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Leoste J, Strömberg-Järvis K, Robal T, Marmor K, Kangur K, Rebane AM. Testing scenarios for using telepresence robots in healthcare settings. Comput Struct Biotechnol J 2024; 24:105-114. [PMID: 38314026 PMCID: PMC10837455 DOI: 10.1016/j.csbj.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/09/2024] [Accepted: 01/14/2024] [Indexed: 02/06/2024] Open
Abstract
The ageing global population puts heavy pressure on healthcare systems everywhere. Addressing ageing-related chronic conditions requires employment of novel innovative solutions. Telehealth technologies, including telepresence robots (TPRs), are being rapidly developed to provide healthcare services efficiently wherever needed. This article explores the role of TPRs in addressing the challenges of providing healthcare to an ageing population, emphasizing their potential advantages and drawbacks. Employing an exploratory research approach with qualitative data collection techniques, we tested three TPR usage scenarios in simulated healthcare settings: anamnesis, measurements, and falls and frailty. The study employed a non-random purposive sample comprising 25 participants, and was conducted at a medical facility in June 2023. The findings suggest that TPRs offer promising solutions for healthcare professionals and patients, especially in scenarios when physical presence is impossible or physical isolation is required to prevent contagion. However, the technology is not yet ready to substitute fully human medical workers, potentially causing patient reluctance and emphasizing the need for patient-centered approaches to technology adoption. In addition, more studies are needed to address ethical, privacy, and scalability concerns.
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Affiliation(s)
- Janika Leoste
- Tallinn University, Narva rd 25, 10120 Tallinn, Estonia
- Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia
| | | | - Tarmo Robal
- Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia
| | - Kristel Marmor
- Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia
| | - Katrin Kangur
- Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia
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2
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Tan S, Mills G. Designing Chinese hospital emergency departments to leverage artificial intelligence-a systematic literature review on the challenges and opportunities. FRONTIERS IN MEDICAL TECHNOLOGY 2024; 6:1307625. [PMID: 38577009 PMCID: PMC10991761 DOI: 10.3389/fmedt.2024.1307625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 03/08/2024] [Indexed: 04/06/2024] Open
Abstract
Artificial intelligence (AI) has witnessed rapid advances in the healthcare domain in recent years, especially in the emergency field, where AI is likely to radically reshape medical service delivery. Although AI has substantial potential to enhance diagnostic accuracy and operational efficiency in hospitals, research on its applications in Emergency Department building design remains relatively scarce. Therefore, this study aims to investigate Emergency Department facility design by identifying the challenges and opportunities of using AI. Two systematic literature reviews are combined, one in AI and the other in sensors, to explore their potential application to support decision-making, resource optimisation and patient monitoring. These reviews have then informed a discussion on integrating AI sensors in contemporary Emergency Department designs for use in China to support the evidence base on resuscitation units, emergency operating rooms and Emergency Department Intensive Care Unit (ED-ICU) design. We hope to inform the strategic implementation of AI sensors and how they might transform Emergency Department design to support medical staff and enhance the patient experience.
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Affiliation(s)
- Sijie Tan
- Bartlett School of Sustainable Construction, Bartlett Faculty of the Built Environment, University College London, London, United Kingdom
| | - Grant Mills
- Bartlett School of Sustainable Construction, Bartlett Faculty of the Built Environment, University College London, London, United Kingdom
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3
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Rothstein MA. Should Chatbots Be Used to Obtain Informed Consent for Research? Ethics Hum Res 2023; 45:46-50. [PMID: 37988278 PMCID: PMC11050738 DOI: 10.1002/eahr.500190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
Chatbots have become increasingly common in diverse settings as a substitute for human conversation. They are being developed and tested for obtaining informed consent for research. An initial study indicated that chatbots saved time and were successful in knowledge transfer, but the informed consent process serves other purposes, such as building trust and respecting the autonomy and dignity of potential research participants. Additional research and possible regulation are necessary before chatbots should be routinely used in health research.
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Affiliation(s)
- Mark A Rothstein
- Director of translational bioethics at the Institute for Clinical and Translational Science at the University of California, Irvine (UCI). He serves as contributing editor for the Ethics in Translational Research series and wrote this essay before assuming his position at UCI
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Ahmed Kamal M, Ismail Z, Shehata IM, Djirar S, Talbot NC, Ahmadzadeh S, Shekoohi S, Cornett EM, Fox CJ, Kaye AD. Telemedicine, E-Health, and Multi-Agent Systems for Chronic Pain Management. Clin Pract 2023; 13:470-482. [PMID: 36961067 PMCID: PMC10037594 DOI: 10.3390/clinpract13020042] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/12/2023] [Accepted: 03/16/2023] [Indexed: 03/25/2023] Open
Abstract
Telemedicine, telehealth, and E-health all offer significant benefits for pain management and healthcare services by fostering the physician-patient relationship in otherwise challenging circumstances. A critical component of these artificial-intelligence-based health systems is the "agent-based system", which is rapidly evolving as a means of resolving complicated or straightforward problems. Multi-Agent Systems (MAS) are well-established modeling and problem-solving modalities that model and solve real-world problems. MAS's core concept is to foster communication and cooperation among agents, which are broadly considered intelligent autonomous factors, to address diverse challenges. MAS are used in various telecommunications applications, including the internet, robotics, healthcare, and medicine. Furthermore, MAS and information technology are utilized to enhance patient-centered palliative care. While telemedicine, E-health, and MAS all play critical roles in managing chronic pain, the published research on their use in treating chronic pain is currently limited. This paper discusses why telemedicine, E-health, and MAS are the most critical novel technologies for providing healthcare and managing chronic pain. This review also provides context for identifying the advantages and disadvantages of each application's features, which may serve as a useful tool for researchers.
