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Biassoni F, Gnerre M. Exploring ChatGPT's communication behaviour in healthcare interactions: A psycholinguistic perspective. PATIENT EDUCATION AND COUNSELING 2025; 134:108663. [PMID: 39854890 DOI: 10.1016/j.pec.2025.108663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2025] [Accepted: 01/12/2025] [Indexed: 01/27/2025]
Abstract
OBJECTIVES Conversational artificial agents such as ChatGPT are commonly used by people seeking healthcare information. This study investigates whether ChatGPT exhibits distinct communicative behaviors in healthcare settings based on the nature of the disorder (medical or psychological) and the user communication style (neutral vs. expressing concern). METHOD Queries were conducted with ChatGPT to gather information on the diagnosis and treatment of two conditions (arthritis and anxiety) using different styles (neutral vs. expressing concern). ChatGPT's responses were analyzed using Linguistic Inquiry and Word Count (LIWC) to identify linguistic markers of the agent's adjustment to different inquiries and interaction modes. Statistical analyses, including repeated measures ANOVA and k-means cluster analysis, identified patterns in ChatGPT's responses. RESULTS ChatGPT used more engaging language in treatment contexts and psychological inquiries. It exhibited more analytical thinking in neutral contexts while demonstrating higher levels of empathy in psychological conditions and when the user expressed concern. Wellness-related language was more prevalent in psychological and treatment contexts, whereas illness-related language was more common in diagnostic interactions for physical conditions. Cluster analysis revealed two distinct patterns: high empathy and engagement in psychological/expressing-concern scenarios, and lower empathy and engagement in neutral/physical disease contexts. CONCLUSIONS These findings suggest that ChatGPT's responses vary according to disorder type and interaction context, potentially improving its effectiveness in patient engagement. PRACTICE IMPLICATIONS Through context and user-concern language adaptation, ChatGPT can enhance patient engagement.
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Affiliation(s)
- Federica Biassoni
- Department of Psychology, Catholic University of the Sacred Heart, Largo Gemelli 1, Milan, Italy; Research Center in Communication Psychology, Catholic University of the Sacred Heart, Milan 20123, Italy.
| | - Martina Gnerre
- Department of Psychology, Catholic University of the Sacred Heart, Largo Gemelli 1, Milan, Italy
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Brown JS, Mumford S, Alexis DA, Ross AG, Higginbotham EJ. Gender Differences in Patient-Physician Communication in Ophthalmic Practice, Pre- and Post-COVID-19 Period. Am J Ophthalmol 2025; 275:114-120. [PMID: 40096876 DOI: 10.1016/j.ajo.2025.03.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2024] [Revised: 02/18/2025] [Accepted: 03/10/2025] [Indexed: 03/19/2025]
Abstract
PURPOSE Physician communication patterns can increase patient satisfaction and adherence to therapy in the primary care setting. This study investigated gender differences in ophthalmologist communication patterns before and after the COVID-19 pandemic. DESIGN Retrospective cohort study. METHODS Messages sent by ophthalmic patients at Penn Medicine from 2017 to 2022 were collected. Differences in the number of physician messages sent for a given patient, median response length, and response time to patient inquiries and messages were examined based on year and physician gender. RESULTS Female ophthalmologists sent longer response messages to their patients (median [25th, 75th percentiles] response length for women vs men: 672 [492-965] characters vs 637 [460, 918] characters; P < .0001) and a higher number of response messages per patient than their male counterparts (mean [SD] for women vs men: 5.5 [2.9] vs 3.0 [1.5]; P = .04). There was an increase in this gender difference in the peri- and post-COVID-19 period (ie, 2020-2022) (P = .007). Male ophthalmologists sent a higher percentage of same-day responses from 2017 to 2020 (P < .0001), whereas female ophthalmologists sent a higher percentage of same-day responses from 2021 to 2022 (P < .0001). The largest gender difference in same-day responses occurred in 2020 (34% for men vs 30% for women; P < .0001). CONCLUSIONS Gender differences exist in ophthalmologist communication patterns, and the COVID-19 pandemic impacted these differences. Future studies will be helpful in determining the potential association of these specific communication patterns with patient satisfaction assessments, eye health outcomes, and physician burnout.
