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Roy I, Salles J, Neveu E, Lariviére-Bastien D, Blondin A, Levac D, Beauchamp MH. Exploring the perspectives of health care professionals on digital health technologies in pediatric care and rehabilitation. J Neuroeng Rehabil 2024; 21:156. [PMID: 39261920 PMCID: PMC11391714 DOI: 10.1186/s12984-024-01431-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 07/24/2024] [Indexed: 09/13/2024] Open
Abstract
BACKGROUND Digital health technologies are increasingly used by healthcare professionals working in pediatric hospital and rehabilitation settings. Multiple factors may affect the implementation and use of digital health technologies in these settings. However, such factors have not been identified in a multidisciplinary, pediatric context. The objective of this study was to describe actual use and to identify the factors that promote or hinder the intention to use digital health technologies (mobile learning applications, virtual/augmented reality, serious games, robotic devices, telehealth applications, computerized assessment tools, and wearables) among pediatric healthcare professionals. METHODS An online survey evaluating opinions, current use, and future intentions to use digital health technologies was completed by 108 professionals at one of Canada's largest pediatric institutes. Mann-Whitney U tests were used to compare the attitudes of healthcare professionals who intend to increase their use of digital health technologies and those who do not. Linear regression analyses were used to determine predictors of usage success. RESULTS Healthcare professionals reported mostly using mobile and tablet learning applications (n = 43, 38.1%), telehealth applications (n = 49, 43.4%), and computerized assessment tools (n = 33, 29.2%). Attitudes promoting the intention to increase the use of digital health technologies varied according to technology type. Healthcare professionals who wished to increase their use of digital health technologies reported a more positive attitude regarding benefits in clinical practice and patient care, but were also more critical of potential negative impacts on patient-professional relationships. Ease of use (β = 0.374; p = 0.020) was a significant predictor of more favorable usage success. The range of obstacles encountered was also a significant predictor (β = 0.342; p = 0.032) of less favorable evaluation of usage success. Specific factors that hinder successful usage are lack of training (β = 0.303; p = 0.033) and inadequate infrastructure (β = 0.342; p = 0.032). CONCLUSIONS When working with children, incorporating digital health technologies can be effective for motivation and adherence. However, it is crucial to ensure these tools are implemented properly. The findings of this study underscore the importance of addressing training and infrastructure needs when elaborating technology-specific strategies for multidisciplinary adoption of digital health technologies in pediatric settings.
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Affiliation(s)
| | | | | | | | | | - Danielle Levac
- Université de Montréal, Montreal, Canada
- CHU Sainte Justine Azrieli Research Center, Montreal, Canada
| | - Miriam H Beauchamp
- Université de Montréal, Montreal, Canada.
- CHU Sainte Justine Azrieli Research Center, Montreal, Canada.
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Minian N, Mehra K, Earle M, Hafuth S, Ting-A-Kee R, Rose J, Veldhuizen S, Zawertailo L, Ratto M, Melamed OC, Selby P. AI Conversational Agent to Improve Varenicline Adherence: Protocol for a Mixed Methods Feasibility Study. JMIR Res Protoc 2023; 12:e53556. [PMID: 38079201 PMCID: PMC10750231 DOI: 10.2196/53556] [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: 10/10/2023] [Revised: 11/10/2023] [Accepted: 11/23/2023] [Indexed: 12/28/2023] Open
Abstract
BACKGROUND Varenicline is a pharmacological intervention for tobacco dependence that is safe and effective in facilitating smoking cessation. Enhanced adherence to varenicline augments the probability of prolonged smoking abstinence. However, research has shown that one-third of people who use varenicline are nonadherent by the second week. There is evidence showing that behavioral support helps with medication adherence. We have designed an artificial intelligence (AI) conversational agent or health bot, called "ChatV," based on evidence of what works as well as what varenicline is, that can provide these supports. ChatV is an evidence-based, patient- and health care provider-informed health bot to improve adherence to varenicline. ChatV has been programmed to provide medication reminders, answer questions about varenicline and smoking cessation, and track medication intake and the number of cigarettes. OBJECTIVE This study aims to explore the feasibility of the ChatV health bot, to examine if it is used as intended, and to determine the appropriateness of proceeding with a randomized controlled trial. METHODS We will conduct a mixed methods feasibility study where we will pilot-test ChatV with 40 participants. Participants will be provided with a standard 12-week varenicline regimen and access to ChatV. Passive data collection will include adoption measures (how often participants use the chatbot, what features they used, when did they use it, etc). In addition, participants will complete questionnaires (at 1, 4, 8, and 12 weeks) assessing self-reported smoking status and varenicline adherence, as well as questions regarding the acceptability, appropriateness, and usability of the chatbot, and participate in an interview assessing acceptability, appropriateness, fidelity, and adoption. We will use "stop, amend, and go" progression criteria for pilot studies to decide if a randomized controlled trial is a reasonable next step and what modifications are required. A health equity lens will be adopted during participant recruitment and data analysis to understand and address the differences in uptake and use of this digital health solution among diverse sociodemographic groups. The taxonomy of implementation outcomes will be used to assess feasibility, that is, acceptability, appropriateness, fidelity, adoption, and usability. In addition, medication adherence and smoking cessation will be measured to assess the preliminary treatment effect. Interview data will be analyzed using the framework analysis method. RESULTS Participant enrollment for the study will begin in January 2024. CONCLUSIONS By using predetermined progression criteria, the results of this preliminary study will inform the determination of whether to advance toward a larger randomized controlled trial to test the effectiveness of the health bot. Additionally, this study will explore the acceptability, appropriateness, fidelity, adoption, and usability of the health bot. These insights will be instrumental in refining the intervention and the health bot. TRIAL REGISTRATION ClinicalTrials.gov NCT05997901; https://classic.clinicaltrials.gov/ct2/show/NCT05997901. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/53556.
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Affiliation(s)
- Nadia Minian
- INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Kamna Mehra
- INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Mackenzie Earle
- INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Sowsan Hafuth
- INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Ryan Ting-A-Kee
- INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Jonathan Rose
- INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Edward S Rogers Sr Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Scott Veldhuizen
- INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
| | - Laurie Zawertailo
- INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Matt Ratto
- Faculty of Information, University of Toronto, Toronto, ON, Canada
- Schwartz Reisman Institute for Technology and Society, University of Toronto, Toronto, ON, Canada
| | - Osnat C Melamed
- INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Peter Selby
- INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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Bonanno M, Militi A, La Fauci Belponer F, De Luca R, Leonetti D, Quartarone A, Ciancarelli I, Morone G, Calabrò RS. Rehabilitation of Gait and Balance in Cerebral Palsy: A Scoping Review on the Use of Robotics with Biomechanical Implications. J Clin Med 2023; 12:jcm12093278. [PMID: 37176718 PMCID: PMC10179520 DOI: 10.3390/jcm12093278] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/22/2023] [Accepted: 04/29/2023] [Indexed: 05/15/2023] Open
Abstract
Cerebral palsy (CP) is a congenital and permanent neurological disorder due to non-progressive brain damage that affects gross motor functions, such as balance, trunk control and gait. CP gross motor impairments yield more challenging right foot placement during gait phases, as well as the correct direction of the whole-body center of mass with a stability reduction and an increase in falling and tripping. For these reasons, robotic devices, thanks to their biomechanical features, can adapt easily to CP children, allowing better motor recovery and enjoyment. In fact, physiotherapists should consider each pathological gait feature to provide the patient with the best possible rehabilitation strategy and reduce extra energy efforts and the risk of falling in children affected by CP.
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Affiliation(s)
- Mirjam Bonanno
- IRCCS Centro Neurolesi "Bonino-Pulejo", Via Palermo, SS 113, C. da Casazza, 98124 Messina, Italy
| | - Angela Militi
- Department of Biomedical and Dental Sciences and Morphological and Functional Imaging, University of Messina, 98125 Messina, Italy
| | - Francesca La Fauci Belponer
- Neuropsichiatria Infantile, Azienda Ospedaliera Universitaria (AOU), Policlinico "Gaetano Martino", 98125 Messina, Italy
| | - Rosaria De Luca
- IRCCS Centro Neurolesi "Bonino-Pulejo", Via Palermo, SS 113, C. da Casazza, 98124 Messina, Italy
| | - Danilo Leonetti
- Department of Biomedical, Dental and Morphological and Functional Images, Section of Orthopaedic and Traumatology, University of Messina, 98125 Messina, Italy
| | - Angelo Quartarone
- IRCCS Centro Neurolesi "Bonino-Pulejo", Via Palermo, SS 113, C. da Casazza, 98124 Messina, Italy
| | - Irene Ciancarelli
- Department of Life, Health and Environmental Sciences, University of L'Aquila, 67100 L'Aquila, Italy
- ASL 1 Abruzzo (Avezzano-Sulmona-L'Aquila), 67100 L'Aquila, Italy
| | - Giovanni Morone
- Department of Life, Health and Environmental Sciences, University of L'Aquila, 67100 L'Aquila, Italy
- San Raffaele Institute of Sulmona, 67039 Sulmona, Italy
| | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi "Bonino-Pulejo", Via Palermo, SS 113, C. da Casazza, 98124 Messina, Italy
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Minian N, Mehra K, Rose J, Veldhuizen S, Zawertailo L, Ratto M, Lecce J, Selby P. Cocreation of a conversational agent to help patients adhere to their varenicline treatment: A study protocol. Digit Health 2023; 9:20552076231182807. [PMID: 37377562 PMCID: PMC10291536 DOI: 10.1177/20552076231182807] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 06/01/2023] [Indexed: 06/29/2023] Open
Abstract
Objective Varenicline is the most efficacious approved smoking cessation medication, making it one of the most cost-effective clinical interventions for reducing tobacco-related morbidity and mortality. Adhering to varenicline is strongly associated with smoking cessation. Healthbots have the potential to help people adhere to their medications by scaling up evidence-based behavioral interventions. In this protocol, we outline how we will follow the UK's Medical Research Council's guidance to codesign a theory-informed, evidence-based, and patient-centered healthbot to help people adhere to varenicline. Methods The study will utilize the Discover, Design and Build, and Test framework and will include three phases: (a) a rapid review and interviews with 20 patients and 20 healthcare providers to understand barriers and facilitators to varenicline adherence (Discover phase); (b) Wizard of Oz test to design the healthbot and get a sense of the questions that chatbot has to be able to answer (Design phase); and (c) building, training, and beta-testing the healthbot (Building and Testing phases) where the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability framework will be used to develop the healthbot using the simplest sensible solution, and 20 participants will beta test the healthbot. We will use the Capability, Opportunity, Motivation-Behavior (COM-B) model of behavior change and its associated framework, the Theoretical Domains Framework, to organize the findings. Conclusions The present approach will enable us to systematically identify the most appropriate features for the healthbot based on a well-established behavioral theory, the latest scientific evidence, and end users' and healthcare providers' knowledge.
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Affiliation(s)
- Nadia Minian
- INTREPID Lab (formerly Nicotine Dependence Service), Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Kamna Mehra
- INTREPID Lab (formerly Nicotine Dependence Service), Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Jonathan Rose
- Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Scott Veldhuizen
- INTREPID Lab (formerly Nicotine Dependence Service), Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Laurie Zawertailo
- INTREPID Lab (formerly Nicotine Dependence Service), Centre for Addiction and Mental Health, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Matt Ratto
- Faculty of Information, University of Toronto, Toronto, ON, Canada
- Schwartz Reisman Institute for Technology and Society, University of Toronto, Toronto, ON, Canada
| | - Julia Lecce
- INTREPID Lab (formerly Nicotine Dependence Service), Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Peter Selby
- INTREPID Lab (formerly Nicotine Dependence Service), Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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Tennant R, Tetui M, Grindrod K, Burns CM. Multi-Disciplinary Design and Implementation of a Mass Vaccination Clinic Mobile Application to Support Decision-Making. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 11:60-69. [PMID: 36654771 PMCID: PMC9842226 DOI: 10.1109/jtehm.2022.3224740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 10/26/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022]
Abstract
Mass vaccination clinics are complex systems that combine professionals who do not typically work together. Coordinating vaccine preparation and patient intake is critically important to maintain patient flow equilibrium, requiring continuous communication and shared decision-making to reduce vaccine waste. OBJECTIVES (1) To develop a mobile application (app) that can address the information needs of vaccination clinic stakeholders for end-of-day doses decision-making in mass immunization settings; and (2) to understand usability and clinical implementation among multi-disciplinary users. METHODS Contextual inquiry guided 71.5 hours of observations to inform design characteristics. Rapid iterative testing and evaluation were performed to validate and improve the design. Usability and integration were evaluated through observations, interviews, and the system usability scale. RESULTS Designing the app required consolidating contextual factors to support information and workload needs. Twenty-four participants used the app at four clinics who reported its effectiveness in reducing stress and improving communication efficiency and satisfaction. They also discussed positive workflow changes and design recommendations to improve its usefulness. The average system usability score was 87 (n = 22). DISCUSSION There is significant potential for mobile apps to improve workflow efficiencies for information sharing and decision-making in vaccination clinics when designed for established cultures and usability, thereby providing frontline workers with greater time to focus on patient care and immunization needs. However, designing and implementing digital systems for dynamic settings is challenging when healthcare teams constantly adapt to evolving complexities. System-level barriers to adoption require further investigation. Future research should explore the implementation of the app within global contexts.
