Informatics in Emergency Medicine: A Literature Review

Abstract

In recent years, the integration of informatics in emergency medicine has led to significant improvements in clinical decision-making, patient management, and overall healthcare delivery. This literature review explores the most recent trends and applications of informatics in the field of emergency medicine, including electronic health records, telemedicine, artificial intelligence, and mobile health technologies. The goal is to provide a comprehensive overview of the state-of-the-art technologies, their current implementations, and the challenges that remain to be addressed.

1. Introduction

Informatics has revolutionized many fields of healthcare over the past decade, bringing about a paradigm shift in the way that medical information is processed, analyzed, and applied in clinical settings. Among the various medical specialties, emergency medicine (EM) has been particularly impacted by this digital transformation. Emergency departments (EDs) represent high-stakes environments characterized by the need for immediate, life-saving interventions, high patient volumes, resource limitations, and time-sensitive decision-making. In these conditions, the ability to leverage advanced informatics systems is crucial in enhancing both patient care and operational efficiency. Digital integration in EM has evolved rapidly, progressing from simple electronic documentation and data retrieval systems to sophisticated platforms capable of predictive analytics, real-time patient monitoring, and artificial intelligence (AI)-driven decision support. This evolution is enabling emergency clinicians to navigate complex and dynamic environments with greater precision and insight.

EM is unique due to its inherent unpredictability. Clinicians are often required to make rapid decisions with limited information while simultaneously managing multiple critical patients. This situation requires immediate access to accurate and up-to-date patient data, a need that is well addressed by electronic health records (EHRs). EHRs have become the backbone of clinical information systems in EDs, providing physicians with a comprehensive view of a patient’s medical history, previous treatments, and current health status. For instance, when patients are unconscious or unable to communicate their medical history, EHRs allow physicians to quickly retrieve vital information such as medication allergies, chronic conditions, and previous surgeries. This instant access reduces delays in treatment and minimizes the risk of administering inappropriate or harmful therapies. In a study conducted across multiple hospitals, the introduction of EHRs in EDs was shown to reduce the documentation time by 20%, while also improving the accuracy of patient records [1]. In addition to EHRs, the integration of decision support systems (DSS) has further augmented the capabilities of EM clinicians. DSS tools provide real-time, evidence-based recommendations tailored to individual patients by analyzing the data stored in EHRs. These systems can flag critical conditions, suggest diagnostic tests, or warn against potential adverse events such as drug interactions. For example, in cases of suspected sepsis, a DSS can alert clinicians to abnormal lab results—such as elevated white blood cell counts or lactate levels—prompting earlier interventions that could prevent deterioration. A study on DSS in EM demonstrated that the use of these tools can reduce diagnostic errors by up to 30% and improve adherence to clinical guidelines, particularly in the management of high-risk conditions such as myocardial infarction and pulmonary embolism [2]. This automation of clinical protocols not only enhances patient safety but also frees up clinicians to focus on more complex aspects of patient care.

AI and machine learning are being increasingly integrated into EM, representing one of the most groundbreaking advancements in the field of health informatics. AI-driven systems, leveraging large datasets and complex algorithms, can assist clinicians by identifying patterns and making predictions based on real-time and historical patient data. For instance, the Cardiac Arrest Risk Triage (CART) algorithm is a prime example of AI applied in the ED. By analyzing data such as vital signs, lab results, and demographic information, CART can predict a patient’s likelihood of experiencing cardiac arrest with high accuracy. This prediction allows clinicians to prioritize high-risk patients and initiate early interventions that could prevent catastrophic events [3]. Another notable AI application is in the detection of sepsis, a condition where early diagnosis is critical for survival. AI algorithms have been developed to identify subtle patterns in patient data that might be missed by human clinicians, allowing for the earlier detection and treatment of sepsis. Studies have demonstrated that AI-driven sepsis prediction systems can detect the condition up to 12 h earlier than traditional methods, reducing mortality rates by 20% [4].

The role of informatics extends beyond individual patient care to operational efficiency and resource management within EDs. One of the persistent challenges faced by EDs globally is overcrowding, which leads to longer wait times, compromised patient safety, and increased stress on healthcare providers. Predictive analytics, powered by informatics, is being used to mitigate this issue by forecasting patient volumes and resource needs. These predictive models analyze historical data, seasonal trends, and external factors such as local events or disease outbreaks to anticipate surges in patient arrivals. For instance, during flu season, predictive models can help EDs to prepare for higher-than-normal patient volumes by optimizing staffing levels, ensuring sufficient bed capacity, and stockpiling necessary medications such as antivirals [5]. Additionally, real-time data from wearable health devices and Internet of Things (IoT) technologies are being integrated into ED systems, enabling the continuous monitoring of patients before they even arrive at the hospital. Wearable devices that track vital signs such as the heart rate, blood pressure, and oxygen saturation can alert healthcare providers to early warning signs of deterioration, triggering preemptive interventions that could prevent emergency visits altogether. For instance, wearable ECG monitors have been used to detect early arrhythmias in patients with cardiovascular conditions, allowing for timely interventions that prevent more severe complications such as stroke or cardiac arrest [6].

The COVID-19 pandemic accelerated the adoption of telemedicine in emergency care, highlighting the importance of informatics in providing remote and virtual care. Telemedicine platforms allow for remote triage and consultations, reducing the strain on overcrowded EDs and limiting the risk of viral transmission. In rural or underserved areas, where access to specialist care is limited, telemedicine has become a critical tool in bridging healthcare gaps. Tele-stroke networks, for example, enable community hospitals to consult with neurologists at major stroke centers in real time, facilitating the faster diagnosis and treatment of stroke patients. In these cases, early intervention with tissue plasminogen activator (tPA) can significantly reduce the risk of long-term disability [7]. These telemedicine solutions demonstrate how informatics can extend the reach of emergency care beyond the physical walls of the hospital.

Despite the numerous advancements in informatics and AI within EM, several challenges remain. One major obstacle is the issue of data interoperability. Different healthcare institutions often use incompatible EHR systems, which hinders the seamless exchange of patient information between facilities. This fragmentation can lead to delays in care, misdiagnosis, and repeated tests or procedures. Addressing this challenge requires the development of standardized data formats and communication protocols that allow for the seamless exchange of health information across institutions. Additionally, concerns around data security and patient privacy must be addressed. As healthcare data become increasingly digitized and interconnected, the risk of data breaches and cyberattacks grows. Robust encryption standards, access control mechanisms, and regular security audits are necessary to safeguard patient information.

Looking forward, the future of informatics in EM holds immense potential. AI algorithms will continue to improve, offering more accurate predictions, faster diagnoses, and even autonomous decision-making in certain clinical scenarios. The expansion of wearable health technologies and IoT devices will provide clinicians with continuous streams of real-time data, enabling a more proactive approach to emergency care. Telemedicine platforms will likely evolve to become more integrated with hospital systems, facilitating better continuity of care for patients across different settings. Additionally, as predictive models incorporate more diverse datasets—including social determinants of health—emergency care can become more personalized, addressing not just immediate clinical needs but also broader factors that impact health outcomes. Thus, the integration of informatics and AI into EM represents a critical shift towards more efficient, predictive, and patient-centered care. From the use of EHRs and DSS to AI algorithms, predictive analytics, and telemedicine, these tools are fundamentally reshaping the way in which emergency care is delivered. As technological advancements continue to unfold, the role of informatics in EM will only expand, offering new possibilities in terms of improving patient outcomes and addressing the complexities of modern healthcare delivery.

We conduct a systematic literature review of the model fusion methodologies employed in EM through informatics, mainly in the last decade. The main goal of this study is to identify the scope, trends, and methods used in the field of informatics to ultimately enhance EM and thus improve our understanding of this domain through a comprehensive analysis of relevant methods. To achieve these objectives, we formulate the following research questions.

• Compared to conventional approaches, how has the incorporation of informatics significantly changed the workflow in emergency care?
• What are the main advantages and drawbacks of using EHRs in emergency care?
• Are there particular DSS characteristics that have worked well in EM situations?
• What are the main obstacles to using AI-powered solutions in emergency care, specifically concerning clinical decision support?
• How might predictive analytics models help EDs to manage patient loads and resource allocation, particularly during peak hours?
• How has telemedicine changed how emergency treatment is provided, especially in underserved or rural areas?
• How may the difficulties of data interoperability across various healthcare organizations be addressed?
• With the increasing use of digital health technology, how can emergency rooms protect patient privacy and ensure data security?
• Based on current research, how have developments in AI and informatics significantly affected patient outcomes in EM?
• Are there noteworthy case studies showing practical AI usage in emergency rooms?
• How might AI help to optimize resource management beyond personnel, such as allocating beds and equipment in emergencies?
• What proof is there that AI technologies in emergency care have improved the diagnosis accuracy?
• As AI and informatics advance, what long-term effects can be anticipated for emergency care?
• What are the main obstacles to these technologies’ broad use in emergency care, and how may they be removed?

Contributions

Our main contributions are as follows:

• We provide a comprehensive and systematic review of informatics techniques in EM, including a detailed taxonomy and literature survey, offering a comprehensive overview of recent work in the field;
• We explore the impact of EHRs in EM (Section 3) and discuss telemedicine and its role in remote diagnostics and consultation (Section 4);
• We delve into the applications of AI in EM (Section 5), and we examine the use and applications of mobile health technologies in EM (Section 6).

The structure of this work is shown in Figure 1.

2. Research Methodology

To assess the use and impact of informatics in EM, a systematic review of the literature was performed. This process followed a structured approach to identify and examine relevant studies in the chosen field, ensuring the review was methodical, transparent, and reproducible, in line with established practices [8]. The review adhered to expert-recommended guidelines, ensuring thoroughness and effectiveness. Particularly, the guidelines suggested by Siddaway et al. [9] and the PRISMA framework by Moher et al. [10] were followed. In [9] an eight-step process for conducting a systematic review is outlined, including the definition of research questions, setting inclusion and exclusion criteria, conducting a literature search, screening the studies, extracting data, evaluating the studies’ quality, analyzing and synthesizing the findings, and presenting the results. Conversely, the PRISMA framework offers a checklist for systematic reviews and meta-analyses, covering elements such as the title, abstract, introduction, methodology, results, discussion, and funding. The following sections detail the search strategy and criteria used to identify studies relevant to this literature review. The research questions, initially outlined in the Introduction, provided direction throughout the entire review process.

At the beginning of the literature search, we decide to utilize the Scopus and Web of Science databases, given their comprehensive coverage of papers. To ensure the comprehensive coverage of the relevant literature, the primary search term ‘Emergency Medicine’ was used to search the titles, abstracts, and keywords of the literature. Furthermore, in order to account for the possibility that some researchers may use the terms ‘Informatics’ and its synonyms interchangeably, the secondary search term ‘Information Science’ OR ‘Computer Science’ OR ‘Artificial Intelligence’ was employed to search all sections of the literature. It is worth noting that, despite using these search keywords, it is possible that not every relevant publication was captured without omission. This is because some researchers preferred to name their addressed sub-tasks (i.e., problems) instead of explicitly using the term ‘emergency medicine’. The classification of literature by topic is provided in

Table 1. Classification of literature by topic.

Main Topic	References by Sub-Topic
Electronic Health Records (EHRs)	EHRs in Emergency Medicine [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] Emerging Trends [31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49]
Telemedicine	Platforms and Infrastructure [50,51,52,53,54,55,56,57,58,59,60,61] AI-Powered Diagnostics and DSS [62,63,64,65,66,67,68,69,70,71] Patient Monitoring and Wearable Technologies [72,73,74,75,76,77,78,79,80,81,82,83,84]
Applications of AI	Importance of AI [85,86] Clinical Decision Support [87,88,89,90,91,92,93,94,95,96] Emergency Medical Services [69,97,98,99,100] Interpretability and Explainability [101,102,103]
Mobile Health Technologies (MHT)	Importance of MHT [104,105,106,107,108,109,110] Data Collection and Management [79,83,111,112,113,114,115,116,117] AI-Powered Analytics and Decision Support [69,86,118,119,120,121,122,123,124,125] User Experience and Behavior Change [126,127,128,129,130,131,132,133]

.

3. Electronic Health Records in Emergency Medicine

Electronic health records (EHRs) are digital versions of patients’ paper charts and play a crucial role in modern healthcare. They facilitate the comprehensive collection, storage, and management of patient health information, allowing for real-time access and sharing among authorized healthcare providers. EHRs typically include data such as medical histories, medications, allergies, lab results, radiology images, and treatment plans. By enhancing the quality of care, EHRs improve the coordination among healthcare professionals, reduce errors associated with paper records, and enable evidence-based clinical decision-making. Furthermore, EHR systems support functionalities like clinical decision support, patient reminders, and population health management, contributing to more efficient healthcare delivery. Studies have demonstrated that the implementation of EHRs can lead to significant improvements in patient outcomes, including reduced medication errors and enhanced adherence to clinical guidelines.

EHRs have revolutionized the landscape of healthcare delivery, and their impact on EM has been particularly profound. The fast-paced, high-stakes environment of EDs demands rapid access to accurate patient information, efficient documentation, and seamless communication among healthcare providers. EHRs have emerged as a critical tool in meeting these demands, transforming the way in which emergency care is delivered and managed [11]. The adoption of EHRs in EM has been driven by several factors, including the need for improved patient safety, enhanced clinical decision-making, increased operational efficiency, and compliance with regulatory requirements. As of 2017, over 95% of hospitals in the United States had adopted certified EHR technology, with EDs being at the forefront of this digital transformation [12].

3.1. Key Features of EHRs in EM

EHRs in EM are designed to address the unique challenges and requirements of emergency care. Some of the key features that make EHRs particularly valuable in this setting include the following points. One of the most critical aspects of emergency care is the ability to quickly access a patient’s medical history, especially when the patient may be unconscious or unable to provide information. EHRs enable emergency physicians to instantly retrieve vital information such as allergies, current medications, and medical histories. This rapid access to comprehensive patient data can be life-saving in time-critical situations, allowing for more informed decision-making and reducing the risk of medical errors. EHRs in emergency settings often incorporate clinical decision support systems (CDSS) that provide real-time guidance to clinicians. These systems can alert physicians to potential drug interactions, suggest appropriate diagnostic tests based on the presenting symptoms, and provide evidence-based treatment recommendations [13]. For instance, a CDSS might flag a patient’s history of contrast dye allergy when a CT scan is ordered or suggest a sepsis protocol based on vital signs and laboratory values. Such support is invaluable in the high-pressure environment of the ED, where decisions must often be made quickly, with limited information.