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Affiliation(s)
| | - Zainab Ismail
- Faculty of Medicine, Menoufia University, Shiben El Kom 51123, Egypt
| | - Islam Mohammad Shehata
- Department of Anesthesiology, Faculty of Medicine, Ain Shams University, Cairo 11517, Egypt
| | - Soumia Djirar
- Department of Ophthalmology, University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Norris C Talbot
- School of Medicine, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA 71103, USA
| | - Shahab Ahmadzadeh
- Department of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA 71103, USA
| | - Sahar Shekoohi
- Department of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA 71103, USA
| | - Elyse M Cornett
- Department of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA 71103, USA
| | - Charles J Fox
- Department of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA 71103, USA
| | - Alan D Kaye
- Department of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA 71103, USA
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Iadanza E, Pasqua G, Piaggio D, Caputo C, Gherardelli M, Pecchia L. A robotic arm for safe and accurate control of biomedical equipment during COVID-19. HEALTH AND TECHNOLOGY 2023; 13:285-300. [PMID: 36624886 PMCID: PMC9813453 DOI: 10.1007/s12553-022-00715-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 12/08/2022] [Indexed: 01/06/2023]
Abstract
Purpose Hospital facilities and social life, along with the global economy, have been severely challenged by COVID-19 since the World Health Organization (WHO) declared it a pandemic in March 2020. Since then, countless ordinary citizens, as well as healthcare workers, have contracted the virus by just coming into contact with infected surfaces. In order to minimise the risk of getting infected by contact with such surfaces, our study aims to design, prototype, and test a new device able to connect users, such as common citizens, doctors or paramedics, with either common-use interfaces (e.g., lift and snack machine keyboards, traffic light push-buttons) or medical-use interfaces (e.g., any medical equipment keypad). Method To this purpose, the device was designed with the help of Unified Modelling Language (UML) schemes, and was informed by a risk analysis, that highlighted some of its essential requirements and specifications. Consequently, the chosen constructive solution of the robotic system, i.e., a robotic-arm structure, was designed and manufactured using computer-aided design and 3D printing. Result The final prototype included a properly programmed micro-controller, linked via Bluetooth to a multi-platform mobile phone app, which represents the user interface. The system was then successfully tested on different physical keypads and touch screens. Better performance of the system can be foreseen by introducing improvements in the industrial production phase. Conclusion This first prototype paves the way for further research in this area, allowing for better management and preparedness of next pandemic emergencies.
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Affiliation(s)
- Ernesto Iadanza
- Department of medical biotechnologies, University of Siena, via Banchi di Sotto 55, Siena, 53100 Tuscany Italy
| | - Giammarco Pasqua
- Department of Information Engineering, University of Florence, Via di Santa Marta 3, Firenze, 50139 Tuscany Italy
| | - Davide Piaggio
- School of Engineering, University of Warwick, Library road, Coventry, CV56GB England UK
| | - Corrado Caputo
- School of Engineering, University of Warwick, Library road, Coventry, CV56GB England UK
| | - Monica Gherardelli
- Department of Information Engineering, University of Florence, Via di Santa Marta 3, Firenze, 50139 Tuscany Italy
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Library road, Coventry, CV56GB England UK.,School of Engineering, Campus Biomedico of Rome, Via Álvaro del Portillo 21, Roma, 00128 Lazio Italy
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Dino MJS, Davidson PM, Dion KW, Szanton SL, Ong IL. Nursing and human-computer interaction in healthcare robots for older people: An integrative review. INTERNATIONAL JOURNAL OF NURSING STUDIES ADVANCES 2022; 4:100072. [PMID: 38745638 PMCID: PMC11080351 DOI: 10.1016/j.ijnsa.2022.100072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 10/18/2022] Open
Abstract
Objectives This study examined the published works related to healthcare robotics for older people using the attributes of health, nursing, and the human-computer interaction framework. Design An integrative literature review. Methods A search strategy captured 55 eligible articles from databases (CINAHL, Embase, IEEE Xplore, and PubMed) and hand-searching approaches. Bibliometric and content analyses grounded on the health and nursing attributes and human-computer interaction framework were performed using MAXQDA. Finally, results were verified using critical friend feedback by a second reviewer. Results Most articles were from multiple authorship, published in non-nursing journals, and originating from developed economies. They primarily focused on applying healthcare robots in practice settings, physical health, and communication tasks. Using the human-computer interaction framework, it was found that older adults frequently served as the primary users while nurses, healthcare providers, and researchers functioned as secondary users and operators. Research articles focused on the usability, functionality, and acceptability of robotic systems. At the same time, theoretical papers explored the frameworks and the value of empathy and emotion in robots, human-computer interaction and nursing models and theories supporting healthcare practice, and gerontechnology. Current robotic systems are less anthropomorphic, operated through real-time direct and supervisory inputs, and mainly equipped with visual and auditory sensors and actuators with limited capability in performing health assessments. Conclusion Results communicate the need for technological competency among nurses, advancements in increasing healthcare robot humanness, and the importance of conscientious efforts from an interdisciplinary research team in improving robotic system usability and utility for the care of older adults.
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Affiliation(s)
- Michael Joseph S. Dino
- School of Nursing, Johns Hopkins University, 525 N Wolfe St, Baltimore, MD 21205, USA
- Our Lady of Fatima University, 120 McArthur Highway, Marulas, Valenzuela City 1440, Philippines
| | - Patricia M. Davidson
- School of Nursing, Johns Hopkins University, 525 N Wolfe St, Baltimore, MD 21205, USA
- University of Wollongong, The Vice-Chancellor's Unit Building 36, University of Wollongong, NSW 2522, Australia
| | - Kenneth W. Dion
- School of Nursing, Johns Hopkins University, 525 N Wolfe St, Baltimore, MD 21205, USA
| | - Sarah L. Szanton
- School of Nursing, Johns Hopkins University, 525 N Wolfe St, Baltimore, MD 21205, USA
| | - Irvin L. Ong
- School of Nursing, Johns Hopkins University, 525 N Wolfe St, Baltimore, MD 21205, USA
- Our Lady of Fatima University, 120 McArthur Highway, Marulas, Valenzuela City 1440, Philippines
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Dao TH, Hoang HY, Hoang VN, Tran DT, Tran DN. Human Activity Recognition System For Moderate Performance Microcontroller Using Accelerometer Data And Random Forest Algorithm. EAI ENDORSED TRANSACTIONS ON INDUSTRIAL NETWORKS AND INTELLIGENT SYSTEMS 2022. [DOI: 10.4108/eetinis.v9i4.2571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
There has been increasing interest in the application of artificial intelligence technologies to improve the quality of support services in healthcare. Some constraints, such as space, infrastructure, and environmental conditions, present challenges with assistive devices for humans. This paper proposed a wearable-based real-time human activity recognition system to monitor daily activities. The classification was done directly on the device, and the results could be checked over the internet. The accelerometer data collection application was developed on the device with a sampling frequency of 20Hz, and the random forest algorithm was embedded in the hardware. To improve the accuracy of the recognition system, a feature vector of 31 dimensions was calculated and used as an input per time window. Besides, the dynamic window method applied by the proposed model allowed us to change the data sampling time (1-3 seconds) and increase the performance of activity classification. The experiment results showed that the proposed system could classify 13 activities with a high accuracy of 99.4%. The rate of correctly classified activities was 96.1%. This work is promising for healthcare because of the convenience and simplicity of wearables.
