Investigating emergency rescue readiness on Indian national highways: A case study of Jaipur-Jodhpur Highways (India) using a geospatial approach Article Swipe
Giribabu Dandabathula
,
Subham Roy
,
Koushik Ghosh
,
Ishani Chadha
,
Sejal Rathore
,
Gaurav Kumar
,
Nitin Kumar Joshi
,
Pankaj Bhardwaj
,
Apurba Kumar Bera
,
S. K. Srivastav
,
Madhu Raikwar
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.4103/jfmpc.jfmpc_1024_24
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.4103/jfmpc.jfmpc_1024_24
A BSTRACT Background: Highways represent the most significant capital asset that any country’s public sector holds. A system of national highways with easy access to ambulance and trauma centers is termed emergency rescue ready and helps reduce the mortality due to road accidents. Given the rich impetus for road infrastructure development by the government of India, there is a need to consider emergency rescue readiness on highways. Previous research suggests that on typical Indian highways, an ambulance can reach the accident scene within 30 minutes; the remaining 30 minutes of golden hour is essential for saving the life of the injured by admitting to a healthcare facility with a trauma unit. Objective: To investigate the emergency rescue readiness for a stretch of highway between two cities in India. The main objective is to determine the possibility of transporting the injured due to the accident on the highway to the nearest available trauma center within 30 minutes. Methods: A Geographic Information System (GIS)-based network analysis method has been adopted to find the possibility of transporting the injured anywhere on the circuit of a national highway to the nearest healthcare unit within 30 minutes. Results: A map representing the status of emergency rescue readiness has been presented for the study area. For a circuit of national highway with a length of 805 km, in a stretch of 89.5% (718.5 km), the injured can be transported to the healthcare unit within 30 minutes. In the remaining 10.5% of the highway, the healthcare units are significantly far from the highways and may hamper the post-crash rescue operations. Conclusion: There exists a wide disparity in trauma care delivery in the study area. Currently, the healthcare units established to serve the areas with dense populations also serve as emergency rescues during accidents on the national highways. Using GIS techniques for highway stretches with difficulty reaching the trauma centers, a national-level assessment is highly recommended.
Related Topics
Concepts
Medicine
Medical emergency
Geospatial analysis
Emergency rescue
Unit (ring theory)
Geographic information system
Capital city
Government (linguistics)
Transport engineering
Health care
Emergency medical services
Geography
Engineering
Cartography
Economic growth
Linguistics
Economics
Philosophy
Mathematics
Mathematics education
Economic geography
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4103/jfmpc.jfmpc_1024_24
- OA Status
- gold
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404432858
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404432858Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.4103/jfmpc.jfmpc_1024_24Digital Object Identifier
- Title
-
Investigating emergency rescue readiness on Indian national highways: A case study of Jaipur-Jodhpur Highways (India) using a geospatial approachWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-01Full publication date if available
- Authors
-
Giribabu Dandabathula, Subham Roy, Koushik Ghosh, Ishani Chadha, Sejal Rathore, Gaurav Kumar, Nitin Kumar Joshi, Pankaj Bhardwaj, Apurba Kumar Bera, S. K. Srivastav, Madhu RaikwarList of authors in order
- Landing page
-
https://doi.org/10.4103/jfmpc.jfmpc_1024_24Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.4103/jfmpc.jfmpc_1024_24Direct OA link when available
- Concepts
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Medicine, Medical emergency, Geospatial analysis, Emergency rescue, Unit (ring theory), Geographic information system, Capital city, Government (linguistics), Transport engineering, Health care, Emergency medical services, Geography, Engineering, Cartography, Economic growth, Linguistics, Economics, Philosophy, Mathematics, Mathematics education, Economic geographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.minutes. | 155, 191, 239 |
| abstract_inverted_index.minutes; | 84 |
| abstract_inverted_index.national | 19, 182, 213, 298 |
| abstract_inverted_index.reaching | 308 |
| abstract_inverted_index.research | 68 |
| abstract_inverted_index.suggests | 69 |
| abstract_inverted_index.accidents | 295 |
| abstract_inverted_index.admitting | 102 |
| abstract_inverted_index.ambulance | 25, 76 |
| abstract_inverted_index.available | 150 |
| abstract_inverted_index.determine | 133 |
| abstract_inverted_index.disparity | 268 |
| abstract_inverted_index.emergency | 31, 62, 115, 199, 292 |
| abstract_inverted_index.essential | 93 |
| abstract_inverted_index.highways, | 74 |
| abstract_inverted_index.highways. | 66, 299 |
| abstract_inverted_index.mortality | 38 |
| abstract_inverted_index.objective | 130 |
| abstract_inverted_index.presented | 204 |
| abstract_inverted_index.readiness | 64, 117, 201 |
| abstract_inverted_index.remaining | 86, 242 |
| abstract_inverted_index.represent | 4 |
| abstract_inverted_index.stretches | 305 |
| abstract_inverted_index.Currently, | 277 |
| abstract_inverted_index.Geographic | 158 |
| abstract_inverted_index.Objective: | 111 |
| abstract_inverted_index.accidents. | 42 |
| abstract_inverted_index.assessment | 314 |
| abstract_inverted_index.difficulty | 307 |
| abstract_inverted_index.government | 53 |
| abstract_inverted_index.healthcare | 105, 187, 235, 248, 279 |
| abstract_inverted_index.post-crash | 260 |
| abstract_inverted_index.techniques | 302 |
| abstract_inverted_index.(GIS)-based | 161 |
| abstract_inverted_index.Background: | 2 |
| abstract_inverted_index.Conclusion: | 263 |
| abstract_inverted_index.Information | 159 |
| abstract_inverted_index.country’s | 12 |
| abstract_inverted_index.development | 50 |
| abstract_inverted_index.established | 281 |
| abstract_inverted_index.investigate | 113 |
| abstract_inverted_index.operations. | 262 |
| abstract_inverted_index.populations | 288 |
| abstract_inverted_index.possibility | 135, 171 |
| abstract_inverted_index.significant | 7 |
| abstract_inverted_index.transported | 232 |
| abstract_inverted_index.recommended. | 317 |
| abstract_inverted_index.representing | 195 |
| abstract_inverted_index.transporting | 137, 173 |
| abstract_inverted_index.significantly | 251 |
| abstract_inverted_index.infrastructure | 49 |
| abstract_inverted_index.national-level | 313 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 11 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.41397009 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |