Improving Medical Accessibility in Mountainous Regions through Helicopter Emergency Medical Services: A Retrospective Evaluation of Clinical Efficiency and Economic Viability

Improving Medical Accessibility in Mountainous Regions through Helicopter Emergency Medical Services: A Retrospective Evaluation of Clinical Efficiency and Economic Viability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Improving Medical Accessibility in Mountainous Regions through Helicopter Emergency Medical Services: A Retrospective Evaluation of Clinical Efficiency and Economic Viability Shun-Bin Luo, Hua-Fu Wang, Cheng-Xi Liu, Xiao Pan, Xu-Wei He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9373335/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background In mountainous regions, geographic topography creates significant structural barriers to timely emergency medical care. The “golden hour” for critical conditions is often compromised by the intrinsic inefficiency of ground ambulance transport in complex terrains. This study aims to evaluate the operational efficiency, clinical necessity, and economic viability of a government-led Helicopter Emergency Medical Services (HEMS) model in Lishui, a representative mountainous prefecture in East China. Methods A retrospective observational study was conducted on 39 HEMS cases between July 2024 and December 2025. Clinical severity was assessed using the Injury Severity Score (ISS), Glasgow Coma Scale (GCS), and Revised Trauma Score (RTS). Transport efficiency and health economic metrics—including total operational costs and out-of-pocket (OOP) expenses—were compared between HEMS and simulated ground transport using the Wilcoxon signed-rank test. Spatial-temporal gain was calculated across eight counties to assess regional accessibility. Results The study cohort exhibited high clinical urgency, with a median ISS of 21 (IQR: 17.25–29.25) and a predominance of cardiovascular/respiratory arrest (41.03%). HEMS demonstrated a profound time-compression effect, reducing the mean transport time by 61.92% compared to ground alternatives (29.70 vs. 78.00 min; Z = -5.44, p < 0.001). Notably, while the total operational cost of HEMS was significantly higher (Mean: 2,463.75 vs. 905.50 CNY, p < 0.001), the implementation of a targeted 90% reimbursement policy effectively reversed the financial burden for patients. The mean OOP expenditure for HEMS was significantly lower than that for ground transport (246.38 vs. 575.50 CNY; Z = -3.408, p = 0.001), representing a 57.2% reduction in individual financial liability. Conclusions The Lishui HEMS model provides a robust template for achieving medical service equity in topographically challenging areas. By integrating high-efficiency air medical assets with progressive health insurance policies, this model successfully de-links clinical urgency from socioeconomic constraints, ensuring that life-saving interventions are both physically reachable and economically accessible for rural populations. Mountainous regions Ground ambulance transport Helicopter Emergency Medical Services (HEMS) Operational efficiency Economic viability Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Geographical topography remains a fundamental structural determinant of health inequity, particularly in the provision of time-sensitive emergency medical care [ 1 – 5 ] . In mountainous regions, the “distance penalty”—characterized by sinuous road networks and significant elevation changes—frequently compromises the “golden hour” for patients with life-threatening conditions [ 6 – 8 ] . Conventional ground ambulance services in these terrains are often constrained by prolonged transport times, fluctuating traffic conditions, and the logistical challenges of reaching remote rural enclaves [ 9 ] . Consequently, patients situated in environments with complex terrain face a disproportionately higher risk of morbidity and mortality compared to their urban counterparts, necessitating a more efficient regional emergency response infrastructure. Consequently, compared to urban residents, patients situated in environments with complex terrain face a disproportionately high risk of morbidity and mortality, thereby creating an urgent need to establish a more efficient regional emergency response mechanism [ 10 , 11 ] . Helicopter Emergency Medical Services (HEMS) have emerged as a pivotal intervention to bridge this pre-hospital care gap. It provides rapid, point-to-point transport that bypasses terrestrial obstacles. Despite the acknowledged clinical advantages of HEMS in reducing transport latency, its global implementation has been persistently hindered by two major barriers: high operational costs and financial unsustainability [ 1 , 7 , 12 ] . In many health systems, HEMS is viewed as an elite resource, often leading to catastrophic health expenditures for patients [ 9 ] . It is a critical paradox where the patients with the highest clinical need in the most remote areas are often the least likely to access or afford air-medical rescue [ 2 , 3 , 11 ] . As a prefecture in East China defined by its “nine mountains, half water, and half farmland” landscape, Lishui provides a unique laboratory for evaluating a government-led, insurance-integrated HEMS model. To address the inequities in accessing emergency care in mountainous regions, the local administration implemented a proactive health policy that integrates HEMS into the public medical insurance framework with a targeted 90% reimbursement rate. This strategy aims to improve the utilization of high-efficiency medical assets and mitigate the impact of socioeconomic status on healthcare utilization among the rural population. To evaluate the synergistic effect of HEMS on both clinical efficiency and economic accessibility in this specific policy context, we analyzed 39 HEMS cases from 2024 to 2025. Our goal was to evaluate whether this policy-driven model can effectively reconcile the tension between high-tech medical intervention and universal financial protection. Through the assessment of spatial-temporal gains and out-of-pocket expenditures, this study aims to establish a universal framework for advancing medical service equity in resource-limited settings with difficult terrain. Methods Study Design and Setting This retrospective observational study was conducted at the First Affiliated Hospital of Lishui University (Lishui People's Hospital), the leading trauma center in a representative mountainous prefecture of East China. The study period spanned from July 2024 to December 2025. During this period, the project aimed to develop an innovative, government-led aero-medical insurance model supported by multi-party funding. Under this model, eligible residents of Lishui City meeting the criteria for aero-medical rescue were required to bear only 10% of the total service costs. The study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines to ensure methodological rigor. Participants and Data Sources A total of 39 patients transported to the emergency department via HEMS were included in the study. Inclusion criteria stipulated that HEMS activation was primarily based on clinical urgency or geographic inaccessibility, as determined by the regional emergency dispatch center. Data