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Over the past several years the perception of telehealth – and its role in healthcare delivery – has changed dramatically. Previously limited to just a few use cases including low-acuity virtual urgent care and chronic outpatient disease management, telehealth now plays some role in virtually every medical specialty and has seen considerable growth in technologies beyond the simple video visit. In this chapter, we highlight the forces that have driven telehealth's rapid growth and adoption. First, we discuss the evolution of the telehealth landscape in the years leading up to the COVID‐19 pandemic, including increasing consumer demand for virtual services, the emergence of new payment models that promote telehealth use, advancements in technical capabilities, and new structures that enabled reimbursement of digital health activities. Then we cover advancements in telehealth directly related to the pandemic and important considerations for continued growth including provider workflow integration, accessibility and equity, and clarity around reimbursement. Finally, we discuss technological innovations and new modes of care delivery – such as digital therapeutics and virtual-first health plans – that are likely to enhance the sophistication and expand the role of telehealth services over the coming years. © 2023 Elsevier Inc. All rights reserved.
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Long COVID is a post-viral illness where symptoms are still experienced more than three months after an infection of COVID 19. In line with a recent shift within HCI and research on self-tracking towards first-person methodologies, I present the results of an 18-month long autoethnographic study of using a Fitbit fitness tracker whilst having long COVID. In contrast to its designed intentions, I misused my Fitbit to do less in order to pace and manage my illness. My autoethnography illustrates three modes of using fitness tracking technologies to do less and points to the new design space of technologies for reducing, rather than increasing, activity in order to manage chronic illnesses where over-exertion would lead to a worsening of symptoms. I propose that these "pacing technologies"should acknowledge the interoceptive and fluctuating nature of the user's body and support user's decision-making when managing long-term illness and maintaining quality of life. © 2023 Owner/Author.
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Since its introduction in 1955, artificial intelligence (AI) has continued its growth and expansion across all industries and societal sectors. It took the COVID-19 pandemic for AI and its subsets to take the center stage in medicine and health care. AI is a broad discipline and encompasses machine learning (ML), deep learning (DL), and other techniques. Advancements in AI enabled, facilitated, and accelerated the expansion of telehealth. Telehealth describes the wide array of digital information and communication technologies and systems that allow the delivery of health and health-related services. There are three distinct subtypes of telehealth: synchronous, asynchronous, and remote (tele) monitoring. The overarching goal of telehealth is to break down barriers in delivery of high value care by overcoming challenges resulting from time or location constraints. The end goal is not to replace in-person care, rather to commoditize and democratize high quality, high value care. On the other hand, there remain significant limitations and pitfalls, particularly regulatory and technological. Examples include best practice guidelines on the adaptation of standards regulating data exchange, expansion of reimbursement and importantly ethical challenges. The latter include critical issues such as data privacy, security, and governance, AI-introduced bias, the black box nature of some AI/ML algorithms and the impact of AI technologies/algorithms on health disparities and inequities. Disparities in access to and use of tele-health were already known but highlighted during the COVID-19 pandemic. Recognition of this hurdle led to the emerging and rapidly growing field of digital determinants of health, which comprise factors like digital literacy, access to AI/technology, and community infrastructure like access to WiFi/broadband internet. © 2023 Elsevier Inc. All rights reserved.
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Sporadic occurrences of transmissible diseases have severe and long-lasting effects on humankind throughout history. These outbreaks have molded the political, economic, and social aspects of human life. Pandemics have redefined some of the basic beliefs of modern healthcare, pushing researchers and scientists to develop innovative solutions to be better equipped for future emergencies. Numerous attempts have been made to fight Covid-19-like pandemics using technologies such as the Internet of Things, wireless body area network, blockchain, and machine learning. Since the disease is highly contagious, novel research in patients' health monitoring system is essential for the constant monitoring of pandemic patients with minimal or no human intervention. With the ongoing pandemic of SARS-CoV-2, popularly known as Covid-19, innovations for monitoring of patients' vitals and storing them securely have risen more than ever. Analyzing the stored patients' data can further assist healthcare workers in their decision-making process. In this paper, we surveyed the research works on remote monitoring of pandemic patients admitted in hospitals or quarantined at home. First, an overview of pandemic patient monitoring is given followed by a brief introduction of enabling technologies i.e. Internet of Things, blockchain, and machine learning to implement the system. The reviewed works have been classified into three categories; remote monitoring of pandemic patients using IoT, blockchain-based storage or sharing platforms for patients' data, and processing/analyzing the stored patients' data using machine learning for prognosis and diagnosis. We also identified several open research issues to set directions for future research.