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Affiliation(s)
- Ryan Tennant
- Department of Systems Design EngineeringUniversity of Waterloo Waterloo ON N2L 3G1 Canada
| | - Moses Tetui
- Department of Epidemiology and Global HealthUmeå University 901 87 Umeå Sweden
- School of PharmacyUniversity of Waterloo Waterloo ON N2G 1C5 Canada
| | - Kelly Grindrod
- School of PharmacyUniversity of Waterloo Waterloo ON N2G 1C5 Canada
| | - Catherine M Burns
- Department of Systems Design EngineeringUniversity of Waterloo Waterloo ON N2L 3G1 Canada
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Tennant R, Tetui M, Grindrod K, Burns CM. Understanding Human Factors Challenges on the Front Lines of Mass COVID-19 Vaccination Clinics: Human-Systems Modelling Study (Preprint). JMIR Hum Factors 2022; 9:e39670. [DOI: 10.2196/39670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/15/2022] [Accepted: 10/06/2022] [Indexed: 11/05/2022] Open
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Turolla A, Kiper P, Mazzarotto D, Cecchi F, Colucci M, D'Avenio G, Facciorusso S, Gatti R, Giansanti D, Iosa M, Bonaiuti D, Boldrini P, Mazzoleni S, Posteraro F, Benanti P, Castelli E, Draicchio F, Falabella V, Galeri S, Gimigliano F, Grigioni M, Mazzon S, Morone G, Petrarca M, Picelli A, Senatore M, Turchetti G, Molteni F. Reference theories and future perspectives on robot-assisted rehabilitation in people with neurological conditions: A scoping review and recommendations from the Italian Consensus Conference on Robotics in Neurorehabilitation (CICERONE). NeuroRehabilitation 2022; 51:681-691. [PMID: 36530100 DOI: 10.3233/nre-220160] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Robot-based treatments are developing in neurorehabilitation settings. Recently, the Italian National Health Systems recognized robot-based rehabilitation as a refundable service. Thus, the Italian neurorehabilitation community promoted a national consensus on this topic. OBJECTIVE To conceptualize undisclosed perspectives for research and applications of robotics for neurorehabilitation, based on a qualitative synthesis of reference theoretical models. METHODS A scoping review was carried out based on a specific question from the consensus Jury. A foreground search strategy was developed on theoretical models (context) of robot-based rehabilitation (exposure), in neurological patients (population). PubMed and EMBASE® databases were searched and studies on theoretical models of motor control, neurobiology of recovery, human-robot interaction and economic sustainability were included, while experimental studies not aimed to investigate theoretical frameworks, or considering prosthetics, were excluded. RESULTS Overall, 3699 records were screened and finally 9 papers included according to inclusion and exclusion criteria. According to the population investigated, structured information on theoretical models and indications for future research was summarized in a synoptic table. CONCLUSION The main indication from the Italian consensus on robotics in neurorehabilitation is the priority to design research studies aimed to investigate the role of robotic and electromechanical devices in promoting neuroplasticity.
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Affiliation(s)
- Andrea Turolla
- Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater Studiorum Università di Bologna, Bologna, Italy
- Division of Occupational Medicine, IRCCS Policlinico Sant'Orsola-Malpighi, Bologna, Italy
| | | | - Deborah Mazzarotto
- Medicina Fisica e Riabilitazione, ULSS 4 Veneto Orientale, San Donà di Piave, Italy
| | - Francesca Cecchi
- Dipartimento di Medicina Sperimentale e Clinica, Università degli Studi di Firenze, Florence, Italy
- IRCSS Fondazione Don Carlo Gnocchi, Firenze, Italy
| | | | - Giuseppe D'Avenio
- National Center for Innovative Technologies in Public Health, Italian National Institute of Health, Rome, Italy
| | | | - Roberto Gatti
- Humanitas University, Department of Biomedical Sciences, via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Milan, Italy
- Humanitas Clinical and Research Center - IRCCS, Milan, Italy
| | - Daniele Giansanti
- National Center for Innovative Technologies in Public Health, Italian National Institute of Health, Rome, Italy
| | - Marco Iosa
- Department of Psychology, Sapienza Università di Roma, Rome, Italy
- Smart Lab, IRCSS Santa Lucia Foundation, Rome, Italy
| | | | - Paolo Boldrini
- Italian Society of Physical and Rehabilitation Medicine (SIMFER), Rome, Italy
| | - Stefano Mazzoleni
- Department of Electrical and Information Engineering, Politecnico di Bari, Bari, Italy
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Federico Posteraro
- Department of Rehabilitation, AUSL Toscana Nord Ovest - Camaiore, Versilia Hospital, Lucca, Italy
| | | | - Enrico Castelli
- Department of Neurorehabilitation and Robotics, IRCCS Bambino Gesù Children's Hospital, Rome, Italy
| | - Francesco Draicchio
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Rome, Italy
| | - Vincenzo Falabella
- Italian Federation of Persons with Spinal Cord Injuries (FAIP Onlus), Rome, Italy
| | | | - Francesca Gimigliano
- Department of Mental, Physical Health and Preventive Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Mauro Grigioni
- National Center for Innovative Technologies in Public Health, Italian National Institute of Health, Rome, Italy
| | - Stefano Mazzon
- Rehabilitation Unit, ULSS (Local Health Authority) Euganea, Camposampiero Hospital, Padua, Italy
| | - Giovanni Morone
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
- San Raffaele Institute of Sulmona, Sulmona, Italy
| | - Maurizio Petrarca
- Movement Analysis and Robotics Laboratory (MARlab), IRCCS Bambino Gesù Children's Hospital, Rome, Italy
| | - Alessandro Picelli
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Michele Senatore
- Associazione Italiana dei Terapisti Occupazionali (AITO), Rome, Italy
| | | | - Franco Molteni
- Villa Beretta Rehabilitation Center, Valduce Hospital, Lecco, Italy
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Salman OH, Taha Z, Alsabah MQ, Hussein YS, Mohammed AS, Aal-Nouman M. A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106357. [PMID: 34438223 DOI: 10.1016/j.cmpb.2021.106357] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 08/10/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND With the remarkable increasing in the numbers of patients, the triaging and prioritizing patients into multi-emergency level is required to accommodate all the patients, save more lives, and manage the medical resources effectively. Triaging and prioritizing patients becomes particularly challenging especially for the patients who are far from hospital and use telemedicine system. To this end, the researchers exploiting the useful tool of machine learning to address this challenge. Hence, carrying out an intensive investigation and in-depth study in the field of using machine learning in E-triage and patient priority are essential and required. OBJECTIVES This research aims to (1) provide a literature review and an in-depth study on the roles of machine learning in the fields of electronic emergency triage (E-triage) and prioritize patients for fast healthcare services in telemedicine applications. (2) highlight the effectiveness of machine learning methods in terms of algorithms, medical input data, output results, and machine learning goals in remote healthcare telemedicine systems. (3) present the relationship between machine learning goals and the electronic triage processes specifically on the: triage levels, medical features for input, outcome results as outputs, and the relevant diseases. (4), the outcomes of our analyses are subjected to organize and propose a cross-over taxonomy between machine learning algorithms and telemedicine structure. (5) present lists of motivations, open research challenges and recommendations for future intelligent work for both academic and industrial sectors in telemedicine and remote healthcare applications. METHODS An intensive research is carried out by reviewing all articles related to the field of E-triage and