EDs are known for their high patient turnover and the need for rapid, accurate documentation. EHRs offer templates and structured data entry forms that can significantly speed up the documentation process while ensuring completeness and accuracy [14]. Moreover, many EHR systems include features for the automatic coding of diagnoses and procedures, which is crucial for billing and regulatory compliance. This not only saves time but also helps to maximize reimbursement and reduce billing errors. EHRs facilitate better communication among healthcare providers within the ED and across different departments and healthcare facilities. They enable the seamless sharing of patient information, test results, and treatment plans, which is particularly important when transferring patients or consulting with specialists. For example, if a patient needs to be admitted from the ED to an inpatient unit, all the information collected in the ED can be instantly available to the admitting team, ensuring continuity of care.

Modern EHRs in EM are typically integrated with other critical hospital systems, such as laboratory information systems, radiology information systems, and pharmacy management systems [15]. This integration allows for the automated ordering of tests and medications, real-time notification of results, and closed-loop medication administration processes. Such seamless integration can significantly reduce the turnaround times for diagnostic tests and treatments, which is crucial in emergency care. The implementation of EHRs has had a profound impact on the practice of EM, influencing various aspects of care delivery and departmental operations. Numerous studies have demonstrated the positive impact of EHRs on patient safety and quality of care in emergency settings. For instance, a study found that the implementation of an EHR system in an ED was associated with a significant reduction in medication errors, from 10.3 per 100 medication orders to 3.9 per 100 orders [16]. Another study showed that EHR use was associated with improved adherence to clinical guidelines for the management of pneumonia in the ED [17].

EHRs also have been shown to improve the completeness and accuracy of clinical documentation in emergency care. A study by Banet et al. found that the implementation of an EHR system led to significant improvements in the documentation of key clinical elements in ED records, including chief complaints, past medical histories, and physical examination findings [18]. The impact of EHRs on ED operational efficiency has been the subject of considerable research. While some studies have reported initial decreases in efficiency during the EHR implementation phase, many have demonstrated long-term improvements in various operational metrics [19]. For example, a study by Daniel et al. found that EHR implementation was associated with a 13% reduction in the ED length of stay for discharged patients [20]. Another study by Feblowitz et al. showed that EHR use led to a significant reduction in laboratory turnaround times in the ED, with the median time from order to result decreasing from 79 min to 62 min [21].

EHRs also have been shown to improve resource utilization in emergency care. A study by Zlabek et al. found that EHR implementation was associated with a 15.2% reduction in radiology utilization and a 13.3% reduction in laboratory utilization in the ED [22]. This suggests that EHRs may help to reduce unnecessary testing and improve the appropriateness of resource use in emergency care. The integration of clinical DSS within EHRs has had a significant impact on clinical decision-making in EM. These systems can provide real-time alerts and recommendations based on patient-specific data and evidence-based guidelines, potentially improving the diagnostic accuracy and treatment decisions. A systematic review by Kawamoto et al. found that CDSS significantly improved clinical practice in 68% of trials [13]. In the context of EM, a study by Drescher et al. demonstrated that an EHR-based clinical decision support system for pulmonary embolism improved the adherence to evidence-based diagnostic strategies and reduced unnecessary CT pulmonary angiography studies [23]. EHRs have greatly enhanced communication and care coordination in EM, both within the ED and across different healthcare settings. A study by Everson et al. found that hospitals with more advanced EHR capabilities had better performance in measures of care coordination, including the timely transmission of discharge summaries to outpatient providers [24]. In the ED setting, EHRs facilitate better handoffs between providers during shift changes, ensuring that critical patient information is accurately communicated. A study by Gonzalez et al. found that the implementation of an EHR-based handoff tool in an ED was associated with improved provider satisfaction and perceived handoff quality [25].

While EHRs have brought numerous benefits to EM, their implementation and use are not without challenges and limitations. The implementation of EHR systems in EDs can be complex and disruptive, often requiring significant changes to existing workflows and practices. A study by McAlearney et al. identified several challenges in EHR implementation, including resistance to change, inadequate training, and technical issues [26]. These challenges can lead to temporary decreases in efficiency and provider satisfaction during the implementation phase. The design and usability of EHR systems have been frequent sources of frustration for emergency physicians. A study by Ratwani et al. found significant usability issues in several widely used EHR systems, including difficulties in data entry, information retrieval, and alert fatigue [27]. These usability issues can lead to inefficiencies, errors, and decreased provider satisfaction.

There are concerns that EHR use in the ED may negatively impact provider–patient interactions. A study by Ratanawongsa et al. found that higher computer use by clinicians was associated with lower patient satisfaction scores [28]. Emergency physicians must balance the need for documentation and EHR use with maintaining eye contact and personal connection with patients. The digitization of health records has raised concerns about data privacy and security. EDs, with their high patient volumes and need for rapid access to information, may be particularly vulnerable to data breaches. A study by Kruse et al. identified several common threats to EHR security, including hacking, unauthorized access, and human errors [29].

Despite progress in health information exchange, interoperability remains a significant challenge for EHRs in EM. The inability to seamlessly share patient information across different healthcare systems can lead to incomplete medical histories, duplicate testing, and potential medical errors [30].

3.2. Future Directions and Emerging Trends

Interoperability and semantic integration represent fundamental challenges in the implementation and effective utilization of EHRs, constituting significant barriers in realizing their full potential in the contemporary healthcare landscape [31]. The heterogeneity of EHR systems, characterized by different architectures, coding standards, and data models, generates substantial fragmentation in the digital healthcare ecosystem [32]. This lack of standardization compromises the ability to effectively share and aggregate clinical data across different healthcare institutions, thus limiting continuity of care and the optimization of clinical decision-making processes [33]. Semantic integration, in particular, presents specific criticalities related to the uniform interpretation of medical terminology and consistent mapping of clinical concepts across different systems [34].

Concurrently, the issue of bias in artificial intelligence algorithms applied to EHRs raises significant concerns regarding the fairness and accuracy of AI-supported clinical decisions [35]. Biases can manifest through multiple mechanisms: from the under-representation of specific populations in training datasets to the presence of historical prejudices embedded in clinical data and to systematic distortions in data collection and documentation practices [36]. These biases can perpetuate and potentially amplify existing healthcare disparities, particularly affecting marginalized populations and minority groups [37].

The integration of diverse data sources and formats within EHR systems poses additional challenges related to data quality, completeness, and consistency [38]. The variability in documentation practices, coding systems, and clinical terminologies across different healthcare providers complicates the establishment of standardized data exchange protocols [39]. Moreover, legacy systems and proprietary data formats continue to impede seamless information flows, necessitating complex interface solutions and data transformation processes [40].

To address these challenges effectively, a multifaceted approach incorporating technical, organizational, and policy interventions is required [41]. This includes the development and adoption of robust interoperability standards, the implementation of sophisticated semantic mapping tools, and the establishment of governance frameworks for data sharing and AI deployment in healthcare [42]. Additionally, systematic efforts to identify and mitigate biases in healthcare AI systems must be integrated into the development and deployment lifecycle, including comprehensive validation protocols and the ongoing monitoring of algorithmic performance across diverse patient populations [43]. It is worth noting that, in addressing these challenges, active collaboration among stakeholders, including healthcare providers, technology vendors, researchers, and regulatory bodies, is required. Standardization efforts must balance the need for consistency with the flexibility required to accommodate evolving clinical practices and technological innovations. The successful resolution of these challenges will significantly impact the ability of healthcare systems to leverage EHRs and AI technologies effectively, ultimately improving patient care and outcomes and the efficiency of healthcare delivery [44].