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Background: Remote patient monitoring (RPM) has been increasingly adopted over the last decade, with the COVID-19 pandemic fostering its rapid development. As RPM implementation is recognised as complex and highly demanding in terms of resources and processes, there are multiple challenges in providing RPM in an integrated logic. Methods: To examine the structural elements that are relevant for implementing RPM integrated care, a scoping review was conducted in PubMed, Scopus, and Web of Science, leveraging a search strategy that combines terms relative to (1) conceptual models and real-life initiatives;(2) RPM;and (3) care integration. Results: 28 articles were included, covering nine conceptual models and 19 real-life initiatives. Eighteen structural elements of RPM integrated care implementation were identified among conceptual models, defining a structure for assessing real-life initiatives. 78.9% of those initiatives referred to at least ten structural elements, with patient education and self-monitoring promotion, multidisciplinary core workforce, ICTs (information and communications technologies) and telemonitoring (TM) devices, and health indicators measurement being present in all studies, and therefore being core elements to the design of RPM initiatives. Conclusion: RPM goes far beyond technology, with underlying processes and involved actors playing a central role in care provision. The structural elements identified can guide RPM implementation and promote maturity in adoption. Future research may focus on assessing design completeness, evaluating impacts, and analysing related financial arrangements.
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While the exploration into biomolecules for diagnostic and prognostic devices continues to develop, many molecules continue to be examined for individual diseases or treatments. Consequently, it can be difficult to fully understand the scope of one individual molecule's current and potential clinical utilization. The scope of this study aimed to assess the potential of Interferon Gamma-induced Protein 10 (IP-10) as a biomarker in a wide variety of diseases, both as a main and supplemental indicator of disease infection and progression. IP-10 is a chemokine secreted in response to IFN-gamma playing a major role in the activation and regulation of inflammatory and immune responses within the body. Currently, IP-10 has displayed potential application in diseases such as COVID-19, tuberculosis, sepsis, Kawasaki disease, cancer, and many more. Molecular assays developed for the detection of IP-10 take longer testing time, sophisticated instrument utilization, and need more sample volumes. These cannot be utilized for bedside patient monitoring during the illness state of the patient. Biosensing tools are alternative methods used at clinical sites due to their rapid results. Though many types of sensing mechanisms established for the detection of disease biomarkers such as optical, piezoelectric sensors, and electrochemical biosensors are far beyond the other sensing methods due to their ease of mechanism, rapid results, and portable nature. IP-10 has been a promising biomarker in different diseases, evaluation of IP-10 levels at different time points of treatments is necessary. To achieve this, current conventional methods cannot be used and thus a portable device that provides rapid results is in demand. Such point-of-care (POC) device development for IP-10 analysis is very crucial in the current scenario. Beyond this, the clarification of its physiological role in healthy and infected individuals could allow for more proper utilization in clinical diagnoses, prognoses, treatment monitoring, and more. Overall, this study was developed to summarize the associations currently created between levels of IP-10 and other biomolecules and diseases.Copyright © 2023 The Author(s)
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Remote Patient Monitoring has enjoyed strong growth to new heights driven by several factors, such as the COVID-19 pandemic or advances in technology, allowing consumers and patients to continuously record health data by themselves. This does not come without its challenges, however. A literature review was completed and highlights usability gaps when using wearables or home use medical devices in a virtual environment. Based on these findings, the Pi-CON methodology was applied to close these gaps by utilizing a novel sensor that allows the acquisition of vital signs at a distance, without any sensors touching the patient. Pi-CON stands for passive, continuous and non-contact, and describes the ability to acquire vital signs continuously and passively, with limited user interaction. The preference of vital sign acquisition with a newly developed sensor was tested and compared to vital sign tests taken with patient generated health-data devices (ear thermometer, pulse oximeter) measuring heart rate, respiratory rate and body temperature. In addition, the amount of operator errors and the user interfaces were tested and compared. Results show that participants preferred vital signs acquisition with the novel sensor and the developed user interface of the sensor. Results also revealed that participants had a mean error of .85 per vital sign measurement with the patient-generated health data devices and .33 with the developed sensor, confirming the beneficial impact available when using the developed sensor based on the Pi-CON methodology.
