Health Parameters Of Patient Research Articles

Muscle matters: Prognostic implications of malnutrition and muscle health parameters in patients with cancer. A secondary analysis of a randomised trial

BackgroundLow muscle mass and malnutrition are independently associated with an increased risk of adverse outcomes in patients with cancer. However, it is not yet clear which parameter is most indicative of these risks. This study investigates the prognostic significance of different parameters reflecting malnutrition and muscle health in a well-characterised oncology population at nutritional risk. MethodsThis preplanned secondary analysis included patients with cancer from a Swiss-wide, randomised-controlled nutritional trial. We investigated associations among malnutrition markers (i.e., malnutrition diagnosis based on modified Global Leadership Initiative on Malnutrition (GLIM) criteria, albumin concentration) and muscle health markers (i.e., hand grip strength, computed tomography (CT)-based muscle mass and radiodensity) with 180-day all-cause mortality (primary outcome). ResultsWe included 269 patients with a main admission diagnosis of cancer and available CT scans. In a mutually adjusted model, four parameters contributed to risk assessment including modified malnutrition diagnosis (GLIM) (HR 1.78 (95%CI 1.17 to 2.69), p = 0.007, AUC 0.58), low albumin concentration (HR 1.58 (95%CI 1.08 to 2.31), p = 0.019, AUC 0.62), low handgrip strength (HR 2.05 (95%CI 1.43 to 2.93), p = 0.001, AUC 0.62) and low muscle radiodensity (HR 1.39 (95%CI 0.90 to 2.16), p = 0.139, AUC 0.63). Combining these parameters resulted in a model with high prognostic power regarding 180-day mortality (overall AUC 0.71). ConclusionsIn this study of inpatients with cancer at nutritional risk, several malnutrition and muscle health parameters emerged as independent prognostic indicators for mortality. The use of these parameters may improve risk stratification and guide nutritional interventions in this vulnerable population. Trial registrationClinicalTrials.gov, number NCT02517476.

Computational Hemodynamics-Based Growth Prediction for Small Abdominal Aortic Aneurysms: Laminar Simulations Versus Large Eddy Simulations.

Prior studies have shown that computational fluid dynamics (CFD) simulations help assess patient-specific hemodynamics in abdominal aortic aneurysms (AAAs); patient-specific hemodynamic stressors are frequently used to predict an AAA's growth. Previous studies have utilized both laminar and turbulent simulation models to simulate hemodynamics. However, the impact of different CFD simulation models on the predictive modeling of AAA growth remains unknown and is thus the knowledge gap that motivates this study. Specifically, CFD simulations were performed for 70 AAA models derived from 70 patients' computed tomography angiography (CTA) data with known growth status (i.e., fast-growing [> 5mm/yr] or slowly growing [< 5mm/yr]). We used laminar and large eddy simulation (LES) models to obtain hemodynamic parameters to predict AAAs' growth status. Predicting the growth status of AAAs was based on morphological, hemodynamic, and patient health parameters in conjunction with three classical machine learning (ML) classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and generalized linear model (GLM). Our preliminary results estimated aneurysmal flow stability and wall shear stress (WSS) were comparable in both laminar and LES flow simulations. Moreover, computed WSS and velocity-related hemodynamic variables obtained from the laminar and LES simulations showed comparable abilities in differentiating the growth status of AAAs. More importantly, the predictive modeling performance of the three ML classifiers mentioned above was similar, with less than a 2% difference observed (p-value>0.05). In closing, our findings suggest that two different flow simulations investigated did not significantly affect outcomes of computational hemodynamics and predictive modeling of AAAs' growth status, given the data investigated.

NGHIÊN CỨU XÂY DỰNG THIẾT BỊ Y TẾ HỖ TRỢ THEO DÕI TỪ XA CHO BỆNH NHÂN TIM MẠCH

This article focuses on researching and developing a medical support device with functions to monitor various health parameters of patients directly or remotely, such as body temperature, heart rate, and blood oxygen saturation. The implementation method includes designing algorithm diagrams, hardware circuit design, component selection, and building a display application on smartphones or a website interface that allows monitoring the parameters quickly and accurately. Actual testing initially showed that, the device essentially operates quite accurately according to the design and programming with many advantages such as: The system operates stably, meeting design requirements; Results can be monitored visually in a variety of ways and conveniently on LCD screen, App, Web; Each measurement data is saved with data security assurance; The system is evaluated to have minimal errors, low power consumption, and is cost-effective, along with the convenience of result monitoring, making it suitable for various user groups. Furthermore, the research results can serve as a practical model for students in the Biomedical Engineering field at educational institutions where there is currently a shortage of devices catering to learning needs.