remote priority systems that utilise machine learning algorithms and sensors. We have searched all related keywords to investigate the databases of Science Direct, IEEE Xplore, Web of Science, PubMed, and Medline for the articles, which have been published from January 2012 up to date. RESULTS A new crossover matching between machine learning methods and telemedicine taxonomy is proposed. The crossover-taxonomy is developed in this study to identify the relationship between machine learning algorithm and the equivalent telemedicine categories whereas the machine learning algorithm has been utilized. The impact of utilizing machine learning is composed in proposing the telemedicine architecture based on synchronous (real-time/ online) and asynchronous (store-and-forward / offline) structure. In addition to that, list of machine learning algorithms, list of the performance metrics, list of inputs data and outputs results are presented. Moreover, open research challenges, the benefits of utilizing machine learning and the recommendations for new research opportunities that need to be addressed for the synergistic integration of multidisciplinary works are organized and presented accordingly. DISCUSSION The state-of-the-art studies on the E-triage and priority systems that utilise machine learning algorithms in telemedicine architecture are discussed. This approach allows the researchers to understand the modernisation of healthcare systems and the efficient use of artificial intelligence and machine learning. In particular, the growing worldwide population and various chronic diseases such as heart chronic diseases, blood pressure and diabetes, require smart health monitoring systems in E-triage and priority systems, in which machine learning algorithms could be greatly beneficial. CONCLUSIONS Although research directions on E-triage and priority systems that use machine learning algorithms in telemedicine vary, they are equally essential and should be considered. Hence, we provide a comprehensive review to emphasise the advantages of the existing research in multidisciplinary works of artificial intelligence, machine learning and healthcare services.
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Affiliation(s)
- Omar H Salman
- Network Department, Faculty of Engineering, AL Iraqia University, Baghdad, Iraq.
| | - Zahraa Taha
- Network Department, Faculty of Engineering, AL Iraqia University, Baghdad, Iraq
| | - Muntadher Q Alsabah
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 4ET, United Kingdom
| | - Yaseein S Hussein
- Information Systems and Computer Science Department, Ahmed Bin Mohammed Military College (ABMMC), P.O. Box: 22988, Doha Qatar
| | - Ahmed S Mohammed
- Information Systems and Computer Science Department, Ahmed Bin Mohammed Military College (ABMMC), P.O. Box: 22988, Doha Qatar
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Jamin A, Abraham P, Humeau-Heurtier A. Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples. Clin Physiol Funct Imaging 2020; 41:113-127. [PMID: 33316137 DOI: 10.1111/cpf.12686] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 11/01/2020] [Accepted: 12/01/2020] [Indexed: 12/13/2022]
Abstract
The evidence-based medicine allows the physician to evaluate the risk-benefit ratio of a treatment through setting and data. Risk-based choices can be done by the doctor using different information. With the emergence of new technologies, a large amount of data is recorded offering interesting perspectives with machine learning for predictive data analytics. Machine learning is an ensemble of methods that process data to model a learning problem. Supervised machine learning algorithms consist in using annotated data to construct the model. This category allows to solve prediction data analytics problems. In this paper, we detail the use of supervised machine learning algorithms for predictive data analytics problems in medicine. In the medical field, data can be split into two categories: medical images and other data. For brevity, our review deals with any kind of medical data excluding images. In this article, we offer a discussion around four supervised machine learning approaches: information-based, similarity-based, probability-based and error-based approaches. Each method is illustrated with detailed cardiovascular and nuclear medicine examples. Our review shows that model ensemble (ME) and support vector machine (SVM) methods are the most popular. SVM, ME and artificial neural networks often lead to better results than those given by other algorithms. In the coming years, more studies, more data, more tools and more methods will, for sure, be proposed.
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Affiliation(s)
- Antoine Jamin
- COTTOS Médical, Avrillé, France.,LERIA-Laboratoire d'Etude et de Recherche en Informatique d'Angers, Univ. Angers, Angers, France.,LARIS-Laboratoire Angevin de Recherche en Ingénierie des Systèmes, Univ. Angers, Angers, France
| | - Pierre Abraham
- Sports Medicine Department, UMR Mitovasc CNRS 6015 INSERM 1228, Angers University Hospital, Angers, France
| | - Anne Humeau-Heurtier
- LARIS-Laboratoire Angevin de Recherche en Ingénierie des Systèmes, Univ. Angers, Angers, France
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10
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Erdemir A, Mulugeta L, Ku JP, Drach A, Horner M, Morrison TM, Peng GCY, Vadigepalli R, Lytton WW, Myers JG. Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective. J Transl Med 2020; 18:369. [PMID: 32993675 PMCID: PMC7526418 DOI: 10.1186/s12967-020-02540-4] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 09/21/2020] [Indexed: 11/10/2022] Open
Abstract
The complexities of modern biomedicine are rapidly increasing. Thus, modeling and simulation have become increasingly important as a strategy to understand and predict the trajectory of pathophysiology, disease genesis, and disease spread in support of clinical and policy decisions. In such cases, inappropriate or ill-placed trust in the model and simulation outcomes may result in negative outcomes, and hence illustrate the need to formalize the execution and communication of modeling and simulation practices. Although verification and validation have been generally accepted as significant components of a model’s credibility, they cannot be assumed to equate to a holistic credible practice, which includes activities that can impact comprehension and in-depth examination inherent in the development and reuse of the models. For the past several years, the Committee on Credible Practice of Modeling and Simulation in Healthcare, an interdisciplinary group seeded from a U.S. interagency initiative, has worked to codify best practices. Here, we provide Ten Rules for credible practice of modeling and simulation in healthcare developed from a comparative analysis by the Committee’s multidisciplinary membership, followed by a large stakeholder community survey. These rules establish a unified conceptual framework for modeling and simulation design, implementation, evaluation, dissemination and usage across the modeling and simulation life-cycle. While biomedical science and clinical care domains have somewhat different requirements and expectations for credible practice, our study converged on rules that would be useful across a broad swath of model types. In brief, the rules are: (1) Define context clearly. (2) Use contextually appropriate data. (3) Evaluate within context. (4) List limitations explicitly. (5) Use version control. (6) Document appropriately. (7) Disseminate broadly. (8) Get independent reviews. (9) Test competing implementations. (10) Conform to standards. Although some of these are common sense guidelines, we have found that many are often missed or misconstrued, even by seasoned practitioners. Computational models are already widely used in basic science to generate new biomedical knowledge. As they penetrate clinical care and healthcare policy, contributing to personalized and precision medicine, clinical safety will require established guidelines for the credible practice of modeling and simulation in healthcare.