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Objective To explore the application of ultrasound-guided arterial line placement in severe patients with COVID-19. Methods From February to April 2020, we retrospectively collected and analyzed the clinical data of critical patients with COVID-19 with an indwelling peripheral arterial catheter treated by the medical team of Peking Union Medical College Hospital. Patients with ultrasound-guided peripheral arterial catheterization were taken as the study group, while patients whose arterial catheter was placed by traditional palpation were taken as the control group. The puncture condition and complication rate were compared between the two groups. Results A total of 60 severe patients with COVID-19 who met the inclusion and exclusion criteria were enrolled in this study. There were 30 cases in the study group and 30 cases in the control group. In the study group, the success rate of the first catheterization of the peripheral artery (63.3% vs. 26.7%) and the total puncture success rate [(79.43+/- 25.79)% vs. (53.07+/-30.21)%] were higher than those in the control group (all P < 0.05), the puncture times(1.43+/-0.56 vs. 2.50+/-1.28) were less than those of the control group (P < 0.05). The rates of 24-hour disuse (6.7% vs. 30.0%), local hematoma (10.0% vs. 36.7%), occlusion, and tortuous (3.3% vs. 40.0%) in the study group were lower than those in the control group (all P < 0.05). Conclusion Under the three-level protection, ultrasound-guided arterial catheter placement for severe patients with COVID-19 can improve the success rate of catheter placement, reduce puncture times, and reduce the incidence of complications.Copyright © 2021, Peking Union Medical College Hospital. All rights reserved.
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This work focuses on the development of a portable physiological monitoring framework that can continuously monitor the patient's heartbeat, oxygen levels, temperature, ECG measurement, blood pressure, and other fundamental patient's data. As a result of this, the workload and the chances of being infected by COVID-19 of the health workers will be reduced and an efficient patient monitoring system can be maintained. In this paper, an IoT based continuous monitoring system has been developed to monitor all COVID-19 patient conditions and store patient data in the cloud server using Wi-Fi Module-based remote communication. In this monitoring system, data stored on IoT platform can be accessed by an authorized individual and ailments can be examined by the doctors from a distance based on the values obtained. If a patient's physical condition deteriorates, the doctor will immediately receive the emergency alert notification. This model proposed in this research work would be extremely important in dealing with the Corona epidemic around the world. © 2022 IEEE.
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To prevent coronavirus disease 2019 (COVID-19) and enhance the nutrition management for patients, the Beijing Quality Control and Improvement Center for Clinical Nutrition Therapy organized relevant experts to formulate "The Nutrition Management of Patients with Coronavirus Disease 2019 in the Hospital: An Expert Opinion (2020)". It clearly stated that food safety, food hygiene, and nutrition management should be incorporated into the whole process of prevention, control, treatment, and rehabilitation of COVID-19. The reasonable and standardized pathway of nutrition management, which includes nutrition-risk screening, malnutrition diagnosis, nutritional support therapy and nutrition monitoring, should be established to improve the immune status, clinical outcome, and quality of life of patients with COVID-19.Copyright © 2021, Peking Union Medical College Hospital. All rights reserved.