Automatic Medical Dispatcher with Dynamic Telemonitoring System Using IoT in Rural Areas

Accessing quality healthcare in rural areas can be challenging, often requiring individuals to travel long distances to nearby towns or cities. To address this issue, the Anytime Medical Machine (AMM) project was developed, leveraging Internet of Things (IoT) technology to provide on-the-spot medical consultations. AMM, a part of the Internet of Medical Things (IoMT), is designed for primary deployment in rural areas. Through AMM, individuals can receive consultations from qualified doctors without the need to travel. Doctors can prescribe medications and provide treatment recommendations, reducing both time and expenses associated with travel. The system collects patient data using sensors for temperature, heart rate, and blood oxygen saturation (SPO2), transmitting this information to doctors via a Wi-Fi module. The doctor-patient interaction is facilitated through secure video conferencing, requiring a unique username and password for each user to prevent misuse. After analyzing the patient's health parameters, the doctor prescribes appropriate medication, which is then dispensed through AMM. Patient information is securely uploaded to a cloud server for future reference, and the system is maintained by an administrator who refills medications as needed. Keywords- Rural Healthcare, Remote Consultation, IoT Healthcare, Medical IoT, Telemedicine, Remote Monitoring, Cloud-Based Healthcare, Healthcare Accessibility

Patient Reminder System

Overview Patient Health Monitoring on ESP8266 Healthcare technology is very popular in this pandemic situation because of coronavirus. Actually, health care technology is rapidly being revolutionized with the help of the Internet of Things (IoT). Monitoring the health status of a covid patient is a hard task because of our busy schedule and our daily work. Mostly, the elderly covid patients should be monitored periodically. So, I thought to make an innovative system in this lockdown to automate the task. This device uses an ESP8266 webserver to track patient health using this monitoring system. Hence, patient health parameters such as body temperature, heart rate (BPM), blood oxygen levels (Sp02) as well as room temperature and humidity can be monitored from any device (like Smartphone, PC, Laptop, Smart TV,.) That support browsing capabilities. In this project, we will learn how to build an ESP8266 based Patient Health Monitoring System. To measure Heart Rate/Pulse (BPM) and Blood Oxygen Level (SpO2), we use the MAX30100 pulse oximeter sensor. Similarly, to measure body temperature, we use the DS18B20 temperature sensor. Meanwhile, the patient is inside the room. So we need to monitor room temperature and humidity level as well. We should keep them in a room with a certain temperature and humidity level to not feel uncomfortable. Hence, we use the DHT22 Temperature &amp; Humidity sensor. A Smart Patient Health Monitoring System using IoT (Internet of Things) is a technology-driven solution designed to continuously collect, transmit, and analyze health-related data from patients. It provides real-time insights to healthcare professionals, patients, and caregivers to improve patient care, enhance early disease detection, and promote overall well-being. One important component of such a system is the alert system, which notifies relevant parties when certain health parameters or conditions require attention.

Exploring real-time patient monitoring and data analytics with IoT-based smart healthcare monitoring

This research study endeavours to explore the dynamic temporal variations in patient health parameters, specifically heart rate, temperature, and Systolic Blood Pressure (SBP), within the context of a synthetic patient dataset. The analysis of heart rate data revealed distinct patterns of temporal variation, characterized by cyclic fluctuations within each hour and an overall increasing trend over the observation period. These findings Highlights the influence of circadian rhythms, potential associations with physical activity and stress, and the broader physiological implications for patient health. The examination of temperature data exhibited analogous cyclic patterns and an increasing trend, prompting considerations of circadian rhythms and external factors such as environmental temperature and physiological health. Conversely, SBP data demonstrated fluctuations without a discernible trend, highlighting the complex nature of blood pressure regulation and its susceptibility to a myriad of physiological and external influences. This study provides a foundational understanding of patient health parameter dynamics and emphasizes the need for more extensive research to elucidate the underlying mechanisms and clinical implications. The insights gleaned from this investigation hold the potential to inform healthcare practitioners and researchers in the realms of patient monitoring and personalized healthcare, ultimately enhancing the quality of patient care and decision-making in clinical settings.

