Patient Health Monitoring Research Articles

Care of Transgender Patients: A General Practice Quality Improvement Approach

Background: With increasing numbers of gender minority/ trans patients, primary care must ensure high quality lifelong care is offered. Aim: To improve the quality of medical care for gender minority/ trans patients. Design and Setting: Initiation of a quality improvement project using population census, patient cohort and audit of standards in a single general practice. Method: Retrospective notes review against an audit standard. Results: One practice has 68 trans patients (population prevalence of 337/100 000). Monitoring guidelines from multiple private and NHS gender identity clinics (GICs) were found to be inconsistent. It was not possible to use an appropriate primary care audit standard as no UK-wide guidance was identified. Initial audit against an international secondary care guideline which was developed following a recognised process suggests health monitoring of transgender patients may be substandard in nearly two thirds (62%) of cases. Patients with gender dysphoria are experiencing long waits for NHS GICs (mean 26 months) and frequently self refer to private services. They receive a mean 2.7 assessment appointments prior to treatment, which consists of 85% hormone prescription uptake. 20% of patients stopped hormone treatment, of which half detransitioned. All detransition has occurred in the last three years. Conclusion: There is a need to develop consistent, national evidence-based primary care guidance that includes standards for monitoring and dealing with complications, including detransition. Funding: None to declare. Declaration of Interest: None to declare. Ethics Approval Statement: The study went through IRAS process, but formal ethics committee permission was not required for a quality improvement retrospective review of notes auditing against standards.

COVID-19 Patient Health Monitoring System

COVID-19 Patient Health Monitoring System

Home health monitoring during the COVID pandemic: Results from a feasibility study in Alberta primary care.

The expansive geography of Central Alberta presents many barriers to optimal care, including limited resources and access issues. In response to the COVID-19 pandemic, primary care networks (PCNs) within Central Alberta partnered with a technology provider to rapidly implement home health monitoring (HHM) for patients with chronic diseases. In the 37 patients evaluated in phase 1 (90 days), diabetes was most common (73%), followed by hypertension (38%), chronic obstructive pulmonary disease (27%), and heart failure (11%). Overall, patients were comfortable using the HHM technology, and >60% reported improved quality of life after follow-up. Patients also made fewer visits to their family physician/emergency department compared with the pre-enrolment period. In January 2021, the HHM initiative was expanded to a larger patient cohort (phase 2; n = 500). Interim results for 90 patients from eight PCNs up to the end of May 2021 show similar findings to phase 1.

Edge Intelligent Agent Assisted Hybrid Hierarchical Blockchain for continuous healthcare monitoring & recommendation system in 5G WBAN-IoT

Wireless Body Area Network (WBAN) is an emerging technology that faces many challenges for monitoring patient health state information and early diagnosis of any disease (e.g. COVID-19). In addition, WBAN has many issues such as high energy consumption, overhead and latency due to the scarcity in resources, for that we bring edge assisted 5G environment in WBAN. The patient information is sensitive and easily traced by the attackers due to insecurity, for that, we proposed the EiA-H2B model (Edge intelligent Agent Hybrid Hierarchical Blockchain) which includes five phases. Firstly we proposed Biological PUF based sensor authentication, which is a strong credential that provides high security. All the sensors are authenticated by the virtual authenticator that is deployed by a blockchain ledger. Due to the mobility of WBAN, we focused on handover that is done by Bi-Partite One to N matching theory. Secondly, we proposed high voting based virtual clustering, in this phase high voting sensor is elected as a super node and that is used to create a virtual cluster. Thirdly, we proposed duty cycle MAC scheduling protocol is used to predict the original state of the sensors which is done by Attention Matrix based Gated Recurrent Unit (AM-GRU), and then the timeslots are allocated for avoiding packet loss and retransmission. Fourth, we perform relay selection and routing that is done by Global Optimization based Artificial Electric Field Algorithm (AEFA). And finally, we predict emergency data and generate an alert in EiA using Human Learning aided State Action Reward State Action (SARSA) algorithm that finds the current state and predicts the corresponding action. Based on that current state information SARSA provides an efficient recommendation to the patients. Experiments are conducted by OMNeT ++ network simulator and evaluate the performance of the proposed EiA-H2B model in terms of energy consumption, success rate, network throughput, end to end delay, packet loss rate, authentication time and processing time.