on demographics, clinical outcomes, flight metrics, and billing information were extracted by cross-referencing administrative records, electronic medical records (EMR), and HEMS flight logs. Clinical Severity and Outcome Measures To quantify the physiological and anatomical severity of injuries, three standardized scoring systems were utilized: the Glasgow Coma Scale (GCS) for neurological assessment, the Revised Trauma Score (RTS) for physiological status, and the Injury Severity Score (ISS) for anatomical injury quantification. The ISS was calculated by trained trauma registrars based on the Abbreviated Injury Scale (AIS-2008). Clinical outcomes were categorized into four groups: recovered, improved, died, and discharged against medical advice. Operational and Geospatial Metrics The primary efficiency metric in this study was the actual flight time, defined as the duration from helicopter takeoff at the dispatch point to landing at the receiving hospital’s helipad. To evaluate the spatial-temporal advantage of air medical assets, we compared this actual flight time with the estimated ground transport time. The latter was calculated using the Amap geospatial platform, which provided the optimal terrestrial route and travel duration from the dispatch location to the tertiary center under typical traffic conditions. This study specifically focused on the efficiency of the transfer phase to isolate the impact of geographic terrain on medical accessibility. The 'Time Efficiency Gain' was subsequently calculated as the percentage reduction in transport time achieved by HEMS relative to the ground baseline, using the formula: (T ground - T flight ) / T ground ×100%. Health Economic Evaluation and Policy Framework The economic analysis focused on two primary metrics: total cost and out-of-pocket (OOP) expenditure. Total cost represented the total charges for the medical rescue mission, including professional fees and flight costs. Under the local 'Lishui Model' policy, HEMS transport was covered by a specialized insurance pool providing a 90% reimbursement rate for eligible missions. In contrast, ground ambulance reimbursement rates were calculated based on the standard regional public medical insurance scheme (approximately 36.44%). Out-of-pocket expenditure was defined as the final financial liability borne by the patient after insurance settlement. Statistical Analysis Statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY) and R software (version 4.1.2). The normality of continuous data was assessed using the Shapiro-Wilk test. Given the non-normal distribution of temporal and economic data, the Wilcoxon signed-rank test was employed for paired comparisons between HEMS and simulated ground transport metrics (time and cost). Categorical variables were expressed as frequencies and percentages, and compared using Fisher’s exact test for variables with small expected cell counts. A p-value < 0.05 was considered statistically significant. Ethical Considerations The study protocol was approved by the Institutional Review Board (IRB) of the First Affiliated Hospital of Lishui University (Approval No. 2026-04-06). Given the retrospective nature of the study and the use of de-identified administrative data, the requirement for informed consent was waived. All methods were performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki. Results Patient Demographic and Clinical Baseline Characteristics From July 2024 to December 2025, 39 patients were enrolled in this study. The cohort was characterized by a high proportion of males (n = 31, 79.49%) and a mean age of 58.33 ± 15.96 years (Table 1 ). Clinical assessment upon admission revealed significant physiological instability and anatomical injury severity: the median Glasgow Coma Scale (GCS) was 3 (IQR: 3–3), the median Revised Trauma Score (RTS) was 8.5 (IQR: 8–9.25), and the median Injury Severity Score (ISS) reached 21 (IQR: 17.25–29.25). The primary diagnostic categories necessitating HEMS activation were cardiovascular or respiratory arrest (41.03%), cerebrovascular accidents (23.08%), and severe multiple trauma (17.95%). Regarding clinical outcomes, 58.97% of patients were recovered and 12.82% showed clinical improvement, while the mortality rate was recorded at 10.26%. The geographical distribution of rescue cases spanned eight counties, with the highest utilization observed in Yunhe (n = 9) and Suichang (n = 8) (Fig. 1 ). Table 1 Patient demographic and clinical baseline characteristics Characteristics Values Age (Year) / Mean ± SD 58.33 ± 15.96 Sex / n (%) Male 31 (79.49%) Femal 8 (20.51%) Major Disease Classifications (Top 3) / n (%) Cardiovascular/respiratory arrest 16 (41.03%) cerebrovascular accident 9 (23.08%) severe multiple injuries/trauma 7 (17.95%) Severity score / Media (IQR) GCS Score 3 (IQR: 3–3) RTS Score 8.5 (IQR: 8-9.25) ISS Score 21 (IQR: 17.25–29.25) Outcomes (Top 3) Recovered 23 (58.97%) Improved 5 (12.82%) Death 4 (10.26%) Discharged Against Advice 7 (17.95%) Number of rescue cases in different regions / n Qingtian 3 Longquan 1 Yunhe 9 Qingyuan 5 Jinyun 3 Suichang 8 Songyang 6 Jingning 4 Operational Efficiency: Spatial-Temporal Gains in Mountainous Terrain The implementation of HEMS effectively overcame the geographical barriers inherent in Lishui’s “nine mountains, half water” topography. As Table 2 and Fig. 2 illustrate, the mean actual flight distance was 66.00 km, representing a 22.88 km reduction compared to the optimal terrestrial route (mean: 88.88 km). The operational efficiency gain was most pronounced in the temporal dimension: the mean aeromedical rescue time (from take-off to landing) was 29.70 minutes, whereas the simulated ground transport time for the same missions was 78.00 minutes (Z = -5.44, p < 0.001). This resulted in an average Time Efficiency Gain of 61.92%. Subgroup analysis by region showed that the most remote area, Qingyuan County, derived the highest benefit, with a 67.41% reduction in transport time (46.60 min vs. 143.00 min) and a distance gap of 55 km. These findings indicate that HEMS effectively compresses the “rescue life radius” in topographically complex regions. Table 2 Comparative Analysis of Transport Efficiency and Economic Burden: HEMS vs. Ground Ambulance County Distance Gap, km Time Gain, % Total Cost, CNY Out-of-Pocket Cost, CNY Qingtian 17 (50 vs 67) 54.57% ( 28.17 vs 62) 1,935 vs 818 193.5 vs 488 Longquan 22 (90 vs 112) 61.46% ( 37.00 vs 96) 3,375 vs 998 337.5 vs 668 Yunhe 5 (55 vs 60) 60.82% ( 22.72 vs 58) 2,115 vs 790 211.5 vs 460 Qingyuan 55 (130 vs 185) 67.41% ( 46.60 vs 143) 4,275 vs 1,290 427.5 vs 960 Jinyun 13 (25 vs 38) 58.00% ( 21.00 vs 50) 1,215 vs 702 121.5 vs 372 Suichang 36 (68 vs 104) 61.24% ( 32.56 vs 84) 2,565 vs 966 256.5 vs 636 Songyang 25 (46 vs 71) 66.28% ( 21.58 vs 64) 1,845 vs 834 184.5 vs 504 Jingning 10 (64 vs 74) 58.21% ( 28.00 vs 67) 2,385 vs 846 238.5 vs 516 Mean 22.88 (66.00 vs 88.88) 61.92% ( 29.70 vs 78) 2,463.75 vs 905.50 246.38 vs 575.50 Health Economic Evaluation and Policy-Driven Financial Protection The economic viability of HEMS was assessed by comparing total mission costs and patient OOP liabilities. While the mean Total Operational Cost (TOC) for HEMS was significantly higher than that of ground ambulances (2,463.75 