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Zhao Y, Rokhani FZ, Sazlina SG, Devaraj NK, Su J, Chew BH. Defining the concepts of a smart nursing home and its potential technology utilities that integrate medical services and are acceptable to stakeholders: a scoping review. BMC Geriatr 2022; 22:787. [PMID: 36207705 PMCID: PMC9540152 DOI: 10.1186/s12877-022-03424-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 08/29/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Smart technology in nursing home settings has the potential to elevate an operation that manages more significant number of older residents. However, the concepts, definitions, and types of smart technology, integrated medical services, and stakeholders' acceptability of smart nursing homes are less clear. This scoping review aims to define a smart nursing home and examine the qualitative evidence on technological feasibility, integration of medical services, and acceptability of the stakeholders. METHODS Comprehensive searches were conducted on stakeholders' websites (Phase 1) and 11 electronic databases (Phase 2), for existing concepts of smart nursing home, on what and how technologies and medical services were implemented in nursing home settings, and acceptability assessment by the stakeholders. The publication year was inclusive from January 1999 to September 2021. The language was limited to English and Chinese. Included articles must report nursing home settings related to older adults ≥ 60 years old with or without medical demands but not bed-bound. Technology Readiness Levels were used to measure the readiness of new technologies and system designs. The analysis was guided by the Framework Method and the smart technology adoption behaviours of elder consumers theoretical model. The results were reported according to the PRISMA-ScR. RESULTS A total of 177 literature (13 website documents and 164 journal articles) were selected. Smart nursing homes are technology-assisted nursing homes that allow the life enjoyment of their residents. They used IoT, computing technologies, cloud computing, big data and AI, information management systems, and digital health to integrate medical services in monitoring abnormal events, assisting daily living, conducting teleconsultation, managing health information, and improving the interaction between providers and residents. Fifty-five percent of the new technologies were ready for use in nursing homes (levels 6-7), and the remaining were proven the technical feasibility (levels 1-5). Healthcare professionals with higher education, better tech-savviness, fewer years at work, and older adults with more severe illnesses were more acceptable to smart technologies. CONCLUSIONS Smart nursing homes with integrated medical services have great potential to improve the quality of care and ensure older residents' quality of life.
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Affiliation(s)
- Yuanyuan Zhao
- Department of Family Medicine, Faculty of Medicine & Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor Malaysia
- Global Century Science Group, Hong Kong, China
| | - Fakhrul Zaman Rokhani
- Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia
- Malaysian Research Institute on Ageing (MyAgeingTM), Universiti Putra Malaysia, Serdang, Malaysia
| | - Shariff-Ghazali Sazlina
- Department of Family Medicine, Faculty of Medicine & Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor Malaysia
- Malaysian Research Institute on Ageing (MyAgeingTM), Universiti Putra Malaysia, Serdang, Malaysia
| | - Navin Kumar Devaraj
- Department of Family Medicine, Faculty of Medicine & Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor Malaysia
- Malaysian Research Institute on Ageing (MyAgeingTM), Universiti Putra Malaysia, Serdang, Malaysia
| | - Jing Su
- College of Public Health, Hainan Medical University, Haikou, China
| | - Boon-How Chew
- Department of Family Medicine, Faculty of Medicine & Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor Malaysia
- Clinical Research Unit, Hospital Pengajar Universiti Putra Malaysia (HPUPM Teaching Hospital), Serdang, Malaysia
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Schünke LC, Mello B, da Costa CA, Antunes RS, Rigo SJ, Ramos GDO, Righi RDR, Scherer JN, Donida B. A rapid review of machine learning approaches for telemedicine in the scope of COVID-19. Artif Intell Med 2022; 129:102312. [PMID: 35659388 PMCID: PMC9055383 DOI: 10.1016/j.artmed.2022.102312] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/05/2022] [Accepted: 04/26/2022] [Indexed: 02/08/2023]
Abstract
The COVID-19 pandemic has rapidly spread around the world. The rapid transmission of the virus is a threat that hinders the ability to contain the disease propagation. The pandemic forced widespread conversion of in-person to virtual care delivery through telemedicine. Given this gap, this article aims at providing a literature review of machine learning-based telemedicine applications to mitigate COVID-19. A rapid review of the literature was conducted in six electronic databases published from 2015 through 2020. The process of data extraction was documented using a PRISMA flowchart for inclusion and exclusion of studies. As a result, the literature search identified 1.733 articles, from which 16 articles were included in the review. We developed an updated taxonomy and identified challenges, open questions, and current data types. Our taxonomy and discussion contribute with a significant degree of coverage from subjects related to the use of machine learning to improve telemedicine in response to the COVID-19 pandemic. The evidence identified by this rapid review suggests that machine learning, in combination with telemedicine, can provide a strategy to control outbreaks by providing smart triage of patients and remote monitoring. Also, the use of telemedicine during future outbreaks could be further explored and refined.