Assessment of the Quality Management System for Clinical Nutrition in Jiangsu: Survey Study

BackgroundAn electronic system that automatically collects medical information can realize timely monitoring of patient health and improve the effectiveness and accuracy of medical treatment. To our knowledge, the application of artificial intelligence (AI) in medical service quality assessment has been minimally evaluated, especially for clinical nutrition departments in China. From the perspective of medical ethics, patient safety comes before any other factors within health science, and this responsibility belongs to the quality management system (QMS) within medical institutions.ObjectiveThis study aims to evaluate the QMS for clinical nutrition in Jiangsu, monitor its performance in quality assessment and human resource management from a nutrition aspect, and investigate the application and development of AI in medical quality control.MethodsThe participants for this study were the staff of 70 clinical nutrition departments of the tertiary hospitals in Jiangsu Province, China. These departments are all members of the Quality Management System of Clinical Nutrition in Jiangsu (QMSNJ). An online survey was conducted on all 341 employees within all clinical nutrition departments based on the staff information from the surveyed medical institutions. The questionnaire contains five sections, and the data analysis and AI evaluation were focused on human resource information.ResultsA total of 330 questionnaires were collected, with a response rate of 96.77% (330/341). A QMS for clinical nutrition was built for clinical nutrition departments in Jiangsu and achieved its target of human resource improvements, especially among dietitians. The growing number of participating departments (an increase of 42.8% from 2018 to 2020) and the significant growth of dietitians (t93.4=–0.42; P=.02) both show the advancements of the QMSNJ.ConclusionsAs the first innovation of an online platform for quality management in Jiangsu, the Jiangsu Province Clinical Nutrition Management Platform was successfully implemented as a QMS for this study. This multidimensional electronic system can help the QMSNJ and clinical nutrition departments achieve quality assessment from various aspects so as to realize the continuous improvement of clinical nutrition. The use of an online platform and AI technology for quality assessment is worth recommending and promoting in the future.

A Home-Care Service for Frail Older Adults: Findings from a Quasi-Experiment in Milan

Population aging is particularly pronounced in Italy. Recently, home-care services emerged as one of the desirable strategy when dealing with such situations of fragility. In this framework, we present the evaluation of a home-care service which was experimentally implemented by Fondazione Sacra Famiglia and Casa di Cura Ambrosiana in the 2017–2018 biennium. The service consisted of a twice-weekly nursing visit intended to monitor patient health conditions and to gather data constantly supervised by a geriatrician. The eligible population consisted of the users of San Carlo Hospital Emergency Department (91 individuals). Twenty of these individuals had access to the experimental home-care service. The results show the smaller probability of mortality for the treatment group compared to the control group, but similar probabilities in admissions to ER and in hospitalizations. These findings suggest that health home-care policies could reduce mortality by lessening the negative effects of relational isolation.

Intelligent Health Monitoring System for Detection of Symptomatic/Asymptomatic COVID-19 Patient

The outbreak of the coronavirus is in its growing stage due to the lack of standard diagnosis for the patients. The situation of any populous area in a geographic location is very critical due to the quick virus spread from an infected individual to the rest. Currently, medical administration is at a crisis point due to the rapidly increasing number of cases and limited medical facilities. Thus, it is time to explore and design an intelligent model to monitor patient health symptoms remotely and predict and detect the abnormality of the patient's health status in quick succession. Thus, the health status of a coronavirus-affected patient can be identified via a well-adjusted predictive model by analyzing the observed parameters of the health. In the proposed model, an Auto-regressive Integrated Moving Average is incorporated to design a predictive model to find the kth forecast of the observed health symptoms of a patient, and Akaike Information Criteria based selection is introduced to find the current best-fit prediction model. Further, the features are extracted from the forecast over each symptom to find a pattern of each patient, and the patterns are learned by the K-Means algorithm to detect the symptomatic and asymptomatic patient intelligently. To demonstrate the efficiency of the proposed model, we evaluate the model using a synthetic dataset, generated from the health symptoms of 400 patients and compare the performance of the model with the standard methods.

Stakeholders' Perspectives on eHealth Support in Colorectal Cancer Survivorship: Qualitative Interview Study.