vs. 905.50 CNY, p < 0.001) (Table 2 and Fig. 3 ), the implementation of the 90% reimbursement policy under the “Lishui Model” significantly reduced the financial burden for individuals. Specifically, the mean OOP expenditure for HEMS patients was only 246.38 CNY, representing a statistically significant decrease compared to the simulated OOP cost of ground transport (575.50 CNY; p = 0.001). This 57.2% reduction in individual financial liability effectively challenges the traditional assumption that high-tech air medical resources inevitably lead to catastrophic health expenditures for rural populations. Multi-dimensional Cost-Benefit Analysis and Correlation Radar chart analysis revealed the superior performance of HEMS across five key dimensions: reimbursement rate (90% vs. 36%), OOP savings, clinical targeting (85% clinical need), temporal gain (62%), and clinical outcomes (72%). As illustrated in the cost-benefit radar chart (Fig. 4 ), the HEMS cohort exhibited a multidimensional superiority. While the total operational cost is higher, the area representing benefits was significantly expanded by the 90% reimbursement policy, effectively converting high-end medical technology into an affordable emergency tool for the rural population. Discussion The findings of this study provide empirical evidence for the transformative impact of a government-led, insurance-integrated HEMS model in a topographically challenging prefecture of East China. Our analysis demonstrates that HEMS not only effectively overcomes the “distance penalty” inherent in mountainous terrains, through strategic policy intervention, but also bridges the gap between high-efficiency medical technology and socioeconomic accessibility. The unique, rugged topography of the Lishui region, often described as “nine mountains, half water,” poses a formidable structural barrier to timely emergency medical intervention, effectively transforming geographic distance into a critical determinant of health outcomes [ 13 , 14 ] . Our spatial analysis confirms that HEMS achieved a profound “time-compression” effect, reducing mean transport times by 61.92%—effectively condensing a 78-minute ground transport into a 29.7-minute direct flight. This gain was most pronounced in remote counties such as Qingyuan and Longquan, where the “distance gap” between sinuous terrestrial road networks and aerodynamic flight paths reached up to 55 km. From a clinical perspective, this reduction is vital for time-sensitive pathologies, such as cardiac arrest and severe trauma (which comprised over 58% of our cohort, characterized by a median ISS of 21), where survival is inextricably linked to the “golden hour” [ 15 , 16 ] . By leveraging aerial capabilities to bypass the “distance penalty” inherent in mountainous environments, HEMS functions not merely as a high-cost transport alternative but as a structural necessity for life-saving care [ 17 , 18 ] . This transition from terrestrial to aerial evacuation serves as a powerful tool for redistributing critical care resources, ensuring that physiological urgency, rather than topographical isolation, dictates the timeline of medical response [ 19 , 20 ] . Beyond clinical efficiency, the “Lishui Model” represents a significant paradigm shift in health economics, transforming HEMS from an elite medical luxury into a universally accessible utility through strategic policy intervention. Our findings reveal a compelling “economic paradox”: despite the significantly higher total operational cost of HEMS compared to ground transport (2,463.75 vs. 905.50 CNY), the actual OOP expenditure for patients was 57.2% lower. This counter-intuitive outcome is directly attributable to the establishment of a specialized insurance-funding pool that provides a 90% reimbursement rate, effectively decoupling clinical necessity from an individual’s socioeconomic status. By establishing the government and health insurance systems as the primary risk absorbers, the model effectively shields vulnerable rural populations from “catastrophic health expenditure,” thereby aligning with the core tenets of universal health coverage. Furthermore, the strategic transition from “procuring services” to “securing coverage” ensures long-term sustainability through a multi-party contribution framework and a centralized municipal command system. By institutionalizing standardized triage and quality control, the Lishui Model provides a scalable and replicable template for regional health systems seeking to harmonize high-tech medical assets with the imperatives of social equity and public risk protection. Despite the significant findings, this study has several limitations. First, the sample size (N = 39) is relatively small, representing the early operational phase of the HEMS system at Lishui People's Hospital. While the results are statistically significant, a larger cohort would be required to perform more granular subgroup analyses on long-term survival rates. Second, our comparison of ground transport costs relied on estimated administrative data rather than actual patient receipts for ambulance services, which may introduce potential discrepancies. Finally, this retrospective study focused on a single prefecture; therefore, the generalizability of the “Lishui Model” to other regions with different insurance structures or less challenging topography should be interpreted with caution. Conclusion Two years of operational experience in Lishui demonstrates that a government-led, insurance-integrated HEMS model can successfully overcome both geographic and financial barriers to emergency care. Our data confirms that HEMS provides superior clinical efficiency by reducing transfer times by over 60% while simultaneously lowering the out-of-pocket financial burden for patients through a targeted 90% reimbursement policy. This model offers a replicable template for other mountainous or resource-limited regions seeking to achieve universal health coverage and equitable access to medical services. Future research should focus on the long-term cost-effectiveness and the potential for integrating AI-driven dispatch systems to further optimize the aeromedical rescue network. Declarations Conflicts of interest disclosure No conflicts of interest have been declared. Funding This study was funded by Zhejiang Provincial Key R&D Program “Pioneer” and “Leading Goose” (Project No. 2024C03186) and the Zhejiang Provincial Soft Science Research Program Project (No.2024C35112). Author Contribution SBL: Conceptualization, data curation, formal analysis, and writing original draft; HFW: Formal analysis and manuscript review; CXL: Formal analysis; XP: Data acquisition and curation; XWH: Conceptualization, methodology, supervision, project administration, funding acquisition, and writing – review & editing. References Klocker E, et al. High-altitude HEMS missions—a retrospective analysis of 3,564 air rescue missions conducted between 2011 and 2021. Scand J Trauma Resusc Emerg Med. 2025;33(1):97. 