Airway Management in ICU

Chaudhary, Anamika; Munjal, Manish; Gaur, Neeru; Rungta, Neena; Rungta, Narendra

doi:10.5005/jp/books/12670_3

Cited by 2 publications

(1 citation statement)

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“…12 There are 12,046 members of the ISCCM registered, though it is unknown how many doctors in India have received intensive care training (including trainees). 8 In contrast to other states, which have fewer than 50 members, Maharashtra has a higher representation (2,944 members), making up a portion of the membership that is not evenly distributed. Similar limitations apply to nursing capacity, and there are significant shortages of qualified critical care nurses.…”

Section: Discussionmentioning

confidence: 99%

Need Analysis of Indian Critical Health Care Delivery in Government Sectors and Its Impact on the General Public: A Time to Revamp Public Health Care Infrastructure

Sindhu¹,

Rajmohan²,

Bharathwaj³

et al. 2023

Indian Journal of Critical Care Medicine

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A bstract Background Poverty is directly linked to public health care delivery in many ways and dimensions. Every aspect of the human sphere is preplanned, but a health crisis is the only emergency which pushes humanity into severe economic stress. Therefore, every nation aims to safeguard its citizens from a health crisis. In this aspect, India needs to improve its public health infrastructure in order to protect its citizens and save them from poverty. Objectives (1) To assess the current pitfalls in public critical health care delivery, (2) to analyze whether the health care delivery matches the requirements of its population in every state, (3) to produce solutions and guidelines to overcome the stress in this priority area. Materials and methods Data regarding the critical care workforce, which includes critical care doctors and nurses, were taken from official websites and other sources. Critical care infrastructure data were retrieved from the Internet sources. Data were validated by consulting state government sources and cross-checked for bias elimination. The data were analyzed using the “Statistical Package for Social Sciences” software version 20, and were presented using descriptive statistics. Results There is a 1:10 percentage of deficit in the case of critical care workforce and infrastructure when compared with its need analysis. Critical care medicine specialists are in 1:75 when compared to other specialties. Conclusion Overall, the public sector critical care needs a total boost through out of box solutions. According to the Stockholm International Peace Research Institute (SIPRI), India spent the third most on defense in the world in 2021. India spent 76.6 billion dollars on its military in 2021, up 33% from 2012 and 0.9% from 2020. However, since India is considered a fast-growing economy, there is still a huge disparity in critical care. Without resetting critical health care, India cannot grow in welfare indices even if it is among the top gross domestic product (GDP) countries. How to cite this article Prabu D, Gousalya V, Rajmohan M, Dinesh MD, Bharathwaj VV, Sindhu R, et al. Need Analysis of Indian Critical Health Care Delivery in Government Sectors and its Impact on the General Public: A Time to Revamp Public Health Care Infrastructure. Indian J Crit Care Med 2023;27(4):237–245.

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Section: Discussionmentioning

confidence: 99%

Need Analysis of Indian Critical Health Care Delivery in Government Sectors and Its Impact on the General Public: A Time to Revamp Public Health Care Infrastructure

Sindhu¹,

Rajmohan²,

Bharathwaj³

et al. 2023

Indian Journal of Critical Care Medicine

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Playing the pipes: acoustic sensing and machine learning for performance feedback during endotracheal intubation simulation

Steffensen,

Bartnes,

Fuglstad

et al. 2023

Front. Robot. AI

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Objective: In emergency medicine, airway management is a core skill that includes endotracheal intubation (ETI), a common technique that can result in ineffective ventilation and laryngotracheal injury if executed incorrectly. We present a method for automatically generating performance feedback during ETI simulator training, potentially augmenting training outcomes on robotic simulators.Method: Electret microphones recorded ultrasonic echoes pulsed through the complex geometry of a simulated airway during ETI performed on a full-size patient simulator. As the endotracheal tube is inserted deeper and the cuff is inflated, the resulting changes in geometry are reflected in the recorded signal. We trained machine learning models to classify 240 intubations distributed equally between six conditions: three insertion depths and two cuff inflation states. The best performing models were cross validated in a leave-one-subject-out scheme.Results: Best performance was achieved by transfer learning with a convolutional neural network pre-trained for sound classification, reaching global accuracy above 98% on 1-second-long audio test samples. A support vector machine trained on different features achieved a median accuracy of 85% on the full label set and 97% on a reduced label set of tube depth only.Significance: This proof-of-concept study demonstrates a method of measuring qualitative performance criteria during simulated ETI in a relatively simple way that does not damage ecological validity of the simulated anatomy. As traditional sonar is hampered by geometrical complexity compounded by the introduced equipment in ETI, the accuracy of machine learning methods in this confined design space enables application in other invasive procedures. By enabling better interaction between the human user and the robotic simulator, this approach could improve training experiences and outcomes in medical simulation for ETI as well as many other invasive clinical procedures.

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Airway Management in ICU

Cited by 2 publications

References 0 publications

Need Analysis of Indian Critical Health Care Delivery in Government Sectors and Its Impact on the General Public: A Time to Revamp Public Health Care Infrastructure

Need Analysis of Indian Critical Health Care Delivery in Government Sectors and Its Impact on the General Public: A Time to Revamp Public Health Care Infrastructure

Playing the pipes: acoustic sensing and machine learning for performance feedback during endotracheal intubation simulation

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