Narrow Results

Telemedicine (TM) is used to treat patients in a remote location by using telecommunication technology. It exchanges the medical information and data from one location to another through advanced technological innovation. During this COVID-19 pandemic, there is a lockdown in almost all countries. TM is beneficial to healthcare to minimize social distance. This review paper briefs about TM and discusses how this technology works for the COVID-19 pandemic and its significant benefits. An extensive search is made on the known research engines of PubMed, SCOPUS, Google Scholar, and ResearchGate using the appropriate keywords to extract meaningful and relevant articles. Ten major applications of TM for COVID-19 are identified and discussed with a brief description of each provided. The major technological processes involved in TM, which create advancement in the medical field, are also discussed. This technology helps avoid visits to the doctor and hospital during the lockdown and provides a suitable treatment option. It collects the medical information and data, which can be helpful for better treatment of the patient. Telemedicine adopts virtualized treatment approaches for the patient. Now patients can receive better quality treatment without leaving their homes during COVID-19 lockdown.

Problem Statement: Improper triage and prioritization of big-data patients may result in erroneous strategic decisions. An example of such wrong decision making includes the triage of patients with chronic heart disease to low-priority groups. Incorrect decisions may jeopardize the patients’ health.

Objective: This study aims to evaluate and score the big data of patients with chronic heart disease and of those who require urgent attention. The assessment is based on multicriteria decision making in a telemedical environment to improve the triage and prioritization processes.

Methods: A hands-on study was performed. A total of 500 patients with chronic heart disease manifested in different symptoms and under various emergency levels were evaluated on the basis of the following four main measures. An electrocardiogram sensor was used to measure the electrical signals of the contractile activity of the heart over time. A SpO₂ sensor was employed to determine the blood oxygen saturation levels of the patients. A blood pressure sensor was used to obtain the physiological data of the systolic and diastolic blood pressures of the patients. Finally, a non-sensory measurement (text frame) was conducted to assess chest pain and breathing. The patients were prioritized on the basis of a set of measurements by utilizing integrated back-forward adjustment for weight computation and technique for order performance by similarity to ideal solution.

Discussion Results: Patients with the most urgent cases were given the highest priority level, whereas those with the least urgent cases were assigned with the lowest priority level among all patients’ scores. The first three patients assigned to the medical committee of doctors were proven to be the most critical emergency cases with the highest priority level on the basis of their clinical symptoms. By contrast, the last three patients were proven to be the least critical emergency cases and given the lowest priority levels relative to other patients. The throughput measurement in terms of scalability based on our proposed algorithm was more efficient than that of the benchmark algorithm. Finally, the new method for determining the “big data” patients characteristics based on “4Vs” was suggested.

Telehealth is an increasingly critical component of the health care ecosystem, especially due to the COVID-19 pandemic. Rapid adoption of telehealth has exposed limitations in the existing infrastructure. In this paper, we study and highlight photo quality as a major challenge in the telehealth workflow. We focus on teledermatology, where photo quality is particularly important; the framework proposed here can be generalized to other health domains. For telemedicine, dermatologists request that patients submit images of their lesions for assessment. However, these images are often of insufficient quality to make a clinical diagnosis since patients do not have experience taking clinical photos. A clinician has to manually triage poor quality images and request new images to be submitted, leading to wasted time for both the clinician and the patient. We propose an automated image assessment machine learning pipeline, TrueImage, to detect poor quality dermatology photos and to guide patients in taking better photos. Our experiments indicate that TrueImage can reject ~50% of the sub-par quality images, while retaining ~80% of good quality images patients send in, despite heterogeneity and limitations in the training data. These promising results suggest that our solution is feasible and can improve the quality of teledermatology care.

Past decades’ rapid development of technological innovations can ease individual’s everyday lives, but they can also convey a sense of frustration. The aim of the present study was to investigate attitudes toward technologies that are expected to become widespread in the near future. The Technology Readiness Index was administered to a sample of Hungarian respondents to measure the capacity to adopt technologies. The results add significant novelties about the specific patterns related to perceptions of considerably different future technologies, emphasizing the unique role of optimism. Derivations are further specified by demographic characteristics, future directions and practical implications are also discussed.

