From Conflict to Care - Telemedicine Utilization During Wartime: A Retrospective Cohort Study
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".