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Affiliation(s)
- Jasmine S Brown
- From the Perelman School of Medicine (J.S.B.); Department of Ophthalmology, Scheie Eye Institute (J.S.B., A.G.R., E.J.H.).
| | - Sunni Mumford
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, Philadelphia, Pennsylvania (S.M.), USA
| | - Dominique A Alexis
- Office of Inclusion, Diversity and Equity, Perelman School of Medicine (D.A.A., E.J.H.), University of Pennsylvania, Philadelphia, Pennsylvania; Norton College of Medicine, SUNY Upstate Medical University (D.A.A.), Syracuse, New York
| | - Ahmara G Ross
- Department of Ophthalmology, Scheie Eye Institute (J.S.B., A.G.R., E.J.H.); Department of Neurology (A.G.R.)
| | - Eve J Higginbotham
- Department of Ophthalmology, Scheie Eye Institute (J.S.B., A.G.R., E.J.H.); Office of Inclusion, Diversity and Equity, Perelman School of Medicine (D.A.A., E.J.H.), University of Pennsylvania, Philadelphia, Pennsylvania
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Kapur I, Kennedy R, Hickman C. Artificial Intelligence Algorithms, Bias, and Innovation: Implications for Social Work. JOURNAL OF EVIDENCE-BASED SOCIAL WORK (2019) 2025:1-23. [PMID: 40008407 DOI: 10.1080/26408066.2025.2470903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2025]
Abstract
PURPOSE Artificial Intelligence (AI) technologies are rapidly expanding across diverse contexts. As the reach of AI continues to grow, there is a need to examine student perspectives on the increasing prevalence of AI and AI-based practice approaches in social work. MATERIALS AND METHODS In this qualitative study, we conducted structured interviews with 15 students in bachelors and masters social work programs. We developed an interview guide with a list of questions to ask students and no prior knowledge of AI was required by the students. The study was framed based on an interpretive phenomenological analysis approach. RESULTS Through thematic analysis, five key themes were developed, including 1) Risks associated with AI, 2) Ethical Concerns in AI and Technology Use, 3) Bias and Fairness in AI, 4) Applications and Possibilities of AI in Social Work, and 5) Training and Awareness of AI in Social Work. DISCUSSION Social workers can help disadvantaged clients by ensuring access to the various AI technologies and facilitating social welfare interventions created using these technologies. There is a need to address the gap in the existing literature about the use of AI in social work practice and education. Social work researchers can explore and conduct future studies that utilize mixed methods methodologies that can evaluate the use of AI in social work domains. CONCLUSION This study highlights the need to increase awareness of AI in social work education and practice settings given the potential of these technologies to aid various aspects of social work practice.
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Affiliation(s)
- Ishita Kapur
- College of Social Work, The University of Tennessee, Knoxville, USA
| | - Reeve Kennedy
- School of Social Work, East Carolina University, North Carolina, USA
| | - Christy Hickman
- College of Social Work, The University of Tennessee, Knoxville, USA
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Tripathi D, Hajra K, Mulukutla A, Shreshtha R, Maity D. Artificial Intelligence in Biomedical Engineering and Its Influence on Healthcare Structure: Current and Future Prospects. Bioengineering (Basel) 2025; 12:163. [PMID: 40001682 PMCID: PMC11851410 DOI: 10.3390/bioengineering12020163] [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: 11/28/2024] [Revised: 01/25/2025] [Accepted: 02/04/2025] [Indexed: 02/27/2025] Open
Abstract
Artificial intelligence (AI) is a growing area of computer science that combines technologies with data science to develop intelligent, highly computation-able systems. Its ability to automatically analyze and query huge sets of data has rendered it essential to many fields such as healthcare. This article introduces you to artificial intelligence, how it works, and what its central role in biomedical engineering is. It brings to light new developments in medical science, why it is being applied in biomedicine, key problems in computer vision and AI, medical applications, diagnostics, and live health monitoring. This paper starts with an introduction to artificial intelligence and its major subfields before moving into how AI is revolutionizing healthcare technology. There is a lot of emphasis on how it will transform biomedical engineering through the use of AI-based devices like biosensors. Not only can these machines detect abnormalities in a patient's physiology, but they also allow for chronic health tracking. Further, this review also provides an overview of the trends of AI-enabled healthcare technologies and concludes that the adoption of artificial intelligence in healthcare will be very high. The most promising are in diagnostics, with highly accurate, non-invasive diagnostics such as advanced imaging and vocal biomarker analyzers leading medicine into the future.