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Affiliation(s)
- Luana Carine Schünke
- Software Innovation Lab. (SOFTWARELAB), Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo 93022-750, Brazil
| | - Blanda Mello
- Software Innovation Lab. (SOFTWARELAB), Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo 93022-750, Brazil
| | - Cristiano André da Costa
- Software Innovation Lab. (SOFTWARELAB), Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo 93022-750, Brazil,Corresponding author
| | - Rodolfo Stoffel Antunes
- Software Innovation Lab. (SOFTWARELAB), Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo 93022-750, Brazil
| | - Sandro José Rigo
- Software Innovation Lab. (SOFTWARELAB), Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo 93022-750, Brazil
| | - Gabriel de Oliveira Ramos
- Software Innovation Lab. (SOFTWARELAB), Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo 93022-750, Brazil
| | - Rodrigo da Rosa Righi
- Software Innovation Lab. (SOFTWARELAB), Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo 93022-750, Brazil
| | - Juliana Nichterwitz Scherer
- Collective Health Graduate Program, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo 93022-750, Brazil
| | - Bruna Donida
- Grupo Hospitalar Conceição (GHC), Porto Alegre 91350-200, Brazil
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"Medical Assistance in Contextual Awareness" (AMICO): A Project for a Better Quality of Care for Patients in Cardiac Rehabilitation Unit. High Blood Press Cardiovasc Prev 2022; 29:305-309. [PMID: 35314963 DOI: 10.1007/s40292-022-00512-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 03/04/2022] [Indexed: 10/18/2022] Open
Abstract
The "Medical Assistance in Contextual Awareness" (AMICO) project proposes an infrastructure, called an "instrumented environment", consisting of the home environment and the person, both of which are suitably equipped with sensors, a telemedicine service platform (in the Internet of Things) and a Robot who acts as a mediator/master between the person, the surrounding environment and the external environment. This infrastructure, oriented to the citizen's well-being, can offer both services oriented to the person in his home environment, monitoring their behavior and psycho-physical state, and telemedicine services to support the remote monitoring of citizens affected by a cardiovascular event undergoing rehabilitation therapies from part of doctors or caregiver.
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Humayun M, Jhanjhi NZ, Almotilag A, Almufareh MF. Agent-Based Medical Health Monitoring System. SENSORS 2022; 22:s22082820. [PMID: 35458805 PMCID: PMC9024934 DOI: 10.3390/s22082820] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 12/05/2022]
Abstract
One of the leading healthcare concerns worldwide is the aging population. Aged patients require more significant healthcare resources because they are more likely to have chronic diseases that result in higher healthcare expenses. The design and implementation of e-health solutions, which offer patients mobile services to assist and enhance their treatment based on monitoring specific physiological data, is one of the key achievements in medical information technology. In the last few decades, there have been tremendous advancements in healthcare technology regarding mobility, size, speed, and communication. However, the critical drawback of today’s e-Health monitoring systems is that patients are confined to smart rooms and beds with monitoring gadgets. Such tracking is not widespread due to chronic patients’ mobility, privacy, and flexibility issues. Further, health monitoring devices that are fastened to a patient’s body do not give any analysis or advice. To improve the health monitoring process, a multi-agent-based system for health monitoring is provided in this study, which entails a group of intelligent agents that gather patient data, reason together, and propose actions to patients and medical professionals in a mobile context. A multi-agent-based framework presented in this study is evaluated through a case study. The results show that the proposed system provides an efficient health monitoring system for chronic, aged, and remote patients. Further, the proposed approach outperforms the existing mHealth system, allowing for timely health facilities for remote patients using 5G technology.
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Affiliation(s)
- Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 42421, Saudi Arabia; (A.A.); (M.F.A.)
- Correspondence:
| | - Noor Z. Jhanjhi
- School of Computer Science and Engineering (SCE), Taylor’s University, Subang Jaya 47500, Malaysia;
| | - Abdullah Almotilag
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 42421, Saudi Arabia; (A.A.); (M.F.A.)
| | - Maram Fahhad Almufareh
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 42421, Saudi Arabia; (A.A.); (M.F.A.)
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Multi-Agent Robot System to Monitor and Enforce Physical Distancing Constraints in Large Areas to Combat COVID-19 and Future Pandemics. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11167200] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Random outbreaks of infectious diseases in the past have left a persistent impact on societies. Currently, COVID-19 is spreading worldwide and consequently risking human lives. In this regard, maintaining physical distance has turned into an essential precautionary measure to curb the spread of the virus. In this paper, we propose an autonomous monitoring system that is able to enforce physical distancing rules in large areas round the clock without human intervention. We present a novel system to automatically detect groups of individuals who do not comply with physical distancing constraints, i.e., maintaining a distance of 1 m, by tracking them within large areas to re-identify them in case of repetitive non-compliance and enforcing physical distancing. We used a distributed network of multiple CCTV cameras mounted to the walls of buildings for the detection, tracking and re-identification of non-compliant groups. Furthermore, we used multiple self-docking autonomous robots with collision-free navigation to enforce physical distancing constraints by sending alert messages to those persons who are not adhering to physical distancing constraints. We conducted 28 experiments that included 15 participants in different scenarios to evaluate and highlight the performance and significance of the present system. The presented system is capable of re-identifying repetitive violations of physical distancing constraints by a non-compliant group, with high accuracy in terms of detection, tracking and localization through a set of coordinated CCTV cameras. Autonomous robots in the present system are capable of attending to non-compliant groups in multiple regions of a large area and encouraging them to comply with the constraints.
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Wang XV, Wang L. A literature survey of the robotic technologies during the COVID-19 pandemic. JOURNAL OF MANUFACTURING SYSTEMS 2021; 60:823-836. [PMID: 33612914 PMCID: PMC7881735 DOI: 10.1016/j.jmsy.2021.02.005] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/08/2021] [Accepted: 02/09/2021] [Indexed: 05/06/2023]
Abstract
Since the late 2019, the COVID-19 pandemic has been spread all around the world. The pandemic is a critical challenge to the health and safety of the general public, the medical staff and the medical systems worldwide. It has been globally proposed to utilise robots during the pandemic, to improve the treatment of patients and leverage the load of the medical system. However, there is still a lack of detailed and systematic review of the robotic research for the pandemic, from the technologies' perspective. Thus a thorough literature survey is conducted in this research and more than 280 publications have been reviewed, with the focus on robotics during the pandemic. The main contribution of this literature survey is to answer two research questions, i.e. 1) what the main research contributions are to combat the pandemic from the robotic technologies' perspective, and 2) what the promising supporting technologies are needed during and after the pandemic to help and guide future robotics research. The current achievements of robotic technologies are reviewed and discussed in different categories, followed by the identification of the representative work's technology readiness level. The future research trends and essential technologies are then highlighted, including artificial intelligence, 5 G, big data, wireless sensor network, and human-robot collaboration.