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Affiliation(s)
- Ahmet Erdemir
- Department of Biomedical Engineering and Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue (ND20), Cleveland, OH, 44195, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Lealem Mulugeta
- InSilico Labs LLC, 2617 Bissonnet St. Suite 435, Houston, TX, 77005, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Joy P Ku
- Department of Bioengineering, Clark Center, Stanford University, 318 Campus Drive, Stanford, CA, 94305-5448, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Andrew Drach
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E. 24th st, Austin, TX, 78712, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Marc Horner
- ANSYS, Inc, 1007 Church Street, Suite 250, Evanston, IL, 60201, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Tina M Morrison
- Division of Applied Mechanics, United States Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Grace C Y Peng
- National Institute of Biomedical Imaging & Bioengineering, Suite 200, MSC 6707 Democracy Blvd5469, Bethesda, MD, 20892, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Rajanikanth Vadigepalli
- Department of Pathology, Anatomy and Cell Biology, Daniel Baugh Institute for Functional Genomics/Computational Biology, Thomas Jefferson University, 1020 Locust St, Philadelphia, PA, 19107, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - William W Lytton
- State University of New York, Kings County Hospital, 450 Clarkson Ave., MSC 31, Brooklyn, NY, 11203, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Jerry G Myers
- Human Research Program, Cross-Cutting Computational Modeling Project, National Aeronautics and Space Administration - John H. Glenn Research Center, 21000 Brookpark Road, Cleveland, OH, 44135, USA. .,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA.
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11
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Gremyr A, Andersson Gäre B, Greenhalgh T, Malm U, Thor J, Andersson AC. Using Complexity Assessment to Inform the Development and Deployment of a Digital Dashboard for Schizophrenia Care: Case Study. J Med Internet Res 2020; 22:e15521. [PMID: 32324143 PMCID: PMC7206515 DOI: 10.2196/15521] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 11/21/2019] [Accepted: 02/03/2020] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Health care is becoming more complex. For an increasing number of individuals, interacting with health care means addressing more than just one illness or disorder, engaging in more than one treatment, and interacting with more than one care provider. Individuals with severe mental illnesses such as schizophrenia are disproportionately affected by this complexity. Characteristic symptoms can make it harder to establish and maintain relationships. Treatment failure is common even where there is access to effective treatments, increasing suicide risk. Knowledge of complex adaptive systems has been increasingly recognized as useful in understanding and developing health care. A complex adaptive system is a collection of interconnected agents with the freedom to act based on their own internalized rules, affecting each other. In a complex health care system, relevant feedback is crucial in enabling continuous learning and improvement on all levels. New technology has potential, but the failure rate of technology projects in health care is high, arguably due to complexity. The Nonadoption, Abandonment, and challenges to Scale-up, Spread, and Sustainability (NASSS) framework and complexity assessment tool (NASSS-CAT) have been developed specifically to help identify and manage complexity in technology-related development projects in health care. OBJECTIVE This study aimed to use a pilot version of the NASSS-CAT instrument to inform the development and deployment of a point-of-care dashboard supporting schizophrenia care in west Sweden. Specifically, we report on the complexity profile of the project, stakeholders' experiences with using NASSS-CAT, and practical implications. METHODS We used complexity assessment to structure data collection and feedback sessions with stakeholders, thereby informing an emergent approach to the development and deployment of the point-of-care dashboard. We also performed a thematic analysis, drawing on observations and documents related to stakeholders' use of the NASSS-CAT to describe their views on its usefulness. RESULTS Application of the NASSS framework revealed different types of complexity across multiple domains, including the condition, technology, value proposition, organizational tasks and pathways, and wider system. Stakeholders perceived the NASSS-CAT tool as useful in gaining perspective and new insights, covering areas that might otherwise have been neglected. Practical implications derived from feedback sessions with managers and developers are described. CONCLUSIONS This case study shows how stakeholders can identify and plan to address complexities during the introduction of a technological solution. Our findings suggest that NASSS-CAT can bring participants a greater understanding of complexities in digitalization projects in general.