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Affiliation(s)
- Divya Tripathi
- School of Health Sciences, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India; (D.T.)
| | - Kasturee Hajra
- School of Public Health, SRM Medical College, Chennai 603203, Tamil Nadu, India
| | - Aditya Mulukutla
- School of Health Sciences, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India; (D.T.)
| | - Romi Shreshtha
- School of Health Sciences, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India; (D.T.)
| | - Dipak Maity
- Integrated Nanosystems Development Institute, Indiana University Indianapolis, Indianapolis, IN 46202, USA
- Department of Chemistry and Chemical Biology, Indiana University Indianapolis, Indianapolis, IN 46202, USA
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Parveen S, Amjad M, Rauf SA, Arbab S, Jamalvi SA, Saleem SEUR, Ali SK, Bai J, Mustansir M, Danish F, Khalil MA, Haque MA. Surgical decision-making in the digital age: the role of telemedicine - a narrative review. Ann Med Surg (Lond) 2025; 87:242-249. [PMID: 40109606 PMCID: PMC11918621 DOI: 10.1097/ms9.0000000000002874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2024] [Accepted: 12/07/2024] [Indexed: 03/22/2025] Open
Abstract
This narrative review delves into the transformative role of telemedicine in the realm of surgical decision-making. Telemedicine, a significant innovation in healthcare services, leverages electronic information and communication technologies to provide healthcare services when distance separates the participants. It addresses the challenges of increased healthcare demands, an aging population, and budget constraints. Telemedicine technologies are employed for pre- and postoperative consultations, monitoring, and international surgical teleconferencing and education. They enhance healthcare access, particularly in remote areas, and facilitate knowledge sharing among healthcare professionals. The review also provides a historical context and discusses the technological advancements in telemedicine, including the rise of digital health technologies and the integration of artificial intelligence and machine learning in healthcare. It delves into the details of telemedicine technologies such as telesurgery, telerobotics, telepathology, teleimaging, remote patient monitoring, and virtual and augmented reality. Despite the numerous benefits, the implementation of telemedicine is often hindered by various complex and diverse ethical and legal concerns, including privacy and data security. The review highlights the need for further evidence on health outcomes and cost savings, bridging the digital divide, and enacting policies to support telemedicine reimbursement. It also emphasizes the need for incorporating telemedicine modules in medical education. It recommends that policy-making bodies consider utilizing telemedicine to address healthcare coverage gaps, particularly in rural areas.
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Affiliation(s)
| | - Maryam Amjad
- Liaquat National Medical College, Karachi, Pakistan
| | | | | | | | | | | | - Jaiwanti Bai
- Liaquat National Medical College, Karachi, Pakistan
| | | | - Fnu Danish
- Jinnah Sindh Medical University, Karachi, Pakistan
| | | | - Md Ariful Haque
- Department of Public Health, Atish Dipankar University of Science and Technology, Dhaka, Bangladesh
- Voice of Doctors Research School, Dhaka, Bangladesh
- Department of Orthopaedic Surgery, Yan'an Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, China
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Choi J, Woo S, Ferrell A. Artificial intelligence assisted telehealth for nursing: A scoping review. J Telemed Telecare 2025; 31:140-149. [PMID: 37071572 DOI: 10.1177/1357633x231167613] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
BACKGROUND Due to the COVID-19 pandemic, telehealth resurfaced as a convenient efficient healthcare delivery method. Researchers indicate that Artificial Intelligence (AI) could further facilitate delivering quality care in telehealth. It is essential to find supporting evidence to use AI-assisted telehealth interventions in nursing. OBJECTIVES This scoping review focuses on finding users' satisfaction and perception of AI-assisted telehealth intervention, performances of AI algorithms, and the types of AI technology used. METHODS A structured search was performed in six databases, PubMed, CINAHL, Web of Science, OVID, PsycINFO, and ProQuest, following the guidance of the Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews. The quality of the final reviewed studies was assessed using the Medical Education Research Study Quality Instrument. RESULTS Eight of the 41 studies published between 2017 and 2022 were included in the final review. Six studies were conducted in the United States, one in Japan, and one in South Korea. Four studies collected data from participants (n = 3014). Two studies used image data (n = 1986), and two used sensor data from smart homes to detect patients' health events for nurses (n = 35). The quality of studies implied moderate to high-quality study (mean = 10.1, range = 7.7-13.7). Two studies reported high user satisfaction, three assessed user perception of AI in telehealth, and only one showed high AI acceptability. Two studies revealed the high performance of AI algorithms. Five studies used machine learning algorithms. CONCLUSIONS AI-assisted telehealth interventions were efficient and promising and could be an effective care delivery method in nursing.
