Bigger Than a Hen's Egg: A Case of Bouveret Syndrome.

Bouveret syndrome, a rare complication of cholelithiasis resulting in gallstone ileus, presents diagnostic and therapeutic challenges due to its low incidence and nonspecific symptoms. We report a case of Bouveret syndrome in a middle-aged male without significant medical history, emphasizing the need for heightened clinical suspicion. Diagnostic imaging, including computed tomography and upper endoscopy, revealed gastric outlet obstruction and a cholecystoduodenal fistula. Treatment involved unsuccessful endoscopic lithotripsy followed by surgical intervention. This case underscores the importance of interdisciplinary collaboration for successful management. With no standardized approach, individualized treatment strategies, including endoscopic and surgical interventions, are crucial for favorable outcomes in Bouveret syndrome.

Duodenal Carcinosarcoma: An Apple-Core Lesion Causing Gastric Outlet Obstruction.

We present a case of a 58-year-old male with a rare duodenal carcinosarcoma causing gastric outlet obstruction. Despite its aggressive nature and poor prognosis, with only 12 documented cases in the literature, this report sheds light on the clinical presentation and challenges in diagnosis and treatment. Carcinosarcoma, characterized by both carcinomatous and sarcomatous elements, poses difficulties in management due to its diverse tissue characteristics. Surgical resection remains the primary treatment, although the prognosis remains grim, emphasizing the need for further research into advanced therapeutic strategies to improve patient outcomes. This case underscores the rarity and clinical complexities associated with duodenal carcinosarcomas.

The Hepatic Imposter: Solitary Necrotic Nodules Masquerading as Hepatic Metastasis.

Solitary necrotic nodules in the liver present a diagnostic challenge due to their rarity and resemblance to metastatic tumors. We report a case where imaging revealed multiple hepatic lesions suggestive of malignancy, prompting a needle biopsy. Histopathology confirmed necrosis without malignancy. Despite advancements in imaging modalities, distinguishing solitary necrotic nodules from metastases remains difficult. Recognition of characteristic imaging features and consideration of biopsy are crucial for accurate diagnosis and management. This case underscores the importance of thorough evaluation and differential diagnosis in liver lesions to prevent unnecessary surgical interventions and ensure appropriate clinical care.

Post-transplant lymphoproliferative disorder: Update on treatment and novel therapies.

Post-transplant lymphoproliferative disorder (PTLD) is rare and heterogeneous lymphoid proliferations that occur as a result of immunosuppression following solid organ transplant (SOT) and haematopoietic stem cell transplant (HSCT) with the majority being driven by EBV. Although some histologies are similar to lymphoid neoplasms seen in immunocompetent patients, treatment of PTLD may be different due to difference in pathobiology and higher risk of treatment complications. The most common treatment approach in SOT PTLD after failing immunosuppression reduction (RIS) takes into consideration a risk-stratified sequential algorithm with rituximab +/- chemotherapy based on phase 2 studies. In HSCT PTLD, RIS alone and chemotherapy are usually ineffective making rituximab +/- RIS as the gold standard of frontline treatment. In this review, we give an update on the treatment of PTLD beyond RIS. We highlight the most recent studies that attempted to incorporate more aggressive chemotherapy regimens and novel treatments into the traditional risk-stratified sequential approach. We also discuss the role of EBV-cytotoxic T lymphocytes in treatment of EBV-driven PTLD. Other novel agents with potential role in PTLD will be discussed in addition to the challenges that could arise with chimeric antigen receptor T-cell therapy and immune checkpoint inhibitors in this population.

CDC's COVID-19 International Vaccine Implementation and Evaluation Program and Lessons from Earlier Vaccine Introductions.