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Introduction: Conservative estimates suggest that the cost of poor medication adherence (MA) to healthcare systems in the UK is close to 800Mn annually, however figures may be as high as 920Mn to 224Bn across larger parts of Europe and the US.(1) This may be attributed to the relationship between poor MA and an increased risk of hospital admission.(2) Often, cases are preventable and hence present an opportunity for avoidable costs if appropriately identified and managed, such as in the case of early readmissions (admissions occurring within 30 days of discharge). However, despite the association between MA and admissions, to date no predictive model has been developed that integrates a holistic Patient-Reported Outcome Measure (PROM) of MA. This study evaluated one such PROM, known as SPUR, as a predictor of general admission and early readmission in patients living with Type 2 Diabetes (T2D). Aim(s): This study sought to develop a predictive model of early readmission and general admission risk using the SPUR tool as a PROM of MA in patients living with T2D. Method(s): Using an observational study design, 6-month retrospective and prospective patient monitoring were conducted to assess the number of admissions and early readmissions during the observational period. Outcomes were reported as binary and count variables. Patients were previously recruited from a large London NHS Trust as part of a cross-sectional study to validate SPUR. Covariates of interest included: age, ethnicity, gender, education level, income, the number of medicines and medical conditions, and Covid-19 diagnoses. A Poisson or negative binomial model was employed for count outcomes, with the exponentiated coefficient indicating incident ratios (IR) [95% CI]. For binary outcomes (Coefficient, [95% CI]), a logistic regression model was developed. Result(s): Data were available for 200 patients. The modal age range was 70-79 years (n=74/200, 37.0%). Most participants were GCSE educated (42.5%), white (76.0%), and over a third female (36.0%) identified as female. For general admission risk as a count variable, a higher SPUR score (increased adherence) was significantly associated with a lower number of admissions (IR = 0.98, [0.96, 1.00]). Other factors associated with an increased risk of admission included: age >=80 years (IR = 5.18, [1.01, 26.55]), GCSE education (IR = 2.11, [1.15 - 3.87]), number of medical conditions (IR = 1.07, [1.01, 1.13]), and a positive Covid- 19 diagnosis during follow-up (IR = 1.83, [1.11, 3.02]). SPUR remained significant when modelled as a binary variable (-0.048, [-0.094, -0.003]). For early readmission, only the SPUR score was significantly predictive of the outcome as a binary variable (-0.051, [-0.094, -0.007]), indicating that those with a higher SPUR score were at less risk of an early readmission. Conclusion(s): The study successfully developed a predictive model for both general admission and early readmissions in patients living with T2D using the SPUR tool and several covariates of clinical relevance. However, a small sample size is noted as a limitation. Future work may look to integrate SPUR as a holistic PROM of MA to support the development of tailored interventions to reduce patients' risk of admission.
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BACKGROUND: The pathogenesis of angioedema induced by angiotensin-converting enzyme inhibitors is based on the accumulation of bradykinin as a result of angiotensin-converting enzyme blockade. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds to the angiotensin-converting enzyme 2 receptor, which may inhibit its production and thereby lead to an increase in bradykinin levels. Thus, SARS-CoV-2 infection may be a likely trigger for the development of angioedema. AIMS: This study aimed to analyze cases of hospitalizations of patients with angioedema associated with the use of angiotensin-converting enzyme inhibitors and angiotensin receptor blockers during the coronavirus disease 2019 (COVID-19) pandemic. MATERIALS AND METHODS: This study retrospectively analyzed medical records of patients admitted to the Vitebsk Regional Clinical Hospital between May 2020 and December 2020 with isolated (without urticaria) angioedema while receiving angiotensin-converting enzyme inhibitors or angiotensin receptor blockers. In all patients, smears from the naso and oropharynx for COVID-19 were analyzed by polymerase chain reaction. RESULT(S): Fifteen inpatients (9 men and 6 women) aged 44-72 years were admitted because of emergent events, of which 53.6% had isolated angioedema. In two cases, a concomitant diagnosis of mild COVID-19 infection was established with predominant symptoms of angioedema, including edema localized in the face, tongue, sublingual area, and soft palate. All patients had favorable disease outcomes. CONCLUSION(S): Patients with angiotensin-converting enzyme inhibitor-induced angioedema may require hospitalization to monitor upper respiratory tract patency. There were cases of a combination of angiotensin-converting enzyme inhibitor-induced angioedema and mild COVID-19. Issues requiring additional research include the effect of SARS- CoV-2 infection on the levels of bradykinin and its metabolites, the triggering role of COVID-19 in the development of angioedema in patients receiving angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, recommendations for the management of patients with angiotensin-converting enzyme inhibitor-induced angioedema, and a positive result for COVID-19.Copyright © 2020 Pharmarus Print Media All rights reserved.