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Affiliation(s)
- Jeeyae Choi
- School of Nursing, College of Health and Human Services, University of North Carolina at Wilmington, Wilmington, NC, USA
| | - Seoyoon Woo
- School of Nursing, College of Health and Human Services, University of North Carolina at Wilmington, Wilmington, NC, USA
| | - Anastasiya Ferrell
- School of Nursing, College of Health and Human Services, University of North Carolina at Wilmington, Wilmington, NC, USA
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Busch D, Za'in C, Chan HM, Haryanto A, Agustiono W, Yu K, Hamilton K, Kroon J, Xiang W. A blueprint for large language model-augmented telehealth for HIV mitigation in Indonesia: A scoping review of a novel therapeutic modality. Health Informatics J 2025; 31:14604582251315595. [PMID: 39825860 DOI: 10.1177/14604582251315595] [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: 01/20/2025]
Abstract
Background: The HIV epidemic in Indonesia is one of the fastest growing in Southeast Asia and is characterised by a number of geographic and sociocultural challenges. Can large language models (LLMs) be integrated with telehealth (TH) to address cost and quality of care? Methods: A literature review was performed using the PRISMA-ScR (2018) guidelines between Jan 2017 and June 2024 using the PubMed, ArXiv and semantic scholar databases. Results: Of the 694 records identified, 12 studies met the inclusion criteria. Although the role of eHealth interventions as well as telehealth in HIV management appears well established, there is a significant literature gap on the integration of telehealth and LLM technology. To address this, we provide a blueprint for the safe and ethical integration of LLM-TH into triage, history taking, patient education highlighting opportunities for reduced consultation time and improved quality of care. Conclusions: Variable access to mobile technology and the need for empirical validation stand out as limitations for LLM-TH. However, we argue that the current evidence base suggests the benefits far outweigh the challenges in applying LLM-TH for HIV care in Indonesia. We also argue this novel therapeutic modality is broadly applicable to the subacute general practice setting.
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Affiliation(s)
- Daniel Busch
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia
| | - Choiru Za'in
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia
| | - Hei Man Chan
- School of Medicine, University of Queensland, Herston, QLD, Australia
| | - Agnes Haryanto
- Department of Human Centred Computing, Monash University, Melbourne, VIC, Australia
| | - Wahyudi Agustiono
- Department of Information Systems, University of Trunojoyo Madura, Bangkalan, Indonesia
| | - Kan Yu
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia
| | - Kyra Hamilton
- School of Applied Psychology, Griffith University, Southport, QLD, Australia
| | - Jeroen Kroon
- School of Medicine and Dentistry, Griffith University, Southport, QLD, Australia
| | - Wei Xiang
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia
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Dailah HG, Koriri M, Sabei A, Kriry T, Zakri M. Artificial Intelligence in Nursing: Technological Benefits to Nurse's Mental Health and Patient Care Quality. Healthcare (Basel) 2024; 12:2555. [PMID: 39765983 PMCID: PMC11675209 DOI: 10.3390/healthcare12242555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2024] [Revised: 12/10/2024] [Accepted: 12/17/2024] [Indexed: 01/11/2025] Open
Abstract
Nurses are frontline caregivers who handle heavy workloads and high-stakes activities. They face several mental health issues, including stress, burnout, anxiety, and depression. The welfare of nurses and the standard of patient treatment depends on resolving this problem. Artificial intelligence is revolutionising healthcare, and its integration provides many possibilities in addressing these concerns. This review examines literature published over the past 40 years, concentrating on AI integration in nursing for mental health support, improved patient care, and ethical issues. Using databases such as PubMed and Google Scholar, a thorough search was conducted with Boolean operators, narrowing results for relevance. Critically examined were publications on artificial intelligence applications in patient care ethics, mental health, and nursing and mental health. The literature examination revealed that, by automating repetitive chores and improving workload management, artificial intelligence (AI) can relieve mental health challenges faced by nurses and improve patient care. Practical implications highlight the requirement of using rigorous implementation strategies that address ethical issues, data privacy, and human-centred decision-making. All changes must direct the integration of artificial intelligence in nursing to guarantee its sustained and significant influence on healthcare.
