Telemedicine in the age of the pandemics: The prospects of web-based remote patient monitoring systems for orthopaedic ambulatory care management in the developing economies.

Objective: The goal of this research was to demonstrate the efficacy of telemedicine via design, implementation and evaluation of a web-based remote patient monitoring system (WB-RPMS) across the tertiary/university teaching hospitals in a developing country Nigeria, as a tool to continue to expand access to an affordable and resilient tertiary healthcare system through the challenging times of the COVID-19 pandemic or any future disruptions. Methods: This research employed an agile and human-centred design thinking philosophy, which saw the researchers iteratively collaborate with clinicians across the system development value chain. It also employed qualitative and quantitative research methods for new system evaluations. After the system's development, a 20-patient sample was randomly selected from members of the National Youth Service Corp to evaluate the WB-RPMS Patient Portal for usability and user experience through a survey based on the system usability scale. Again, the COREQ standards for reporting research result were adopted for this study. Results: The evaluation of the WB-RPMS Patient Portal by a select patient sample showed that 95.0% of the respondents believed that they would like to use the system frequently. It was also discovered that 90.0% of all respondents also indicated that they found the Patient Portal to be simple; 85.0% of the respondents believed and indicated that the WB-RPMS Patient Portal was easy to use. Conclusions: The result of the usability evaluation of the developed WB-RPMS Patient Portal showed that it was well received by the select patient sample and by the clinicians who participated in the development process. In fact, the performance of the system shows that it has the potential to remotely support and sustain improved access to affordable healthcare for outpatients in developing countries even during times of uncertainties and disruptions as recently occasioned by COVID-19 pandemic.

Optimizing Data-Centric Healthcare: A Novel Monitoring Framework for High-Risk General Ward Patients.

The digital transformation of healthcare in South Korea, accelerated by COVID-19, has led to increased focus on critically ill patients in large hospitals. To address this, a monitoring system was developed to ensure safe inpatient treatment and improve staff efficiency. This aligns with the Medical Data-Centric Hospitals initiative, which leverages data for healthcare innovation. The case study highlights the implementation of a ward critical care monitoring system, which has improved patient safety, work efficiency, and expanded patient monitoring scope. Key lessons include the importance of addressing technical and user challenges, aligning innovations with national policies, and the potential of data-driven solutions to tackle healthcare challenges.

Wireless Patient Monitoring System Based on Smart Wristbands and Central user Interface Software.

In this article, a patient monitoring system is proposed that is able to obtain heart rate and oxygen saturation (SpO2) levels of patients, identify abnormal conditions, and inform emergency status to the nurses. The proposed monitoring system consists of smart patient wristbands, smart nurse wristbands, central monitoring user interface (UI) software, and a wireless communication network. In the proposed monitoring system, a unique smart wristband is dedicated to each of the patients and nurses. To measure heart rate and SpO2 level, a pulse oximeter sensor is used in the patient wristbands. The output of this sensor is transferred to the wristband's microcontroller where heart rate and SpO2 are calculated through advanced signal processing algorithms. Then, the calculated values are transmitted to central UI software through a wireless network. In the UI software, received values are compared with their normal values and a predefined message is sent to the nurses' wristband if an abnormal condition is identified. Whenever this message is received by a nurse's wristband, an acoustic alarm with vibration is generated to inform an emergency status to the nurse. By doing so, health services are delivered to the patients more quickly and as a result, the probability of the patient recovery is increased effectively.

Electroencephalographic monitoring of anesthesia during surgical procedures in mice using a modified clinical monitoring system.

Monitoring brain activity and associated physiology during the administration of general anesthesia (GA) in mice is pivotal to guarantee postanesthetic health. Clinically, electroencephalogram (EEG) monitoring is a well-established method to guide GA. There are no established methods available for monitoring EEG in mice (Mus musculus) during surgery. In this study, a minimally invasive rodent intraoperative EEG monitoring system was implemented using subdermal needle electrodes and a modified EEG-based commercial patient monitor. EEG recordings were acquired at three different isoflurane concentrations revealing that surgical concentrations of isoflurane anesthesia predominantly contained burst suppression patterns in mice. EEG suppression ratios and suppression durations showed strong positive correlations with the isoflurane concentrations. The electroencephalographic indices provided by the monitor did not support online monitoring of the anesthetic status. The online available suppression duration in the raw EEG signals during isoflurane anesthesia is a straight forward and reliable marker to assure safe, adequate and reproducible anesthesia protocols.

The development and preliminary evaluation of a clinician e-learning training platform for a neonatal sepsis risk monitor for use in ICU settings.

BACKGROUND: Training clinicians on the use of hospital-based patient monitoring systems (PMS) is vital to mitigate the risk of use errors and of frustration using these devices, especially when used in ICU settings. PMS training is typically delivered through face-to-face training sessions in the hospital. However, it is not always feasible to deliver training in this format to all clinical staff given some constraints (e.g., availability of staff and trainers to attend in-person training sessions and the costs associated with face-to-face training). OBJECTIVE: The literature indicates that E-learning has the potential to mitigate barriers associated with time restrictions for trainers and trainees and evidence shows it to be more flexible, and convenient for learners in healthcare settings. This study aimed to develop and carry out a preliminary evaluation via a case study of an e-learning training platform designed for a novel neonatal sepsis risk monitor system (Digi-NewB). METHODS: A multi-modal qualitative research case study approach was used, including the analysis of three qualitative data sources: (i) audio/video recordings of simulation sessions in which participants were asked to operate the system as intended (e.g., update the clinical observations and monitor the sepsis risk), (ii) interviews with the simulation participants and an attending key opinion leader (KOL), who observed all simulation sessions, and (iii) post-simulation survey. RESULTS: After receiving ethical approval for the study, nine neonatal intensive care unit (NICU) nurses completed the online training and participated in the simulation and follow-up interview sessions. The KOL was also interviewed, and seven out of the nine NICU nurses answered the post-simulation survey. The video/audio analysis of the simulations revealed that participants were able to use and interpret the Digi-NewB interface. Interviews with simulation participants and the KOL, and feedback extracted from the survey, revealed that participants were overall satisfied with the training platform and perceived it as an efficient and effective method to deliver medical device training. CONCLUSIONS: This study developed an online training platform to train clinicians in the use of a critical care medical device and carried out a preliminary evaluation of the platform via a case study. The e-learning platform was designed to supplement and enhance other training approaches. Further research is required to evaluate the effectiveness of this approach.

Artificial Intelligence-Based Patient Monitoring System for Medical Support.

PURPOSE: In this paper, we present the development of a monitoring system designed to aid in the management and prevention of conditions related to urination. The system features an artificial intelligence (AI)-based recognition technology that automatically records a user's urination activity. Additionally, we developed a technology that analyzes movements to prevent neurogenic bladder. METHODS: Our approach included the creation of AI-based recognition technology that automatically logs users' urination activities, as well as the development of technology that analyzes movements to prevent neurogenic bladder. Initially, we employed a recurrent neural network model for the urination activity recognition technology. For predicting the risk of neurogenic bladder, we utilized convolutional neural network (CNN)-based AI technology. RESULTS: The performance of the proposed system was evaluated using a study population of 30 patients with urinary tract dysfunction, who collected data over a 60-day period. The results demonstrated an average accuracy of 94.2% in recognizing urinary tract activity, thereby confirming the effectiveness of the recognition technology. Furthermore, the motion analysis technology for preventing neurogenic bladder, which also employed CNN-based AI, showed promising results with an average accuracy of 83%. CONCLUSION: In this study, we developed a urination disease monitoring system aimed at predicting and managing risks for patients with urination issues. The system is designed to support the entire care cycle of a patient by leveraging AI technology that processes various image and signal data. We anticipate that this system will evolve into digital treatment products, ultimately providing therapeutic benefits to patients.

EDDAMAP: efficient data-dependent approach for monitoring asymptomatic patient.

BACKGROUND: A pandemic affects healthcare delivery and consequently leads to socioeconomic complications. During a pandemic, a community where there lives an asymptomatic patient (AP) becomes a potential endemic zone. Assuming we want to monitor the travel and/or activity of an AP in a community where there is a pandemic. Presently, most monitoring algorithms are relatively less efficient to find a suitable solution as they overlook the continuous mobility instances and activities of the AP over time. Conversely, this paper proposes an EDDAMAP as a compelling data-dependent technique and/or algorithm towards efficient continuous monitoring of the travel and/or activity of an AP. METHODS: In this paper, it is assumed that an AP is infected with a contagious disease in which the EDDAMAP technique exploits a GPS-enabled mobile device by tagging it to the AP along with its travel within a community. The technique further examines the Spatio-temporal trajectory of the AP to infer its spatial time-bounded activity. The technique aims to learn the travels of the AP and correlates them to its activities to derive some classes of point of interests (POIs) in a location. Further, the technique explores the natural occurring POIs via modelling to identify some regular stay places (SP) and present them as endemic zones. The technique adopts concurrent object feature localization and recognition, branch and bound formalism and graph theory to cater for the worst error-guaranteed approximation to obtain a valid and efficient query solution and also experiments with a real-world GeoLife dataset to confirm its performance. RESULTS: The EDDAMAP technique proofs a compelling technique towards efficient monitoring of an AP in case of a pandemic. CONCLUSIONS: The EDDAMAP technique will promote the discovery of endemic zones and hence some public healthcare facilities can rely on it to facilitate the design of patient monitoring system applications to curtail a global pandemic.