Incumbents’ inertia in the face of disruptive innovations has been emphasised in prior literature. The relevance of inertia is particularly topical in the context of digital transformation. However, incumbents may be able to invest in disruptive digital innovations appropriately if they possess the motivation and ability to do so. In this paper, I use three streams of research in order to investigate contextual, organisational, and individual antecedents of incumbents’ motivation and ability to adopt and use potentially disruptive digital innovations in health care: institutional theory, the resource-based view, and technology acceptance literature. I employ factor analyses and logistic regressions to test the impact on the adoption and usage of telemedicine applications using a dataset of 9,196 European general practitioners. I examine B2B as well as B2C applications in order to determine the effect of the antecedents on different business models. My findings suggest that only isomorphic pressure, complementary assets, and perceived output quality significantly influence both adoption and usage as well as B2B and B2C business models in the same way. Formal institutions and individual factors yield ambiguous results. These findings provide important implications for the understanding of incumbents’ response to potentially disruptive digital innovations in regulated contexts.

Background: With the emergence of the COVID-19 pandemic, most health-care personnel and resources are redirected to prioritize care for seriously-ill COVID patients. This situation may poorly impact our capacity to care for critically injured patients. We need to devise a strategy to provide rational and essential care to hand trauma victims whilst the access to theatres and anaesthetic support is limited. Our center is a level 1 trauma center, where the pandemic preparedness required reorganization of the trauma services. We aim to summarise the clinical profile and management of these patients and highlight, how we modified our practice to optimize their care.

Methods: This is a single-centre retrospective observational study of all patients with hand injuries visiting the Department of Plastic Surgery from 22^nd March to 31^st May 2020. Patient characteristics, management details, and outcomes were analysed.

Results: A total of 102 hand injuries were encountered. Five patients were COVID-19 positive. The mean age was 28.9 ± 14.8 years and eighty-two (80.4%) were males. Thirty-one injuries involved fractures/dislocations, of which 23 (74.2%) were managed non-operatively. Seventy-five (73.5%) patients underwent wound wash or procedure under local anaesthetic and were discharged as soon as they were comfortable. Seventeen cases performed under brachial-plexus block, were discharged within 24 hours except four cases of finger replantation/ revascularisation and one flap cover which were discharged after monitoring for four days. At mean follow-up of 54.4 ± 21.8 days, the rates of early complication and loss to follow-up were 6.9% and 12.7% respectively.

Conclusions: Essential trauma care needs to continue keeping in mind, rational use of resources while ensuring safety of the patients and health-care professionals. We need to be flexible and dynamic in our approach, by utilising teleconsultation, non-operative management, and regional anaesthesia wherever feasible.

A novel approach to trend monitoring and the identification of promising high-tech solutions is presented in the chapter. It is based on the ontology of a technology/market trend, Hype Cycles methodology, and semantic indicators which provide evidence of a maturity level of a technology as well as of emerging user needs (customer pains) in high-tech industries. This approach forms the basis for text mining software tools implemented in Semantic Hub platform. The algorithms behind these tools allow users to escape from getting too general or garbage results which make it impossible to identify promising technologies at early stages (early detection, weak signals). Besides, these algorithms provide high-quality results in the extraction of complex multiword terms which correspond to technological concepts and user pains forming a trend. The methodology and software developed as a result of this study are applicable to various industries with minor adjustments.

This study aims to adapt the Expectation Disconfirmation Theory and Technology Adoption Model to unveil provocative roles in patients’ satisfaction cognitions and subsequent continuity behaviors pertaining to telemedicine services in rural Bangladesh. A quantitative research model is developed and validated using a two-staged deep neural network and partial least squares structural equation modeling approach. The findings of this study provide evidence that five salient determinants; expectations, disconfirmation, performance, usefulness, and ease of use dominantly contribute to predicting patients’ satisfaction concerning continuity with telemedicine. This contributes to health informatics and behavioral literature by clarifying the complex interplay between patients’ satisfaction and determinants of continuity behavior in telemedicine’s domain. The findings provide novel insights into predictions of complex patients’ attitudes toward telemedicine continuity, and dynamic changes in adoption trends thereby assisting health professionals, global health experts, policymakers, and IS community in making higher quality informed decisions for people-centered future models of care.