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Affiliation(s)
- Xi Vincent Wang
- Department of Production Engineering, KTH Royal Institute of Technology, Sweden
| | - Lihui Wang
- Department of Production Engineering, KTH Royal Institute of Technology, Sweden
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Giri S, Chenn LM, Romero-Ortuno R. Nursing homes during the COVID-19 pandemic: a scoping review of challenges and responses. Eur Geriatr Med 2021; 12:1127-1136. [PMID: 34136990 PMCID: PMC8208072 DOI: 10.1007/s41999-021-00531-2] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 06/09/2021] [Indexed: 12/01/2022]
Abstract
Aim To describe factors that contributed to the spread and mortality of COVID-19 in nursing homes and provide an overview of responses that were implemented. Findings COVID-19 exerted severe challenges on the nursing home population and its staff. Both internal and external factors predisposed nursing homes to an increased propensity of spread. Message Substantial learning occurred that will lead to better pandemic preparedness and improve quality of care for nursing home residents at all times. Introduction COVID-19 has caused unprecedented challenges in nursing homes. In this scoping review, we aimed to describe factors that contributed to the spread and mortality of COVID-19 in nursing homes and provide an overview of responses that were implemented to try to overcome such challenges. Methods The MeSH terms “Nursing homes” and “COVID-19” were searched in MEDLINE Ovid, and English language articles were retrieved that were published between 1 March 2020 and 31 January 2021. Article titles and abstracts were screened by two reviewers, and the results of included articles were grouped by themes. Results The search retrieved 348 articles, of which 76 were included in the thematic review. 8 articles related to COVID-19 disease characteristics (e.g. asymptomatic transmission), 24 to resident-related factors (e.g. comorbidities, nutrition, cognition), 13 to facility characteristics (e.g. physical space, occupancy, for-profit status), 21 to staffing (e.g. staffing levels, staff-to-resident ratio, staff multi-employment), and 10 to external factors (e.g. availability of personal protective equipment, prevailing health and social care policies). In terms of responses, identified themes included widespread testing, isolation and cohorting of residents, staff protection and support, promotion of residents’ well-being, and technological innovations. Conclusion COVID-19 exerted severe challenges on the nursing home population and its staff. Both internal and external factors predisposed nursing homes to an increased propensity of spread. Numerous strategies were employed to attempt to mitigate the negative impacts. Substantial learning occurred that may not only aid future pandemic preparedness but improve quality of care for nursing home residents at all times.
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Affiliation(s)
- Shamik Giri
- School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Lee Minn Chenn
- School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Roman Romero-Ortuno
- School of Medicine, Trinity College Dublin, Dublin, Ireland. .,Discipline of Medical Gerontology, Mercer's Institute for Successful Ageing (MISA), St James's Hospital, 6th Floor, Dublin, 8, Ireland. .,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.
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15
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Gao A, Murphy RR, Chen W, Dagnino G, Fischer P, Gutierrez MG, Kundrat D, Nelson BJ, Shamsudhin N, Su H, Xia J, Zemmar A, Zhang D, Wang C, Yang GZ. Progress in robotics for combating infectious diseases. Sci Robot 2021; 6:6/52/eabf1462. [PMID: 34043552 DOI: 10.1126/scirobotics.abf1462] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 03/09/2021] [Indexed: 12/24/2022]
Abstract
The world was unprepared for the COVID-19 pandemic, and recovery is likely to be a long process. Robots have long been heralded to take on dangerous, dull, and dirty jobs, often in environments that are unsuitable for humans. Could robots be used to fight future pandemics? We review the fundamental requirements for robotics for infectious disease management and outline how robotic technologies can be used in different scenarios, including disease prevention and monitoring, clinical care, laboratory automation, logistics, and maintenance of socioeconomic activities. We also address some of the open challenges for developing advanced robots that are application oriented, reliable, safe, and rapidly deployable when needed. Last, we look at the ethical use of robots and call for globally sustained efforts in order for robots to be ready for future outbreaks.
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Affiliation(s)
- Anzhu Gao
- Institute of Medical Robotics, Shanghai Jiao Tong University, 200240 Shanghai, China.,Department of Automation, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - Robin R Murphy
- Humanitarian Robotics and AI Laboratory, Texas A&M University, College Station, TX, USA
| | - Weidong Chen
- Institute of Medical Robotics, Shanghai Jiao Tong University, 200240 Shanghai, China.,Department of Automation, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - Giulio Dagnino
- Hamlyn Centre for Robotic Surgery, Imperial College London, London SW7 2AZ, UK.,University of Twente, Enschede, Netherlands
| | - Peer Fischer
- Institute of Physical Chemistry, University of Stuttgart, Stuttgart, Germany.,Micro, Nano, and Molecular Systems Laboratory, Max Planck Institute for Intelligent Systems, Stuttgart, Germany
| | | | - Dennis Kundrat
- Hamlyn Centre for Robotic Surgery, Imperial College London, London SW7 2AZ, UK
| | | | | | - Hao Su
- Biomechatronics and Intelligent Robotics Lab, Department of Mechanical Engineering, City University of New York, City College, New York, NY 10031, USA
| | - Jingen Xia
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, 100029 Beijing, China.,National Center for Respiratory Medicine, 100029 Beijing, China.,Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, 100029 Beijing, China.,National Clinical Research Center for Respiratory Diseases, 100029 Beijing, China
| | - Ajmal Zemmar
- Department of Neurosurgery, Henan Provincial People's Hospital, Henan University People's Hospital, Henan University School of Medicine, 7 Weiwu Road, 450000 Zhengzhou, China.,Department of Neurosurgery, University of Louisville, School of Medicine, 200 Abraham Flexner Way, Louisville, KY 40202, USA
| | - Dandan Zhang
- Hamlyn Centre for Robotic Surgery, Imperial College London, London SW7 2AZ, UK
| | - Chen Wang
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, 100029 Beijing, China.,National Center for Respiratory Medicine, 100029 Beijing, China.,Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, 100029 Beijing, China.,National Clinical Research Center for Respiratory Diseases, 100029 Beijing, China.,Chinese Academy of Medical Sciences, Peking Union Medical College, 100730 Beijing, China
| | - Guang-Zhong Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, 200240 Shanghai, China.