BackgroundeHealth interventions may represent the way forward in following up patients with colorectal cancer (CRC) after hospital discharge to support them in coping with the illness, strengthen their self-management, and increase their quality of life. By involving end users of eHealth in cocreation processes when designing eHealth solutions, an acceptable and relevant product can be secured. Stakeholders’ perspectives could aid in closing the gap between research-developed products and the implementation of eHealth services in real-life scenarios.ObjectiveThis study aims to explore the views of patients with CRC, their informal caregivers, and health care professionals (HCPs) on information technology and the design of eHealth support in CRC care.MethodsA qualitative, explorative design was used to conduct 31 semistructured individual interviews with 41% (13/31) patients with CRC, 29% (9/31) informal caregivers, and 29% (9/31) HCPs recruited from the gastrosurgical ward of a university hospital in southwestern Norway. A semistructured interview guide was used for data collection, and the data were analyzed by systematic text condensation.ResultsParticipants described the diverse experiences of patients with CRC seeking web-based information. Age and digital competence were highlighted as influencers of the use of information technology. Patients rarely received advice from HCPs about relevant and secure websites containing information on CRC diagnosis and treatment. Features of desired eHealth interventions in following up patients with CRC were patient education, health monitoring, and communication with HCPs.ConclusionsSeveral elements affect the activities of patients with CRC seeking health information. Age, inexperience with computer technology, and lack of access to web-based health information may reduce the ability of patients with CRC to engage in decision-making processes regarding illness and treatment. An eHealth service for patients with CRC should comprise features for information, education, and support for self-management and should aim to be individually adapted to the patient’s age and digital competence. Involving end users of eHealth services is necessary to ensure high-quality tailored services that are perceived as user friendly and relevant to the end users.

Application of a Machine Learning Algorithms in a Wrist-Wearable Sensor for Patient Health Monitoring during Autonomous Hospital Bed Transport

Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors has been a growing trend exacerbated by the COVID crisis. In this exploratory study, a polynomial regression (PR) machine learning (ML) RAM algorithm based on a Dreyfusian descriptor for immediate wellbeing monitoring was proposed for the autonomous hospital bed transport (AHBT) application. This method was preferred over several other AI algorithm for its simplicity and quick computation. The algorithm quantified historical data using supervised photoplethysmography (PPG) data for 5 min just before the start of the autonomous journey, referred as pre-journey (PJ) dataset. During the transport process, the algorithm continued to quantify immediate measurements using non-overlapping sets of 30 PPG waveforms, referred as in-journey (IJ) dataset. In combination, this algorithm provided a binary decision condition that determined if AHBT should continue its journey to destination by checking the degree of polynomial (DoP) between PJ and IJ. Wrist PPG was used as algorithm’s monitoring parameter. PPG data was collected simultaneously from both wrists of 35 subjects, aged 21 and above in postures mimicking that in AHBT and were given full freedom of upper limb and wrist movement. It was observed that the top goodness-of-fit which indicated potentials for high data accountability had 0.2 to 0.6 cross validation score mean (CVSM) occurring at 8th to 10th DoP for PJ datasets and 0.967 to 0.994 CVSM at 9th to 10th DoP for IJ datasets. CVSM was a reliable metric to pick out the best PJ and IJ DoPs. Central tendency analysis showed that coinciding DoP distributions between PJ and IJ datasets, peaking at 8th DoP, was the precursor to high algorithm stability. Mean algorithm efficacy was 0.20 as our proposed algorithm was able to pick out all signals from a conscious subject having full freedom of movement. This efficacy was acceptable as a first ML proof of concept for AHBT. There was no observable difference between subjects’ left and right wrists.