Background: A major consequence of the COVID-19 pandemic on the U.S. healthcare system has been the rapid transition away from in-person healthcare visits to telehealth. This study analyzed patient and surgeon satisfaction in the utilization of telehealth within the hand surgery division during the COVID-19 pandemic.

Methods: All hand surgery patients who completed a telemedicine visit from March 30^th, 2020 through April 30^th, 2020 completed a 14-question survey via e-mail. Hand surgeons who participated in telemedicine completed a separate 14-question survey. Survey results were presented descriptively (mean ± standard deviation) and patient factors influencing satisfaction were determined using univariate and multivariate proportional modeling.

Results: 89 patients and five surgeons completed the surveys. Patients were very satisfied with their telemedicine visits (4.21/5.00 ± 0.89). Multivariate proportional modeling determined patients who found it “very easy” (5/5) to arrange telemedicine visits had greater satisfaction (OR = 4.928; 95% CI = 0.94 to 25.84) compared to those who found it “difficult” (2/5) (p = 0.059). Patients who believed they could ask/relay questions/concerns “extremely effectively” (5/5) had greater satisfaction (OR = 55.236; CI = 11.39 to 267.80) compared to those who asked/relayed questions only “slightly effective” to “moderately effectively” (p < 0.001). Surgeons were similarly satisfied with their telemedicine experience (4.00/5.00 ± 0.89) and were confident in their diagnoses (4.20/5.00 ± 0.84). All surgeons responded they will continue using telemedicine. 30.7% of patients would choose telemedicine over an inperson visit.

Conclusions: Telemedicine provides a viable platform for healthcare delivery with high patient and surgeon satisfaction. Most patients still prefer in-person visits for the post-pandemic future.

Telemedicine is an emerging industry with the potential to revolutionize the delivery of healthcare for the benefit of consumers, providers and payors. In general, telemedicine refers to the use of information and telecommunication technologies to distribute both information and expertise necessary for healthcare service provision, collaboration or delivery among geographically separated participants — physicians and patients. In short, it is a generic term which is used to define various aspects of healthcare at a distance. Telemedicine has been heralded as one of several possible solutions to some of the medical dilemmas that are faced by many developing countries. In this paper, we will discuss the current state of telemedicine in developing countries in South-East Asia (SEA) in general, with Malaysia and Indonesia in particular.

Telecystoscopy can lower the barrier to access critical urologic diagnostics for patients around the world. A major challenge for robotic control of flexible cystoscopes and intuitive teleoperation is the pose estimation of the scope tip. We propose a novel real-time camera localization method using video recordings from a prior cystoscopy and 3D bladder reconstruction to estimate cystoscope pose within the bladder during follow-up telecystoscopy. We map prior video frames into a low-dimensional space as a dictionary so that a new image can be likewise mapped to efficiently retrieve its nearest neighbor among the dictionary images. The cystoscope pose is then estimated by the correspondence among the new image, its nearest dictionary image, and the prior model from 3D reconstruction. We demonstrate performance of our methods using bladder phantoms with varying fidelity and a servo-controlled cystoscope to simulate the use case of bladder surveillance through telecystoscopy. The servo-controlled cystoscope with 3 degrees of freedom (angulation, roll, and insertion axes) was developed for collecting cystoscope videos from bladder phantoms. Cystoscope videos were acquired in a 2.5D bladder phantom (bladder-shape cross-section plus height) with a panorama of a urothelium attached to the inner surface. Scans of the 2.5D phantom were performed in separate arc trajectories each of which is generated by actuation on the angulation with a fixed roll and insertion length. We further included variance in moving speed, imaging distance and existence of bladder tumors. Cystoscope videos were also acquired in a water-filled 3D silicone bladder phantom with hand-painted vasculature. Scans of the 3D phantom were performed in separate circle trajectories each of which is generated by actuation on the roll axis under a fixed angulation and insertion length. These videos were used to create 3D reconstructions, dictionary sets, and test data sets for evaluating the computational efficiency and accuracy of our proposed method in comparison with a method based on global Scale-Invariant Feature Transform (SIFT) features, named SIFT-only. Our method can retrieve the nearest dictionary image for 94–100% of test frames in under 55ms per image, whereas the SIFT-only method can only find the image match for 56–100% of test frames in 6000–40000ms per image depending on size of the dictionary set and richness of SIFT features in the images. Our method, with a speed of around 20 Hz for the retrieval stage, is a promising tool for real-time image-based scope localization in robotic cystoscopy when prior cystoscopy images are available.