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16
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Sustainable Human–Robot Collaboration Based on Human Intention Classification. SUSTAINABILITY 2021. [DOI: 10.3390/su13115990] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Sustainable manufacturing plays a role in ensuring products’ economic characteristics and reducing energy and resource consumption by improving the well-being of human workers and communities and maintaining safety. Using robots is one way for manufacturers to increase their sustainable manufacturing practices. Nevertheless, there are limitations to directly replacing humans with robots due to work characteristics and practical conditions. Collaboration between robots and humans should accommodate human capabilities while reducing loads and ineffective human motions to prevent human fatigue and maximize overall performance. Moreover, there is a need to establish early and fast communication between humans and machines in human–robot collaboration to know the status of the human in the activity and make immediate adjustments for maximum performance. This study used a deep learning algorithm to classify muscular signals of human motions with accuracy of 88%. It indicates that the signal could be used as information for the robot to determine the human motion’s intention during the initial stage of the entire motion. This approach can increase not only the communication and efficiency of human–robot collaboration but also reduce human fatigue by the early detection of human motion patterns. To enhance human well-being, it is suggested that a human–robot collaboration assembly line adopt similar technologies for a sustainable human–robot collaboration workplace.
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17
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De Raeve P, Davidson PM, Shaffer FA, Pol E, Pandey AK, Adams E. Leveraging the trust of nurses to advance a digital agenda in Europe: a critical review of health policy literature. OPEN RESEARCH EUROPE 2021; 1:26. [PMID: 37645160 PMCID: PMC10446062 DOI: 10.12688/openreseurope.13231.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/10/2021] [Indexed: 08/31/2023]
Abstract
This article is a critical and integrative review of health policy literature examining artificial intelligence (AI) and its implications for healthcare systems and the frontline nursing workforce. A key focus is on co-creation as essential for the deployment and adoption of AI. Our review hinges on the European Commission's White Paper on Artificial Intelligence from 2020, which provides a useful roadmap. The value of health data spaces and electronic health records (EHRs) is considered; and the role of advanced nurse practitioners in harnessing the potential of AI tools in their practice is articulated. Finally, this paper examines "trust" as a precondition for the successful deployment and adoption of AI in Europe. AI applications in healthcare can enhance safety and quality, and mitigate against common risks and challenges, once the necessary level of trust is achieved among all stakeholders. Such an approach can enable effective preventative care across healthcare settings, particularly community and primary care. However, the acceptance of AI tools in healthcare is dependent on the robustness, validity and reliability of data collected and donated from EHRs. Nurse stakeholders have a key role to play in this regard, since trust can only be fostered through engaging frontline end-users in the co-design of EHRs and new AI tools. Nurses hold an intimate understanding of the direct benefits of such technology, such as releasing valuable nursing time for essential patient care, and empowering patients and their family members as recipients of nursing care. This article brings together insights from a unique group of stakeholders to explore the interaction between AI, the co-creation of data spaces and EHRs, and the role of the frontline nursing workforce. We identify the pre-conditions needed for successful deployment of AI and offer insights regarding the importance of co-creating the future European Health Data Space.
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Affiliation(s)
- Paul De Raeve
- European Federation of Nurses Associations, Brussels, 1050, Belgium
| | | | | | - Eric Pol
- aNewGovernance, Brussels, 1050, Belgium
| | - Amit Kumar Pandey
- Socients AI and Robotics (SAS), 185 RUE DES GROS GRES, Colombes, 92700, France
| | - Elizabeth Adams
- European Federation of Nurses Associations, Brussels, 1050, Belgium
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18
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Navaz AN, Serhani MA, El Kassabi HT, Al-Qirim N, Ismail H. Trends, Technologies, and Key Challenges in Smart and Connected Healthcare. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:74044-74067. [PMID: 34812394 PMCID: PMC8545204 DOI: 10.1109/access.2021.3079217] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 05/05/2021] [Indexed: 05/04/2023]
Abstract
Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.
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Affiliation(s)
- Alramzana Nujum Navaz
- Department of Information Systems and SecurityCollege of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates
| | - Mohamed Adel Serhani
- Department of Information Systems and SecurityCollege of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates
| | - Hadeel T. El Kassabi
- Department of Computer Science and Software EngineeringCollege of Information TechnologyUAE UniversityAl AinUnited Arab Emirates
| | - Nabeel Al-Qirim
- Department of Information Systems and SecurityCollege of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates
| | - Heba Ismail
- Department of Computer Science and Information Technology (CS-IT)College of EngineeringAbu Dhabi UniversityAl AinUnited Arab Emirates
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19
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Thomas MJ, Lal V, Baby AK, Rabeeh Vp M, James A, Raj AK. Can technological advancements help to alleviate COVID-19 pandemic? a review. J Biomed Inform 2021; 117:103787. [PMID: 33862231 PMCID: PMC8056973 DOI: 10.1016/j.jbi.2021.103787] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 03/22/2021] [Accepted: 04/10/2021] [Indexed: 12/18/2022]
Abstract
The COVID-19 pandemic is continuing, and the innovative and efficient contributions of the emerging modern technologies to the pandemic responses are too early and cannot be completely quantified at this moment. Digital technologies are not a final solution but are the tools that facilitate a quick and effective pandemic response. In accordance, mobile applications, robots and drones, social media platforms (such as search engines, Twitter, and Facebook), television, and associated technologies deployed in tackling the COVID-19 (SARS-CoV-2) outbreak are discussed adequately, emphasizing the current-state-of-art. A collective discussion on reported literature, press releases, and organizational claims are reviewed. This review addresses and highlights how these effective modern technological solutions can aid in healthcare (involving contact tracing, real-time isolation monitoring/screening, disinfection, quarantine enforcement, syndromic surveillance, and mental health), communication (involving remote assistance, information sharing, and communication support), logistics, tourism, and hospitality. The study discusses the benefits of these digital technologies in curtailing the pandemic and 'how' the different sectors adapted to these in a shorter period. Social media and television's role in ensuring global connectivity and serving as a common platform to share authentic information among the general public were summarized. The World Health Organization and Governments' role globally in-line with the prevention of propagation of false news, spreading awareness, and diminishing the severity of the COVID-19 was discussed. Furthermore, this collective review is helpful to investigators, health departments, Government organizations, and policymakers alike to facilitate a quick and effective pandemic response.
