Abstract P120: Feasibility Of An Asynchronous, Semi-Automated Remote Patient Monitoring Blood Pressure Management System

Introduction: While remote patient monitoring (RPM) for hypertension (HTN) continues to grow in the United States, most systems are third party, employer-directed, or do not directly lead to changes in medication management. Systems that address these issues may reduce therapeutic inertia and lead to more rapid control of blood pressure (BP). We developed a clinician-facing HTN RPM system with evidence-based customizable medication titration protocols that integrate with a patient mobile app and Bluetooth®-connected BP cuff. We report interim results from the pilot implementation of this system. Hypothesis: In a pilot study, an RPM system with patient and clinician-facing platforms and a semi-automated protocol will achieve high engagement with actionable user feedback. Methods: We performed a single arm, single center study with five clinicians from primary care (3) and cardiology (2). Eligible patients had essential hypertension (BP >130/80 mmHg) and a smartphone (iPhone, Android). Patients used a Bluetooth®-connected cuff that sent readings to a patient app and a clinician dashboard. Based on BP and comorbidities, a protocol provided medication titration recommendations for clinicians. In this 12-week study, we assessed feasibility through user feedback, user engagement (defined as the number of BP measurements), and changes in systolic (SBP, mmHg) and diastolic BP (DBP, mmHg). Results: We enrolled 18 patients (age 51 + 11y; 94% male; 29% White). Baseline SBP was 133 + 7.8 and DBP was 87 + 7.1. At a mean follow-up of 4.7 weeks, there were 15 + 11 weekly BP measurements per patient. Mean per-patient decreases in SBP and DBP were 12 (95% CI 5.8-18, p<0.001) and 7.1 (95% CI 3.1-11, p = 0.002), respectively. A total of 77.8% (14/18) patients continued BP measurements without attrition. Key feedback included improved cuff-mobile app connectivity (patients) and increased medication choices in protocols (clinicians). Conclusions: In interim results of a pilot study, an RPM HTN system was implemented with high engagement, evidence of BP reduction, and actionable feedback. Complete results including medication and BP changes are anticipated by September 2020 and will guide a planned, funded, large, multicenter cluster randomized trial.

Early Identification of COVID-19 using Remote Cardiorespiratory Monitoring: Three Case Reports (Preprint)

BACKGROUND The adoption of remote patient monitoring (RPM) into routine medical care requires an increased understanding of how the physiologic changes accompanying disease development and what proactive interventions will improve outcomes. OBJECTIVE We present three case reports which highlight the capability of RPM to allow for early identification of viral infection with COVID-19 in chronic respiratory disease patients. METHODS Patients at a large pulmonary practice were identified who were enrolled in a respiratory RPM program and who had contracted COVID-19. The physiologic data was retrospectively reviewed and three instances were identified where the RPM system had notified clinicians of physiologic deviation due to the viral infection. RESULTS Physiologic deviations from respective patient baselines occurred during infection onset and, despite the infection manifesting differently in each case, had been identified by the RPM system. In one case, the patient was symptomatic, in another the patient was pre-symptomatic, and in the final the patient varied from asymptomatic to mildly symptomatic. CONCLUSIONS RPM systems meant for long-term use and which utilize patient-specific baselines can highlight physiologic changes early in the course of acute disease, such as COVID-19 infection. The cases demonstrate opportunities for earlier diagnosis, treatment, and isolation. This supports the need for further research into how RPM can be effectively integrated into clinical practice. CLINICALTRIAL

Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward.

Performance evaluation and application of a new phase permeability improver system

A novel hydrophobically associating polymer (HHA-RPM) was synthesized by a solution polymerization method with a modified acrylamide (AM) as the main component and implanting an anionic group. The static adsorptivity of the polymer was measured by an ultraviolet visible light photometer with an adsorption amount of about 11.0 mg/g. The influence of concentration, temperature and salinity on the apparent viscosity of the polymer solution system was investigated indoorally, and the HHA-RPM system was prepared. In view of the on-site requirements for the timeliness of the phase permeability improver, an antioxidant was added to the system to evaluate the durability of the HHA-RPM system.