As EHR technology continues to evolve, several emerging trends are shaping the application of electronic health records in EM. The integration of AI and machine learning algorithms into EHR systems holds great promise for EM. These technologies can analyze vast amounts of patient data to predict clinical outcomes, identify high-risk patients, and provide more sophisticated decision support [45]. For example, an AI-powered EHR system might be able to predict the likelihood of sepsis or acute coronary syndrome based on a patient’s presenting symptoms and historical data. Natural language processing (NLP) technologies are being developed to improve the efficiency of clinical documentation and information retrieval in EHRs. These systems can automatically extract relevant clinical information from free-text notes, potentially reducing the documentation burden on emergency physicians [46]. The trend towards mobile and cloud-based EHR solutions is particularly relevant for EM, where providers need access to patient information from various locations within and outside the hospital. Mobile EHR applications allow emergency physicians to access and update patient records using smartphones or tablets, potentially improving the efficiency and flexibility in care delivery [47].

There is a growing emphasis on patient engagement and access to health information. Future EHR systems in EM may include features that allow patients to access their ED visit summaries, follow-up instructions, and test results through patient portals or mobile applications [48]. The integration of data from wearable devices and Internet of Things (IoT) sensors into EHRs could provide emergency physicians with real-time, continuous patient data. This could be particularly valuable in monitoring patients with chronic conditions or those at risk of acute events [49]. Electronic health records have become an integral part of EM practice, transforming the way in which care is delivered in this critical healthcare setting. While EHRs have brought significant benefits in terms of patient safety, clinical decision-making, and operational efficiency, they also present challenges that need to be addressed. As EHR technology continues to evolve, emerging trends such as AI, natural language processing, and integration with wearable devices promise to further enhance the capabilities of these systems in emergency care.

The successful implementation and optimization of EHRs in EM require a multifaceted approach that addresses technological, organizational, and human factors. Continued research, user-centered design, and ongoing education and training for healthcare providers will be crucial in realizing the full potential of EHRs to improve emergency care delivery and patient outcomes.

4. Telemedicine in Emergency Medicine

Telemedicine refers to the remote delivery of healthcare services using telecommunications technology, enabling real-time interaction between patients and healthcare providers across geographical distances. Its application in EM has gained significant attention due to its ability to provide timely medical care, particularly in underserved areas and during critical situations where immediate access to specialists may not be possible. Telemedicine encompasses various technologies, including video conferencing, mobile health applications, and remote monitoring systems, which allow for the assessment, diagnosis, and treatment of patients without the need for physical proximity [50].

In recent years, the adoption of telemedicine in emergency settings has accelerated, driven by advancements in digital health technologies and the growing demand for efficient and scalable healthcare solutions. Survey studies have highlighted its benefits, such as improved access to care, reduced patient wait times, and enhanced coordination between healthcare providers [51]. Moreover, telemedicine has shown particular promise in rural and remote areas, where patients may face significant delays in receiving emergency care [52]. These studies underscore the transformative potential of telemedicine in EM, yet they also point out the need for robust informatics infrastructure to support its integration into routine practice. The role of informatics and AI in telemedicine is particularly significant. Informatics systems provide the backbone for telemedicine platforms, enabling real-time access to critical patient information, such as electronic health records (EHRs), imaging, and laboratory results. AI further enhances telemedicine’s capabilities by offering powerful tools for data analysis, predictive modeling, and clinical decision support. For example, AI-powered algorithms can assist healthcare providers in remotely diagnosing critical conditions, such as sepsis or myocardial infarction, by analyzing patient data in real time, thus facilitating timely and informed decision-making.

This section delves into the integration of informatics and AI within telemedicine for EM, examining the key advancements, challenges, and future prospects for these technologies in enhancing emergency care.

4.1. Telemedicine Platforms and Infrastructure

Telemedicine platforms serve as the backbone of remote emergency care, enabling real-time communication between patients, emergency medical services (EMS) personnel, and healthcare providers. These platforms have evolved significantly in recent years, incorporating advanced features that enhance the quality and efficiency of emergency care delivery. One of the primary components of telemedicine infrastructure is high-quality video conferencing systems. These systems allow for the visual assessment of patients, which is crucial in emergency scenarios where physical examination is not possible [53]. Advanced video conferencing tools now incorporate features such as zoom capabilities, high-definition imaging, and multi-party conferencing, enabling more accurate remote diagnoses and facilitating collaboration among healthcare professionals [54].

In addition to video conferencing, modern telemedicine platforms integrate various data streams to provide a comprehensive view of a patient’s condition. This includes real-time vital sign monitoring, electronic health record (EHR) access, and integration with portable diagnostic devices [55]. For instance, wearable devices and IoT sensors can transmit critical patient data such as heart rate, blood pressure, and oxygen saturation levels directly to the telemedicine platform, allowing for continuous monitoring and the early detection of deterioration [56]. The effectiveness of telemedicine platforms in emergency care largely depends on the reliability and speed of the underlying network infrastructure. With the advent of 5G technology, telemedicine applications are poised to benefit from ultra-low latency and high-bandwidth connections, enabling the seamless transmission of large data files, such as medical imaging, and supporting real-time video consultations without lags or interruptions [57].

Interoperability remains a critical challenge in telemedicine infrastructure. To address this, efforts are underway to develop standardized protocols and APIs that facilitate seamless data exchange between different telemedicine systems, EHRs, and healthcare devices [58]. Initiatives such as the Fast Healthcare Interoperability Resources (FHIR) standard are playing a crucial role in promoting interoperability and enabling more efficient information sharing in emergency telemedicine settings [59]. Security and privacy considerations are paramount in telemedicine infrastructure design. As the volume of sensitive patient data transmitted through these platforms increases, robust encryption methods, secure authentication mechanisms, and comprehensive audit trails have become essential components of telemedicine systems [60]. Compliance with regulations such as HIPAA in the United States and GDPR in Europe is driving the development of more secure and privacy-preserving telemedicine solutions [61].

4.2. AI-Powered Diagnostic and Decision Support Systems

AI has emerged as a game-changer in emergency telemedicine, offering powerful tools for diagnosis, triage, and clinical decision support. These AI-driven systems are designed to augment human expertise, improve accuracy, and expedite care delivery in time-critical situations.

One of the most promising applications of AI in emergency telemedicine is in image analysis and interpretation. Deep learning algorithms, particularly convolutional neural networks (CNNs), could demonstrate remarkable accuracy in analyzing medical imaging data such as X-rays, and CT scans. In the context of EM, these AI systems can rapidly detect critical conditions such as intracranial hemorrhages, pulmonary embolisms, or fractures, potentially reducing diagnostic errors and improving patient outcomes [62].

Natural language processing (NLP) is another area where AI is making significant contributions to emergency telemedicine. NLP algorithms can analyze unstructured clinical notes, patient complaints, and medical histories to extract relevant information and identify potential red flags [63]. This capability is particularly valuable in telemedicine triage systems, where AI-powered chatbots or virtual assistants can conduct initial patient assessments, prioritize cases based on severity, and route patients to appropriate care pathways [64].