Internet of Things (IoT)-Based Smart Healthcare System for Efficient Diagnostics of Health Parameters of Patients in Emergency Care

The Internet of Things has been instrumental in bringing about several advancements and innovations in the domain of healthcare. Healthcare professionals are essentially life savers when it comes to handling emergency cases such as accidents, heart attacks, etc. Only the patient’s vital parameters generally characterize emergency cases, and the doctors must wait for additional details for a wholesome diagnosis. As a result, the treatment processes and procedures sometimes get hastened and, in turn, put the patients’ lives at risk. It would always be helpful for doctors to be equipped with the medical requirements in advance for deciding the right course of action, thereby increasing the scope and chances of recovery. In this work, Multi-Model IoT (MMIoT) devices are deployed to monitor and collect health data from different body parts simultaneously. The healthcare data comprises signals and imagery captured from the MMIoT devices. Both the U-Net model and LSTM model are used to analyze the data automatically. The data processing is carried out by the server connected to the MMIoT network. All the medical IoT devices experimented with in this work are interconnected using a potential 5G network for optimal data transmission. The output obtained from the U-Net and the LSTM are channelized through a dense layer to classify the health anomalies accurately. It would not only facilitate but also educate medical professionals to handle unseen and typical cases in the future confidently. It can improve the overall quality of treatment and save lives with the best available resources.

Impact of physical activity on self-reported health parameters in peripheral artery disease in two periods of the COVID-19 pandemic

BackgroundProlonged social isolation intended to mitigate the spread of Coronavirus 2019 (COVID-19), may potentially affect the physical activity level and health of patients with peripheral artery disease (PAD). ObjectivesTo analyze the impact of physical activity practice on longitudinal changes in self-reported health parameters during the COVID-19 pandemic in patients with PAD. DesignLongitudinal study. SettingThe database of studies developed by our group involving patients with PAD from public hospitals in São Paulo, Brazil. MethodsIn this longitudinal study, 99 patients with PAD were evaluated in two periods during the COVID-19 pandemic (i.e., May to August 2020 and May to August 2021). Patients were interviewed by telephone, and information was obtained regarding physical activity practice and self-reported health (i.e., current global, physical, and mental health). Patients were divided into two groups: the physically inactive group (patients who were or became physically inactive, n=76, 51.3% male, 67±10 years old, 8±7 years of disease duration) and the physically active group (patients who started or continued practicing physical activity, n=23, 65.2% male, 71±8years old, 7±6 years of disease). ResultsThe physically inactive group more frequently self-reported their current health as poor (6.6 vs. 17.1%, P=0.045) and were more frequently hospitalized for reasons other than COVID-19 (6.6 vs. 27.6%, P=0.001) between the two periods evaluated during the COVID-19 pandemic. The physically active group self-reported lower declines in walking capacity (13.0 vs. 43.5%, P=0.022) between the two periods evaluated. We did not observe any differences in other parameters in either group between the two periods evaluated (p>0.05). ConclusionsThe practice of physical activity during the COVID-19 pandemic can help maintain or mitigate the negative impacts on self-reported global and physical health parameters in patients with PAD.