The US Centers for Disease Control and Prevention (CDC) supports international partners in introducing vaccines, including those against SARS-CoV-2 virus. CDC contributes to the development of global technical tools, guidance, and policy for COVID-19 vaccination and has established its COVID-19 International Vaccine Implementation and Evaluation (CIVIE) program. CIVIE supports ministries of health and their partner organizations in developing or strengthening their national capacities for the planning, implementation, and evaluation of COVID-19 vaccination programs. CIVIE's 7 priority areas for country-specific technical assistance are vaccine policy development, program planning, vaccine confidence and demand, data management and use, workforce development, vaccine safety, and evaluation. We discuss CDC's work on global COVID-19 vaccine implementation, including priorities, challenges, opportunities, and applicable lessons learned from prior experiences with Ebola, influenza, and meningococcal serogroup A conjugate vaccine introductions.

Knowledge, attitudes, and self-reported practices (KAP) towards hand hygiene in medical students versus the public.

BACKGROUND: The COVID-19 pandemic and its associated morbidity, mortality, and economic disruption has reignited interest in simple protective and preventive measures. AIMS: The aim of this study was to assess the knowledge, attitudes, and practices (KAP) of hand hygiene in a sample of medical students in Ireland and members of the public to evaluate these within the context of the COVID-19 pandemic. We also explored any differences between the two groups. METHODS: A 35-question survey was formulated and circulated to potential participants comprising Irish medical students and members of the public. The data was analysed using Microsoft Excel with P-values being calculated using chi-squared goodness-of-fit analysis. RESULTS: There were 356 responses to the survey, categorised into medical students and general public populations. Incomplete surveys were removed leaving 303 responses. There was no statistical difference between the groups for attitudes and self-reported practices towards hand hygiene. Statistical differences were found between the two groups in terms of knowledge. CONCLUSIONS: The study showed that medical students and the public had a good knowledge base and positive attitude in regards to hand hygiene. Both groups displayed consensus that the practices are essential, especially within the current pandemic context. However, larger studies, involving multiple universities and a larger portion of the public, may be useful to ascertain whether there is a true difference in the KAP between healthcare students and the general public.

Discrete flower pollination algorithm for patient admission scheduling problem.

This paper aims to tackle the Patient Admission Scheduling Problem (PASP) using the Discrete Flower Pollination Algorithm (DFPA), a new, meta-heuristic optimization method based on plant pollination. PASP is one of the most important problems in the field of health care. It is a highly constrained and combinatorial optimization problem of assigning patients to medical resources in a hospital, subject to predefined constraints, while maximizing patient comfort. While the flower pollination algorithm was designed for continuous optimization domains, a discretization of the algorithm has been carried out for application to the PASP. Various neighborhood structures have been employed to enhance the method, and to explore more solutions in the search space. The proposed method has been tested on six instances of benchmark datasets for comparison against another algorithm using the same dataset. The prospective method is shown to be very efficient in solving any scheduling problem.

An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource.

BACKGROUND: Clear language makes communication easier between any two parties. A layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical terminology which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. OBJECTIVE: Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. In this paper, we present an automatic method to enrich laymen's vocabularies that has the benefit of being able to be applied to vocabularies in any domain. METHODS: Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies. Our approach further improves the consumer health vocabularies by incorporating synonyms and hyponyms from the WordNet ontology. The basic GloVe and our novel algorithms incorporating WordNet were evaluated using two laymen datasets from the National Library of Medicine (NLM), Open-Access Consumer Health Vocabulary (OAC CHV) and MedlinePlus Healthcare Vocabulary. RESULTS: The results show that GloVe was able to find new laymen terms with an F-score of 48.44%. Furthermore, our enhanced GloVe approach outperformed basic GloVe with an average F-score of 61%, a relative improvement of 25%. Furthermore, the enhanced GloVe showed a statistical significance over the two ground truth datasets with P < 0.001. CONCLUSIONS: This paper presents an automatic approach to enrich consumer health vocabularies using the GloVe word embeddings and an auxiliary lexical source, WordNet. Our approach was evaluated used healthcare text downloaded from MedHelp.org, a healthcare social media platform using two standard laymen vocabularies, OAC CHV, and MedlinePlus. We used the WordNet ontology to expand the healthcare corpus by including synonyms, hyponyms, and hypernyms for each layman term occurrence in the corpus. Given a seed term selected from a concept in the ontology, we measured our algorithms' ability to automatically extract synonyms for those terms that appeared in the ground truth concept. We found that enhanced GloVe outperformed GloVe with a relative improvement of 25% in the F-score.

