Evaluating Efficiency of a Provincial Telerehabilitation Service in Improving Access to Care During the COVID-19 Pandemic.

Scope: Early in the COVID-19 pandemic, community rehabilitation stakeholders from a provincial health system designed a novel telerehabilitation service. The service provided wayfinding and self-management advice to individuals with musculoskeletal concerns, neurological conditions, or post-COVID-19 recovery needs. This study evaluated the efficiency of the service in improving access to care. Methodology: We used multiple methods including secondary data analyses of call metrics, narrative analyses of clinical notes using artificial intelligence (AI) and machine learning (ML), and qualitative interviews. Conclusions: Interviews revealed that the telerehabilitation service had the potential to positively impact access to rehabilitation during the COVID-19 pandemic, for individuals living rurally, and for individuals on wait lists. Call metric analyses revealed that efficiency may be enhanced if call handling time was reduced. AI/ML analyses found that pain was the most frequently-mentioned keyword in clinical notes, suggesting an area for additional telerehabilitation resources to ensure efficiency.

Evaluating the impact of a novel telerehabilitation service to address neurological, musculoskeletal, or coronavirus disease 2019 rehabilitation concerns during the coronavirus disease 2019 pandemic.

Introduction: A novel telerehabilitation service provides wayfinding and self-management advice to persons with neurological, musculoskeletal, or coronavirus disease 2019 related rehabilitation needs. Method: We utilized multiple methods to evaluate the impact of the service. Surveys clarified health outcomes (quality of life, self-efficacy, social support) and patient experience (telehealth usability; general experience) 3-months post-call. We analysed associations between, and within, demographics and survey responses. Secondary analyses described health care utilization during the first 6 months. Results: Sixty-eight callers completed the survey (42% response rate). Self-efficacy was significantly related to quality of life, interpersonal support and becoming productive quickly using the service. Becoming productive quickly was significantly related to quality of life. Education level was related to ethnicity. Survey respondents' satisfaction and whether they followed the therapist's recommendations were not significantly associated with demographics. Administrative data indicated there were 124 callers who visited the emergency department before, on, or after their call. The average (SD) frequency of emergency department visits before was 1.298 times (1.799) compared to 0.863 times (1.428) after. Discussion: This study offers insights into the potential impact of the telerehabilitation service amidst pandemic restrictions. Usability measurements showed that callers were satisfied, corroborating literature from pre-pandemic contexts. The satisfaction and acceptability of the service does not supplant preferences for in-person visits. The survey sample reported lower quality of life compared with the provincial population, conflicting with pre-pandemic research. Findings may be due to added stressors associated with the pandemic. Future research should include population-level comparators to better clarify impact.

Evaluating Community-Facing Virtual Modalities to Support Complex Neurological Populations During the COVID-19 Pandemic: Protocol for a Mixed Methods Study.

BACKGROUND: The COVID-19 pandemic and concomitant governmental responses have created the need for innovative and collaborative approaches to deliver services, especially for populations that have been inequitably affected. In Alberta, Canada, two novel approaches were created in Spring 2020 to remotely support patients with complex neurological conditions and rehabilitation needs. The first approach is a telehealth service that provides wayfinding and self-management advice to Albertans with physical concerns related to existing neurological or musculoskeletal conditions or post-COVID-19 recovery needs. The second approach is a webinar series aimed at supporting self-management and social connectedness of individuals living with spinal cord injury. OBJECTIVE: The study aims to evaluate the short- and long-term impacts and sustainability of two virtual modalities (telehealth initiative called Rehabilitation Advice Line [RAL] and webinar series called Alberta Spinal Cord Injury Community Interactive Learning Seminars [AB-SCILS]) aimed at advancing self-management, connectedness, and rehabilitation needs during the COVID-19 pandemic and beyond. METHODS: We will use a mixed-methods evaluation approach. Evaluation of the approaches will include one-on-one semistructured interviews and surveys. The evaluation of the telehealth initiative will include secondary data analyses and analysis of call data using artificial intelligence. The evaluation of the webinar series will include analysis of poll questions collected during the webinars and YouTube analytics data. RESULTS: The proposed study describes unique pandemic virtual modalities and our approaches to evaluating them to ensure effectiveness and sustainability. Implementing and evaluating these virtual modalities synchronously allows for the building of knowledge on the complementarity of these methods. At the time of submission, we have completed qualitative and quantitative data collection for the telehealth evaluation. For the webinar series, so far, we have distributed the evaluation survey following three webinars and have conducted five attendee interviews. CONCLUSIONS: Understanding the impact and sustainability of the proposed telehealth modalities is important. The results of the evaluation will provide data that can be actioned and serve to improve other telehealth modalities in the future, since health systems need this information to make decisions on resource allocation, especially in an uncertain pandemic climate. Evaluating the RAL and AB-SCILS to ensure their effectiveness demonstrates that Alberta Health Services and the health system care about ensuring the best practice even after a shift to primarily virtual care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/28267.

