From Conflict to Care - Telemedicine Utilization During Wartime: A Retrospective Cohort Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Armed conflict poses severe challenges to healthcare delivery, requiring rapid adaptation. This study evaluates how telemedicine enabled continuity of care during the October 7, 2023, war in Israel, and assess regional and service-specific utilization patterns in relation to conflict intensity. METHODS: A retrospective cohort study of 7.19 million healthcare interactions from an Israeli HMO covering one-third of Israel's population. The study compared three periods: (T0) the first month of the war, (T1) the month before, and (T2) the same period last year. Interactions included visits and inquiries in primary care, secondary care, mental health, and allied health services. Data were categorized by service type and geographic conflict zones. Chi-square tests and effect sizes assessed trends. RESULTS: Telemedicine utilization increased significantly during the war, especially in primary conflict zones (13-20%, p < 0.01). Remote consultations in mental health tripled (10-30%, p < 0.01), and nutrition services reached the highest telemedicine adoption (27-52%, p < 0.01). Family medicine, pediatrics, and gynecology also showed significant increases. Digital inquiries surged in family medicine but declined in pediatrics. CONCLUSION: This study offers timely insights into telemedicine's role in maintaining access during armed conflict within a digitally advanced system. By examining service utilization across medical domains and conflict zones, it highlights how remote care supports system adaptability in crises. Notably, patient satisfaction remained high, suggesting telemedicine preserved access and perceived care quality. Findings may inform digital health planning to strengthen continuity, equity, and resilience in future emergencies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it