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Affiliation(s)
- Hamad Ghaleb Dailah
- College of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi Arabia; (M.K.); (A.S.); (T.K.)
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Spina S, Facciorusso S, Cinone N, Santoro L, Castagna A, Ramella M, Molteni F, Santamato A. Integrating Telemedicine in Botulinum Toxin Type-A Treatment for Spasticity Management: Perspectives and Challenges from Italian Healthcare Professionals. Toxins (Basel) 2024; 16:529. [PMID: 39728787 PMCID: PMC11679457 DOI: 10.3390/toxins16120529] [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: 11/16/2024] [Revised: 11/27/2024] [Accepted: 12/05/2024] [Indexed: 12/28/2024] Open
Abstract
(1) Background: Telemedicine is a vital tool for enhancing healthcare accessibility and outcomes at reduced costs. This study aimed to assess the usability of the Maia Connected Care telemedicine platform for managing spasticity in patients receiving botulinum toxin type-A, focusing on the perspectives of Italian physiatrists with expertise in this treatment. (2) Methods: Conducted from March 2023 to June 2023, this multicenter survey involved 15 Italian physicians who used the platform for teleconsultations. Data collected included demographic details, responses to the Telemedicine Usability Questionnaire, and physician insights on patient satisfaction, treatment effectiveness, and implementation challenges in telehealth. (3) Results: The platform demonstrated high usability, with strong physician satisfaction due to its user-friendly interface and quality of interactions. A majority expressed willingness to continue telehealth for spasticity management, noting its effectiveness in improving patient satisfaction and outcomes. Challenges included replicating the depth of in-person consultations and addressing issues like reimbursement and telehealth standardization. (4) Conclusions: This study highlights telemedicine's potential for spasticity management and clinician satisfaction, while underscoring the need for improvements in simulating in-person experiences and addressing systemic issues. The absence of patient perspectives represents a limitation, advocating for future research to optimize telemedicine practices and evaluate long-term clinical impacts.
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Affiliation(s)
- Stefania Spina
- Spasticity and Movement Disorders “ReSTaRt”, Physical Medicine and Rehabilitation Section, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy; (S.S.)
| | - Salvatore Facciorusso
- Spasticity and Movement Disorders “ReSTaRt”, Physical Medicine and Rehabilitation Section, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy; (S.S.)
| | - Nicoletta Cinone
- Spasticity and Movement Disorders “ReSTaRt”, Physical Medicine and Rehabilitation Section, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy; (S.S.)
| | - Luigi Santoro
- Spasticity and Movement Disorders “ReSTaRt”, Physical Medicine and Rehabilitation Section, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy; (S.S.)
| | - Anna Castagna
- IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy
| | | | - Franco Molteni
- Villa Beretta Rehabilitation Center, Valduce Hospital Como, 23845 Costa Masnaga, Italy
| | - Andrea Santamato
- Spasticity and Movement Disorders “ReSTaRt”, Physical Medicine and Rehabilitation Section, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy; (S.S.)