Facial expression monitoring system for predicting patient's sudden movement during radiotherapy using deep learning.

PURPOSE: Imaging, breath-holding/gating, and fixation devices have been developed to minimize setup errors so that the prescribed dose can be exactly delivered to the target volume in radiotherapy. Despite these efforts, additional patient monitoring devices have been installed in the treatment room to view patients' whole-body movement. We developed a facial expression recognition system using deep learning with a convolutional neural network (CNN) to predict patients' advanced movement, enhancing the stability of the radiation treatment by giving warning signs to radiation therapists. MATERIALS AND METHODS: Convolutional neural network model and extended Cohn-Kanade datasets with 447 facial expressions of source images for training were used. Additionally, a user interface that can be used in the treatment control room was developed to monitor real-time patient's facial expression in the treatment room, and the entire system was constructed by installing a camera in the treatment room. To predict the possibility of patients' sudden movement, we categorized facial expressions into two groups: (a) uncomfortable expressions and (b) comfortable expressions. We assumed that the warning sign about the sudden movement was given when the uncomfortable expression was recognized. RESULTS: We have constructed the facial expression monitoring system, and the training and test accuracy were 100% and 85.6%, respectively. In 10 patients, their emotions were recognized based on their comfortable and uncomfortable expressions with 100% detection rate. The detected various emotions were represented by a heatmap and motion prediction accuracy was analyzed for each patient. CONCLUSION: We developed a system that monitors the patient's facial expressions and predicts patient's advanced movement during the treatment. It was confirmed that our patient monitoring system can be complementarily used with the existing monitoring system. This system will help in maintaining the initial setup and improving the accuracy of radiotherapy for the patients using deep learning in radiotherapy.

How to Design a Remote Patient Monitoring System? A French Case Study.

BACKGROUND: Remote Patient Monitoring Systems (RPMS) based on e-health, Nurse Navigators (NNs) and patient engagement can improve patient follow-up and have a positive impact on quality of care (by limiting adverse events) and costs (by reducing readmissions). However, the extent of this impact depends on effective implementation which is often restricted. This is partly due to the lack of attention paid to the RPMS design phase prior to implementation. The content of the RPMS can be carefully designed at this stage and various obstacles anticipated. Our aim was to report on an RPMS design case to provide insights into the methodology required in order to manage this phase. METHODS: This study was carried out at Gustave Roussy, a comprehensive cancer centre, in France. A multidisciplinary team coordinated the CAPRI RPMS design process (2013-2015) that later produced positive outcomes. Data were collected during eight studies conducted according to the Medical Research Council (MRC) framework. This project was approved by the French National Data Protection Authorities. RESULTS: Based on the study results, the multidisciplinary team defined strategies for resolving obstacles prior to the implementation of CAPRI. Consequently, the final CAPRI design includes a web app with two interfaces (patient and health care professionals) and two NNs. The NNs provide regular follow-up via telephone or email to manage patients' symptoms and toxicity, treatment compliance and care packages. Patients contact the NNs via a secure messaging system. Eighty clinical decision support tools enable NNs to prioritise and decide on the course of action to be taken. CONCLUSION: In our experience, the RPMS design process and, more generally, that of any complex intervention programme, is an important phase that requires a sound methodological basis. This study is also consistent with the notion that an RPMS is more than a technological innovation. This is indeed an organizational innovation, and principles identified during the design phase can help in the effective use of a RPMS (e.g. locating NNs if possible within the care organization; recruiting NNs with clinical and managerial skills; defining algorithms for clinical decision support tools for assessment, but also for patient decision and orientation).

Mobile-Based Patient Monitoring Systems: A Prioritisation Framework Using Multi-Criteria Decision-Making Techniques.