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Affiliation(s)
- Andreas Gremyr
- Department of Schizophrenia Spectrum Disorders (Psykiatri Psykos), Sahlgrenska University Hospital, Mölndal, Sweden
- Jönköping Academy for Improvement of Health and Welfare, School of Health and Welfare, Jönköping University, Jönköping, Sweden
| | - Boel Andersson Gäre
- Jönköping Academy for Improvement of Health and Welfare, School of Health and Welfare, Jönköping University, Jönköping, Sweden
- Futurum Academy for Health and Care, Region Jönköping County, Jönköping, Sweden
| | - Trisha Greenhalgh
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Ulf Malm
- Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Johan Thor
- Jönköping Academy for Improvement of Health and Welfare, School of Health and Welfare, Jönköping University, Jönköping, Sweden
| | - Ann-Christine Andersson
- Jönköping Academy for Improvement of Health and Welfare, School of Health and Welfare, Jönköping University, Jönköping, Sweden
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12
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Jacob C, Sanchez-Vazquez A, Ivory C. Social, Organizational, and Technological Factors Impacting Clinicians' Adoption of Mobile Health Tools: Systematic Literature Review. JMIR Mhealth Uhealth 2020; 8:e15935. [PMID: 32130167 PMCID: PMC7059085 DOI: 10.2196/15935] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 12/03/2019] [Accepted: 12/31/2019] [Indexed: 01/22/2023] Open
Abstract
Background There is a growing body of evidence highlighting the potential of mobile health (mHealth) in reducing health care costs, enhancing access, and improving the quality of patient care. However, user acceptance and adoption are key prerequisites to harness this potential; hence, a deeper understanding of the factors impacting this adoption is crucial for its success. Objective The aim of this review was to systematically explore relevant published literature to synthesize the current understanding of the factors impacting clinicians’ adoption of mHealth tools, not only from a technological perspective but also from social and organizational perspectives. Methods A structured search was carried out of MEDLINE, PubMed, the Cochrane Library, and the SAGE database for studies published between January 2008 and July 2018 in the English language, yielding 4993 results, of which 171 met the inclusion criteria. The Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines and the Cochrane handbook were followed to ensure a systematic process. Results The technological factors impacting clinicians’ adoption of mHealth tools were categorized into eight key themes: usefulness, ease of use, design, compatibility, technical issues, content, personalization, and convenience, which were in turn divided into 14 subthemes altogether. Social and organizational factors were much more prevalent and were categorized into eight key themes: workflow related, patient related, policy and regulations, culture or attitude or social influence, monetary factors, evidence base, awareness, and user engagement. These were divided into 41 subthemes, highlighting the importance of considering these factors when addressing potential barriers to mHealth adoption and how to overcome them. Conclusions The study results can help inform mHealth providers and policymakers regarding the key factors impacting mHealth adoption, guiding them into making educated decisions to foster this adoption and harness the potential benefits.
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Affiliation(s)
- Christine Jacob
- Anglia Ruskin University, Cambridge, United Kingdom.,University of Applied Sciences Northwestern Switzerland, Brugg, Switzerland
| | - Antonio Sanchez-Vazquez
- Innovation and Management Practice Research Centre, Anglia Ruskin University, Cambridge, United Kingdom
| | - Chris Ivory
- Innovation and Management Practice Research Centre, Anglia Ruskin University, Cambridge, United Kingdom
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13
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Jacob C, Sanchez-Vazquez A, Ivory C. Social, Organizational, and Technological Factors Impacting Clinicians' Adoption of Mobile Health Tools: Systematic Literature Review. JMIR Mhealth Uhealth 2020. [PMID: 32130167 DOI: 10.2196/preprints.15935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND There is a growing body of evidence highlighting the potential of mobile health (mHealth) in reducing health care costs, enhancing access, and improving the quality of patient care. However, user acceptance and adoption are key prerequisites to harness this potential; hence, a deeper understanding of the factors impacting this adoption is crucial for its success. OBJECTIVE The aim of this review was to systematically explore relevant published literature to synthesize the current understanding of the factors impacting clinicians' adoption of mHealth tools, not only from a technological perspective but also from social and organizational perspectives. METHODS A structured search was carried out of MEDLINE, PubMed, the Cochrane Library, and the SAGE database for studies published between January 2008 and July 2018 in the English language, yielding 4993 results, of which 171 met the inclusion criteria. The Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines and the Cochrane handbook were followed to ensure a systematic process. RESULTS The technological factors impacting clinicians' adoption of mHealth tools were categorized into eight key themes: usefulness, ease of use, design, compatibility, technical issues, content, personalization, and convenience, which were in turn divided into 14 subthemes altogether. Social and organizational factors were much more prevalent and were categorized into eight key themes: workflow related, patient related, policy and regulations, culture or attitude or social influence, monetary factors, evidence base, awareness, and user engagement. These were divided into 41 subthemes, highlighting the importance of considering these factors when addressing potential barriers to mHealth adoption and how to overcome them. CONCLUSIONS The study results can help inform mHealth providers and policymakers regarding the key factors impacting mHealth adoption, guiding them into making educated decisions to foster this adoption and harness the potential benefits.
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Affiliation(s)
- Christine Jacob
- Anglia Ruskin University, Cambridge, United Kingdom
- University of Applied Sciences Northwestern Switzerland, Brugg, Switzerland
| | - Antonio Sanchez-Vazquez
- Innovation and Management Practice Research Centre, Anglia Ruskin University, Cambridge, United Kingdom
| | - Chris Ivory
- Innovation and Management Practice Research Centre, Anglia Ruskin University, Cambridge, United Kingdom
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14
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Mc Ardle R, Del Din S, Donaghy P, Galna B, Thomas A, Rochester L. Factors That Influence Habitual Activity in Mild Cognitive Impairment and Dementia. Gerontology 2019; 66:197-208. [DOI: 10.1159/000502288] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 07/23/2019] [Indexed: 11/19/2022] Open
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15
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Jacob C, Sanchez-Vazquez A, Ivory C. Clinicians' Role in the Adoption of an Oncology Decision Support App in Europe and Its Implications for Organizational Practices: Qualitative Case Study. JMIR Mhealth Uhealth 2019; 7:e13555. [PMID: 31066710 PMCID: PMC6524456 DOI: 10.2196/13555] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 03/13/2019] [Accepted: 04/03/2019] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Despite the existence of adequate technological infrastructure and clearer policies, there are situations where users, mainly physicians, resist mobile health (mHealth) solutions. This is of particular concern, bearing in mind that several studies, both in developed and developing countries, showed that clinicians' adoption is the most influential factor in such solutions' success. OBJECTIVE The aim of this study was to focus on understanding clinicians' roles in the adoption of an oncology decision support app, the factors impacting this adoption, and its implications for organizational and social practices. METHODS A qualitative case study of a decision support app in oncology, called ONCOassist, was conducted. The data were collected through 17 in-depth interviews with clinicians and nurses in the United Kingdom, Ireland, France, Italy, Spain, and Portugal. RESULTS This case demonstrates the affordances and constraints of mHealth technology at the workplace, its implications for the organization of work, and clinicians' role in its constant development and adoption. The research findings confirmed that factors such as app operation and stability, ease of use, usefulness, cost, and portability play a major role in the adoption decision; however, other social factors such as endorsement, neutrality of the content, attitude toward technology, existing workload, and internal organizational politics are also reported as key determinants of clinicians' adoption. Interoperability and cultural views of mobile usage at work are the key workflow disadvantages, whereas higher efficiency and performance, sharpened practice, and location flexibility are the main workflow advantages. CONCLUSIONS Several organizational implications emerged, suggesting the need for some actions such as fostering a work culture that embraces new technologies and the creation of new digital roles for clinicians both on the hospitals or clinics and on the development sides but also more collaboration between health care organizations and digital health providers to enable electronic medical record integration and solving of any interoperability issues. From a theoretical perspective, we also suggest the addition of a fourth step to Leonardi's methodological guidance that accounts for user engagement; embedding the users in the continuous design and development processes ensures the understanding of user-specific affordances that can then be made more obvious to other users and increase the potential of such tools to go beyond their technological features and have a higher impact on workflow and the organizing process.