A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work.

BACKGROUND: With the remarkable increasing in the numbers of patients, the triaging and prioritizing patients into multi-emergency level is required to accommodate all the patients, save more lives, and manage the medical resources effectively. Triaging and prioritizing patients becomes particularly challenging especially for the patients who are far from hospital and use telemedicine system. To this end, the researchers exploiting the useful tool of machine learning to address this challenge. Hence, carrying out an intensive investigation and in-depth study in the field of using machine learning in E-triage and patient priority are essential and required. OBJECTIVES: This research aims to (1) provide a literature review and an in-depth study on the roles of machine learning in the fields of electronic emergency triage (E-triage) and prioritize patients for fast healthcare services in telemedicine applications. (2) highlight the effectiveness of machine learning methods in terms of algorithms, medical input data, output results, and machine learning goals in remote healthcare telemedicine systems. (3) present the relationship between machine learning goals and the electronic triage processes specifically on the: triage levels, medical features for input, outcome results as outputs, and the relevant diseases. (4), the outcomes of our analyses are subjected to organize and propose a cross-over taxonomy between machine learning algorithms and telemedicine structure. (5) present lists of motivations, open research challenges and recommendations for future intelligent work for both academic and industrial sectors in telemedicine and remote healthcare applications. METHODS: An intensive research is carried out by reviewing all articles related to the field of E-triage and remote priority systems that utilise machine learning algorithms and sensors. We have searched all related keywords to investigate the databases of Science Direct, IEEE Xplore, Web of Science, PubMed, and Medline for the articles, which have been published from January 2012 up to date. RESULTS: A new crossover matching between machine learning methods and telemedicine taxonomy is proposed. The crossover-taxonomy is developed in this study to identify the relationship between machine learning algorithm and the equivalent telemedicine categories whereas the machine learning algorithm has been utilized. The impact of utilizing machine learning is composed in proposing the telemedicine architecture based on synchronous (real-time/ online) and asynchronous (store-and-forward / offline) structure. In addition to that, list of machine learning algorithms, list of the performance metrics, list of inputs data and outputs results are presented. Moreover, open research challenges, the benefits of utilizing machine learning and the recommendations for new research opportunities that need to be addressed for the synergistic integration of multidisciplinary works are organized and presented accordingly. DISCUSSION: The state-of-the-art studies on the E-triage and priority systems that utilise machine learning algorithms in telemedicine architecture are discussed. This approach allows the researchers to understand the modernisation of healthcare systems and the efficient use of artificial intelligence and machine learning. In particular, the growing worldwide population and various chronic diseases such as heart chronic diseases, blood pressure and diabetes, require smart health monitoring systems in E-triage and priority systems, in which machine learning algorithms could be greatly beneficial. CONCLUSIONS: Although research directions on E-triage and priority systems that use machine learning algorithms in telemedicine vary, they are equally essential and should be considered. Hence, we provide a comprehensive review to emphasise the advantages of the existing research in multidisciplinary works of artificial intelligence, machine learning and healthcare services.

Progress in Immunization Safety Monitoring - Worldwide, 2010-2019.