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Sutham K, et al. Barriers to helicopter emergency medical services in a Haze-Prone, mountainous region of Northern Thailand. Scand J Trauma Resusc Emerg Med. 2025;33(1):182. Chittawatanarat K, et al. Critical care transport and management in Earthquake catastrophes: Lessons from Japan: Earthquake catastrophes: Lessons from Japan. Clin Crit Care. 2026;34:e260001–260001. Matejić T, et al. Mass casualty incidents and healthcare system challenges: Capacity adaptation and emergency medical coordination. Crisis Management Days; 2025. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviewers invited by journal 20 Apr, 2026 Editor invited by journal 15 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 09 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9373335","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630698432,"identity":"cddbecc5-5927-440f-aaa5-22f5bed0a6bf","order_by":0,"name":"Shun-Bin Luo","email":"","orcid":"","institution":"The First Affiliated Hospital of Lishui University, Lishui People's Hospital,","correspondingAuthor":false,"prefix":"","firstName":"Shun-Bin","middleName":"","lastName":"Luo","suffix":""},{"id":630698433,"identity":"a39a7040-d1a9-4a5e-a6c6-1c300fec3661","order_by":1,"name":"Hua-Fu Wang","email":"","orcid":"","institution":"The First 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2","display":"","copyAsset":false,"role":"figure","size":203083,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOperational efficiency of air medical rescue vs. land transportation in mountainous areas of Lishui\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9373335/v1/af9a13c8b09226b22006b6a4.jpeg"},{"id":108384038,"identity":"ce1f73b2-7b5c-4931-8466-504392ca53f5","added_by":"auto","created_at":"2026-05-04 05:51:10","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":224319,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHealth economic evaluation of air medical rescue vs. land transportation in mountainous areas of Lishui\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9373335/v1/43e0cca8bc423703e91e2c71.jpeg"},{"id":108493113,"identity":"5c325a6f-8f78-470a-8467-dd8faef7babd","added_by":"auto","created_at":"2026-05-05 09:59:25","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":313196,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRadar chart of cost-benefit analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9373335/v1/393be646dc4b17e765a1fa3a.jpeg"},{"id":108804303,"identity":"d653eadd-f1be-497a-a8d3-0e939dbb0ea3","added_by":"auto","created_at":"2026-05-08 15:19:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2591477,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9373335/v1/2f800568-867f-49e4-904c-50aa09b9451d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improving Medical Accessibility in Mountainous Regions through Helicopter Emergency Medical Services: A Retrospective Evaluation of Clinical Efficiency and Economic Viability","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGeographical topography remains a fundamental structural determinant of health inequity, particularly in the provision of time-sensitive emergency medical care\u003csup\u003e[\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. In mountainous regions, the \u0026ldquo;distance penalty\u0026rdquo;\u0026mdash;characterized by sinuous road networks and significant elevation changes\u0026mdash;frequently compromises the \u0026ldquo;golden hour\u0026rdquo; for patients with life-threatening conditions\u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Conventional ground ambulance services in these terrains are often constrained by prolonged transport times, fluctuating traffic conditions, and the logistical challenges of reaching remote rural enclaves\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Consequently, patients situated in environments with complex terrain face a disproportionately higher risk of morbidity and mortality compared to their urban counterparts, necessitating a more efficient regional emergency response infrastructure. Consequently, compared to urban residents, patients situated in environments with complex terrain face a disproportionately high risk of morbidity and mortality, thereby creating an urgent need to establish a more efficient regional emergency response mechanism\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHelicopter Emergency Medical Services (HEMS) have emerged as a pivotal intervention to bridge this pre-hospital care gap. It provides rapid, point-to-point transport that bypasses terrestrial obstacles. Despite the acknowledged clinical advantages of HEMS in reducing transport latency, its global implementation has been persistently hindered by two major barriers: high operational costs and financial unsustainability\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In many health systems, HEMS is viewed as an elite resource, often leading to catastrophic health expenditures for patients\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. It is a critical paradox where the patients with the highest clinical need in the most remote areas are often the least likely to access or afford air-medical rescue\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs a prefecture in East China defined by its \u0026ldquo;nine mountains, half water, and half farmland\u0026rdquo; landscape, Lishui provides a unique laboratory for evaluating a government-led, insurance-integrated HEMS model. To address the inequities in accessing emergency care in mountainous regions, the local administration implemented a proactive health policy that integrates HEMS into the public medical insurance framework with a targeted 90% reimbursement rate. This strategy aims to improve the utilization of high-efficiency medical assets and mitigate the impact of socioeconomic status on healthcare utilization among the rural population.\u003c/p\u003e \u003cp\u003eTo evaluate the synergistic effect of HEMS on both clinical efficiency and economic accessibility in this specific policy context, we analyzed 39 HEMS cases from 2024 to 2025. Our goal was to evaluate whether this policy-driven model can effectively reconcile the tension between high-tech medical intervention and universal financial protection. Through the assessment of spatial-temporal gains and out-of-pocket expenditures, this study aims to establish a universal framework for advancing medical service equity in resource-limited settings with difficult terrain.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis retrospective observational study was conducted at the First Affiliated Hospital of Lishui University (Lishui People's Hospital), the leading trauma center in a representative mountainous prefecture of East China. The study period spanned from July 2024 to December 2025. During this period, the project aimed to develop an innovative, government-led aero-medical insurance model supported by multi-party funding. Under this model, eligible residents of Lishui City meeting the criteria for aero-medical rescue were required to bear only 10% of the total service costs. The study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines to ensure methodological rigor.