Objective: To conduct a preliminary study of the hierarchical diagnosis and treatment of patients with obstructive sleep apnea/hypopnea syndrome (OSAHS) using the Internet of Things (IoT) medical technology and to explore the feasibility of the hierarchical diagnosis, treatment, and management of OSAHS patients using IoT medicine in primary hospitals.

Methods: The IoT technology and a remote medical monitoring system were used to observe and compare the respiratory and sleep parameters before and after a three-month intervention in 47 patients with OSAHS who met the diagnostic criteria and were selected in the Kashgar region. All parameters were compared based on the severity (mild, moderate, and severe) of OSAHS.

Results: The Epworth Sleepiness Scale (ESS) score, apnea–hypopnea index (AHI), and nighttime minimum oxygen saturation (lowest SaO₂min) improved in patients with OSAHS from before to after treatment ($p<0.05$). The improvements were more profound in OSAHS patients with cardiovascular disease such as hypertension.

Conclusion: The IoT medical technology can help to hierarchically diagnose, treat, and manage patients with OSAHS. It is feasible for primary hospitals in rural regions to use the IoT technology for the hierarchical diagnosis and treatment of OSAHS patients.

This paper presents the design and development of a prototype for remote ECG data transmission based on Internet-enabled health care services and telemedicine fundamentals. An ECG acquisition system developed by the authors is used to acquire the ECG signal in lead-II configuration from patient and store it in .lvm format in a PC interfaced to patient module through RS232. This unit (data server) on the patient side then transfers the data to a remote client (on doctor's side) using TCP/IP as network protocol on LabVIEW 8.20 environment. Using this device, a specialist doctor can telematically move to the patient site and instruct medical personnel when handling a patient. During the last years, more and more modern tools have found their ways to different tasks during the design, the realization, and data processing in the area of Internet access to the e-health services. The telemedicine system demonstrated in this work is a combined real-time and store-and-forward facility.

The ubiquity of broadband wireless coverage and widespread usage of smart phones around the world carry a potential for transforming health care services, reducing health care cost, and ensuring faster care for urgent cases. To these objectives, we present a mobile-based health monitoring solution that takes advantage of the mobile's increasing processing capability to address the rising cost of health care. The solution enables health care providers to easily analyze and diagnose a patient's data. This is possible due to the low cost in integrating a powerful data analysis tool with the mobile device. This paper presents a proof of concept that has been developed to monitor, record, and analyze the heart rate. The design enables a physician to develop custom analysis and monitoring to collect key indicators or set alerts without a need for infrastructure implementations to store or transfer the data.

An electrocardiogram (ECG) signal is an important diagnostic tool for cardiologists to detect the abnormality. In continuous monitoring, an ambulatory huge amount of ECG data is involved. This leads to high storage requirements and transmission costs. Hence, to reduce the storage and transmission cost, there is a requirement for an efficient compression or coding technique. One of the most promising compression techniques is Compressive Sensing (CS) which makes efficient compression of signals. By this methodology, a signal can easily be reconstructed if it has a sparse representation. This paper presents the Block Sparse Bayesian Learning (BSBL)-based multiscale compressed sensing (MCS) method for the compression of ECG signals. The main focus of the proposed technique is to achieve a reconstructed signal with less error and more energy efficiency. The ECG signal is sparsely represented by wavelet transform. MIT-BIH Arrhythmia database is used for testing purposes. The Huffman technique is used for encoding and decoding. The signal recovery is appropriate up to 75% of compression. The quality of the signal is ascertained using the standard performance measures such as signal-to-noise ratio (SNR) and Percent root mean square difference (PRD). The quality of the reconstructed ECG signal is also validated through the visual method. This method is most suitable for telemedicine applications.