EDITORIAL


https://doi.org/10.5005/njem-11015-0018
National Journal of Emergency Medicine
Volume 1 | Issue 3 | Year 2023

Health Informatics and the Importance of Artificial Intelligence in the Field of Emergency Medicine


Saravana Kumar1https://orcid.org/0000-0003-0569-3582, Srinath Kumar Tumsi Sanjeeva2https://orcid.org/0000-0002-6712-5700, Meenakshisundaram Rajadurai3https://orcid.org/0000-0002-5811-3062, Patibandla Sowjanya4https://orcid.org/0009-0005-1888-6991

1Department of Emergency Medicine, Dr Mehta’s Multispeciality Hospitals, Chennai, Tamil Nadu, India

2Department of Emergency Medicine, Yashoda Hospitals, Hyderabad, Telangana, India

3Department of Emergency Medicine and Critical Care, Apollo KH Hospital, Vellore, Tamil Nadu, India

4Chief Medical Intelligence Officer, Vigocare Private Limited, Hyderabad, Telangana, India

Corresponding Author: Meenakshisundaram Rajadurai, Department of Emergency Medicine and Critical Care, Apollo KH Hospital, Vellore, Tamil Nadu, India, Phone: +91 9600055552, e-mail: drmrajadurai@gmail.com

How to cite this article: Kumar S, Sanjeeva SKT, Rajadurai M, et al. Health Informatics and the Importance of Artificial Intelligence in the Field of Emergency Medicine. Natl J Emerg Med 2023;1(3):61–62.

Source of support: Nil

Conflict of interest: None

PREAMBLE

The realm of healthcare is in a state of perpetual metamorphosis, continually adjusting and responding to novel advancements in technology and innovations. Among the most consequential breakthroughs in recent times is the assimilation of health informatics and artificial intelligence (AI) into the domain of exigency medicine. This convergence can overhaul emergency care and enhance patient prognoses. In the following discourse, we shall scrutinise the sundry means through which health informatics and AI are being harnessed in exigency medicine, and the dividends they bestow.

Contextual Information

Health informatics pertains to the procurement, preservation, retrieval, and utilisation of data in the healthcare sector. It encompasses a spectrum of technologies and methodologies, comprising digital health records (DHRs), health data exchange (HDE), and clinical judgment assistance systems (CJAS). These instruments aid in simplifying healthcare operations and enhancing the standard of patient care.1

Conversely, AI is a technology that empowers machines to execute functions that conventionally necessitate human intellect, for example, visual acuity, speech discernment, and discretion. In the domain of healthcare, AI can scrutinise extensive volumes of patient information and detect patterns and tendencies that may otherwise evade human experts.

ARTICLE OBJECTIVE

The objective of this article is to present a survey of the modalities by which health informatics and AI are being employed in exigency medicine and the dividends they yield. We shall examine the cardinal domains where these technologies are being harnessed, in addition to the obstacles and constraints that require resolution.2

In the principal segment of this paper, we shall scrutinise three cardinal themes germane to health informatics and AI in exigency medicine:

Each of these realms embodies an indispensable ingredient of modern emergency medicine, and the assimilation of health informatics and AI can refine patient prognoses and simplify the dispensation of care.

The Use of Predictive Analytics in Triage

Triage is the process of determining the priority of patients’ treatments based on the severity of their condition. In emergency medicine, triage decisions can be the difference between life and death. The use of predictive analytics in triage can help to identify patients who are at high risk for adverse outcomes and ensure that they receive timely and appropriate care.

Predictive analytics involves using machine learning algorithms to analyze large datasets and identify patterns and trends. In emergency medicine, predictive analytics can be used to identify patients who are at high risk for complications, such as sepsis or cardiac arrest. By identifying these patients early, healthcare providers can take action to prevent adverse outcomes, and improve patient outcomes.3

Example: The University of Utah Health System has implemented a predictive analytics system in its Emergency Department. The system uses machine learning algorithms to analyze patient data, such as vital signs and lab results, and predict which patients are at high risk for adverse outcomes. This allows healthcare providers to prioritise patients based on their risk level and provide timely and appropriate care.

The Implementation of Clinical Decision Support Systems

Clinical decision support systems (CDSS) are computer programs that provide healthcare providers with information and tools to support clinical decision-making. Clinical decision support systems can help to improve the accuracy and consistency of diagnoses, reduce medical errors, and improve patient outcomes.

In emergency medicine, CDSS can be used to assist healthcare providers in making triage decisions, diagnosing conditions, and selecting appropriate treatment options. Clinical decision support systems can also help to ensure that healthcare providers follow evidence-based guidelines and best practices.4

Example: The Emergency Department at Massachusetts General Hospital implemented a CDSS system that uses machine learning algorithms to provide real-time clinical decision support.