IoT Based Tracking System for Patients

Abstract-Health-care Environment has developed in science and patients require ongoing monitoring of their ECG readings, body temperature, blood pressure (BP), heart rate, and other vital indicators. So, the primary goal of the working model is to use wireless technology to transmit the patient's health parameters to the doctor in case anything is abnormal. All the characteristics will be sensed, even if the patient is unconscious, and relayed to the medical professionals. So, we are proposing an innovative project to dodge such sudden death rates by using Patient Health Monitoring that uses sensor technology and uses the internet to communicate with assistance in case of problems. This system uses Temperature, Spo2, gyroscope, and heartbeat sensors for tracking patients’ health. These sensors are connected to the Arduino-Uno. A microcontroller that tracks the patient's health which is connected to an LCD display and a wireless network to transmit the information to a web server. In case of any abrupt changes in the patient’s heart rate, temperature, or blood oxygen saturation level, an alert message is sent about the patient using IoT. This tracking system also shows patients’ temperature, angular movements, and heartbeat tracked live data with timestamps over the Internetwork. Consequently, the IoT-based patient health tracking system actively monitors patient health through the internet, assists users in keeping an eye on their patients while at work, and perhaps saves lives. technology- based knowledge focused on Wireless-Sensing nodes. Key Words: Monitoring, Arduino-Uno, LCD display, temperature alert, Ubidots.

Design of a randomized cross-over study evaluating effects of carbohydrate intake on glycemic control in persons with type 1 diabetes.

Diet is an important factor in managing glycemic control in type 1 diabetes (T1D). Reducing carbohydrate intake may be important for stabilizing blood glucose levels in certain groups of patients with T1D. There are few studies examining the effects of a low carbohydrate diet in patients with T1D. The aim of this study is to investigate the effects of carbohydrate intake on glucose control in adults with T1D. Adults with T1D (N = 54) and inadequate glycemic control (HbA1c ≥ 7.5%; 58 mmol/mol) were randomized in a cross-over design to a moderate carbohydrate diet (30 percent of total energy from carbohydrates) versus a traditional diabetes diet (50 percent of total energy from carbohydrates) for 4 weeks with a between wash-out period of 4 weeks. Masked continuous glucose monitoring was used throughout the study to evaluate effects on mean blood glucose levels, time-in-range, hypoglycemia, hyperglycemia, and glycemic variability. Diabetes treatment satisfaction, hypoglycemic confidence, and physical activity were measured using questionnaires during different phases of the trial. HbA1c, blood lipids, blood pressure, and ketone levels were also measured. The primary endpoint is the difference in mean blood glucose level between the diet periods. Study completion is anticipated during winter 2022. The study seeks to increase knowledge about the effects of dietary carbohydrate intake on glycemic control and other health parameters in patients with T1D. If beneficial effects on mean blood glucose level without elevated risk of hypoglycemia or ketoacidosis are shown, a moderate carbohydrate diet may be a treatment option for people with T1D that have unsatisfactory blood glucose levels.Clinical Trials Registration: www.clinicaltrials.gov, ID: NCT03400618.

IoT Based Covid Patient Health Monitoring System

The COVID-19 pandemic situation has made timely Health monitoring a major concern for saving a life. The busy routine life is a major challenge for doctors to regularly monitor the Health of a COVID Patient. To overcome this problem faced by the family members and doctors in this pandemic situation, we have designed a smart Health monitoring system using IoT (Internet of Things). This system immediately checks and alerts about the critical situations faced by the COVID Patients. This system uses NODEMCU ESP8266 controller, for continuous tracking of the Patient’s Blood Oxygen saturation (SpO 2 ) level. Additionally, Patient Health parameters such as Body Temperature, Heart rate (BPM), as well as Room Temperature and Humidity can also be measured. The Patient Health data are collected and stored in an IoT platform (BLYNK). These Patient Health parameters can be monitored from anywhere by any devices such as Smartphones, Laptop, PC, Smart TV’s etc… that supports browsing capabilities. If the levels of Blood Oxygen Saturation (SpO 2 ) gets lower, it sends a warning immediately to the family members, doctors and to the nearest hospital through mail, phone call and SMS (Short Message Service). By Checking the data obtained from the system, necessary actions could be taken before the problem becomes severe. Thus, the Patient’s life can be saved in time by using this proposed system.

The association of psoriasis and psoriatic arthritis with periodontitis: A hospital-based case-control study.