High levels of coverage with safe and effective immunizations are critical to the successful control and prevention of vaccine-preventable diseases worldwide. In addition to stringent standards to regulate the safety of vaccines, robust postlicensure monitoring systems help ensure that the benefits of vaccines continue to outweigh the risks for the populations who receive them. National Expanded Programmes on Immunization (EPI) are typically responsible for identifying and investigating adverse events following immunization (AEFI), including assessment of causality. National regulatory authorities (NRAs) are mandated to perform postlicensure surveillance of adverse drug reactions, including those associated with receipt of vaccines. This report describes global progress toward meeting World Health Organization (WHO) indicators on minimal country capacity for vaccine safety surveillance and coordination of AEFI reporting between countries' EPI and NRAs. In 2019, among 194 countries, 129 (66.5%) reported having an operational national AEFI causality review committee, compared with 94 (48.5%) in 2010. During 2010-2019, the proportion of countries reporting ≥10 AEFI per 100,000 surviving infants per year (an indicator of country capacity to monitor immunization safety) increased, from 41.2% to 56.2%. In 2019, however, only 46 (23.7%) countries reported AEFI data from both EPI and NRAs. Although global progress has been made toward strengthening systems for vaccine safety monitoring over the past decade, new indicators for monitoring global immunization safety performance are needed to better reflect program functionality. Continued global efforts will be vital to address barriers to routine reporting of AEFI, build national capacity for AEFI investigation and data management, and improve sharing of AEFI data at national, regional, and global levels.

Dieulafoy lesions as cause of upper gastrointestinal bleeding in a patient with portal hypertension.

Dieulafoy's lesion is an abnormally large and tortuous submucosal artery that protrudes through a small mucosal defect resulting in gastrointestinal bleeding. We present a case of a 53-year-old man with a history of HIV and alcohol abuse who presented to the emergency room with hematemesis and melena. Upper endoscopy revealed an actively bleeding dieulafoy lesion, but due to uncontrolled bleeding, embolization of the left artery was necessitated. The incidence of dieulafoy lesions is about 0.3% to 6.7% within the stomach. The etiology remains uncertain but has been linked to alcoholism and antiplatelet drugs. We are emphasizing the importance of considering uncommon causes of upper gastrointestinal bleeding in patients with portal hypertension.

Formulating multi diseases dataset for identifying, triaging and prioritizing patients to multi medical emergency levels: Simulated dataset accompanied with codes.

This paper provides simulated datasets for triaging and prioritizing patients that are essentially required to support multi emergency levels. To this end, four types of input signals are presented, namely, electrocardiogram (ECG), blood pressure, and oxygen saturation (SpO2), where the latter is text. To obtain the aforementioned signals, the PhysioNet online library [1], is used, which is considered as one of the most reliable and relevant libraries in the healthcare services and bioinformatics sciences. In particular, this library contains collections of several databases and signals, where some of these signals are related to ECG, blood pressure, and SpO2 sensor. The simulated datasets, which are accompanied by codes, are presented in this paper. The contributions of our work, which are related to the presented dataset, can be summarized as follow. (1) The presented dataset is considered as an essential feature that is extracted from the signal records. Specifically, the dataset includes medical vital features such as: QRS width; ST elevation; peaks number; cycle interval from ECG signal; SpO2 level from SpO2 signal; high blood (systolic) pressure value; and low-pressure (diastolic) value from blood pressure signal. These essential features have been extracted based on our machine learning algorithms. In addition, new medical features are added based on medical doctors' recommendations, which are given as text-inputs, e.g., chest pain, shortness of breath, palpitation, and whether the patient at rest or not. All these features are considered to be significant symptoms for many diseases such as: heart attack or stroke; sleep apnea; heart failure; arrhythmia; and blood pressure chronic diseases. (2) The formulated dataset is considered in the doctor diagnostic procedures for identifying the patients' emergency level. (3) In the PhysioNet online library [1], the ECG, blood pressure, and SpO2 have been represented as signals. In contrast, we use some signal processing techniques to re-present the dataset by numeric values, which enable us to extract the essential features of the dataset in Excel sheet representations. (4) The dataset is re-organized and re-formatted to be presented in a useful structure feasible format. Specifically, the dataset is re-presented in terms of tables to illustrate the patient's profile and the type of diseases. (5) The presented dataset is utilized in the evaluation of medical monitoring and healthcare provisioning systems [2]. (6) Some simulated codes for feature extractions are also provided in this paper.

Reducing waiting time for remote patients in telemedicine with considering treated patients in emergency department based on body sensors technologies and hybrid computational algorithms: Toward scalable and efficient real time healthcare monitoring system.