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants and Data Sources\u003c/h3\u003e\n\u003cp\u003eA total of 39 patients transported to the emergency department via HEMS were included in the study. Inclusion criteria stipulated that HEMS activation was primarily based on clinical urgency or geographic inaccessibility, as determined by the regional emergency dispatch center. Data on demographics, clinical outcomes, flight metrics, and billing information were extracted by cross-referencing administrative records, electronic medical records (EMR), and HEMS flight logs.\u003c/p\u003e\n\u003ch3\u003eClinical Severity and Outcome Measures\u003c/h3\u003e\n\u003cp\u003eTo quantify the physiological and anatomical severity of injuries, three standardized scoring systems were utilized: the Glasgow Coma Scale (GCS) for neurological assessment, the Revised Trauma Score (RTS) for physiological status, and the Injury Severity Score (ISS) for anatomical injury quantification. The ISS was calculated by trained trauma registrars based on the Abbreviated Injury Scale (AIS-2008). Clinical outcomes were categorized into four groups: recovered, improved, died, and discharged against medical advice.\u003c/p\u003e\n\u003ch3\u003eOperational and Geospatial Metrics\u003c/h3\u003e\n\u003cp\u003eThe primary efficiency metric in this study was the actual flight time, defined as the duration from helicopter takeoff at the dispatch point to landing at the receiving hospital\u0026rsquo;s helipad. To evaluate the spatial-temporal advantage of air medical assets, we compared this actual flight time with the estimated ground transport time. The latter was calculated using the Amap geospatial platform, which provided the optimal terrestrial route and travel duration from the dispatch location to the tertiary center under typical traffic conditions.\u003c/p\u003e \u003cp\u003eThis study specifically focused on the efficiency of the transfer phase to isolate the impact of geographic terrain on medical accessibility. The 'Time Efficiency Gain' was subsequently calculated as the percentage reduction in transport time achieved by HEMS relative to the ground baseline, using the formula: (T\u003csub\u003eground\u003c/sub\u003e - T\u003csub\u003eflight\u003c/sub\u003e) / T\u003csub\u003eground\u003c/sub\u003e \u0026times;100%.\u003c/p\u003e\n\u003ch3\u003eHealth Economic Evaluation and Policy Framework\u003c/h3\u003e\n\u003cp\u003eThe economic analysis focused on two primary metrics: total cost and out-of-pocket (OOP) expenditure. Total cost represented the total charges for the medical rescue mission, including professional fees and flight costs. Under the local 'Lishui Model' policy, HEMS transport was covered by a specialized insurance pool providing a 90% reimbursement rate for eligible missions. In contrast, ground ambulance reimbursement rates were calculated based on the standard regional public medical insurance scheme (approximately 36.44%). Out-of-pocket expenditure was defined as the final financial liability borne by the patient after insurance settlement.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY) and R software (version 4.1.2). The normality of continuous data was assessed using the Shapiro-Wilk test. Given the non-normal distribution of temporal and economic data, the Wilcoxon signed-rank test was employed for paired comparisons between HEMS and simulated ground transport metrics (time and cost). Categorical variables were expressed as frequencies and percentages, and compared using Fisher\u0026rsquo;s exact test for variables with small expected cell counts. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e The study protocol was approved by the Institutional Review Board (IRB) of the First Affiliated Hospital of Lishui University (Approval No. 2026-04-06). Given the retrospective nature of the study and the use of de-identified administrative data, the requirement for informed consent was waived. All methods were performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePatient Demographic and Clinical Baseline Characteristics\u003c/h2\u003e \u003cp\u003eFrom July 2024 to December 2025, 39 patients were enrolled in this study. The cohort was characterized by a high proportion of males (n\u0026thinsp;=\u0026thinsp;31, 79.49%) and a mean age of 58.33\u0026thinsp;\u0026plusmn;\u0026thinsp;15.96 years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Clinical assessment upon admission revealed significant physiological instability and anatomical injury severity: the median Glasgow Coma Scale (GCS) was 3 (IQR: 3\u0026ndash;3), the median Revised Trauma Score (RTS) was 8.5 (IQR: 8\u0026ndash;9.25), and the median Injury Severity Score (ISS) reached 21 (IQR: 17.25\u0026ndash;29.25). The primary diagnostic categories necessitating HEMS activation were cardiovascular or respiratory arrest (41.03%), cerebrovascular accidents (23.08%), and severe multiple trauma (17.95%). Regarding clinical outcomes, 58.97% of patients were recovered and 12.82% showed clinical improvement, while the mortality rate was recorded at 10.26%. The geographical distribution of rescue cases spanned eight counties, with the highest utilization observed in Yunhe (n\u0026thinsp;=\u0026thinsp;9) and Suichang (n\u0026thinsp;=\u0026thinsp;8) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient demographic and clinical baseline characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValues\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (Year) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.33\u0026thinsp;\u0026plusmn;\u0026thinsp;15.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex / n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (79.49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (20.51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMajor Disease Classifications (Top 3) / n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular/respiratory arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (41.03%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecerebrovascular accident\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (23.08%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esevere multiple injuries/trauma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (17.95%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeverity score / Media (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (IQR: 3\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRTS Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.5 (IQR: 8-9.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISS Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (IQR: 17.25\u0026ndash;29.