AI-driven CDSS are increasingly being integrated into telemedicine platforms to assist healthcare providers in making informed decisions. These systems analyze vast amounts of patient data, including vital signs, lab results, and medical histories, to generate personalized treatment recommendations and alert clinicians to potential risks or contraindications [65]. In emergency settings, where time is critical and information may be limited, AI-powered CDSS can help healthcare providers to make more accurate and timely decisions [66].

Predictive analytics is another area where AI is making significant contributions in emergency telemedicine. Machine learning models trained on large datasets can predict patient outcomes, identify individuals at high risk of deterioration, and forecast resource needs [67]. For example, AI algorithms have been developed to predict the likelihood of cardiac arrest, sepsis, or other critical conditions based on real-time patient data, enabling proactive interventions and potentially saving lives [68].

Despite the promising advancements in AI-powered diagnostics and DSS, several challenges remain. One significant concern is the potential for bias in AI algorithms, which can lead to disparities in care delivery [69]. Efforts are underway to develop more transparent and explainable AI models, as well as to ensure diverse and representative training datasets to mitigate biases [70]. Another challenge is the integration of AI systems into existing clinical workflows. While AI tools have the potential to improve efficiency and accuracy, they must be designed and implemented in a way that complements rather than disrupts established practices [71]. User-centered design approaches and ongoing collaboration between AI developers and healthcare professionals are essential to ensure the successful adoption of these technologies in emergency telemedicine settings.

4.3. Remote Patient Monitoring and Wearable Technologies

The proliferation of wearable devices and Internet of Things (IoT) technologies has opened up new possibilities for remote patient monitoring in EM. These technologies enable the continuous tracking of vital signs and other health parameters, providing valuable data for the early detection of emergencies and ongoing management of chronic conditions. Wearable devices such as smartwatches, fitness trackers, and specialized medical wearables can monitor a wide range of physiological parameters, including the heart rate, blood pressure, respiratory rate, and blood glucose levels [72]. In the context of emergency telemedicine, these devices serve as an extension of the healthcare provider’s reach, allowing for the real-time monitoring of patients in their homes or other remote locations [73]. Advanced wearable technologies are now capable of detecting specific emergency conditions. For example, some smartwatches can identify atrial fibrillation and alert users to potential cardiac issues [74]. Similarly, wearable devices equipped with fall detection algorithms can automatically notify emergency services if a fall is detected, which is particularly valuable for elderly patients or those with mobility issues [75]. The integration of wearable devices with telemedicine platforms allows for more comprehensive and context-aware emergency care. For instance, when a patient initiates a telemedicine consultation due to chest pain, the healthcare provider can instantly access real-time and historical data from the patient’s wearable device, providing valuable insights into the patient’s heart rate, activity levels, and other relevant parameters [76]. IoT-enabled home monitoring systems are extending the capabilities of remote patient monitoring beyond wearable devices. These systems can include a variety of sensors and devices, such as smart pill dispensers, connected blood pressure cuffs, and environmental sensors, to create a comprehensive picture of a patient’s health and living conditions [77]. In emergency scenarios, these systems can provide crucial information about the patient’s environment and recent activities, aiding in diagnosis and treatment decisions. The vast amount of data generated by wearable devices and IoT sensors presents both opportunities and challenges for emergency telemedicine. On one hand, this wealth of data can provide unprecedented insights into patient health and enable more personalized and proactive care [78]. On the other hand, managing and interpreting these data requires sophisticated analytics tools and AI algorithms to extract meaningful insights and avoid information overload for healthcare providers [79].

Privacy and security concerns are particularly acute in the realm of remote patient monitoring, given the sensitive nature of the data collected and the potential vulnerabilities in IoT devices [80]. Ensuring end-to-end encryption, implementing robust authentication mechanisms, and providing patients with granular control over their data-sharing preferences are essential considerations in the design of remote monitoring systems for emergency telemedicine [81]. The reliability and accuracy of wearable devices and IoT sensors remain ongoing challenges. False alarms can lead to unnecessary anxiety for patients and potentially overwhelm emergency services [82]. Conversely, missed detections of critical events could have severe consequences. Ongoing research is focused on improving the sensitivity and specificity of these devices through advanced signal processing techniques and machine learning algorithms [83]. As remote patient monitoring technologies continue to evolve, their integration with other emerging technologies, such as 5G networks and edge computing, is expected to further enhance their capabilities. These advances will enable more sophisticated real-time analytics, reduced latency in data transmission, and improved reliability, ultimately leading to more effective emergency telemedicine services [84].

In conclusion, the integration of informatics and AI in telemedicine has significantly enhanced the capabilities of EM, enabling more efficient, accurate, and accessible care delivery. Telemedicine platforms provide the infrastructure for remote consultations and data exchange, while AI-powered diagnostics and DSS augment clinical decision-making. Remote patient monitoring technologies extend the reach of healthcare providers, enabling continuous surveillance and the early detection of emergencies. However, challenges remain in areas such as interoperability, privacy and security, and the seamless integration of AI into clinical workflows. Addressing these challenges will require ongoing collaboration between technologists, healthcare professionals, and policymakers. As these technologies continue to evolve, they hold the promise of further transforming EM, improving patient outcomes, and increasing access to high-quality emergency care, particularly in underserved and remote areas.

5. Applications of Artificial Intelligence in Emergency Medicine

The field of EM is characterized by its fast-paced, high-stakes environment, where rapid decision-making is crucial. In recent years, AI has emerged as a powerful tool to support emergency care providers in various aspects of their work. AI technologies, including machine learning, deep learning, and natural language processing, are being applied to address challenges in diagnosis, treatment, resource management, and overall patient care in EDs [85].

The potential of AI in EM is vast, ranging from enhancing clinical decision-making to optimizing the operational efficiency. As the volume of medical data continues to grow exponentially, AI offers the promise of harnessing this information to improve patient outcomes, reduce medical errors, and streamline ED workflows [86]. This section explores the various applications of AI in EM, discussing the current implementations, challenges, and future prospects.

5.1. AI in Clinical Decision Support

One of the most promising applications of AI in EM is in the realm of clinical decision support. AI-powered systems can analyze vast amounts of patient data, including medical histories, symptoms, vital signs, and laboratory results, to assist emergency physicians in making accurate diagnoses and treatment decisions.

AI algorithms have shown significant potential in improving the triage process in EDs. Machine learning models can analyze patient data to predict the severity of a patient’s condition and prioritize care accordingly. For instance, Levin et al. developed a machine learning-based triage system that outperformed traditional triage methods in predicting patient outcomes and resource needs [87]. The system analyzed various patient parameters, including vital signs, chief complaints, and medical histories, to assign more accurate triage scores. Similarly, AI models have been developed to predict patient deterioration and identify high-risk patients in the ED. A study by Goto et al. demonstrated the effectiveness of a deep learning model in predicting critical care admissions from the ED [88]. The model, which analyzed real-time vital signs and laboratory data, showed superior performance compared to traditional scoring systems in identifying patients at risk of requiring intensive care.