Background Periodontitis can trigger and perpetuate inflammation in several chronic inflammatory diseases. The association of periodontitis with psoriasis has been investigated earlier, but data are incomplete and the influence of confounders has not been fully evaluated. We examined the relationship of dental and periodontal health parameters in patients with psoriasis and/or psoriatic arthritis. Methods This hospital-based cross-sectional analytical study was conducted in patients with chronic plaque psoriasis, psoriatic arthritis or both, and compared with controls. Dental and periodontal health parameters were assessed based on the WHO oral health assessment method. Multivariate logistic regression was done on variables with significant or near-significant values to find the association between periodontitis and psoriasis and/or psoriatic arthritis after adjusting for confounders. Results Psoriasis and/or psoriatic arthritis were independently and significantly associated with periodontal pockets ≥4 mm in depth. Limitations Causality and temporal relationship cannot be established as this was a cross-sectional study. As in all observational studies, the possibility of unmeasured or unknown confounders exists. Psoriatic arthritis was present only in a small subset of patients. Conclusion Patients with psoriasis and/or psoriatic arthritis have significant periodontal inflammation. This needs to be addressed by dental examination and intervention.

Reliability of smartphone measurements of peripheral oxygen saturation and heart rate in hypotensive patients measurement of vital signs with smartphones

ObjectiveWith the increasing use of wearable technologies (smartphones and smartwatches), it has become possible to measure vital signs outside healthcare institutions without the need for an additional medical device. With the advancement in technologies, the accuracy of vital signs measured by smartphones and smartwatches has also increased. In this study, the accuracy of smart devices in the measurement of heart rate and saturation, which are two vital signs that are difficult to detect in conditions such as hypotension were investigated. Materials and methodsThe study was prospectively conducted in a tertiary healthcare center. In hypotensive patients who presented to the emergency department (ED) and required an arterial blood gas evaluation, oxygen saturation and heart rate values measured by a smartphone, those measured with a vital signs monitor (VSM) at the time of admission to the ED and oxygen saturation values measured by a blood gas analyzer (BGA) were compared. ResultsA total of 200 patients, 117 women and 83 men, were included in the study. It was determined that the correlation coefficients of the heart rate values measured by the vital signs monitor and smartphone were in a high statistical agreement. When the saturation values measured by the vital signs monitor, smartphone, and blood gas analyzer were compared, it was found that the intra-class correlation coefficients of the saturation values measured by the smartphone with reference to the blood gas analyzer and vital signs monitor were 0.957 and 0.949, respectively, indicating an excellent agreement. ConclusionSmartphones have as high efficiency as reference devices in measuring heart rate and saturation in hypotensive patients. In this way, hypotensive patients who need medical help can also have the opportunity to measure their vital parameters with their smartphones, without the need for any other medical device, before applying to the hospital or emergency health system. This may contribute to the improvement of the quality of life of the patients and the early and accurate information of the health care providers about the patient's health parameters.

Pulmonary hypertension predicts higher mortality in patients admitted with severe COVID-19 infection.

Patients with underlying conditions are predicted to have worse outcomes with COVID-19. A strong association between baseline cardiovascular disease and COVID-19-related mortality has been shown by a number of studies. In the current retrospective study, we aim to identify whether patients with pulmonary hypertension have worse outcomes compared with patients without pulmonary hypertension. Data from patients of ⩾18 years of age with COVID was retrospectively collected and analyzed (n = 679). Patients who underwent transthoracic echocardiography, at the discretion of the medical team, were identified and the transthoracic echocardiography was reviewed for the presence of pulmonary hypertension. Patient health parameters and outcomes were measured and statistically analyzed. Of 679 consecutive patients identified with a diagnosis of COVID-19, 57 underwent transthoracic echocardiography, 32 of which were found to have pulmonary hypertension. Patients who underwent transthoracic echocardiography had a significantly higher intensive care unit admission rate (73.7% versus 25.4%, p < 0.001) and increased presence of acute respiratory distress syndrome (63.2% versus 21.6%, p > 0.001). These patients had longer intensive care unit length of stay, longer mechanical ventilation time, longer hospital length of stay, and a significantly higher mortality rate when compared to those not undergoing transthoracic echocardiography (59.7% versus 32.3%, p < 0.001). Among patients who underwent transthoracic echocardiography, those with pulmonary hypertension had significantly higher mortality compared to those without pulmonary hypertension (80% versus 43.8%, p < 0.01). COVID-19 in patients with pulmonary hypertension was associated with high in-hospital mortality even when adjusted for confounding factors. A number of mechanisms have been proposed for the worse outcomes in patients with pulmonary hypertension and right ventricular dysfunction, including right ventricle overload and indirect pro-inflammatory cytokine storm. Further, large-scale studies are required to evaluate the impact of right ventricular dysfunction in COVID-19 patients and to elucidate the associated mechanisms.