Wearable IoTs and Geo-Fencing Based Framework for COVID-19 Remote Patient Health Monitoring and Quarantine Management to Control the Pandemic

The epidemic disease of Severe Acute Respiratory Syndrome (SARS) called COVID-19 has become a more frequently active disease. Managing and monitoring COVID-19 patients is still a challenging issue for advanced technologies. The first and foremost critical issue in COVID-19 is to diagnose it timely and cut off the chain of transmission by isolating the susceptible and patients. COVID-19 spreads through close interaction and contact with an infected person. It has affected the entire world, and every country is facing the challenges of having adequate medical facilities along with the availability of medical staff in rural and urban areas that have a high number of patients due to the pandemic. Due to the invasive method of treatment, SARS-COVID is spreading swiftly. In this paper, we propose an intelligent health monitoring framework using wearable Internet of Things (IoT) and Geo-fencing for COVID-19 susceptible and patient monitoring, and isolation and quarantine management to control the pandemic. The proposed system consists of four layers, and each layer has different functionality: a wearable sensors layer, IoT gateway layer, cloud server layer, and client application layer for visualization and analysis. The wearable sensors layer consists of wearable biomedical and GPS sensors for physiological parameters, and GPS and Wi-Fi Received Signal Strength Indicator acquisition for health monitoring and user Geo-fencing. The IoT gateway layer provides a Bluetooth and Wi-Fi based wireless body area network and IoT environment for data transmission anytime and anywhere. Cloud servers use Raspberry Pi and ThingSpeak cloud for data analysis and web-based application layers for remote monitoring based on user consent. The susceptible and patient conditions, real-time sensor’s data, and Geo-fencing enables minimizing the spread through close interaction. The results show the effectiveness of the proposed framework.

Review of Wearable Devices and Data Collection Considerations for Connected Health.

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.

SS-FD: Internet of medical things based patient health monitoring system

Internet of Medical Things (IoMT) consists of connected devices used to collect patient health information in a real-time environment. The IoMT device effectively handles medical issues by using health wearable and medical-grade wearables. Although IoMT can process the collected data, it has few pitfalls, such as interoperability of data, standardization issues, and computation complexity while detecting disease. By considering these issues, in this work, IoMT is utilized in the field of the remote patient monitoring system. Initially, the IoMT devices are placed on the human body and collect their health information continuously. The gathered details are processed using a salp swarm optimized fuzzy deep neural network (SS-FD). This system supports the patient health monitoring process with minimum low-cost consumption. The SS-FD classifier processes the obtained data; primary and emergency data is classified according to the fuzzy rule. This process improves the remote patient health data analysis and reduces the difficulties involved in the patient health analysis. Then the efficiency of the system is evaluated using experimental results

A Scheduling Mechanism Based on Optimization Using IoT-Tasks Orchestration for Efficient Patient Health Monitoring.

Over the past years, numerous Internet of Things (IoT)-based healthcare systems have been developed to monitor patient health conditions, but these traditional systems do not adapt to constraints imposed by revolutionized IoT technology. IoT-based healthcare systems are considered mission-critical applications whose missing deadlines cause critical situations. For example, in patients with chronic diseases or other fatal diseases, a missed task could lead to fatalities. This study presents a smart patient health monitoring system (PHMS) based on an optimized scheduling mechanism using IoT-tasks orchestration architecture to monitor vital signs data of remote patients. The proposed smart PHMS consists of two core modules: a healthcare task scheduling based on optimization and optimization of healthcare services using a real-time IoT-based task orchestration architecture. First, an optimized time-constraint-aware scheduling mechanism using a real-time IoT-based task orchestration architecture is developed to generate autonomous healthcare tasks and effectively handle the deployment of emergent healthcare tasks. Second, an optimization module is developed to optimize the services of the e-Health industry based on objective functions. Furthermore, our study uses Libelium e-Health toolkit to monitors the physiological data of remote patients continuously. The experimental results reveal that an optimized scheduling mechanism reduces the tasks starvation by 14% and tasks failure by 17% compared to a conventional fair emergency first (FEF) scheduling mechanism. The performance analysis results demonstrate the effectiveness of the proposed system, and it suggests that the proposed solution can be an effective and sustainable solution towards monitoring patient’s vital signs data in the IoT-based e-Health domain.

A Lightweight and Secure Anonymous User Authentication Protocol for Wireless Body Area Networks

The recent development of wireless body area network (WBAN) technology plays a significant role in the modern healthcare system for patient health monitoring. However, owing to the open nature of the wireless channel and the sensitivity of the transmitted messages, the data security and privacy threats in WBAN have been widely discussed and must be solved. In recent years, many authentication protocols had been proposed to provide security and privacy protection in WBANs. However, many of these schemes are not computationally efficient in the authentication process. Inspired by these studies, a lightweight and secure anonymous authentication protocol is presented to provide data security and privacy for WBANs. The proposed scheme adopts a random value and hash function to provide user anonymity. Besides, the proposed protocol can provide user authentication without a trusted third party, which makes the proposed scheme have no computational bottleneck in terms of architecture. Finally, the security and performance analyses demonstrate that the proposed scheme can meet security requirements with low computational and communication costs.