AI systems are increasingly being used to assist emergency physicians in making accurate diagnoses, particularly for complex or rare conditions. These systems can analyze patients’ symptoms, medical histories, and test results to generate differential diagnoses and suggest appropriate diagnostic tests. For example, an AI system developed by Fauw et al. demonstrated the ability to diagnose over 50 eye diseases from retinal scans, with accuracy comparable to that of expert ophthalmologists [89]. Such systems could be particularly valuable in emergency settings, where specialist expertise may not be immediately available. In the context of acute conditions, AI has shown promise in the early detection of time-sensitive emergencies. A deep learning algorithm developed by Hannun et al. was able to detect atrial fibrillation from single-lead ECG data with greater accuracy than cardiologists [90]. This technology could potentially expedite the diagnosis and treatment of cardiac arrhythmias in the ED. AI systems can also provide treatment recommendations based on the latest clinical guidelines and patient-specific factors. These systems can help emergency physicians to navigate complex treatment protocols and avoid potential medication errors. A study by Kang et al. demonstrated the effectiveness of an AI-powered clinical decision support system in improving antibiotic prescribing practices in the ED [91]. The system provided personalized antibiotic recommendations based on local antimicrobial resistance patterns and patient characteristics, leading to more appropriate antibiotic use and improved patient outcomes.

EDs rely heavily on medical imaging for the rapid diagnosis of various conditions. AI technologies, particularly deep learning algorithms, have shown remarkable capabilities in analyzing medical images, potentially expediting the diagnostic process and improving the accuracy. AI algorithms have been developed to interpret various types of medical images, including X-rays, CT scans, and MRI. These systems can assist radiologists in identifying abnormalities and prioritizing urgent cases. In the context of neurological emergencies, AI has shown promise in the early detection of intracranial hemorrhages on CT scans. A study by Chilamkurthy et al. demonstrated the effectiveness of a deep learning algorithm in detecting critical findings on head CT scans, including intracranial hemorrhages and mass effects [92]. The algorithm’s performance was comparable to that of neuroradiologists, suggesting its potential to expedite the diagnosis of life-threatening neurological conditions in the ED. AI can also be used to triage imaging studies, ensuring that the most urgent cases are prioritized for radiologist review. This approach can be particularly useful in managing large volumes of imaging studies in busy EDs. A study by Annarumma et al. demonstrated the effectiveness of a deep learning system in triaging chest X-rays in the ED [93]. The system was able to accurately identify normal chest X-rays, allowing radiologists to focus their attention on abnormal cases and reducing the overall reporting times. Beyond clinical applications, AI is being utilized to optimize ED operations and resource management, addressing challenges such as overcrowding, long wait times, and resource allocation.

AI models can analyze historical data and real-time inputs to predict ED patient volumes and optimize staffing levels. This function can help EDs to better prepare for surges in patient arrivals and allocate resources more effectively. A study by Jiang et al. developed a machine learning model to predict hourly ED visit volumes with high accuracy [94]. The model incorporated various factors, including temporal patterns, weather conditions, and local events, to generate accurate forecasts that could inform staffing decisions and resource allocation. Predicting patients’ length of stay (LOS) is crucial for efficient bed management and patient flow in the ED. AI models have shown promise in accurately estimating the LOS based on patient characteristics and ED conditions. For example, Lucini et al. developed a machine learning model that outperformed traditional statistical methods in predicting the ED LOS [95]. The model considered various factors, including patient demographics, chief complaints, and ED crowding metrics, to generate accurate LOS predictions. AI can assist in optimizing the use of ED resources, including staff, equipment, and beds. Machine learning algorithms can analyze patterns in resource utilization to suggest more efficient allocation strategies. A study by Peck et al. demonstrated the use of machine learning to optimize ED physician scheduling, reducing wait times and improving the patient flow [96]. The algorithm considered factors such as historical patient arrival patterns, physician productivity, and patient acuity to generate optimized scheduling recommendations.

5.2. AI in Emergency Medical Services

The application of AI extends beyond the hospital ED to pre-hospital emergency medical services (EMS). AI technologies are being explored to enhance decision-making and patient care in the field. AI algorithms can assist in optimizing EMS dispatch by predicting the likelihood of various emergency scenarios and suggesting the most appropriate resource allocation. A study by Naoum-Sawaya et al. developed a machine learning model to predict the severity of emergency calls and optimize ambulance dispatches [97]. The model analyzed historical data and call characteristics to prioritize dispatches and improve the response times for high-acuity cases.

AI-powered decision support tools can assist paramedics and emergency medical technicians in assessing patients and making treatment decisions in the field. For instance, Blomberg et al. developed a machine learning model to predict the need for critical care interventions during air medical transport [98]. The model analyzed patient vital signs and other clinical data to identify high-risk patients who might require advanced interventions during transport. Natural language processing (NLP) techniques are being applied to various aspects of EM, from analyzing clinical notes to improving documentation processes. NLP algorithms can extract valuable information from unstructured clinical notes, potentially uncovering important patterns or risk factors that might be missed in manual review. A study by Horng et al. demonstrated the use of NLP to analyze ED triage notes for the early identification of patients with severe sepsis [99]. The system showed high sensitivity in detecting sepsis cases, potentially enabling earlier intervention and improved outcomes.

AI-powered systems are being developed to assist in clinical documentation, potentially reducing the administrative burden on emergency physicians and improving the quality of medical records. For example, Klann et al. developed an NLP system to automatically generate clinical notes from doctor–patient conversations in the ED [100]. While still in the early stages, such technologies could significantly streamline the documentation process and allow physicians to focus more on patient care. While the potential of AI in EM is significant, there are several challenges and ethical considerations that need to be addressed. The performance of AI algorithms is heavily dependent on the quality and representativeness of the data used for training. Biases in training data can lead to algorithmic biases, potentially exacerbating healthcare disparities [69].

5.3. Interpretability, Explainability, and Future Directions

Many AI algorithms, particularly deep learning models, operate as ‘black boxes’, making it difficult to understand how they arrive at their conclusions. This lack of interpretability can be problematic in healthcare settings, where the rationale behind decisions is crucial [86]. The successful implementation of AI in EM requires seamless integration with existing clinical workflows. Poorly designed AI systems can add to the cognitive burden of healthcare providers, rather than alleviating it [65]. The use of AI in healthcare also raises important ethical and legal questions, including issues of patient privacy, informed consent, and liability in case of errors [43].

The future of AI in EM holds exciting possibilities. Future AI systems may integrate data from multiple sources, including clinical notes, imaging studies, and real-time physiological monitoring, to provide more comprehensive and accurate clinical decision support [101]. AI could enable more personalized approaches to emergency care, taking into account individual patient characteristics, genetic factors, and social determinants of health to tailor treatment strategies [102]. The development of AI systems that can continuously learn and adapt based on new data and feedback could lead to more robust and up-to-date clinical decision support tools [103]. AI has the potential to revolutionize EM, enhancing clinical decision-making, improving the operational efficiency, and ultimately leading to better patient outcomes.

From triage and diagnosis to resource management and documentation, AI applications are being explored across various aspects of emergency care. However, the successful integration of AI into EM practice requires the careful consideration of technical, ethical, and practical challenges. As AI technologies continue to evolve, ongoing research, interdisciplinary collaboration, and thoughtful implementation strategies will be crucial in realizing the full potential of AI to improve emergency care delivery. The future of AI in EM is promising, with the potential for more sophisticated, integrated, and personalized approaches to emergency care. As we move forward, it will be essential to balance the enthusiasm for these new technologies with the rigorous evaluation of their effectiveness and careful consideration of their ethical implications.

6. Mobile Health Technologies

Mobile health (mHealth) technologies have emerged as a transformative force in healthcare delivery, leveraging the ubiquity of smartphones and wearable devices to provide accessible, personalized, and real-time health services [104]. These technologies encompass a wide range of applications, from simple text message-based interventions to sophisticated AI-powered diagnostic tools and remote monitoring systems [105].