BACKGROUND: Scalability challenge in real time healthcare monitoring system relates to several issues. One of the insistent issues is the increasing in the number of patients. Increasing in the patients' number causes long queue and increase the waiting time for the patients in their seeking for healthcare services. Thus, an ethical issue raises as the healthcare providers should provide fast services for all patients. Recent studies have proposed scalable models that are limited to (1) triaging remote patients for the optimal emergency level and (2) prioritizing remote patients with the highest triage level to receive immediate healthcare services. However, these studies have shown limitations, that is, (1) they have not addressed the waiting time for all patients with different triage levels in the same waiting queue; and (2) they have not considered Emergency Department EDs patients. Therefore, considering the remote patients with the treated patients in EDs in one healthcare system is a demand, to efficiently handle all the patients' requests and productively manage the medical resources. OBJECTIVE: This study aims to reduce the waiting time for the remote patients in telemedicine with considering treated patients in EDs. The study presents a scalable telemedicine model to improve the ability of real time healthcare monitoring system in accommodating the increasing number of patients with chronic heart disease by reducing their waiting time for healthcare services, prioritizing the patients who have the most emergency cases and provide all the patients by fast healthcare services. The proposed model called Triaging and Prioritizing Model "TPM". METHOD: The proposed model "TPM" considers triaging and prioritizing all patients (remote and EDs patients) as two sequential processes. The TPM was formulated to triage the patients based on hybrid algorithms which combine Evidence-Theory with Fuzzy Cluster Means (FCM) and then prioritize the patients based on dedicated computational algorithm. A simulation, on 580 chronic heart diseases patients, was implemented. The patients considered as they have different emergency levels based on four vital data acquisition tools: electrocardiogram sensor, blood pressure sensor, oxygen saturation sensor and a text input as non-sensory based acquisition tool. RESULTS: Computational results show the superiority of the proposed model (TPM) in accommodating large numbers of patients and reducing their waiting time for services compared with relevant benchmark studies. In 1,185 min, TPM managed the (580) patients' requests. By contrast, the benchmark managed only 256 patients at the same amount of time. In addition to that, TPM shows improvements in terms of waiting time and services provisioning rates compared with benchmark methods. CONCLUSION: All patients with the different emergency levels receive services with less waiting time compared with the relevant studies. The proposed model (TPM) model considers both of remote patients and treated patients in EDs efficiently. TPM improves response time for the medical services, reduces waiting time for all patients and consequently, saves more lives.

A review of the costs of delivering maternal immunisation during pregnancy.

BACKGROUND: Routine maternal immunisation against influenza and pertussis are recommended by the WHO to protect mother and child, and new vaccines are under development. Introducing maternal vaccines into national programmes requires an understanding of vaccine delivery costs - particularly in low resource settings. METHODS: We searched Medline, Embase, Econlit, and Global Health for studies reporting costs of delivering vaccination during pregnancy but excluded studies that did not separate the vaccine purchase price. Extracted costs were inflated and converted to 2018 US dollars. RESULTS: Sixteen studies were included, of which two used primary data to estimate vaccine delivery costs. Costs per dose ranged from $0.55 to $0.64 in low-income countries, from $1.25 to $6.55 for middle-income countries, and from $5.76 to $39.87 in high-income countries. CONCLUSIONS: More research is needed on the costs of delivering maternal immunisation during pregnancy, and of integrating vaccine delivery into existing programmes of antenatal care especially in low and middle-income countries.

Lessons from humanitarian clusters to strengthen health system responses to mass displacement in low and middle-income countries: A scoping review.