Symptoms of Anxiety and Depression and Sense of Coherence in Patients Undergoing Carotid Artery Stenting.

Background: The sense of coherence is lower in patients with somatic diseases and psychiatric disorders. Purpose: The purpose of this study was to evaluate the intensity of depression and anxiety symptoms and their relationship with the sense of coherence and to try to determine the relationship between the strength of sense of coherence and symptoms of depression and anxiety with the presence of symptoms of carotid atherosclerosis in a group of patients undergoing carotid artery stenting. Methods: 35 patients, including 13 with symptomatic atherosclerosis in the carotid arteries, completed self-report tests: Hospital Scale of Depression and Anxiety (HADS) and the SOC-29 Life Orientation Questionnaire (SOC-29), 22 of whom also rated their subjective feelings of anxiety and depression on a scale included in the author’s questionnaire. Results: Both symptomatic and asymptomatic patients did not differ significantly in the severity of depression, but they differed in anxiety levels as assessed by the HADS scale. There were no differences in the overall strength of sense of coherence and its individual components. Nearly 12% of those undergoing carotid artery stenting have pronounced anxiety symptoms, and just over 14% have pronounced depression symptoms. A higher overall sense of coherence and its components are associated with lower severity of depression symptoms. Lower severity of anxiety correlates negatively with a higher sense of understanding, meaningfulness, and holistic Sense of Coherence (SOC). Manageability appeared higher in men. Conclusions: SOC is an important health-promoting factor that is preferably related to mental health parameters of patients with carotid atherosclerosis.

The Impact of Foehn Wind on Mental Distress among Patients in a Swiss Psychiatric Hospital.

Psychiatric patients are particularly vulnerable to strong weather stimuli, such as foehn, a hot wind that occurs in the alps. However, there is a dearth of research regarding its impact on mental health. This study investigated the impact of foehn wind among patients of a psychiatric hospital located in a foehn area in the Swiss Alps. Analysis was based on anonymized datasets obtained from routine records on admission and discharge, including the Brief Symptom Checklist (BSCL) questionnaire, as well as sociodemographic parameters (age, sex, and diagnosis). Between 2013 and 2020, a total of 10,456 admission days and 10,575 discharge days were recorded. All meteorological data were extracted from the database of the Federal Office of Meteorology and Climatology of Switzerland. We estimated the effect of foehn on the BSCL items using a distributed lag model. Significant differences were found between foehn and non-foehn admissions in obsession–compulsion, interpersonal sensitivity, depression, anxiety, phobic anxiety, paranoid ideation, and general severity index (GSI) (p < 0.05). Our findings suggest that foehn wind events may negatively affect specific mental health parameters in patients. More research is needed to fully understand the impact of foehn’s events on mental health.

An IoT Based Health Monitoring System to Tackle COVID-19 in a Contagious Ward of Hospital

l: l: Email:l: Email:l: Regarding the highly contiguous novel coronavirus disease (COVID-19), it is unsafe for health professionals, being involved in a hospital isolation ward. The healthcare workers, stationed in a contagious ward provide relentless monitoring of specific health parameters of those patients. The rate of contagion proportionately depends on the time spent by the health workers in an isolation ward. This challenging task is fairly manageable by employing promising Internet-of-Things (IoT) based autonomous robots (i.e. smart bots) thus reducing the hazards of contamination to health workers. In this research paper, we introduce a smart bot that can periodically measure the health parameters of COVID-19 patients, for instance, body temperature, oxygen saturation levels, blood pressure, respiration rate, heart rate, blood glucose level, etc. The proposed smart bot will transfer from one patient to another in a structured pattern and autonomously collect, forward, store the data of the patients for further analysis. The goal of the work is to involve a significantly inferior number of health workers in contagious wards to control contagion, thus creating stress-free environs for health workers. Moreover, smart bots will offer health professionals to pay attention to non-COVID-19 patients and make things easier for regular health check-ups of individuals in need.