The scope of mHealth includes, but is not limited to, $\left(i\right)$ health-related mobile applications (apps); $\left(ii\right)$ wearable devices for fitness and health monitoring; $\left(iii\right)$ telemedicine platforms; $\left(iv\right)$ remote patient monitoring systems; $\left(v\right)$ health-related text messaging services; and $\left(vi\right)$ mobile-based health information systems. mHealth technologies have shown promise in various healthcare domains, including chronic disease management, mental health support, health education, and public health surveillance [106]. They offer the potential to extend healthcare access to underserved populations, reduce healthcare costs, and improve health outcomes through continuous monitoring and early intervention [107]. Several comprehensive surveys have explored the landscape of mHealth technologies. For instance, Ali et al. [108] provided an extensive review of mHealth apps and systems, focusing on their architectures, frameworks, and impacts on healthcare delivery. Silva et al. [109] conducted a systematic review of mobile health applications, analyzing their purposes, strategies, and outcomes. More recently, Sim [110] examined the intersection of mHealth and AI, highlighting the potential of AI to enhance mHealth interventions.

Building upon these existing surveys, this section delves into the specific roles of informatics and AI in advancing mHealth technologies. We explore how these technological paradigms are shaping the future of mobile health, enhancing its capabilities, and addressing its challenges.

6.1. Data Collection and Management in mHealth

The foundation of effective mHealth solutions lies in robust data collection and management systems. Informatics plays a crucial role in this domain, enabling the capture, storage, retrieval, and analysis of vast amounts of health-related data from mobile devices and wearable sensors. One of the primary challenges in mHealth data collection is ensuring data quality and reliability. Mobile devices and wearable sensors can generate continuous streams of data, but these data may be noisy, incomplete, or inconsistent [111]. Informatics solutions address this challenge through advanced data preprocessing techniques, including noise reduction algorithms, data imputation methods for the handling of missing values, and data fusion approaches for the integration of multiple data streams [83]. Moreover, the heterogeneity of the data sources in mHealth necessitates sophisticated data integration strategies. Health-related data may come from various sources, including wearable sensors, smartphone apps, electronic health records (EHRs), and patient-reported outcomes. Informatics approaches, such as ontology-based data integration and semantic interoperability frameworks, enable the meaningful combination of these diverse data types, providing a comprehensive view of an individual’s health status [79].

Data security and privacy are paramount concerns in mHealth, given the sensitive nature of health information. Informatics solutions play a critical role in implementing robust security measures, including end-to-end encryption, secure authentication mechanisms, and privacy-preserving data sharing protocols [112,113]. Additionally, blockchain technology has emerged as a promising approach in ensuring data integrity and enabling secure, decentralized health data management in mHealth applications [114]. The volume and velocity of the data generated by mHealth technologies also necessitate efficient data storage and retrieval systems.

Cloud-based storage solutions have become increasingly popular in mHealth, offering scalability, accessibility, and cost-effectiveness [115]. However, the use of cloud storage raises additional security and privacy concerns, prompting the development of advanced encryption techniques and access control mechanisms specifically designed for cloud-based health data [116]. Furthermore, edge computing paradigms are gaining traction in mHealth, allowing data processing and analysis to occur closer to the data source (i.e., on the mobile device or wearable sensor itself) [117]. This approach can reduce the latency, conserve the bandwidth, and enhance privacy by minimizing the amount of raw data transmitted to central servers.

As mHealth technologies continue to evolve, the role of informatics in data collection and management will become increasingly critical. Future developments in this area may include the integration of 5G networks for high-speed, low-latency data transmission; the use of federated learning approaches for privacy-preserving distributed data analysis; and the development of more sophisticated data quality assessment and improvement algorithms tailored to the unique characteristics of mHealth data.

6.2. AI-Powered Analytics and Decision Support in mHealth

AI has emerged as a game-changer in mHealth, enabling sophisticated data analytics and intelligent DSS. Machine learning (ML) algorithms, in particular, have demonstrated remarkable capabilities in extracting meaningful insights from the vast amounts of data generated by mobile health technologies. One of the primary applications of AI in mHealth is in predictive analytics. ML models trained on large-scale mHealth datasets can forecast various health outcomes, identify individuals at high risk of developing certain conditions, and predict treatment responses [118]. For instance, Alaa et al. [119] developed a machine learning model that uses data from wearable devices to predict the onset of cardiovascular diseases with high accuracy.

Natural language processing (NLP) techniques have found notable application in mHealth, particularly in the analysis of unstructured data such as patient-reported symptoms, chat interactions with health chatbots, and social media posts related to health issues [86]. NLP algorithms can extract relevant information from these textual sources, enabling sentiment analysis, symptom categorization, and the early detection of mental health issues [120].

Computer vision techniques, coupled with the high-quality cameras in modern smartphones, have opened up new possibilities for mobile-based diagnostic tools. AI-powered image analysis algorithms can assist in the detection and classification of various medical conditions, from skin lesions to eye diseases [121]. For example, Gulshan et al. [122] developed a deep learning algorithm for the detection of diabetic retinopathy using retinal photographs, which can be captured using smartphone-based retinal imaging devices. AI is also enhancing the capabilities of virtual health assistants and chatbots in mHealth applications. These AI-powered conversational agents can provide personalized health advice, medication reminders, and mental health support [123]. Advanced natural language understanding and generation techniques enable these virtual assistants to engage in more natural and context-aware interactions with users.

In the realm of personalized medicine, AI algorithms are being employed to tailor health interventions and recommendations based on an individual’s unique characteristics, including their genetic profile, lifestyle factors, and real-time health data collected through mobile devices [124]. This approach promises to enhance the efficacy of mHealth interventions by providing highly targeted and timely support. However, the integration of AI in mHealth also presents several challenges. One significant concern is the potential for bias in AI algorithms, which can lead to disparities in care delivery [69]. Efforts are underway to develop more transparent and explainable AI models, as well as to ensure diverse and representative training datasets to mitigate bias in mHealth applications. Another challenge lies in the interpretability of complex AI models, particularly deep learning algorithms. While these models can achieve high accuracy, their decision-making processes are often opaque, which can be problematic in healthcare settings, where transparency is crucial [125]. Research is ongoing to develop more interpretable AI models and techniques for the explanation of AI-driven decisions in mHealth applications.

As AI continues to advance, we can expect to see even more sophisticated applications in mHealth. Future developments may include the integration of reinforcement learning for adaptive health interventions, the use of federated learning techniques for privacy-preserving collaborative model training across multiple mobile devices, and the development of more robust and generalizable AI models capable of handling the diverse and dynamic nature of mHealth data.

6.3. User Experience and Behavior Change in AI-Driven mHealth

The success of mHealth technologies ultimately depends on user engagement and their ability to facilitate positive behavior change. Informatics and AI play crucial roles in enhancing the user experience and promoting the sustained use of mHealth applications. User interface (UI) and user experience (UX) design are critical components of mHealth applications. Informatics principles guide the development of intuitive, accessible, and engaging interfaces that cater to diverse user populations, including those with limited technological literacy or disabilities [126]. AI-driven personalization can further enhance the user experience by adapting the interface and content to individual user preferences, habits, and health goals [127]. Gamification, i.e., the application of game design elements in non-game contexts, has emerged as a powerful strategy to promote engagement with mHealth applications [128]. AI algorithms can optimize gamification elements by analyzing user behavior and preferences, dynamically adjusting challenges and rewards to maintain user interest and motivation [129]. Behavior change techniques are at the heart of many mHealth interventions. AI-powered systems can deliver personalized behavior change strategies based on established psychological theories and real-time user data [127]. For instance, just-in-time adaptive interventions (JITAIs) use machine learning algorithms to determine the optimal timing, content, and delivery modes of health interventions based on the user’s current context and state [127].