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Affiliation(s)
- Christine Jacob
- Anglia Ruskin University, Cambridge, United Kingdom.,University of Applied Sciences Northwestern Switzerland, Brugg, Switzerland
| | - Antonio Sanchez-Vazquez
- Innovation and Management Practice Research Centre, Anglia Ruskin University, Cambridge, United Kingdom
| | - Chris Ivory
- Innovation and Management Practice Research Centre, Anglia Ruskin University, Cambridge, United Kingdom
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16
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Mc Ardle R, Morris R, Hickey A, Del Din S, Koychev I, Gunn RN, Lawson J, Zamboni G, Ridha B, Sahakian BJ, Rowe JB, Thomas A, Zetterberg H, MacKay C, Lovestone S, Rochester L. Gait in Mild Alzheimer's Disease: Feasibility of Multi-Center Measurement in the Clinic and Home with Body-Worn Sensors: A Pilot Study. J Alzheimers Dis 2018; 63:331-341. [PMID: 29614664 DOI: 10.3233/jad-171116] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Gait is emerging as a potential diagnostic tool for cognitive decline. The 'Deep and Frequent Phenotyping for Experimental Medicine in Dementia Study' (D&FP) is a multicenter feasibility study embedded in the United Kingdom Dementia Platform designed to determine participant acceptability and feasibility of extensive and repeated phenotyping to determine the optimal combination of biomarkers to detect disease progression and identify early risk of Alzheimer's disease (AD). Gait is included as a clinical biomarker. The tools to quantify gait in the clinic and home, and suitability for multi-center application have not been examined. Six centers from the National Institute for Health Research Translational Research Collaboration in Dementia initiative recruited 20 individuals with early onset AD. Participants wore a single wearable (tri-axial accelerometer) and completed both clinic-based and free-living gait assessment. A series of macro (behavioral) and micro (spatiotemporal) characteristics were derived from the resultant data using previously validated algorithms. Results indicate good participant acceptability, and potential for use of body-worn sensors in both the clinic and the home. Recommendations for future studies have been provided. Gait has been demonstrated to be a feasible and suitable measure, and future research should examine its suitability as a biomarker in AD.
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Affiliation(s)
- Ríona Mc Ardle
- Institute of Neuroscience, Newcastle University, Newcastle, UK
| | - Rosie Morris
- Institute of Neuroscience, Newcastle University, Newcastle, UK
| | - Aodhán Hickey
- Institute of Neuroscience, Newcastle University, Newcastle, UK
| | - Silvia Del Din
- Institute of Neuroscience, Newcastle University, Newcastle, UK
| | - Ivan Koychev
- UK Department of Psychiatry, University of Oxford, UK
| | - Roger N Gunn
- MANOVA Ltd, London, UK
- Department of Medicine, Imperial College, UK
| | | | | | - Basil Ridha
- NIHR Queen Square Dementia Biomedical Research Unit, University College London, UK
| | | | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, UK and MRC Cognition and Brain Sciences Unit, Cambridge, UK
| | - Alan Thomas
- Institute of Neuroscience, Newcastle University, Newcastle, UK
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute, London, UK
| | - Clare MacKay
- UK Department of Psychiatry, University of Oxford, UK
| | | | - Lynn Rochester
- Institute of Neuroscience, Newcastle University, Newcastle, UK
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17
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Pang Z, Yang G, Khedri R, Zhang YT. Introduction to the Special Section: Convergence of Automation Technology, Biomedical Engineering, and Health Informatics Toward the Healthcare 4.0. IEEE Rev Biomed Eng 2018. [DOI: 10.1109/rbme.2018.2848518] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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18
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Lewis JR, Kerridge I, Lipworth W. Use of Real-World Data for the Research, Development, and Evaluation of Oncology Precision Medicines. JCO Precis Oncol 2017; 1:1-11. [DOI: 10.1200/po.17.00157] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Although randomized controlled trials remain the scientific ideal for determining the efficacy and safety of new treatments, they are sometimes insufficient to address the evidentiary requirements of regulators and payers. This is particularly the case when it comes to precision medicines because trials are often small, deliver incomplete insights into outcomes of most interest to policymakers (eg, overall survival), and may fail to address other complex diagnostic and treatment-related questions. Additional methods, both experimental and observational, are increasingly being used to fill critical evidentiary gaps. A number of modified early- and late-phase trial designs have been proposed to better support earlier biomarker validation, patient identification, and selection for regulatory studies, but there is still a need for confirmatory evidence from real-world data sources. These data are usually provided through observational, postapproval, phase IIIB and IV studies, which rely heavily on registries and other electronic data sets—most notably data from electronic health records. It is, therefore, crucial to understand what ethical, practical, and scientific challenges are raised by the use of electronic health records to generate evidence about precision medicines.
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Affiliation(s)
- Jan R.R. Lewis
- All authors: Sydney Health Ethics, University of Sydney, Sydney, New South Wales, Australia
| | - Ian Kerridge
- All authors: Sydney Health Ethics, University of Sydney, Sydney, New South Wales, Australia
| | - Wendy Lipworth
- All authors: Sydney Health Ethics, University of Sydney, Sydney, New South Wales, Australia
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19
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Jimison HB, Pavel M. Real-time measures of context to improve fall-detection models. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:574-577. [PMID: 28268396 DOI: 10.1109/embc.2016.7590767] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Real-time fall detection has been a challenging area of research and even more challenging as a viable commercial service, given the need for near perfect classification algorithms. True fall events are rare is monitored data sets, whereas confounding events for automated algorithms are quite frequent. In this paper we describe a decision theoretic approach to classification and alerting that incorporates context, such as location and activities, to improve probability and utility estimates for new classes, including near falls and known confounding events. We describe how to use monitored context to provide real-time assessment of true patient state to improve training data sets, as well as the use of context in improving classification, detection and alerting.
