The growing worldwide population has increased the need for technologies, computerised software algorithms and smart devices that can monitor and assist patients anytime and anywhere and thus enable them to lead independent lives. The real-time remote monitoring of patients is an important issue in telemedicine. In the provision of healthcare services, patient prioritisation poses a significant challenge because of the complex decision-making process it involves when patients are considered 'big data'. To our knowledge, no study has highlighted the link between 'big data' characteristics and real-time remote healthcare monitoring in the patient prioritisation process, as well as the inherent challenges involved. Thus, we present comprehensive insights into the elements of big data characteristics according to the six 'Vs': volume, velocity, variety, veracity, value and variability. Each of these elements is presented and connected to a related part in the study of the connection between patient prioritisation and real-time remote healthcare monitoring systems. Then, we determine the weak points and recommend solutions as potential future work. This study makes the following contributions. (1) The link between big data characteristics and real-time remote healthcare monitoring in the patient prioritisation process is described. (2) The open issues and challenges for big data used in the patient prioritisation process are emphasised. (3) As a recommended solution, decision making using multiple criteria, such as vital signs and chief complaints, is utilised to prioritise the big data of patients with chronic diseases on the basis of the most urgent cases.
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.
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.
The healthcare industry is streamlining processes to offer more timely and effective services to all patients. Computerized software algorithm and smart devices can streamline the relation between users and doctors by providing more services inside the healthcare telemonitoring systems. This paper proposes a multi-sources framework to support advanced healthcare applications. The proposed framework named Multi Sources Healthcare Architecture (MSHA) considers multi-sources: sensors (ECG, SpO2 and Blood Pressure) and text-based inputs from wireless and pervasive devices of Wireless Body Area Network. The proposed framework is used to improve the healthcare scalability efficiency by enhancing the remote triaging and remote prioritization processes for the patients. The proposed framework is also used to provide intelligent services over telemonitoring healthcare services systems by using data fusion method and prioritization technique. As telemonitoring system consists of three tiers (Sensors/ sources, Base station and Server), the simulation of the MSHA algorithm in the base station is demonstrated in this paper. The achievement of a high level of accuracy in the prioritization and triaging patients remotely, is set to be our main goal. Meanwhile, the role of multi sources data fusion in the telemonitoring healthcare services systems has been demonstrated. In addition to that, we discuss how the proposed framework can be applied in a healthcare telemonitoring scenario. Simulation results, for different symptoms relate to different emergency levels of heart chronic diseases, demonstrate the superiority of our algorithm compared with conventional algorithms in terms of classify and prioritize the patients remotely.
Problem Statement: Improper triage and prioritization of big-data patients may result in erroneous strategic decisions. An example of such wrong decision making includes the triage of patients with chronic heart disease to low-priority groups. Incorrect decisions may jeopardize the patients’ health. Objective: This study aims to evaluate and score the big data of patients with chronic heart disease and of those who require urgent attention. The assessment is based on multicriteria decision making in a telemedical environment to improve the triage and prioritization processes. Methods: A hands-on study was performed. A total of 500 patients with chronic heart disease manifested in different symptoms and under various emergency levels were evaluated on the basis of the following four main measures. An electrocardiogram sensor was used to measure the electrical signals of the contractile activity of the heart over time. A SpO2 sensor was employed to determine the blood oxygen saturation levels of the patients. A blood pressure sensor was used to obtain the physiological data of the systolic and diastolic blood pressures of the patients. Finally, a non-sensory measurement (text frame) was conducted to assess chest pain and breathing. The patients were prioritized on the basis of a set of measurements by utilizing integrated back-forward adjustment for weight computation and technique for order performance by similarity to ideal solution. Discussion Results: Patients with the most urgent cases were given the highest priority level, whereas those with the least urgent cases were assigned with the lowest priority level among all patients’ scores. The first three patients assigned to the medical committee of doctors were proven to be the most critical emergency cases with the highest priority level on the basis of their clinical symptoms. By contrast, the last three patients were proven to be the least critical emergency cases and given the lowest priority levels relative to other patients. The throughput measurement in terms of scalability based on our proposed algorithm was more efficient than that of the benchmark algorithm. Finally, the new method for determining the “big data” patients characteristics based on “4Vs” was suggested.
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.
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