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcomes (Top 3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecovered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (58.97%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (12.82%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (10.26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDischarged Against Advice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (17.95%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of rescue cases in different regions / n\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQingtian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongquan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYunhe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQingyuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJinyun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuichang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSongyang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJingning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eOperational Efficiency: Spatial-Temporal Gains in Mountainous Terrain\u003c/h2\u003e \u003cp\u003eThe implementation of HEMS effectively overcame the geographical barriers inherent in Lishui\u0026rsquo;s \u0026ldquo;nine mountains, half water\u0026rdquo; topography. As Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrate, the mean actual flight distance was 66.00 km, representing a 22.88 km reduction compared to the optimal terrestrial route (mean: 88.88 km). The operational efficiency gain was most pronounced in the temporal dimension: the mean aeromedical rescue time (from take-off to landing) was 29.70 minutes, whereas the simulated ground transport time for the same missions was 78.00 minutes (Z = -5.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This resulted in an average Time Efficiency Gain of 61.92%. Subgroup analysis by region showed that the most remote area, Qingyuan County, derived the highest benefit, with a 67.41% reduction in transport time (46.60 min vs. 143.00 min) and a distance gap of 55 km. These findings indicate that HEMS effectively compresses the \u0026ldquo;rescue life radius\u0026rdquo; in topographically complex regions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative Analysis of Transport Efficiency and Economic Burden: HEMS vs. Ground Ambulance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance Gap, km\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTime Gain, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Cost, CNY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOut-of-Pocket Cost, CNY\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQingtian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (50 vs 67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.57% ( 28.17 vs 62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,935 vs 818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e193.5 vs 488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongquan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (90 vs 112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.46% ( 37.00 vs 96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,375 vs 998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e337.5 vs 668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYunhe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (55 vs 60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.82% ( 22.72 vs 58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,115 vs 790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e211.5 vs 460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQingyuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (130 vs 185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.41% ( 46.60 vs 143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,275 vs 1,290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e427.5 vs 960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJinyun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (25 vs 38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.00% ( 21.00 vs 50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,215 vs 702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121.5 vs 372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuichang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (68 vs 104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.24% ( 32.56 vs 84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,565 vs 966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e256.5 vs 636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSongyang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (46 vs 71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.28% ( 21.58 vs 64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,845 vs 834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e184.5 vs 504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJingning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (64 vs 74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.21% ( 28.00 vs 67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,385 vs 846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e238.5 vs 516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.88 (66.00 vs 88.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.92% ( 29.70 vs 78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,463.75 vs 905.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246.38 vs 575.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHealth Economic Evaluation and Policy-Driven Financial Protection\u003c/h2\u003e \u003cp\u003eThe economic viability of HEMS was assessed by comparing total mission costs and patient OOP liabilities. While the mean Total Operational Cost (TOC) for HEMS was significantly higher than that of ground ambulances (2,463.75 vs. 905.50 CNY, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the implementation of the 90% reimbursement policy under the \u0026ldquo;Lishui Model\u0026rdquo; significantly reduced the financial burden for individuals. Specifically, the mean OOP expenditure for HEMS patients was only 246.38 CNY, representing a statistically significant decrease compared to the simulated OOP cost of ground transport (575.50 CNY; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). This 57.2% reduction in individual financial liability effectively challenges the traditional assumption that high-tech air medical resources inevitably lead to catastrophic health expenditures for rural populations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMulti-dimensional Cost-Benefit Analysis and Correlation\u003c/h2\u003e \u003cp\u003eRadar chart analysis revealed the superior performance of HEMS across five key dimensions: reimbursement rate (90% vs. 36%), OOP savings, clinical targeting (85% clinical need), temporal gain (62%), and clinical outcomes (72%). As illustrated in the cost-benefit radar chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the HEMS cohort exhibited a multidimensional superiority. While the total operational cost is higher, the area representing benefits was significantly expanded by the 90% reimbursement policy, effectively converting high-end medical technology into an affordable emergency tool for the rural population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study provide empirical evidence for the transformative impact of a government-led, insurance-integrated HEMS model in a topographically challenging prefecture of East China. Our analysis demonstrates that HEMS not only effectively overcomes the \u0026ldquo;distance penalty\u0026rdquo; inherent in mountainous terrains, through strategic policy intervention, but also bridges the gap between high-efficiency medical technology and socioeconomic accessibility.