This study presents a prioritisation framework for mobile patient monitoring systems (MPMSs) based on multicriteria analysis in architectural components. This framework selects the most appropriate system amongst available MPMSs for the telemedicine environment. Prioritisation of MPMSs is a challenging task due to (a) multiple evaluation criteria, (b) importance of criteria, (c) data variation and (d) unmeasurable values. The secondary data presented as the decision evaluation matrix include six systems (namely, Yale-National Aeronautics and Space Administration (NASA), advanced health and disaster aid network, personalised health monitoring, CMS, MobiHealth and NTU) as alternatives and 13 criteria (namely, supported number of sensors, sensor front-end (SFE) communication, SFE to mobile base unit (MBU) communications, display of biosignals on the MBU, storage of biosignals on the MBU, intra-body area network (BAN) communication problems, extra-BAN communication problems, extra-BAN communication technology, extra-BAN communication protocols, back-end system communication technology, intended geographic area of use, end-to-end security and reported trial problems) based on the architectural components of MPMSs. These criteria are adopted from the most relevant studies and are found to be applicable to this study. The prioritisation framework is developed in three stages. (1) The unmeasurable values of the MPMS evaluation criteria in the adopted decision evaluation matrix based on expert opinion are represented by using the best-worst method (BWM). (2) The importance of the evaluation criteria based on the architectural components of the MPMS is determined by using the BWM. (3) The VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is utilised to rank the MPMSs according to the determined importance of the evaluation criteria and the adopted decision matrix. For validation, mean ± standard deviation is used to verify the similarity of systematic prioritisations objectively. The following results are obtained. (1) The BWM represents the unmeasurable values of the MPMS evaluation criteria. (2) The BWM is suitable for weighing the evaluation criteria based on the architectural components of the MPMS. (3) VIKOR is suitable for solving the MPMS prioritisation problem. Moreover, the internal and external VIKOR group decision making are approximately the same, with the best MPMS being 'Yale-NASA' and the worst MPMS being 'NTU'. (4) For the objective validation, remarkable differences are observed between the group scores, which indicate the similarity of internal and external prioritisation results.

Survey on Monitoring and Quality Controlling of the Mobile Biosignal Delivery.

A Mobile Patient Monitoring System (MPMS) acquires patient's biosignals and transmits them using wireless network connection to the decision-making module or healthcare professional for the assessment of patient's condition. A variety of wireless network technologies such as wireless personal area networks (e.g., Bluetooth), mobile ad-hoc networks (MANET), and infrastructure-based networks (e.g., WLAN and cellular networks) are in practice for biosignals delivery. The wireless network quality-of-service (QoS) requirements of biosignals delivery are mainly specified in terms of required bandwidth, acceptable delay, and tolerable error rate. An important research challenge in the MPMS is how to satisfy QoS requirements of biosignals delivery in the environment characterized by patient mobility, deployment of multiple wireless network technologies, and variable QoS characteristics of the wireless networks. QoS requirements are mainly application specific, while available QoS is largely dependent on QoS provided by wireless network in use. QoS provisioning refers to providing support for improving QoS experience of networked applications. In resource poor conditions, application adaptation may also be required to make maximum use of available wireless network QoS. This survey paper presents a survey of recent developments in the area of QoS provisioning for MPMS. In particular, our contributions are as follows: (1) overview of wireless networks and network QoS requirements of biosignals delivery; (2) survey of wireless networks' QoS performance evaluation for the transmission of biosignals; and (3) survey of QoS provisioning mechanisms for biosignals delivery in MPMS. We also propose integrating end-to-end QoS monitoring and QoS provisioning strategies in a mobile patient monitoring system infrastructure to support optimal delivery of biosignals to the healthcare professionals.

Some problems with cost-benefit analysis in health care.

...Arnold's article is very ambitious. He suggests rules that could be followed in order to decide whether health care should be rationed. He notes that explicit assessments of costs and benefits using the same monetary unit are rarely used. The main reason, it seems to me, is that the method of cost-benefit analysis is relatively difficult to apply in the context of health care. Arnold does not address the difficulties that are related to his approach. Thus, my impression is that the interest of his provocative article lies more in its ability to foster a useful debate than in the methodology itself. In this brief commentary, I will merely list some of the theoretical problems that occurred to me while reading this article....

Quantifying the value of human life for cost accounting of safeguards: clarifying formulas applied to the clozapine controversy.

Using CPMS as an example, this article has developed mathematical formulations to compare the value of lives saved against the cost of medical safeguards. By a logical comparison of benefit and cost converted to the same units of currency, the article explores three important questions relating to the CPMS example. These mathematical formulations serve to quantify the decision-making process in a logical manner, and might serve as a useful example for clinical and public policy decisions.

Ethical and clinical considerations in selecting patients who will receive clozapine.

In a large state hospital population, more treatment-resistant schizophrenic patients may be clinically eligible to receive clozapine than the hospital can afford to pay for, given the costs of the medication and its mandatory monitoring system. The authors review clinical criteria for selecting patients and discuss two ethical principles that might be useful: select patients on the principle of providing the greatest good for the greatest number of patients (based on expected outcomes such as likelihood of discharge and benefit to the milieu) and give priority to patients for whom the fewest treatment alternatives are available. Issues such as family pressures for administration of clozapine to a relative must also be dealt with.