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Affiliation(s)
- Mervin Joe Thomas
- Dept. of Mechanical Engg., National Institute of Technology Calicut, Kerala 673601, India
| | - Vishnu Lal
- Dept. of Mechanical Engg., National Institute of Technology Calicut, Kerala 673601, India
| | - Ajith Kurian Baby
- Dept. of Mechanical Engg., National Institute of Technology Calicut, Kerala 673601, India
| | - Muhammad Rabeeh Vp
- School of Materials Science and Engg., National Institute of Technology Calicut, Kerala 673601, India
| | - Alosh James
- Solar Energy Center, Dept. of Mechanical Engg., National Institute of Technology Calicut, Kerala 673601, India
| | - Arun K Raj
- Dept. of Mechanical Engg., Indian Institute of Technology Bombay, Maharashtra 400076, India.
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20
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Kaiser FK, Wiens M, Schultmann F. Use of digital healthcare solutions for care delivery during a pandemic-chances and (cyber) risks referring to the example of the COVID-19 pandemic. HEALTH AND TECHNOLOGY 2021; 11:1125-1137. [PMID: 33875933 PMCID: PMC8046498 DOI: 10.1007/s12553-021-00541-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/07/2021] [Indexed: 12/12/2022]
Abstract
During pandemics, regular service provisioning processes in medical care may be disrupted. Digital health promises many opportunities for service provisioning during a pandemic. However, a broad penetration of medical processes with information technology also has drawbacks. Within this work, the authors use the COVID-19 pandemic to analyze the chances and the risks that may come with using digital health solutions for medical care during a pandemic. Therefore, a multi-methods approach is used. First we use a systematic literature review for reviewing the state of the art of digital health applications in healthcare. Furthermore, the usage of digital health applications is mapped to the different processes in care delivery. Here we provide an exemplary process model of oncological care delivery. The analysis shows that including digital health solutions may be helpful for care delivery in most processes of medical care provisioning. However, research on digital health solutions focuses strongly on some few processes and specific disciplines while other processes and medical disciplines are underrepresented in literature. Last, we highlight the necessity of a comprehensive risk-related debate around the effects that come with the use of digital healthcare solutions.
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Affiliation(s)
- Florian Klaus Kaiser
- Institute for Industrial Production (IIP), Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Marcus Wiens
- Institute for Industrial Production (IIP), Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Frank Schultmann
- Institute for Industrial Production (IIP), Karlsruhe Institute of Technology, Karlsruhe, Germany
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21
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Seidita V, Lanza F, Pipitone A, Chella A. Robots as intelligent assistants to face COVID-19 pandemic. Brief Bioinform 2021; 22:823-831. [PMID: 33326994 PMCID: PMC7799200 DOI: 10.1093/bib/bbaa361] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/10/2020] [Accepted: 11/09/2020] [Indexed: 01/24/2023] Open
Abstract
MOTIVATION The epidemic at the beginning of this year, due to a new virus in the coronavirus family, is causing many deaths and is bringing the world economy to its knees. Moreover, situations of this kind are historically cyclical. The symptoms and treatment of infected patients are, for better or worse even for new viruses, always the same: more or less severe flu symptoms, isolation and full hygiene. By now man has learned how to manage epidemic situations, but deaths and negative effects continue to occur. What about technology? What effect has the actual technological progress we have achieved? In this review, we wonder about the role of robotics in the fight against COVID. It presents the analysis of scientific articles, industrial initiatives and project calls for applications from March to now highlighting how much robotics was ready to face this situation, what is expected from robots and what remains to do. RESULTS The analysis was made by focusing on what research groups offer as a means of support for therapies and prevention actions. We then reported some remarks on what we think is the state of maturity of robotics in dealing with situations like COVID-19.