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Laursen SH, Hæsum LKE, Egmose J, Kronborg T, Udsen FW, Hejlesen OK, Hangaard S. Implementation of an algorithm for predicting exacerbations in telemonitoring: A multimethod study of patients' and clinicians' experiences. INTERNATIONAL JOURNAL OF NURSING STUDIES ADVANCES 2024; 7:100257. [PMID: 39555388 PMCID: PMC11565428 DOI: 10.1016/j.ijnsa.2024.100257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 08/27/2024] [Accepted: 10/21/2024] [Indexed: 11/19/2024] Open
Abstract
Background Prediction algorithms may improve the ability of telehealth solutions to assess the risk of future exacerbations in patients with chronic obstructive pulmonary disease. Learning from patients' and clinicians' evaluations and experiences about the use of such algorithms is essential to evaluate its potential and examine factors that could potentially influence the implementation and sustained use. Objective To investigate the patients' and clinicians' perceptions and satisfaction with an algorithm for predicting exacerbations in patients with chronic obstructive pulmonary disease. Design Multimethod study. Setting Three community nursing sites in Aalborg Municipality, Denmark. Participants One hundred and eleven adults with chronic obstructive pulmonary disease and four clinicians (three nurses and one physiotherapist) specialized in telehealth monitoring of the disease. Methods The study was performed from November 2021 to November 2022 alongside a clinical trial in which a prediction algorithm was integrated into an existing telehealth system. The patients' perspectives were investigated using a self-constructed questionnaire. The clinicians' perspective was explored using semistructured individual interviews. Results Most patients (84.0 %-90.8 %) were satisfied with the algorithm and the additional measurements required by the algorithm. Approximately 71.7 %-75.9 % found that the algorithm could be a useful tool for disease assessment. Patients elaborated that they could see an exacerbation prevention potential in the algorithm. Patients trusted the algorithm and found an increased sense of security. The clinicians showed a positive response toward the algorithm and its user-friendliness. However, they were concerned that the additional measurements could be too demanding for some patients and questioned the accuracy of the measurements. Some felt that the algorithm could risk being time-consuming and harm the overall assessment of the individual patient. They expressed a need for continuous information about the algorithm to understand its functions and alarms. Conclusions Optimal use of the algorithm would require that patients perform additional pulse and oxygen saturation measurements. Furthermore, it will require in-depth insight among clinicians regarding the algorithm's functions and alarms. Registration The study was performed alongside a clinical trial, which was first registered September 9, 2021, at clinicaltrials.gov (registration number NCT05218525). Date of first recruitment was September 28, 2021.
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Affiliation(s)
- Sisse Heiden Laursen
- Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
- Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark
- Department of Nursing, University College of Northern Denmark, Aalborg, Denmark
- Clinical Nursing Research Unit, Aalborg University Hospital, Aalborg, Denmark
| | - Lisa Korsbakke Emtekær Hæsum
- Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
- Department of Nursing, University College of Northern Denmark, Aalborg, Denmark
| | - Julie Egmose
- Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
| | - Thomas Kronborg
- Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
| | - Flemming Witt Udsen
- Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
| | | | - Stine Hangaard
- Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
- Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark
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Habib AR, Crossland G, Sacks R, Singh N, Patel H. Tele-otology for Aboriginal and Torres Strait Islander People Living in Rural and Remote Areas. Laryngoscope 2024; 134:5096-5102. [PMID: 38982868 DOI: 10.1002/lary.31624] [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] [Received: 01/16/2024] [Revised: 05/06/2024] [Accepted: 06/17/2024] [Indexed: 07/11/2024]
Abstract
OBJECTIVE To evaluate a referral-based, tele-otology service in rural and remote areas of the Northern Territory, Australia. METHODS A retrospective observational cohort study was performed of a tele-otology service in 93 Aboriginal and Torres Strait Islander communities (2011 to 2019). Assessments included face-to-face examinations performed by Clinical Nurse Consultants and audiologists, and asynchronous reviews performed by otolaryngologists. Multivariable logistic regression was performed to determine the likelihood of ear disease, adjusted for age and gender. Intra- and inter-rater agreement was assessed between otolaryngologists. RESULTS A total of 3,950 patients were reviewed (6,838 encounters, 13,726 ear assessments). The median age of patients was 9.8 years (interquartile range: 7.2 years). Overall, 62.2% of patients were identified with ear disease and 62.5% identified with hearing loss. Substantial intra- and inter-rater agreement in diagnosis was found between otolaryngologists (κ = 0.71 and κ = 0.78, respectively). The most common ear conditions identified were chronic otitis media (COM, 28.1%) and otitis media with effusion (OME, 16.5%). Topical or oral antibiotics were initiated in 14.1% of all encounters, most often for acute otitis media or COM. Surgery was recommended in 27.7% of all encounters, most often myringoplasty, adenoidectomy, and myringotomy with insertion of tympanostomy tubes. CONCLUSION Tele-otology is a critical component of an integrated approach to evaluating ear disease in Indigenous people living in rural and remote areas. The high prevalence of OME, COM, and surgical recommendations highlights the need for community engagement, regular follow-up, and early interventions to prevent long-term hearing loss. LEVEL OF EVIDENCE NA Laryngoscope, 134:5096-5102, 2024.