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20
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Pavel M, Jimison HB, Korhonen I, Gordon CM, Saranummi N. Behavioral Informatics and Computational Modeling in Support of Proactive Health Management and Care. IEEE Trans Biomed Eng 2015; 62:2763-75. [PMID: 26441408 PMCID: PMC4809752 DOI: 10.1109/tbme.2015.2484286] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Health-related behaviors are among the most significant determinants of health and quality of life. Improving health behavior is an effective way to enhance health outcomes and mitigate the escalating challenges arising from an increasingly aging population and the proliferation of chronic diseases. Although it has been difficult to obtain lasting improvements in health behaviors on a wide scale, advances at the intersection of technology and behavioral science may provide the tools to address this challenge. In this paper, we describe a vision and an approach to improve health behavior interventions using the tools of behavioral informatics, an emerging transdisciplinary research domain based on system-theoretic principles in combination with behavioral science and information technology. The field of behavioral informatics has the potential to optimize interventions through monitoring, assessing, and modeling behavior in support of providing tailored and timely interventions. We describe the components of a closed-loop system for health interventions. These components range from fine grain sensor characterizations to individual-based models of behavior change. We provide an example of a research health coaching platform that incorporates a closed-loop intervention based on these multiscale models. Using this early prototype, we illustrate how the optimized and personalized methodology and technology can support self-management and remote care. We note that despite the existing examples of research projects and our platform, significant future research is required to convert this vision to full-scale implementations.
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Affiliation(s)
- Misha Pavel
- Northeastern University, Boston, MA 02115 USA
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21
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Spruijt-Metz D, Hekler E, Saranummi N, Intille S, Korhonen I, Nilsen W, Rivera DE, Spring B, Michie S, Asch DA, Sanna A, Salcedo VT, Kukakfa R, Pavel M. Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Transl Behav Med 2015; 5:335-46. [PMID: 26327939 PMCID: PMC4537459 DOI: 10.1007/s13142-015-0324-1] [Citation(s) in RCA: 163] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static "snapshots" of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing "gold standard" measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a "knowledge commons," which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.
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Affiliation(s)
- Donna Spruijt-Metz
- />University of Southern California, 635 Downey Way, Suite 305 Building Code: VPD 3332, Los Angeles, CA 90089-3332 USA
| | | | | | | | | | - Wendy Nilsen
- />National Institutes of Health, Bethesda, MD USA
| | | | | | | | - David A. Asch
- />Wharton School, University of Pennsylvania, Philadelphia, PA USA
| | - Alberto Sanna
- />Scientific Institute Hospital San Raffaelle, Milano, Italy
| | | | | | - Misha Pavel
- />VTT Technical Research Centre of Finland, Espoo, Finland
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22
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Zahedi E, Sohani V, Ali MAM, Chellappan K, Beng GK. Experimental feasibility study of estimation of the normalized central blood pressure waveform from radial photoplethysmogram. JOURNAL OF HEALTHCARE ENGINEERING 2015; 6:121-44. [PMID: 25708380 DOI: 10.1260/2040-2295.6.1.121] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
The feasibility of a novel system to reliably estimate the normalized central blood pressure (CBPN) from the radial photoplethysmogram (PPG) is investigated. Right-wrist radial blood pressure and left-wrist PPG were simultaneously recorded in five different days. An industry-standard applanation tonometer was employed for recording radial blood pressure. The CBP waveform was amplitude-normalized to determine CBPN. A total of fifteen second-order autoregressive models with exogenous input were investigated using system identification techniques. Among these 15 models, the model producing the lowest coefficient of variation (CV) of the fitness during the five days was selected as the reference model. Results show that the proposed model is able to faithfully reproduce CBPN (mean fitness = 85.2% ± 2.5%) from the radial PPG for all 15 segments during the five recording days. The low CV value of 3.35% suggests a stable model valid for different recording days.
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Affiliation(s)
- Edmond Zahedi
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM) School of Electrical Engineering, Sharif University of Technology, Iran
| | - Vahid Sohani
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM)
| | - M A Mohd Ali
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM)
| | - Kalaivani Chellappan
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM)
| | - Gan Kok Beng
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM)
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Wang X, Le D, Cheng H, Xie C. All-IP wireless sensor networks for real-time patient monitoring. J Biomed Inform 2014; 52:406-17. [PMID: 25153310 DOI: 10.1016/j.jbi.2014.08.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2013] [Revised: 08/02/2014] [Accepted: 08/08/2014] [Indexed: 11/15/2022]
Abstract
This paper proposes the all-IP WSNs (wireless sensor networks) for real-time patient monitoring. In this paper, the all-IP WSN architecture based on gateway trees is proposed and the hierarchical address structure is presented. Based on this architecture, the all-IP WSN can perform routing without route discovery. Moreover, a mobile node is always identified by a home address and it does not need to be configured with a care-of address during the mobility process, so the communication disruption caused by the address change is avoided. Through the proposed scheme, a physician can monitor the vital signs of a patient at any time and at any places, and according to the IPv6 address he can also obtain the location information of the patient in order to perform effective and timely treatment. Finally, the proposed scheme is evaluated based on the simulation, and the simulation data indicate that the proposed scheme might effectively reduce the communication delay and control cost, and lower the packet loss rate.
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Affiliation(s)
- Xiaonan Wang
- Computer Science Department, Changshu Institute of Technology, Jiangsu, Changshu 215500, China
| | - Deguang Le
- Computer Science Department, Changshu Institute of Technology, Jiangsu, Changshu 215500, China
| | - Hongbin Cheng
- Computer Science Department, Changshu Institute of Technology, Jiangsu, Changshu 215500, China
| | - Conghua Xie
- Computer Science Department, Changshu Institute of Technology, Jiangsu, Changshu 215500, China
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Bricon-Souf N, Conchon E. Trends on integrating framework of applications or data. Findings from the section on health and clinical management. Yearb Med Inform 2014; 9:55-7. [PMID: 25123723 DOI: 10.15265/iy-2014-0032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVE To summarize current excellent research and trends in the field of Health and Clinical management. METHODS Synopsis of the articles selected for the IMIA Yearbook 21014 RESULTS: A comprehensive review of papers published in 2013 was performed by querying PubMed. 1079 were reviewed as papers without authors, without abstract or smaller than 4 pages were excluded from the selection. The editors reviewed all papers and 15 papers selected and provided to to international reviewers. Four papers from international peer-reviewed journals were finally selected for the Health and Clinical Management section. CONCLUSION Many telemedicine applications are tested nowadays in medical situation, but the challenges emphasized by the best papers selection focus on the ability of proposing integrative frameworks for applications or data in order to handle efficiency of health and clinical management.
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