Robotic Ultrasound Scanning With Real-Time Image-Based Force Adjustment: Quick Response for Enabling Physical Distancing During the COVID-19 Pandemic.

During an ultrasound (US) scan, the sonographer is in close contact with the patient, which puts them at risk of COVID-19 transmission. In this paper, we propose a robot-assisted system that automatically scans tissue, increasing sonographer/patient distance and decreasing contact duration between them. This method is developed as a quick response to the COVID-19 pandemic. It considers the preferences of the sonographers in terms of how US scanning is done and can be trained quickly for different applications. Our proposed system automatically scans the tissue using a dexterous robot arm that holds US probe. The system assesses the quality of the acquired US images in real-time. This US image feedback will be used to automatically adjust the US probe contact force based on the quality of the image frame. The quality assessment algorithm is based on three US image features: correlation, compression and noise characteristics. These US image features are input to the SVM classifier, and the robot arm will adjust the US scanning force based on the SVM output. The proposed system enables the sonographer to maintain a distance from the patient because the sonographer does not have to be holding the probe and pressing against the patient's body for any prolonged time. The SVM was trained using bovine and porcine biological tissue, the system was then tested experimentally on plastisol phantom tissue. The result of the experiments shows us that our proposed quality assessment algorithm successfully maintains US image quality and is fast enough for use in a robotic control loop.

Review: How Can Intelligent Robots and Smart Mechatronic Modules Facilitate Remote Assessment, Assistance, and Rehabilitation for Isolated Adults With Neuro-Musculoskeletal Conditions?

Worldwide, at the time this article was written, there are over 127 million cases of patients with a confirmed link to COVID-19 and about 2.78 million deaths reported. With limited access to vaccine or strong antiviral treatment for the novel coronavirus, actions in terms of prevention and containment of the virus transmission rely mostly on social distancing among susceptible and high-risk populations. Aside from the direct challenges posed by the novel coronavirus pandemic, there are serious and growing secondary consequences caused by the physical distancing and isolation guidelines, among vulnerable populations. Moreover, the healthcare system's resources and capacity have been focused on addressing the COVID-19 pandemic, causing less urgent care, such as physical neurorehabilitation and assessment, to be paused, canceled, or delayed. Overall, this has left elderly adults, in particular those with neuromusculoskeletal (NMSK) conditions, without the required service support. However, in many cases, such as stroke, the available time window of recovery through rehabilitation is limited since neural plasticity decays quickly with time. Given that future waves of the outbreak are expected in the coming months worldwide, it is important to discuss the possibility of using available technologies to address this issue, as societies have a duty to protect the most vulnerable populations. In this perspective review article, we argue that intelligent robotics and wearable technologies can help with remote delivery of assessment, assistance, and rehabilitation services while physical distancing and isolation measures are in place to curtail the spread of the virus. By supporting patients and medical professionals during this pandemic, robots, and smart digital mechatronic systems can reduce the non-COVID-19 burden on healthcare systems. Digital health and cloud telehealth solutions that can complement remote delivery of assessment and physical rehabilitation services will be the subject of discussion in this article due to their potential in enabling more effective and safer NMSDK rehabilitation, assistance, and assessment service delivery. This article will hopefully lead to an interdisciplinary dialogue between the medical and engineering sectors, stake holders, and policy makers for a better delivery of care for those with NMSK conditions during a global health crisis including future pandemics.