The humanitarian cluster approach was established in 2005 but clarity on how lessons from humanitarian clusters can inform and strengthen health system responses to mass displacement in low and middle-income countries (LMIC) is lacking. We conducted a scoping review to examine the extent and nature of existing research and identify relevant lessons. We used Arksey and O'Malley's scoping framework with Levac's 2010 revisions and Khalil's 2016 refinements, focussing on identifying lessons from discrete humanitarian clusters that could strengthen health system responses to mass population displacement. We summarised thematically by cluster. Of 186 sources included, 56% were peer-reviewed research articles. Most related to health (37%), protection (18%), or nutrition (13%) clusters. Key lessons for health system responses included the necessity of empowering women; ensuring communities are engaged in decision-making processes (e.g. planning and construction of camps and housing) to strengthen trust and bonds between and within communities; and involving potential end-users in technological innovations development (e.g. geographical information systems) to ensure relevance and applicability. Our review provided evidence that non-health clusters can contribute to improving health outcomes using focussed interventions for implementation by government or humanitarian partners to inform LMIC health system responses to mass displacement.

Systematic Review on the Acute Cost-of-illness of Sepsis and Meningitis in Neonates and Infants.

BACKGROUND: Sepsis and meningitis in neonates and infants are a source of substantial morbidity, mortality and economic loss. The objective of this review is to estimate the acute costs associated with treating sepsis, meningitis and meningococcal septicemia, in neonates and infants, worldwide. METHODS: The electronic databases Medline, Embase and EconLit were searched and exported on November 24, 2018. Studies that reported an average hospitalization cost for confirmed cases of sepsis, meningitis or meningococcal septicemia were eligible for our review. Descriptive data were extracted and reported costs were inflated and converted. A narrative synthesis of the costs was conducted. RESULTS: Our review identified 20 studies reporting costs of sepsis, meningitis and/or meningococcal septicemia. Costs ranged from $55 to $129,632 for sepsis and from $222 to $33,635 for meningitis (in 2017 US dollars). One study estimated the cost of meningococcal septicemia to be $56,286. All reported costs were estimated from the perspective of the healthcare provider or payer. Most studies were from the United States, which also had the highest costs. Only a few studies were identified for low- and middle-income countries, which reported lower costs than high-income countries for both sepsis and meningitis. CONCLUSIONS: Sepsis and meningitis in neonates and infants are associated with substantial costs to the healthcare system and showed a marked difference across global income groups. However, more research is needed to inform costs in low- and middle-income settings and to understand the economic costs borne by families and wider society.

Associations Between Acute Conflict and Maternal Care Usage in Egypt: An Uncontrolled Before-and-After Study Using Demographic and Health Survey Data.

BACKGROUND: United Nations' (UN) data indicate that conflict-affected low- and middle-income countries (LMICs) contribute considerably to global maternal deaths. Maternal care usage patterns during conflict have not been rigorously quantitatively examined for policy insights. This study analysed associations between acute conflict and maternal services usage and quality in Egypt using reliable secondary data (as conflict-affected settings generally lack reliable primary data). METHODS: An uncontrolled before-and-after study used data from the 2014 Egypt Demographic and Health Survey (EDHS). The 'pre-conflict sample' included births occurring from January 2009 to January 2011. The 'peri-conflict sample' included births from February 2011 to December 2012. The hierarchical nature of demographic and household survey (DHS) data was addressed using multi-level modelling (MLM). RESULTS: In total, 2569 pre-conflict and 4641 peri-conflict births were reported. After adjusting for socioeconomic variables, conflict did not significantly affect antenatal service usage. Compared to the pre-conflict period, periconflict births had slightly lower odds of delivery in public institutions (odds ratio [OR]: 0.987; 95% CI: 0.975-0.998; P<.05), institutional postnatal care (OR: 0.995; 95% CI: 0.98-1.00; P=.05), and at least 24 hours post-delivery stay (OR: 0.921; 95% CI: 0.906-0.935; P<.01). Peri-conflict births had relatively higher odds of doctor-assisted deliveries (OR: 1.021; 95% CI: 1.004-1.035; P<.05), institutional deliveries (OR: 1.022; 95% CI: 1.00-1.04; P<.05), private institutional deliveries (OR: 1.035; 95% CI: 1.017-1.05; P<.001), and doctor-assisted postnatal care (OR: 1.015; 95% CI: 1.003-1.027; P<.05). Sensitivity analysis did not change results significantly. CONCLUSION: Maternal care showed limited associations with the acute conflict, generally reflecting pre-conflict usage patterns. Further qualitative and quantitative research could identify the effects of larger conflicts on maternal careseeking and usage, and inform approaches to building health system resilience.

