Emerging and Innovative Technologies

2021 ◽  
pp. 275-286
Author(s):  
Ahmad A. Aalam ◽  
Sam P. Tarassoli ◽  
Damien J. Drury ◽  
Elias G. Carayannis ◽  
Andrew C. Meltzer
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Emergency Medicine ◽  
Augmented Reality ◽  
Remote Patient Monitoring ◽  
The Core ◽  
Unscheduled Care ◽  
Near Term ◽  
Core Mission ◽  
Remote Patient

To provide acute unscheduled care 24 hours per day and 7 days per week is the core mission of emergency medicine. Emergency telehealth is evolving in scope and complexity, no longer constraining care by the walls of the emergency department (ED). Current audio- and video-based communications will advance to support a complex interplay between enhanced video communication, remote patient monitoring, augmented reality, and machine learning. Many of these technologies already exist or are under development for near-term implementation. For those deploying or planning the deployment of emergency telehealth services, this chapter highlights near-term technologies and applications to be considered.

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

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 776
Author(s):  
Xiaohui Tao ◽  
Thanveer Basha Shaik ◽  
Niall Higgins ◽  
Raj Gururajan ◽  
Xujuan Zhou
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Radio Frequency Identification ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Remote Patient Monitoring ◽  
Rfid Technology ◽  
Rpm System ◽  
Frequency Identification ◽  
Remote Patient

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.

scholarly journals Smart Multi-Level Tool for Remote Patient Monitoring Based on a Wireless Sensor Network and Mobile Augmented Reality

Sensors ◽  
2014 ◽  
Vol 14 (9) ◽  
pp. 17212-17234 ◽  
Author(s):  
Fernando González ◽  
Osslan Villegas ◽  
Dulce Ramírez ◽  
Vianey Sánchez ◽  
Humberto Domínguez
Keyword(s):  
Wireless Sensor Network ◽  
Augmented Reality ◽  
Sensor Network ◽  
Patient Monitoring ◽  
Wireless Sensor ◽  
Mobile Augmented Reality ◽  
Remote Patient Monitoring ◽  
Multi Level ◽  
Remote Patient

scholarly journals The core emergency ultrasound curriculum project: A report from the Curriculum Working Group of the CAEP Emergency Ultrasound Committee

CJEM ◽  
2017 ◽  
Vol 20 (2) ◽  
pp. 176-182 ◽  
Author(s):  
Paul Olszynski ◽  
Dan Kim ◽  
Jordan Chenkin ◽  
Louise Rang
Keyword(s):  
Emergency Department ◽  
Emergency Medicine ◽  
Working Group ◽  
Focused Ultrasound ◽  
Position Statement ◽  
Emergency Ultrasound ◽  
Emergency Physicians ◽  
The Core ◽  
Canadian Association ◽  
Curriculum Project

Emergency ultrasound (EUS) is now widely considered to be a “skill integral to the practice of emergency medicine.”1The Canadian Association of Emergency Physicians (CAEP) initially issued a position statement in 1999 supporting the availability of focused ultrasound 24 hours per day in the emergency department (ED).2

scholarly journals Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Dylan M. Richards ◽  
MacKenzie J. Tweardy ◽  
Steven R. Steinhubl ◽  
David W. Chestek ◽  
Terry L. Vanden Hoek ◽  
...  
Keyword(s):  
Machine Learning ◽  
Patient Monitoring ◽  
Standard Of Care ◽  
Sensor Data ◽  
Study Cohort ◽  
Remote Patient Monitoring ◽  
Health Technologies ◽  
Phase Study ◽  
Remote Patient ◽  
Continuous Sensor

AbstractThe COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of various personal health technologies, but only rarely using a multi-parameter chest-patch for continuous monitoring. Here we describe the development and validation of a COVID-19 decompensation index (CDI) model based on chest patch-derived continuous sensor data to predict COVID-19 hospitalizations in outpatient-managed COVID-19 positive individuals, achieving an overall AUC of the ROC Curve of 0.84 on 308 event negative participants, and 22 event positive participants, out of an overall study cohort of 400 participants. We retrospectively compare the performance of CDI to standard of care modalities, finding that the machine learning model outperforms the standard of care modalities in terms of both numbers of events identified and with a lower false alarm rate. While only a pilot phase study, the CDI represents a promising application of machine learning within a continuous remote patient monitoring system.

scholarly journals Tele-transitions of Care (TTOC). A 12-month, Randomized Controlled Trial evaluating the use of Telehealth to achieve Triple Aim Objectives.