Case Report: Utilizing AI and NLP to Assist with Healthcare and Rehabilitation During the COVID-19 Pandemic.

The COVID-19 pandemic has profoundly affected healthcare systems and healthcare delivery worldwide. Policy makers are utilizing social distancing and isolation policies to reduce the risk of transmission and spread of COVID-19, while the research, development, and testing of antiviral treatments and vaccines are ongoing. As part of these isolation policies, in-person healthcare delivery has been reduced, or eliminated, to avoid the risk of COVID-19 infection in high-risk and vulnerable populations, particularly those with comorbidities. Clinicians, occupational therapists, and physiotherapists have traditionally relied on in-person diagnosis and treatment of acute and chronic musculoskeletal (MSK) and neurological conditions and illnesses. The assessment and rehabilitation of persons with acute and chronic conditions has, therefore, been particularly impacted during the pandemic. This article presents a perspective on how Artificial Intelligence and Machine Learning (AI/ML) technologies, such as Natural Language Processing (NLP), can be used to assist with assessment and rehabilitation for acute and chronic conditions.

Intraoperative optimization of seed implantation plan in breast brachytherapy.

PURPOSE: Low-dose-rate permanent-seed (LDR-PS) brachytherapy has shown a great potential for treating breast cancer. An implantation scheme indicating the template pose and needle trajectories is determined before the operation. However, when performing the pre-planned scheme intraoperatively, a change of the patient's posture will cause seed placements away from the desired locations. Hence, the implantation scheme should update based on the current patient's posture. METHODS: A numerical method of optimizing the implantation scheme for the LDR-PS breast brachytherapy is presented here. The proposed algorithm determines the fewest needle trajectories and template poses for delivering the seeds to the intraoperative desired locations. The clinical demand, such as the minimum distance between the chest wall and the needle, is considered in the optimization process. RESULTS: The method was simulated for a given LDR-PS brachytherapy procedure to evaluate the optimal scheme as the number of the template poses changing. The optimization parameters of the needles' number and the implantation errors are used to adjust the algorithm outcome. The results show that the implantation schemes obtained by our method have a satisfactory accuracy in the cases of 2 or 3 template poses. The computation time is about 76s to 150s according to the number of the template poses from 1 to 3. CONCLUSION: The proposed method can find the optimal implantation scheme corresponding to the current desired seed locations immediately once there is a change of patient's posture. This work can be applied to the robot-assisted LDR-PS breast brachytherapy for improving the operation accuracy and efficiency.

Augmented Reality Guided Needle Biopsy of Soft Tissue: A Pilot Study.

Percutaneous biopsies are popular for extracting suspicious tissue formations (primarily for cancer diagnosis purposes) due to the: relatively low cost, minimal invasiveness, quick procedure times, and low risk for the patient. Despite the advantages provided by percutaneous biopsies, poor needle and tumor visualization is a problem that can result in the clinicians classifying the tumor as benign when it was malignant (false negative). The system developed by the authors aims to address the concern of poor needle and tumor visualization through two virtualization setups. This system is designed to track and visualize the needle and tumor in three-dimensional space using an electromagnetic tracking system. User trials were conducted in which the 10 participants, who were not medically trained, performed a total of 6 tests, each guiding the biopsy needle to the desired location. The users guided the biopsy needle to the desired point on an artificial spherical tumor (diameters of 30, 20, and 10 mm) using the 3D augmented reality (AR) overlay for three trials and a projection on a second monitor (TV) for the other three trials. From the randomized trials, it was found that the participants were able to guide the needle tip 6.5 ± 3.3 mm away from the desired position with an angle deviation of 1.96 ± 1.10° in the AR trials, compared to values of 4.5 ± 2.3 mm and 2.70 ± 1.67° in the TV trials. The results indicate that for simple stationary surgical procedures, an AR display is non-inferior a TV display.