Natural language generation (NLG) techniques are being employed to create more engaging and personalized health communications in mHealth applications. These AI-driven systems can generate tailored health messages, progress reports, and motivational content that resonate with individual users, potentially increasing the effectiveness of health interventions [130]. Sentiment analysis and emotion recognition techniques, powered by AI, enable mHealth applications to be more empathetic and responsive to users’ emotional states. This capability is particularly valuable in mental health applications, where understanding and responding to the user’s emotional context is crucial [131]. However, the use of AI in shaping the user experience and behavior also raises ethical concerns. The potential for manipulation and the blurring of the lines between persuasion and coercion need careful consideration. Transparency about the use of AI in mHealth applications and giving users control over their data and the level of AI-driven personalization are important steps in addressing these concerns. Moreover, ensuring the accessibility and inclusivity of AI-driven mHealth technologies across diverse populations remains a challenge. Factors such as a user’s age, cultural background, socioeconomic status, and technological literacy can significantly impact their engagement with mHealth applications. Developing culturally sensitive and adaptable AI systems that can cater to diverse user needs is an ongoing area of research.

As the field of AI continues to evolve, we can anticipate more sophisticated approaches to enhancing the user experience and promoting behavior change in mHealth. Future developments may include more advanced emotion AI capable of detecting and responding to subtle emotional cues, the integration of virtual and augmented reality for immersive health interventions, and the development of AI systems that can adapt to long-term changes in users’ behavior and health statuses.

In conclusion, the integration of informatics and AI in mobile health technologies is reshaping the landscape of healthcare delivery. From sophisticated data management systems to AI-powered analytics and personalized interventions, these technologies offer unprecedented opportunities to improve health outcomes and patient engagement. However, realizing the full potential of AI-driven mHealth requires ongoing research to address challenges related to data quality, algorithm bias, privacy, and ethical considerations. As these technologies continue to evolve, they hold the promise of more accessible, personalized, and effective healthcare solutions delivered through the ubiquitous medium of mobile devices.

7. Conclusions and Future Work

This survey has provided a comprehensive overview of the recent trends and applications of informatics in EM. We have explored the transformative impact of EHRs, telemedicine, AI, and mobile health technologies on EM practices. The integration of these informatics solutions has significantly enhanced clinical decision-making, patient management, and overall healthcare delivery in emergency settings.

To answer our research questions (RQs), we can affirm that informatics has revolutionized emergency room workflows (RQ1) through real-time patient tracking systems, automated triage protocols, and integrated vital sign monitoring. Digital documentation has reduced paperwork times and enabled faster access to patient histories. Computer-assisted clinical decision support helps to prioritize cases and suggest treatment protocols. The main advantages of EHRs (RQ2) include instant access to patient histories, improved care coordination, and reduced medical errors. However, there are drawbacks, such as risks from system downtime, the initial implementation costs, and potential workflow disruptions due to documentation requirements. Some clinicians report an increased cognitive load and time spent on data entry. Successful DSS in emergency care (RQ3) include real-time alerting systems for critical lab values, automated sepsis detection protocols, and triage decision support. Systems that integrate well with existing workflows and provide clear, actionable recommendations have shown the best adoption rates. The main obstacles to AI adoption (RQ4) are data quality and standardization issues, a lack of real-world validation, and integration challenges with existing systems. There are also concerns about liability and over-reliance on automated systems among healthcare workers. Predictive analytics (RQ5) helps to forecast patient arrival patterns based on historical data, optimize staff scheduling, and identify potential surge periods, allowing proactive resource allocation and staffing adjustments. Telemedicine has enabled remote specialist consultations, emergency triage support for rural facilities, and post-discharge patient monitoring (RQ6). It is particularly valuable for initial assessments and follow-up care in areas with limited medical resources. Data interoperability challenges can be addressed by adopting standardized formats like HL7 FHIR, implementing robust health information exchanges, and developing universal patient identifiers (RQ7). Technical standards and regulatory frameworks must align across organizations. Emergency rooms protect data through multi-factor authentication, role-based access controls, and encrypted data transmission (RQ8). Regular security audits, staff training on privacy protocols, and secure mobile device management are essential components of data protection strategies. Expected AI developments (RQ9) include advanced image recognition for faster diagnostics, improved natural language processing for clinical documentation, and more sophisticated predictive models for patient outcomes. Real-time monitoring integrated with AI-driven decision support is likely to become more prevalent. Research shows that AI and informatics have improved the triage accuracy, reduced wait times, and decreased medical errors (RQ10). Studies indicate better outcomes in specific conditions, such as sepsis detection and stroke management, where early intervention is crucial. Notable case studies include Imperial College Healthcare’s implementation of AI-driven sepsis detection, Mount Sinai’s use of AI for COVID-19 patient risk stratification, and Johns Hopkins’ AI-powered capacity management system. AI optimizes resources through predictive maintenance scheduling, automated inventory management, and dynamic bed allocation based on patient acuity and the predicted length of stay (RQ11). Machine learning algorithms can forecast equipment needs and suggest optimal distributions. Studies have shown improved diagnostic accuracy in radiology image interpretation, ECG analysis for cardiac conditions, and early sepsis detection (RQ12). AI systems have demonstrated particular success in pattern recognition and anomaly detection in medical imaging. Long-term effects (RQ13) are likely to include more personalized treatment protocols, the increased automation of routine tasks, and the better integration of pre-hospital and emergency care. We can expect more precise risk stratification and automated decision support to become standard practice. The main obstacles to AI implementation (RQ14) include high costs, resistance to change from healthcare workers, and the lack of standardized validation protocols. Solutions involve better training programs, phased implementation approaches, and the development of clear regulatory frameworks for AI in healthcare.

While the advancements in informatics have significantly improved emergency care, several challenges remain. Data privacy and security concerns, particularly in the context of increased data sharing and cloud-based solutions, need to be addressed. There is also a need for more robust evaluation methodologies to assess the impact of informatics interventions on clinical outcomes, cost-effectiveness, and patient satisfaction in EM. Thus, the field of informatics in EM is rapidly evolving, offering immense potential for improved patient care. Future research should focus on addressing the identified challenges, developing more integrated and interoperable solutions, and exploring emerging technologies such as augmented reality, blockchain for secure health data management, and advanced natural language processing for more efficient clinical documentation. Collaborative efforts between clinicians, computer scientists, and technology developers will be crucial in realizing the full potential of informatics in EM and ultimately enhancing patient outcomes in acute care settings.

Future research should focus on improving the integration and interoperability of different systems, addressing the limitations of AI, and ensuring equitable access to these technologies across diverse populations. Future challenges include the need for the continuous updating and validation of AI models to account for evolving medical knowledge and practices. The integration of AI into existing clinical workflows without disrupting efficiency or overburdening healthcare providers will require careful implementation strategies. Furthermore, addressing the ethical implications of AI in emergency decision-making, particularly in triage situations, will necessitate an ongoing dialog between clinicians, ethicists, policymakers, and AI researchers to establish clear guidelines and protocols.