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Affiliation(s)
- Al-Rahim Habib
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
- Department of Otolaryngology-Head and Neck Surgery, Royal Darwin Hospital, Top End Health Service, Department of Health, Tiwi, Northern Territory, Australia
- Department of Otolaryngology-Head and Neck Surgery, Westmead Hospital, Sydney, New South Wales, Australia
| | - Graeme Crossland
- Department of Otolaryngology-Head and Neck Surgery, Royal Darwin Hospital, Top End Health Service, Department of Health, Tiwi, Northern Territory, Australia
| | - Raymond Sacks
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Narinder Singh
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
- Department of Otolaryngology-Head and Neck Surgery, Westmead Hospital, Sydney, New South Wales, Australia
| | - Hemi Patel
- Department of Otolaryngology-Head and Neck Surgery, Royal Darwin Hospital, Top End Health Service, Department of Health, Tiwi, Northern Territory, Australia
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Valdez RB, Dymek C, Chaney K, Lomotan EA. AHRQ's digital healthcare research program: 20 years of advancing innovation and discovery. J Am Med Inform Assoc 2024; 31:2766-2771. [PMID: 39348281 PMCID: PMC11491618 DOI: 10.1093/jamia/ocae251] [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] [Received: 08/09/2024] [Revised: 09/12/2024] [Accepted: 09/20/2024] [Indexed: 10/02/2024] Open
Abstract
OBJECTIVES To reflect on the achievements of the Agency for Healthcare Research and Quality's (AHRQ) Digital Healthcare Research Program over the past 20 years, evaluate its impact on US healthcare quality and safety, and outline current and future priorities for digital healthcare research and innovation. PROCESS The article reviews key milestones in AHRQ's digital healthcare initiatives, including its founding and its advances in telehealthcare and clinical decision support. It highlights AHRQ's contributions to advancing technology integration in healthcare, promoting patient safety, and addressing equity gaps. The article also examines the evolving role of artificial intelligence (AI) in healthcare delivery. CONCLUSIONS AHRQ's Digital Healthcare Research Program has significantly contributed to improving healthcare quality. As digital technologies evolve, particularly with AI, the program remains focused on enhancing safety, equity, and efficiency in healthcare. Continued research and investment will be essential to maintaining progress and addressing new challenges.
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Affiliation(s)
- R Burciaga Valdez
- Agency for Healthcare Research and Quality, Rockville, MD 20857, United States
| | - Christine Dymek
- Agency for Healthcare Research and Quality, Rockville, MD 20857, United States
| | - Kevin Chaney
- Agency for Healthcare Research and Quality, Rockville, MD 20857, United States
| | - Edwin A Lomotan
- Agency for Healthcare Research and Quality, Rockville, MD 20857, United States
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13
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Sheerah HA, AlSalamah S, Alsalamah SA, Lu CT, Arafa A, Zaatari E, Alhomod A, Pujari S, Labrique A. The Rise of Virtual Health Care: Transforming the Health Care Landscape in the Kingdom of Saudi Arabia: A Review Article. Telemed J E Health 2024; 30:2545-2554. [PMID: 38984415 DOI: 10.1089/tmj.2024.0114] [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: 07/11/2024] Open
Abstract
Background: The rise of virtual healthcare underscores the transformative influence of digital technologies in reshaping the healthcare landscape. As technology advances and the global demand for accessible and convenient healthcare services escalates, the virtual healthcare sector is gaining unprecedented momentum. Saudi Arabia, with its ambitious Vision 2030 initiative, is actively embracing digital innovation in the healthcare sector. Methods: In this narrative review, we discussed the key drivers and prospects of virtual healthcare in Saudi Arabia, highlighting its potential to enhance healthcare accessibility, quality, and patient outcomes. We also summarized the role of the COVID-19 pandemic in the digital transformation of healthcare in the country. Healthcare services provided by Seha Virtual Hospital in Saudi Arabia, the world's largest and Middle East's first virtual hospital, were also described. Finally, we proposed a roadmap for the future development of virtual health in the country. Results and conclusions: The integration of virtual healthcare into the existing healthcare system can enhance patient experiences, improve outcomes, and contribute to the overall well-being of the population. However, careful planning, collaboration, and investment are essential to overcome the challenges and ensure the successful implementation and sustainability of virtual healthcare in the country.