\u003c/p\u003e \u003cp\u003eThe unique, rugged topography of the Lishui region, often described as \u0026ldquo;nine mountains, half water,\u0026rdquo; poses a formidable structural barrier to timely emergency medical intervention, effectively transforming geographic distance into a critical determinant of health outcomes\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Our spatial analysis confirms that HEMS achieved a profound \u0026ldquo;time-compression\u0026rdquo; effect, reducing mean transport times by 61.92%\u0026mdash;effectively condensing a 78-minute ground transport into a 29.7-minute direct flight. This gain was most pronounced in remote counties such as Qingyuan and Longquan, where the \u0026ldquo;distance gap\u0026rdquo; between sinuous terrestrial road networks and aerodynamic flight paths reached up to 55 km. From a clinical perspective, this reduction is vital for time-sensitive pathologies, such as cardiac arrest and severe trauma (which comprised over 58% of our cohort, characterized by a median ISS of 21), where survival is inextricably linked to the \u0026ldquo;golden hour\u0026rdquo;\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. By leveraging aerial capabilities to bypass the \u0026ldquo;distance penalty\u0026rdquo; inherent in mountainous environments, HEMS functions not merely as a high-cost transport alternative but as a structural necessity for life-saving care\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. This transition from terrestrial to aerial evacuation serves as a powerful tool for redistributing critical care resources, ensuring that physiological urgency, rather than topographical isolation, dictates the timeline of medical response\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBeyond clinical efficiency, the \u0026ldquo;Lishui Model\u0026rdquo; represents a significant paradigm shift in health economics, transforming HEMS from an elite medical luxury into a universally accessible utility through strategic policy intervention. Our findings reveal a compelling \u0026ldquo;economic paradox\u0026rdquo;: despite the significantly higher total operational cost of HEMS compared to ground transport (2,463.75 vs. 905.50 CNY), the actual OOP expenditure for patients was 57.2% lower. This counter-intuitive outcome is directly attributable to the establishment of a specialized insurance-funding pool that provides a 90% reimbursement rate, effectively decoupling clinical necessity from an individual\u0026rsquo;s socioeconomic status. By establishing the government and health insurance systems as the primary risk absorbers, the model effectively shields vulnerable rural populations from \u0026ldquo;catastrophic health expenditure,\u0026rdquo; thereby aligning with the core tenets of universal health coverage. Furthermore, the strategic transition from \u0026ldquo;procuring services\u0026rdquo; to \u0026ldquo;securing coverage\u0026rdquo; ensures long-term sustainability through a multi-party contribution framework and a centralized municipal command system. By institutionalizing standardized triage and quality control, the Lishui Model provides a scalable and replicable template for regional health systems seeking to harmonize high-tech medical assets with the imperatives of social equity and public risk protection.\u003c/p\u003e \u003cp\u003eDespite the significant findings, this study has several limitations. First, the sample size (N\u0026thinsp;=\u0026thinsp;39) is relatively small, representing the early operational phase of the HEMS system at Lishui People's Hospital. While the results are statistically significant, a larger cohort would be required to perform more granular subgroup analyses on long-term survival rates. Second, our comparison of ground transport costs relied on estimated administrative data rather than actual patient receipts for ambulance services, which may introduce potential discrepancies. Finally, this retrospective study focused on a single prefecture; therefore, the generalizability of the \u0026ldquo;Lishui Model\u0026rdquo; to other regions with different insurance structures or less challenging topography should be interpreted with caution.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTwo years of operational experience in Lishui demonstrates that a government-led, insurance-integrated HEMS model can successfully overcome both geographic and financial barriers to emergency care. Our data confirms that HEMS provides superior clinical efficiency by reducing transfer times by over 60% while simultaneously lowering the out-of-pocket financial burden for patients through a targeted 90% reimbursement policy. This model offers a replicable template for other mountainous or resource-limited regions seeking to achieve universal health coverage and equitable access to medical services. Future research should focus on the long-term cost-effectiveness and the potential for integrating AI-driven dispatch systems to further optimize the aeromedical rescue network.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of interest disclosure\u003c/h2\u003e \u003cp\u003eNo conflicts of interest have been declared.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by Zhejiang Provincial Key R\u0026amp;D Program \u0026ldquo;Pioneer\u0026rdquo; and \u0026ldquo;Leading Goose\u0026rdquo; (Project No. 2024C03186) and the Zhejiang Provincial Soft Science Research Program Project (No.2024C35112).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSBL: Conceptualization, data curation, formal analysis, and writing original draft; HFW: Formal analysis and manuscript review; CXL: Formal analysis; XP: Data acquisition and curation; XWH: Conceptualization, methodology, supervision, project administration, funding acquisition, and writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKlocker E, et al. High-altitude HEMS missions\u0026mdash;a retrospective analysis of 3,564 air rescue missions conducted between 2011 and 2021. Scand J Trauma Resusc Emerg Med. 2025;33(1):97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou Y, et al. Spatial accessibility of emergency medical services in Chongqing, Southwest China. Front public health. 2023;10:959314.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Dochartaigh D, et al. Determining What Proportion of Helicopter Emergency Medical Services\u0026ndash;Transported Patients Are Urban Versus Rurally Based: A Retrospective 36-Year Geospatial Analysis of a Critical Care Helicopter Emergency Medical Services Organization's Patient Transports. Air Med J. 2024;43(6):575\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSerchen J, et al. Improving health and health care in rural communities: a position paper from the American College of Physicians. Ann Intern Med. 2025;178(5):701\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh S, et al. Evolution of Helicopter Services and Their Development From a Medical Standpoint: Nepal. Air Med J. 2025;44(1):30\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlruwaili A, Alanazy ARM. Prehospital time interval for urban and rural emergency medical services: a systematic literature review. in Healthcare. 2022. MDPI.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMin JG, et al. The Golden Hour is elusive in rural trauma: A 10-year analysis from a Level I trauma center in Montana. The American Journal of Emergency Medicine; 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, et al. Urban Medical Emergency Logistics Drone Base Station Location Selection. Drones. 