An IoT Based Health Monitoring System to Tackle COVID-19 in a Contagious Ward of Hospital

Regarding the highly contiguous novel coronavirus disease (COVID-19), it is unsafe for health professionals, being involved in a hospital isolation ward. The healthcare workers, stationed in a contagious ward provide relentless monitoring of specific health parameters of those patients. The rate of contagion proportionately depends on the time spent by the health workers in an isolation ward. This challenging task is fairly manageable by employing promising Internet-of-Things (IoT) based autonomous robots (i.e. smart bots) thus reducing the hazards of contamination to health workers. In this research paper, we introduce a smart bot that can periodically measure the health parameters of COVID-19 patients, for instance, body temperature, oxygen saturation levels, blood pressure, respiration rate, heart rate, blood glucose level, etc. The proposed smart bot will transfer from one patient to another in a structured pattern and autonomously collect, forward, store the data of the patients for further analysis. The goal of the work is to involve a significantly inferior number of health workers in contagious wards to control contagion, thus creating stress-free environs for health workers. Moreover, smart bots will offer health professionals to pay attention to non-COVID-19 patients and make things easier for regular health check-ups of individuals in need.

A review of required monitoring and management of physical health parameters in patients being treated with clozapine

AimsTo review available standards for physical health monitoring in people taking clozapine To audit current practice against standards To identify changes in practice and facilitate a re-audit to assess impact of any changesMethodStandard: CG178 Psychosis and Schizophrenia in Adults: Prevention and Management – NICE, February 2014Target:100%Exceptions: NoneSample: The original audit included all 58 patients from the Worcester clozapine clinic, as per October 2018. The re-audit reviewed a random sample of all patients attending the clozapine clinics in Worcester, Kidderminster and Redditch, as part of Worcestershire Health and Care NHS Trust, as per October 2019. A total of 66 patients were selected.Data Source: Carenotes and ICEResultAreas of good practice:Monitoring of HbA1c and FBC remains goodThere has been an improvement in monitoring alcohol use, substance misuse and side effectsAreas requiring improvement:There continues to be limited recording of respiratory rateThere has been a decline in recording temperature, BMI and concomitant therapiesPotential reasoning for missing data includes:Staff not knowing the monitoring requirements, which is more likely to be an issue when staff members running the clinics change frequentlyMonitoring being completed but not documentedPatients’ refusal of monitoringData being recorded in alternative locations including general practice, without communication between servicesPatients moving between teams or having inpatient stays may disrupt monitoring regimeConclusionLIMITATIONSThis audit assumes all patients involved to be on a stable dose of clozapine with routine monitoringSome patients may have been transferred between teams or inpatients during the period of data collectionThere is no scope to record when patients refuse monitoringWe may not have access to all notes such as those from general practice for data collectionRECOMMENDATIONSInduction programme for junior doctors to include education on clozapine monitoringTraining for staff involved in clozapine clinics to ensure better understanding of monitoring requirementsProcurement of ECG machines for each site and relevant training for nursing and medical staffCollaboration with GPs for shared dataRe-audit in 1 year

WITHDRAWN: Cloud computing based generic medicine recommendation system for advanced E-Healthcare

Many health institutes manually record and collect patients' data may be misunderstood and at times, inconsistent. Also, patients' alienation from medical details leaves them unaware of the necessary medical costs and inevitably causes them to pay long and ineffective bills. Therefore, it is very important and important to consider the consumers of generic medicinal products – medicinal products of the same chemical composition, but available at low cost. This device proposes a method for sensing the physical wellbeing of patients using sensors after they are admitted to the hospital. This is added to a “cloud” server that stores the health parameters of patients and accesses them whenever possible. The patient's cloud-based databases also include the medicine prescribed by physicians, the diagnosed condition of the patient and the tests suggested by the doctors. The software interfaced between the cloud and the patient data entry displays the results of 'cost-effective generic drugs,' the necessary tests needed for the patient and the hospital to be qualified for further treatment, which are shown to the patients or their associates. Few well-equipped healthcare facilities follow data centralization and database approaches, but their accuracy remains a matter of reliability. Our proposed system offers a way for patients to track their medical costs within their boundaries, using sensors and cloud computers as their technological resources.