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The Covid-19 pandemic disturbed the smooth functioning of healthcare services throughout the world. New practices such as masking, social distancing and so on were followed to prevent the spread. Further, the severity of the problem increases for the elderly people and people having co-morbidities as proper medical care was not possible and as a result many deaths were recorded. Even for those patients who recovered from Covid could not get proper health monitoring in the Post-Covid phase as a result many deaths and severity in health conditions were reported after the Covid recovery i.e., the Post-Covid era. Technical interventions like the Internet of Things (IoT) based remote patient monitoring using Medical Internet of Things (M-IoT) wearables is one of the solutions that could help in the Post-Covid scenarios. The paper discusses a proposed framework where in a variety of IoT sensing devices along with ML algorithms are used for patient monitoring by utilizing aggregated data acquired from the registered Post-Covid patients. Thus, by using M-IoT along with Machine Learning (ML) approaches could help us in monitoring Post-Covid patients with co-morbidities for and immediate medical help. © 2023 SCPE.
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Healthcare monitoring frameworks emerged as one of the most essential frameworks and innovations established over the last decade. As a result of failing to provide adequate clinical attention to patients at the appropriate time, many people are facing the possibility of an untimely death. Ultimately, the goal was to develop an IoT-based integrated healthcare monitoring framework that could be relied upon by healthcare professionals to screen their patients, whether they were in the hospital or at home, to ensure that they were being well-cared for. A mobile phone-based remote healthcare monitoring framework has been constructed with the help of sensors, an information acquisition unit, a microcontroller (such as Arduino), and a product modification. This framework has the potential to provide continuous web-based data regarding a patient's physiological states (i.e., JAVA). Before transmitting it to the specialist's portable device along with the application, the framework examines the patient's temperature, heart rate, and EEG data. It then displays and saves this information. An Internet of Things-based patient monitoring framework may monitor a patient's health condition in an efficient manner and save the patient's life at the appropriate moment. © 2023 IEEE.
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Hepatitis A is a common viral infection worldwide that is transmitted via the fecal-oral route. Since the introduction of an efficient vaccine, the incidence of infection has decreased but the number of cases has risen due to widespread community outbreaks among unimmunized individuals. Classic symptoms include fever, malaise, dark urine, and jaundice, and are more common in older children and adults. People are often most infectious 14 days prior to and 7 days following the onset of jaundice. We will discuss the case of a young male patient, diagnosed with acute hepatitis A, leading to fulminant hepatitis refractory to conventional therapy and the development of subsequent kidney injury. The medical treatment through the course of hospitalization was challenging and included the use of L-ornithine-L-aspartate and prolonged intermittent hemodialysis, leading to a remarkable outcome. Hepatitis A is usually self-limited and vaccine-preventable;supportive care is often sufficient for treatment, and chronic infection or chronic liver disease rarely develops. However, fulminant hepatitis, although rare, can be very challenging to manage as in the case of our patient.Copyright © 2023 The Author(s).
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Nowadays, there is an increasing demand for cloud-based remote clinical services, both for diagnosis and monitoring. The COVID-19 pandemic has dramatically amplified this need. E-government programs should quickly go towards the expansion of this type of services, also to avoid that people (especially elderly) renounce treatment or adequate healthcare. However, to be effective, latency between IoT medical devices and the cloud should be reduced as much as possible. For this reason, fog computing appears the best approach, as part of the elaboration is moved closer to the user. However, some privacy threats arise. Indeed, these services can be delivered only based on secure digital identity and authentication systems, but the intermediate fog layer should learn nothing about the identity of users and the link among different service requests. In this paper, we propose a concrete solution to the above issue by leveraging eIDAS-compliant digital identity and by including a cryptographic protocol to provide anonymity and unlinkability of user's access to fog servers. Copyright © 2023 Inderscience Enterprises Ltd.