Performance Analysis of Nested Multilevel Inverter Topology for 72V Electric Vehicle Applications

In recent years Multilevel Inverter (MLI) getting in popular due to its performance in field of medium and high-power applications. Many MLI’s like Diode-clamped, cascaded H-bridge, flying capacitor and hybrid cascaded H-bridge are introduced in 1970’s. But due to the draw backs like harmonic distortions researchers are concentrated on novel topologies. Recently nested configuration is gaining attention to researchers due to it is having an advantage of 3-phase design with a smaller number of components compared to traditional MLI topologies. Hence, this paper investigates the performance an advanced MLI named as Nested topology for 72V electric vehicle (EV) motor drive application for 1kW/1500 RPM system. It can generate near-sinusoidal voltages with only fundamental switching frequency, there is no electromagnetic interference (EMI) and also it gives easy operating EV and safer conditions. Furthermore, this paper inspects the analysis, benefits and control scheme for nested MLI for the use of EV motor drive applications. The simulations are carried out using Matlab/Simlink.

Intelligent Rework Process Management System under Smart Factory Environment

Rework for defective items is very common in practical shopfloors; however, it generally causes unnecessary energy consumptions and operational costs. In order to address this problem, we propose a novel approach called the intelligent rework process management (i-RPM) system. The proposed system is based on intelligent rework policy, which provides a preventive rework procedure for items with latent defects. Such items can be detected before quality tests by applying conventional classification techniques. Moreover, training sets for the classification algorithms can be collected by using modern information and communications technology (ICT) infrastructures. Items with latent defects are not allowed to proceed to the following processes under intelligent rework policy. Instead, they are returned to the preceding processes for rework in order to avoid unnecessary losses on the shopfloor. Consequently, the proposed system helps to achieve a sustainable manufacturing system. Nevertheless, misclassification by the classification model can degrade the performance of intelligent rework policy. Therefore, the i-RPM system is designed to compare rework policies based on classification accuracy and choose the best one of them. For illustration, we applied the i-RPM system to the rework procedure of a steel manufacturer located in Busan, South Korea, and our experiment results revealed that the cost reduction effect of the intelligent rework policy is affected by several input parameters.

Evaluation of patient specific quality assurance of gated field in field radiation therapy technique using two-dimensional detector array

Introduction: Gated tangential field-in-field (FIF) technique is used to lower the dose to organs at risk for breast cancer radiotherapy (RT). In this study, the authors investigated the accuracy of the delivered treatment plan with and without gating using a two-dimensional detector array for patient-specific verification purposes.Methods: In this study, a 6MV beams were used for the merged FIF RT (forward Intensity Modulated Radiation Therapy). The respiration signals for gated FIF delivery were obtained from the one-dimensional moving phantom using the real-time position management (RPM) system (Varian Medical Systems, Palo Alto, CA). RPM system used for four-dimensional computed tomography scanner light-speed, GE is based on an infrared camera to detect motion of external 6-point marker. The beams were delivered using a Clinac iX (Varian Medical Systems, Palo Alto, CA) with the multileaf collimator Millennium 120. The MapCheck2 (SunNuclear, Florida) was used for the evaluation of treatment plans. MapCheck2 was validated through a comparison with measurements from a farmer-type ion chamber. Gated beams were delivered using a maximum dose rate with varying duty cycles and analyzed the MapCheck2 data to evaluate treatment plan delivery accuracy.Results: Results of the gamma passing rate for relative and absolute dose differences for all ungated and gated beams were between 95.1% and 100%.Conclusion: Gated FIF technique can deliver an accurate dose to a detector during gated breast cancer RT. There is no significance between gated and ungated patient-specific quality assurance (PSQA); one can use ungated PSQA for verification of treatment plan delivery

Blockchain Based Remote Patient Monitoring System

Remote Patient Monitoring system is an approach of a health care system that enables the patient-user of performing a remote periodical check-up. Unfortunately, these types of systems usually don't provide the advantages of securely sharing the patient health information among different health providers. Many types of research aimed to solve this issue by applying the blockchain technique to the existing patient health information records at hospitals. However; none was found regarding the remote patient monitoring system's generated data. Therefore, this proposal aims to integrate the advantages of blockchain and the Remote Patient Monitoring (RPM) system by building a secure blockchain based RPM system.