Secure and efficient transmission of data based on Caesar Cipher Algorithm for Sybil attack in IoT

The Internet of Things (IoT) is an emerging concept in the field of information technology. IoT can integrate any real-time entity with another, using sensing, computing and communication capabilities to offer enhanced services in everyday life. In this article, IoT-based patient health monitoring is considered for use in IoT sensors deployed in devices. These devices are attached to the body of the patient for timely tracking of his or her health condition. During data transfers from devices connected to the patient’s body to the doctor, the data may be susceptible to security threats. IoT devices are subjected to many routing attacks, like blackhole, greyhole, Sybil, sinkhole and wormhole attacks. Sybil attacks are the most dangerous routing attacks. This type of attack involves stealing the identities of legitimate nodes; this, in turn, leads to information loss, misinterpretation in the network and an increase in routing disturbances. Hence, in this paper, we propose the use of the traditional Caesar Cipher Algorithm (CCA) along with the lightweight encryption algorithm (LEA) and the Received Signal Strength Indicator (RSSI) to detect and prevent Sybil attacks in an IoT environment. The proposed algorithm detects the false node in a particular path by announcing the attack to another node. It also prevents the attack by choosing an alternative path by which to forward data packets to the desired users. To ensure authentication, privacy and data integrity, the lightweight encryption algorithm with a 64-bit key is used with AODV as the routing protocol.

Design of a secured telehealth system based on multiple biosignals diagnosis and classification for IoT application

AbstractThe aim of this article is to design a new telehealth system with secured wireless transmission and classification of multiple biosignals using e‐Health sensors platform and Xbee modules with Arduino Uno and Raspberry Pi as acquisition and processing units, respectively. The collected data, such as temperature, airflow, position, Galvanic skin response and oxygen in the blood can be evaluated in order to monitor patient health state using threshold detection. The prediction of the cardiac state based on automatic identification of arrhythmias is validated by the classification of ElectroCardioGram (ECG) signals using Artificial Intelligence (AI) by exploiting TensorFlow and Keras tools. Different AI algorithms and a combination with different Machine Learning (ML) basing to transfer learning approach are tested. These algorithms include Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Support Vector Machine (SVM), K‐Nearest Neighbour (KNN) and Random Forest (RF). At first, ANN and CNN are used to classify ECG‐scalogram images using softmax, then the used CNN model (VGG16) is employed to extract features and pass them to other traditional classifiers (SVM, KNN and RF) allowing to evaluate and select the best classifier, such that the ECG signal can be classified into four categories namely Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Congestive Heart Failure (CHF) and other cardiac arrhythmia (ARR). The proposed method has been evaluated using real recorded signals and four PhysioNet databases. A Graphical User Interface (GUI) has been designed with C# under Visual Studio IDE allowing to display the results using personal computer (PC) or a network linked phone, which makes it possible to transfer the diagnosis with the prediction results to a remote clinic control room as Internet of Things (IoT) system application. The best classification accuracy of 99.56% is attained, confirming that the designed system allows a good trade‐off between low cost and performances in addition, it is easy to use with quick access to multiple biosignals. It has improved vital characteristics monitoring and diagnosis services quality under a robust secured wireless transmission using lightweight chaos‐based algorithm, thus preventing loss of life during critical health situations.

