Surge Capacity and Capability: Flexible Frameworks for Expanding Care Beyond the Hospital Walls
Bibliographic record
Abstract
Background: Traditional hospital-centric surge capacity models are increasingly inadequate for modern mass-casualty events, pandemics, and infrastructure failures. These crises demand the rapid creation of clinical care capacity beyond fixed facilities, requiring a fundamental reimagining of healthcare delivery. Aim: This narrative review synthesizes evidence from 2010-2024 on innovative frameworks for expanding clinical care into alternative settings during surges, analyzing the integration of emergency management systems, paramedic scope expansion, and nursing leadership in non-traditional environments. Methods: A comprehensive search of PubMed, Scopus, CINAHL, and disaster medicine databases was conducted. Thematic analysis integrated literature from public health, emergency medical services, nursing science, and health policy. Results: Evidence identifies three key models: (1) Alternative Care Sites (ACS), including field hospitals and repurposed community venues; (2) Pre-hospital treat-in-place and community paramedicine to decompress emergency departments; and (3) Virtual care surge through telehealth. Successful implementation hinges on pre-event planning, legal/regulatory flexibilities, adaptable clinical protocols, and crucially, the defined roles of paramedics and nurses operating beyond their traditional settings. Conclusion: Effective surge response requires a paradigm shift from "beds inside hospitals" to "care anywhere." This demands integrated systems where emergency management provides the structure, nursing provides the clinical leadership, and paramedicine provides the mobile extension of care. Future resilience depends on investing in these flexible frameworks, standardized training, and policy reforms that enable healthcare to dynamically scale beyond institutional walls.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".