2020 ◽  
Author(s):  
Kimberly Noel ◽  
Catherine Messina ◽  
Wei Hou ◽  
Elinor Schoenfeld ◽  
Gerald Kelly
Keyword(s):  
Emergency Department ◽  
Randomized Controlled Trial ◽  
Hospital Readmission ◽  
Patient Monitoring ◽  
Controlled Trial ◽  
Transitions Of Care ◽  
Emergency Department Utilization ◽  
Remote Patient Monitoring ◽  
Randomized Controlled ◽  
Remote Patient

Abstract Background: Poor transitions of care leads to increased health costs, over-utilization of emergency room departments, increased re-hospitalizations and causes poor patient experiences and outcomes. This study evaluated Telehealth feasibility in improving transitions of care.Methods: This is a 12-month randomized controlled trial, evaluating the use of telehealth (remote patient monitoring and video visits) versus standard transitions of care with the primary outcomes of hospital readmission and emergency department utilization and secondary outcomes of access to care, medication management and adherence and patient engagement. Electronic Medical Record data, Health Information Exchange data and phone survey data was collected. Multi-variable logistic regression models were created to evaluate the effect of Telehealth on hospital readmission, emergency department utilization, medication adherence. Chi-square tests or Fisher’s exact tests were used to compare the percentages of categorical variables between the Telehealth and control groups. T tests or Wilcoxon rank sum tests were used to compared means and medians between the two randomized groups.Results: The study conducted between June 2017 and 2018, included 102 patients. Compared with the standard of care, Telehealth patients were more likely to have medicine reconciliation (p = 0.013) and were 7 times more likely to adhere to medication than the control group (p = 0.03). Telehealth patients exhibited enthusiasm (p = 0.0001), and confidence that Telehealth could improve their healthcare (p= 0.0001). Telehealth showed no statistical significance on emergency department utilization (p = 0.691) nor for readmissions (p = 0.31). 100% of Telehealth patients found the intervention to be valuable, 98% if given the opportunity, reported they would continue using telehealth to manage their healthcare needs, and 94% reported that the remote patient monitoring technology was useful.Conclusions: Telehealth can improve transitions of care after hospital discharge improving patient engagement and adherence to medications. Although this study was unable to show the effect of Telehealth on reduced healthcare utilization, more research needs to be done in order to understand the true impact of Telehealth on preventing avoidable hospital readmission and emergency department visits.

scholarly journals Tele-transitions of Care (TTOC). A 12-month, Randomized Controlled Trial evaluating the use of Telehealth to achieve Triple Aim Objectives

2020 ◽  
Author(s):  
Kimberly Noel ◽  
Catherine Messina ◽  
Wei Hou ◽  
Elinor Schoenfeld ◽  
Gerald Kelly
Keyword(s):  
Emergency Department ◽  
Randomized Controlled Trial ◽  
Hospital Readmission ◽  
Patient Monitoring ◽  
Controlled Trial ◽  
Transitions Of Care ◽  
Emergency Department Utilization ◽  
Remote Patient Monitoring ◽  
Randomized Controlled ◽  
Remote Patient

Abstract Background : Poor transitions of care leads to increased health costs, over-utilization of emergency room departments, increased re-hospitalizations and causes poor patient experiences and outcomes. This study evaluated Telehealth feasibility in improving transitions of care. Methods : This is a 12-month randomized controlled trial, evaluating the use of telehealth (remote patient monitoring and video visits) versus standard transitions of care with the primary outcomes of hospital readmission and emergency department utilization and secondary outcomes of access to care, medication management and adherence and patient engagement. Electronic Medical Record data, Health Information Exchange data and phone survey data was collected. Multi-variable logistic regression models were created to evaluate the effect of Telehealth on hospital readmission, emergency department utilization, medication adherence. Chi-square tests or Fisher’s exact tests were used to compare the percentages of categorical variables between the Telehealth and control groups. T tests or Wilcoxon rank sum tests were used to compared means and medians between the two randomized groups. Results : The study conducted between June 2017 and 2018, included 102 patients. Compared with the standard of care, Telehealth patients were more likely to have medicine reconciliation (p = 0.013) and were 7 times more likely to adhere to medication than the control group (p = 0.03). Telehealth patients exhibited enthusiasm (p = 0.0001), and confidence that Telehealth could improve their healthcare (p= 0.0001). Telehealth showed no statistical significance on emergency department utilization (p = 0.691) nor for readmissions (p = 0.31). 100% of Telehealth patients found the intervention to be valuable, 98% if given the opportunity, reported they would continue using telehealth to manage their healthcare needs, and 94% reported that the remote patient monitoring technology was useful. Conclusions: Telehealth can improve transitions of care after hospital discharge improving patient engagement and adherence to medications. Although this study was unable to show the effect of Telehealth on reduced healthcare utilization, more research needs to be done in order to understand the true impact of Telehealth on preventing avoidable hospital readmission and emergency department visits.

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