IoT based Paralyzed Patient Health and Body Movement Monitoring System

Healthcare systems are a very important part of the economy of any country and for the public health. The IoT-based monitoring system for patients with paralysis, which helps to promote the health condition of a patient with paralysis, in addition to the day-to-day life. India has suffered a stroke, the incidence is much higher than that of the more developed countries, it is home to around 2.1 million Indians suffered from the boom of the (lame) per year. If a patient is suffering from a paralysis attack in all or any part of the body can be turned off in order to move in, which means that their movement is restricted and they can barely communicate with anyone at all, because they can't talk like a normal person. Raccoons will find it difficult to understand what they are saying, and help them deal with their day-to-day needs, such as food, water, etc.). At present, work is in progress on the review of the motion parameters on the legs, arms, and head of the paralytics. This paper investigates the development of an integrated and portable prototype is a model of a system for the monitoring of the various movements of the body, spinal cord injuries, with the help of sensors. The tests were carried out by placing the sensors on the head, arm, and leg of the paralyzed patient the data received from these sensors are sent to the raspberry pi 3 model. In the Android app, you'll receive a verbal warning, and if the patient is in need of help via Bluetooth, which, in turn, is connected to the raspberry pi.

Spectrally filtered photodiode pairs for on-chip ratiometric aptasensing of cytokine dynamics

Miniaturized, cost-effective cytokine sensing devices are essential for health monitoring and critical care of patients with acute immune responses. Here we report an on-chip ratiometric aptasensing device to monitor the dynamics of an essential immune response biomarker, interferon-gamma. Our device is formed by pairs of spectrally filtered silicon photodiodes that are surface functionalized with aptamer probes. These photodiode pairs are able to rapidly detect two-color fluorescence changes transduced from aptamer-cytokine binding events within c.a. 6 min at picomolar levels, which are sufficient to capture the cytokine transients in point-of-care settings. Furthermore, our device can be reset to baseline by simple washing steps, yielding consistent readout in consecutive runs. Combined with their chip-scale construct and real-time operation, our cytokine sensors may have possible use in point-of-care and therapeutic screening applications.

Patient health monitoring system using IoT

Healthcare monitoring systems have been one of the most important system in the last decade, and they have become increasingly technological. Humans are facing various issue and untimely death due to multiple illness, due to a lack of medical treatment for patients. To overcome this issue a real time health monitoring system has proposed based on IOT. The proposed system consists of mobile application and GSM for continuous wireless monitoring of patients. The main aim is to create a dependable patient management system based on IoT so that healthcare professionals can monitor their patients who are either hospitalized or at home using an IOT based integrated healthcare system to ensure quality patient care. Sensors are used to track vital parameters, and the data collected by the sensors is sent to the cloud via a WiFi module. A wireless healthcare monitoring system has created that can provide real time online information about a patients conditions. The system is made up of sensors, a data acquisition unit, a microcontroller (i.e. ESP32) and software. The patients temperature, heart beat rate, ECG, blood pressure and SPO2 are regularly monitored, displayed and stored by the system and the same has been sent to the doctors mobile containing the application. Additionally a message is sent to the doctors mobile if any of the parameter crosses the threshold value. Thus, IOT based real time health monitoring system systematically monitor the condition of patients health and save their life on time.

Microcontroller Based Wireless Patient Health Monitoring System

This paper represents the design and simulation of a wireless patient health monitoring system. Generally, in the hospitals where patient’s body temperature needs to be continuously monitored, which is usually done by a doctor or other paramedical staff by continuously observing the temperature, pulse rate, blood pressure and maintaining its record. This is a very monotonous routine and can really be nerve-racking, especially in overcrowded hospitals. The primary function of this system is to monitor the temperature, blood pressure and pulse rate of a patient’s body, and transmit the information wirelessly to the doctor’s office on the LCD display unit. In this advanced system, a transmitting unit constantly scan patient’s body temperature, blood pressure and pulse rate through digital sensors, and shows them on the transmitter LCD. At the receiving end, a receiver is used to collect the data, decode it and feed them to another microcontroller which then displays it on the doctor’s LCD screen. The receiver unit is kept in the doctor’s office to constantly show the patient’s body temperature, blood pressure and pulse rate wirelessly. An alarm is also activated at the receiver end where the doctor will be, and is activated when the patient’s temperature, blood pressure and pulse rate goes below or above the normal human threshold value, which are 37°C, SBP- less than 120 mm Hg, DBP- less than 80mm Hg and 70 beats per minute. An SMS will be sent to the doctor, just in case he or she is outside the area and to present as a path for continuous update. The advanced system was simulated using Proteus software and programs written in Embedded C language. The result achieved shows a systematic method of relating information to the doctor on duty for urgent attention to patients.