Systematic Review of Real-time Remote Health Monitoring System in Triage and Priority-Based Sensor Technology: Taxonomy, Open Challenges, Motivation and Recommendations.

The new and ground-breaking real-time remote monitoring in triage and priority-based sensor technology used in telemedicine have significantly bounded and dispersed communication components. To examine these technologies and provide researchers with a clear vision of this area, we must first be aware of the utilised approaches and existing limitations in this line of research. To this end, an extensive search was conducted to find articles dealing with (a) telemedicine, (b) triage, (c) priority and (d) sensor; (e) comprehensively review related applications and establish the coherent taxonomy of these articles. ScienceDirect, IEEE Xplore and Web of Science databases were checked for articles on triage and priority-based sensor technology in telemedicine. The retrieved articles were filtered according to the type of telemedicine technology explored. A total of 150 articles were selected and classified into two categories. The first category includes reviews and surveys of triage and priority-based sensor technology in telemedicine. The second category includes articles on the three-tiered architecture of telemedicine. Tier 1 represents the users. Sensors acquire the vital signs of the users and send them to Tier 2, which is the personal gateway that uses local area network protocols or wireless body area network. Medical data are sent from Tier 2 to Tier 3, which is the healthcare provider in medical institutes. Then, the motivation for using triage and priority-based sensor technology in telemedicine, the issues related to the obstruction of its application and the development and utilisation of telemedicine are examined on the basis of the findings presented in the literature.

Based on Real Time Remote Health Monitoring Systems: A New Approach for Prioritization "Large Scales Data" Patients with Chronic Heart Diseases Using Body Sensors and Communication Technology.

This paper presents a new approach to prioritize "Large-scale Data" of patients with chronic heart diseases by using body sensors and communication technology during disasters and peak seasons. An evaluation matrix is used for emergency evaluation and large-scale data scoring of patients with chronic heart diseases in telemedicine environment. However, one major problem in the emergency evaluation of these patients is establishing a reasonable threshold for patients with the most and least critical conditions. This threshold can be used to detect the highest and lowest priority levels when all the scores of patients are identical during disasters and peak seasons. A practical study was performed on 500 patients with chronic heart diseases and different symptoms, and their emergency levels were evaluated based on four main measurements: electrocardiogram, oxygen saturation sensor, blood pressure monitoring, and non-sensory measurement tool, namely, text frame. Data alignment was conducted for the raw data and decision-making matrix by converting each extracted feature into an integer. This integer represents their state in the triage level based on medical guidelines to determine the features from different sources in a platform. The patients were then scored based on a decision matrix by using multi-criteria decision-making techniques, namely, integrated multi-layer for analytic hierarchy process (MLAHP) and technique for order performance by similarity to ideal solution (TOPSIS). For subjective validation, cardiologists were consulted to confirm the ranking results. For objective validation, mean ± standard deviation was computed to check the accuracy of the systematic ranking. This study provides scenarios and checklist benchmarking to evaluate the proposed and existing prioritization methods. Experimental results revealed the following. (1) The integration of TOPSIS and MLAHP effectively and systematically solved the patient settings on triage and prioritization problems. (2) In subjective validation, the first five patients assigned to the doctors were the most urgent cases that required the highest priority, whereas the last five patients were the least urgent cases and were given the lowest priority. In objective validation, scores significantly differed between the groups, indicating that the ranking results were identical. (3) For the first, second, and third scenarios, the proposed method exhibited an advantage over the benchmark method with percentages of 40%, 60%, and 100%, respectively. In conclusion, patients with the most and least urgent cases received the highest and lowest priority levels, respectively.