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AIMS: Postoperative atrial fibrillation (POAF) is a common complication of cardiac surgery, yet difficult to detect in ambulatory patients. The primary aim of this study is to investigate the effect of a mobile health (mHealth) intervention on POAF detection after cardiac surgery. METHODS AND RESULTS: We performed an observational cohort study among 730 adult patients who underwent cardiac surgery at a tertiary care hospital in The Netherlands. Of these patients, 365 patients received standard care and were included as a historical control group, undergoing surgery between December 2017 and September 2018, and 365 patients were prospectively included from November 2018 and November 2020, undergoing an mHealth intervention which consisted of blood pressure, temperature, weight, and electrocardiogram (ECG) monitoring. One physical outpatient follow-up moment was replaced by an electronic visit. All patients were requested to fill out a satisfaction and quality of life questionnaire. Mean age in the intervention group was 62 years, 275 (70.4%) patients were males. A total of 4136 12-lead ECGs were registered. In the intervention group, 61 (16.7%) patients were diagnosed with POAF vs. 25 (6.8%) patients in the control group [adjusted risk ratio (RR) of POAF detection: 2.15; 95% confidence interval (CI): 1.55-3.97]. De novo atrial fibrillation was found in 13 patients using mHealth (6.5%) vs. 4 control group patients (1.8%; adjusted RR 3.94, 95% CI: 1.50-11.27). CONCLUSION: Scheduled self-measurements with mHealth devices could increase the probability of detecting POAF within 3 months after cardiac surgery. The effect of an increase in POAF detection on clinical outcomes needs to be addressed in future research.
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Ever since an anonymous disease broke out in late 2019, the whole world seems to have own ceased functioning. COVID-19 patients are proliferating at an exponential rate, straining healthcare systems around the world. Traditional techniques of screening every patient with a respiratory disease is unfeasible due to the restricted number of testing kits available. We presented a method for recognizing COVID-19 infected patients utilizing data collected from chest X-ray scans to overcome this challenge. This attempt will benefit both patients and doctors significantly. It becomes even more critical in nations where the number of people affected far outnumbers the number of laboratory kits available to test the disease. When current systems are confused whether to retain the patient on the ward with other patients or isolate them in COVID-19 zones, this could be useful in an inpatient setting. Apart from that, it would aid in the identification of patients with a high risk of COVID-19 and a false negative RT-PCR who would require a repeat. Most of the COVID-19 detection methods use traditional image classification models. This has the issue of low detection accuracy and incorrect COVID-19 detection. This method starts with a chest x-ray enhancement procedure like this: Rotation, translation, random conversion. The survey's accuracy has considerably increased as a result of this. For the COVID-19 infection, our model has 97.5 percent accuracy and 100 percent sensitivity (recall). In addition, we used a visualization technique that distinguishes our model from the others by displaying contaminated areas in X-ray pictures. © 2022 IEEE.
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Covid-19 has rapidly spread and affected millions of people worldwide. For that reason, the public healthcare system was overwhelmed and underprepared to deal with this pandemic. Covid-19 also interfered with the delivery of standard medical care, causing patients with chronic diseases to receive subpar care. As chronic heart failure becomes more common, new management strategies need to be developed. Mobile health technology can be utilized to monitor patients with chronic conditions, such as chronic heart failure, and detect early signs of Covid-19, for diagnosis and prognosis. Recent breakthroughs in Artificial Intelligence and Machine Learning, have increased the capacity of data analytics, which may now be utilized to remotely conduct a variety of tasks that previously required the physical presence of a medical professional. In this work, we analyze the literature in this domain and propose an AI-based mHealth application, designed to collect clinical data and provide diagnosis and prognosis of diseases such as Covid-19 or chronic cardiac diseases. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.