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22
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Chai PR, Dadabhoy FZ, Huang HW, Chu JN, Feng A, Le HM, Collins J, da Silva M, Raibert M, Hur C, Boyer EW, Traverso G. Assessment of the Acceptability and Feasibility of Using Mobile Robotic Systems for Patient Evaluation. JAMA Netw Open 2021; 4:e210667. [PMID: 33662134 PMCID: PMC8058534 DOI: 10.1001/jamanetworkopen.2021.0667] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
IMPORTANCE Before the widespread implementation of robotic systems to provide patient care during the COVID-19 pandemic occurs, it is important to understand the acceptability of these systems among patients and the economic consequences associated with the adoption of robotics in health care settings. OBJECTIVE To assess the acceptability and feasibility of using a mobile robotic system to facilitate health care tasks. DESIGN, SETTING, AND PARTICIPANTS This study included 2 components: a national survey to examine the acceptability of using robotic systems to perform health care tasks in a hospital setting and a single-site cohort study of patient experiences and satisfaction with the use of a mobile robotic system to facilitate triage and telehealth tasks in the emergency department (ED). The national survey comprised individuals living in the US who participated in a sampling-based survey via an online analytic platform. Participants completed the national survey between August 18 and August 21, 2020. The single-site cohort study included patients living in the US who presented to the ED of a large urban academic hospital providing quaternary care in Boston, Massachusetts between April and August 2020. All data were analyzed from August to October 2020. EXPOSURES Participants in the national survey completed an online survey to measure the acceptability of using a mobile robotic system to perform health care tasks (facilitating telehealth interviews, acquiring vital signs, obtaining nasal or oral swabs, placing an intravenous catheter, performing phlebotomy, and turning a patient in bed) in a hospital setting in the contexts of general interaction and interaction during the COVID-19 pandemic. Patients in the cohort study were exposed to a mobile robotic system, which was controlled by an ED clinician and used to facilitate a triage interview. After exposure, patients completed an assessment to measure their satisfaction with the robotic system. MAIN OUTCOMES AND MEASURES Acceptability of the use of a mobile robotic system to facilitate health care tasks in a hospital setting (national survey) and feasibility and patient satisfaction regarding the use of a mobile robotic system in the ED (cohort study). RESULTS For the national survey, 1154 participants completed all acceptability questions, representing a participation rate of 35%. After sample matching, a nationally representative sample of 1000 participants (mean [SD] age, 48.7 [17.0] years; 535 women [53.5%]) was included in the analysis. With regard to the usefulness of a robotic system to perform specific health care tasks, the response of "somewhat useful" was selected by 373 participants (37.3%) for facilitating telehealth interviews, 350 participants (35.0%) for acquiring vital signs, 307 participants (30.7%) for obtaining nasal or oral swabs, 228 participants (22.8%) for placing an intravenous catheter, 249 participants (24.9%) for performing phlebotomy, and 371 participants (37.1%) for turning a patient in bed. The response of "extremely useful" was selected by 287 participants (28.7%) for facilitating telehealth interviews, 413 participants (41.3%) for acquiring vital signs, 192 participants (19.2%) for obtaining nasal or oral swabs, 159 participants (15.9%) for placing an intravenous catheter, 167 participants (16.7%) for performing phlebotomy, and 371 participants (37.1%) for turning a patient in bed. In the context of the COVID-19 pandemic, the median number of individuals who perceived the application of robotic systems to be acceptable for completing telehealth interviews, obtaining nasal and oral swabs, placing an intravenous catheter, and performing phlebotomy increased. For the ED cohort study, 51 individuals were invited to participate, and 41 participants (80.4%) enrolled. One participant was unable to complete the study procedures because of a signaling malfunction in the robotic system. Forty patients (mean [SD] age, 45.8 [2.7] years; 29 women [72.5%]) completed the mobile robotic system-facilitated triage interview, and 37 patients (92.5%) reported that the interaction was satisfactory. A total of 33 participants (82.5%) reported that their experience of receiving an interview facilitated by a mobile robotic system was as satisfactory as receiving an in-person interview from a clinician. CONCLUSIONS AND RELEVANCE In this study, a mobile robotic system was perceived to be acceptable for use in a broad set of health care tasks among survey respondents across the US. The use of a mobile robotic system enabled the facilitation of contactless triage interviews of patients in the ED and was considered acceptable among participants. Most patients in the ED rated the quality of mobile robotic system-facilitated interaction to be equivalent to in-person interaction with a clinician.
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Affiliation(s)
- Peter R Chai
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Boston, Massachusetts
- The Fenway Institute, Boston, Massachusetts
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge
| | - Farah Z Dadabhoy
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Hen-Wei Huang
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge
- Division of Gastroenterology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Jacqueline N Chu
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Boston
| | - Annie Feng
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge
| | - Hien M Le
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge
| | - Joy Collins
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge
- Division of Gastroenterology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | | | | | - Chin Hur
- Division of Gastroenterology, Department of Medicine, Columbia University, New York, New York
| | - Edward W Boyer
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- The Fenway Institute, Boston, Massachusetts
| | - Giovanni Traverso
- Division of Gastroenterology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge
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23
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Grunhut J, Wyatt ATM, Marques O. Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes. JOURNAL OF MEDICAL EDUCATION AND CURRICULAR DEVELOPMENT 2021; 8:23821205211036836. [PMID: 34778562 PMCID: PMC8580487 DOI: 10.1177/23821205211036836] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/13/2021] [Accepted: 07/13/2021] [Indexed: 05/05/2023]
Abstract
BACKGROUND As medicine and the delivery of healthcare enters the age of Artificial Intelligence (AI), the need for competent human-machine interaction to aid clinical decisions will rise. Medical students need to be sufficiently proficient in AI, its advantages to improve healthcare's expenses, quality, and access. Similarly, students must be educated about the shortfalls of AI such as bias, transparency, and liability. Overlooking a technology that will be transformative for the foreseeable future would place medical students at a disadvantage. However, there has been little interest in researching a proper method to implement AI in the medical education curriculum. This study aims to review the current literature that covers the attitudes of medical students towards AI, implementation of AI in the medical curriculum, and describe the need for more research in this area. METHODS An integrative review was performed to combine data from various research designs and literature. Pubmed, Medline (Ovid), GoogleScholar, and Web of Science articles between 2010 and 2020 were all searched with particular inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were successively pooled together, recorded, and analyzed quantitatively using a modified Hawkings evaluation form. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses was utilized to help improve reporting. RESULTS A total of 39 articles meeting inclusion criteria were identified. Primary assessments of medical students attitudes were identified (n = 5). Plans to implement AI in the curriculum for the purpose of teaching students about AI (n = 6) and articles reporting actual implemented changes (n = 2) were assessed. Finally, 26 articles described the need for more research on this topic or calling for the need of change in medical curriculum to anticipate AI in healthcare. CONCLUSIONS There are few plans or implementations reported on how to incorporate AI in the medical curriculum. Medical schools must work together to create a longitudinal study and initiative on how to successfully equip medical students with knowledge in AI.
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Affiliation(s)
- Joel Grunhut
- Charles E. Schmidt College of Medicine, Florida Atlantic University, USA
| | - Adam TM Wyatt
- Charles E. Schmidt College of Medicine, Florida Atlantic University, USA
| | - Oge Marques
- College of Engineering and Computer Science, Florida Atlantic University, USA
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24
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HCI for biomedical decision-making: From diagnosis to therapy. J Biomed Inform 2020; 111:103593. [PMID: 33069887 DOI: 10.1016/j.jbi.2020.103593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 10/06/2020] [Indexed: 01/08/2023]
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