The system provides healthcare providers with recommendations for patient care based on evidence-based guidelines and the patient’s specific clinical information. The CDSS system also alerts healthcare providers when a patient’s condition changes or when a critical lab result is received. This helps to ensure that healthcare providers are aware of changes in the patient’s condition and can take appropriate action.

The Role of AI in Diagnostic Imaging

Diagnostic imaging plays a critical role in emergency medicine. It enables healthcare providers to quickly and accurately diagnose a range of conditions, from broken bones to life-threatening conditions such as stroke and pulmonary embolism. However, the interpretation of diagnostic images can be time-consuming and subject to variability between healthcare providers.

Artificial intelligence can help to improve the accuracy and consistency of diagnostic imaging. Machine learning algorithms can be trained to analyze medical images and identify abnormalities with a high degree of accuracy. This can help to reduce the time required to interpret diagnostic images and ensure that healthcare providers receive consistent and accurate results.5,6

Example: The Radiology Department at Stanford University has implemented an AI-powered system for interpreting chest X-rays. The system uses machine learning algorithms to analyze medical images and identify abnormalities, such as lung nodules or pneumonia. The system has been shown to improve the accuracy and efficiency of chest X-ray interpretation, enabling healthcare providers to make more timely and accurate diagnoses.7

Impediments and Obstacles

Despite the manifold benefits of amalgamating health informatics and AI in exigency medicine, several impediments and obstacles necessitate resolution. One of the foremost hurdles is the necessity for accurate and comprehensive data. Machine learning algorithms necessitate ample datasets that precisely mirror the patient populace to function optimally. Nevertheless, many healthcare organisations grapple with the collection and maintenance of data of elevated caliber.8,9

Another hurdle is the need for regulatory supervision. As with any nascent technology, the implementation of health informatics and AI in exigency medicine must be cautiously monitored to ensure patient safety and confidentiality. Further, the necessity for instruction and training is imperative to ensure that healthcare providers acquire the competence to operate these tools competently and securely.

Epilogue

The assimilation of health informatics and AI in exigency medicine possesses the potential to overhaul patient care and augment outcomes. Prognostic analytics, CDSS, and AI-fuelled diagnostic imaging are merely some instances of how these technologies are being employed. Nonetheless, impediments and obstacles such as the necessity for accurate and comprehensive data and regulatory oversight must be resolved. Through surmounting these challenges and capitalizing on the advantages of health informatics and AI, we can enhance the dispensation of exigency care and, ultimately, rescue lives.

ORCID

Saravana Kumar https://orcid.org/0000-0003-0569-3582

Srinath Kumar Tumsi Sanjeeva https://orcid.org/0000-0002-6712-5700

Meenakshisundaram Rajadurai https://orcid.org/0000-0002-5811-3062

Patibandla Sowjanya https://orcid.org/0009-0005-1888-6991

REFERENCES

1. Haux R. Health information systems–Past, present, future. Int J Med Inform 2006;75(3–4):268–281.DOI: 0.1016/j.ijmedinf.2005.08.002.

2. Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine 2019;25(1):44–56.DOI: 0.1038/s41591-018-0300-7.

3. Obermeyer Z, Emanuel E J. Predicting the future—Big data, machine learning, and clinical medicine. N Engl J Med 2016;375(13):1216–1219.DOI: 0.1056/NEJMp1606181.

4. Fairbanks RJ, Bisantz AM, Sunm M. Emergency department communication links and patterns. Ann Emerg Med 2007;50(4):396–406.DOI: 0.1016/j.annemergmed.2007.03.005.

5. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42:60–88.DOI: 0.1016/j.media.2017.07.005.

6. Wong A, Plasek JM, Montecalvo SP, et al. Natural language processing and its implications for the future of medication safety: A narrative review of recent advances and challenges. Pharmacotherapy 2018;38(8):822–841.DOI: 0.1002/phar.2151.

7. Raghupathi W, Raghupathi V. Big data analytics in healthcare: Promise and potential. Health Inf Sci Syst 2014;2(3).DOI: 0.1186/2047-2501-2-3.

8. Erickson BJ, Korfiatis P, Akkus Z, et al. Machine Learning for Medical Imaging. Radiographics 2017;37(2):505–515.DOI: 0.1148/rg.2017160130.

9. Boyle A, Beniuk K, Higginson I, et al. Emergency department crowding: Time for interventions and policy evaluations. Emerg Med Int 2012;2012:838610.DOI: 0.1155/2012/838610.

________________________
© The Author(s). 2023 Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and non-commercial reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.