOPs were in place mainly for orthopedic (21/28 insurers with SecOPs; 75%) and oncologic indications (20/28; 71%). Indications were chosen principally based on their potential impact on a patient (22/28; 79%). The key qualification criterion for second opinion providers was their expertise (30/44 SecOPs; 68%). Second opinions were usually provided based on submitted documents only (21/44; 48%) or on direct contact between a patient and a doctor (20/44; 45%). They were delivered after a median of 9 days (IQR 5–15). A median of 31 (IQR 7–85) insured persons per year used SecOPs. Only 12 of 44 SecOPs were confirmed to have conducted a formal evaluation process (27%) or, if not, plan such a process in the future (10/22; 45%). Conclusion Health insurers’ SecOPs focus on orthopedic and oncologic indications and are based on submitted documents or on direct patient-physician contact. The formal evaluation of SecOPs needs to be expanded and the results should be published. This can allow the evaluation of the impact of SecOPs on insured persons’ health status and satisfaction, as well as on the number of interventions performed. Our results should be interpreted with caution due to the low participation rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".