2025;10(1):17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarki S, Sprinkle DJ. Helicopter emergency medical services during coronavirus disease 2019 in Nepal. Air Med J. 2021;40(4):287\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAusserer J, et al. Physician staffed helicopter emergency medical systems can provide advanced trauma life support in mountainous and remote areas. Injury. 2017;48(1):20\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarhat H, et al. Exploring factors influencing time from dispatch to unit availability according to the transport decision in the pre-hospital setting: an exploratory study. BMC Emerg Med. 2024;24(1):77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong X, Du M, Zhao S. Multi factor assessment of spatial accessibility for rural health equity in Diqing China. Sci Rep. 2025;15(1):40977.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, et al. Estimating the effects of natural and anthropogenic activities on vegetation cover: Analysis of Zhejiang province, china, from 2000 to 2022. Remote Sens. 2025;17(8):1433.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShubietah A, et al. Do Patients With Acute Coronary Syndrome Face Higher Mortality on Weekends Versus Weekdays? A Comprehensive Analysis of Demographic, Geographic, and Temporal Trends in the United States. Clin Cardiol. 2025;48(7):e70175.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThapa G, Gyawali S, Kharel S. Helicopter Emergency Medical Services within Resource-Limited Settings of Nepal: Insights from a Tertiary Care Center. Prehosp Disaster Med. 2026;41(S1):s115\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZIN DHBM, D.M.M.B.M., HATTA. An Analysis of Characteristics of Patient Transported by Helicopter Emergency Medical Services (Hems) in Sabah, Malaysia. Malaysian J Emerg Med. 2025;7(4):7\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLaughlin K, et al. Helicopter Rescue at Very High Altitude: Recommendations of the International Commission for Mountain Emergency Medicine (ICAR MedCom) 2025. High Altitude Medicine \u0026amp; Biology; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSutham K, et al. Barriers to helicopter emergency medical services in a Haze-Prone, mountainous region of Northern Thailand. Scand J Trauma Resusc Emerg Med. 2025;33(1):182.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChittawatanarat K, et al. Critical care transport and management in Earthquake catastrophes: Lessons from Japan: Earthquake catastrophes: Lessons from Japan. Clin Crit Care. 2026;34:e260001\u0026ndash;260001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatejić T, et al. Mass casualty incidents and healthcare system challenges: Capacity adaptation and emergency medical coordination. Crisis Management Days; 2025.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mountainous regions, Ground ambulance transport, Helicopter Emergency Medical Services (HEMS), Operational efficiency, Economic viability","lastPublishedDoi":"10.21203/rs.3.rs-9373335/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9373335/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIn mountainous regions, geographic topography creates significant structural barriers to timely emergency medical care. The \u0026ldquo;golden hour\u0026rdquo; for critical conditions is often compromised by the intrinsic inefficiency of ground ambulance transport in complex terrains. This study aims to evaluate the operational efficiency, clinical necessity, and economic viability of a government-led Helicopter Emergency Medical Services (HEMS) model in Lishui, a representative mountainous prefecture in East China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective observational study was conducted on 39 HEMS cases between July 2024 and December 2025. Clinical severity was assessed using the Injury Severity Score (ISS), Glasgow Coma Scale (GCS), and Revised Trauma Score (RTS). Transport efficiency and health economic metrics\u0026mdash;including total operational costs and out-of-pocket (OOP) expenses\u0026mdash;were compared between HEMS and simulated ground transport using the Wilcoxon signed-rank test. Spatial-temporal gain was calculated across eight counties to assess regional accessibility.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study cohort exhibited high clinical urgency, with a median ISS of 21 (IQR: 17.25\u0026ndash;29.25) and a predominance of cardiovascular/respiratory arrest (41.03%). HEMS demonstrated a profound time-compression effect, reducing the mean transport time by 61.92% compared to ground alternatives (29.70 vs. 78.00 min; Z = -5.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, while the total operational cost of HEMS was significantly higher (Mean: 2,463.75 vs. 905.50 CNY, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the implementation of a targeted 90% reimbursement policy effectively reversed the financial burden for patients. The mean OOP expenditure for HEMS was significantly lower than that for ground transport (246.38 vs. 575.50 CNY; Z = -3.408, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), representing a 57.2% reduction in individual financial liability.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe Lishui HEMS model provides a robust template for achieving medical service equity in topographically challenging areas. By integrating high-efficiency air medical assets with progressive health insurance policies, this model successfully de-links clinical urgency from socioeconomic constraints, ensuring that life-saving interventions are both physically reachable and economically accessible for rural populations.\u003c/p\u003e","manuscriptTitle":"Improving Medical Accessibility in Mountainous Regions through Helicopter Emergency Medical Services: A Retrospective Evaluation of Clinical Efficiency and Economic Viability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 05:51:06","doi":"10.21203/rs.3.rs-9373335/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-15T17:57:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86638910500398393555865659958500903738","date":"2026-05-12T19:57:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289818411955511969797878207746100647810","date":"2026-05-12T10:07:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T22:12:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-15T21:08:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T09:04:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-13T09:04:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-04-10T01:35:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5fb42069-1e24-47df-9452-889518993bfb","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-15T17:57:28+00:00","index":72,"fulltext":""},{"type":"reviewerAgreed","content":"86638910500398393555865659958500903738","date":"2026-05-12T19:57:27+00:00","index":71,"fulltext":""},{"type":"reviewerAgreed","content":"289818411955511969797878207746100647810","date":"2026-05-12T10:07:05+00:00","index":68,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T05:51:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 05:51:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9373335","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9373335","identity":"rs-9373335","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}