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Affiliation(s)
- Haytham A Sheerah
- Ministry of Health, Office of the Vice Minister of Health, Riyadh, Saudi Arabia
| | - Shada AlSalamah
- Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
- Department of Digital Health and Innovation, Science Division, World Health Organization, Geneva, Switzerland
| | - Sara A Alsalamah
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA
| | - Chang-Tien Lu
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA
| | - Ahmed Arafa
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan
- Department of Public Health and Community Medicine, Faculty of Medicine, Beni-Suef University, Beni-Suef, Egypt
| | - Ezzedine Zaatari
- Ministry of Health, Office of the Vice Minister of Health, Riyadh, Saudi Arabia
| | - Abdulaziz Alhomod
- Ministry of Health, SEHA Virtual Hospital, Riyadh, Saudi Arabia
- Emergency Medicine Administration, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Sameer Pujari
- Department of Digital Health and Innovation, Science Division, World Health Organization, Geneva, Switzerland
| | - Alain Labrique
- Department of International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland,United States
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14
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Green RW, Castro H. Transforming Otolaryngology-Head and Neck Surgery: The Pivotal Role of Artificial Intelligence in Clinical Workflows. Otolaryngol Clin North Am 2024; 57:909-918. [PMID: 38719713 DOI: 10.1016/j.otc.2024.04.003] [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: 09/06/2024]
Abstract
Use of artificial intelligence (AI) is expanding exponentially as it pertains to workflow operations. Otolaryngology-Head and Neck Surgery (OHNS), as with all medical fields, is just now beginning to realize the exciting upsides of AI as it relates to patient care but otolaryngologists should also be critical when considering using AI solutions. This paper highlights how AI can optimize clinical workflows in the outpatient, inpatient, and surgical settings while also discussing some of the possible drawbacks with the burgeoning technology.
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15
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Alasmari A, Zhou L. Quality Measurement of Consumer Health Questions: Content and Language Perspectives. J Med Internet Res 2024; 26:e48257. [PMID: 39265162 PMCID: PMC11427880 DOI: 10.2196/48257] [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: 04/17/2023] [Revised: 04/09/2024] [Accepted: 07/10/2024] [Indexed: 09/14/2024] Open
Abstract
BACKGROUND Health information consumers increasingly rely on question-and-answer (Q&A) communities to address their health concerns. However, the quality of questions posted significantly impacts the likelihood and relevance of received answers. OBJECTIVE This study aims to improve our understanding of the quality of health questions within web-based Q&A communities. METHODS We develop a novel framework for defining and measuring question quality within web-based health communities, incorporating content- and language-based variables. This framework leverages k-means clustering and establishes automated metrics to assess overall question quality. To validate our framework, we analyze questions related to kidney disease from expert-curated and community-based Q&A platforms. Expert evaluations confirm the validity of our quality construct, while regression analysis helps identify key variables. RESULTS High-quality questions were more likely to include demographic and medical information than lower-quality questions (P<.001). In contrast, asking questions at the various stages of disease development was less likely to reflect high-quality questions (P<.001). Low-quality questions were generally shorter with lengthier sentences than high-quality questions (P<.01). CONCLUSIONS Our findings empower consumers to formulate more effective health information questions, ultimately leading to better engagement and more valuable insights within web-based Q&A communities. Furthermore, our findings provide valuable insights for platform developers and moderators seeking to enhance the quality of user interactions and foster a more trustworthy and informative environment for health information exchange.
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Affiliation(s)
- Ashwag Alasmari
- Computer Science Department, King Khalid University, Abha, Saudi Arabia
- Center for Artificial Intelligence, King Khalid University, Abha, Saudi Arabia
| | - Lina Zhou
- Department of Business Information Systems and Operations Management, The University of North Carolina at Charlotte, Charlotte, NC, United States
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16
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Zychlinski N, Fluss R, Goldberg Y, Zubli D, Barkai G, Zimlichman E, Segal G. Tele-medicine controlled hospital at home is associated with better outcomes than hospital stay. PLoS One 2024; 19:e0309077. [PMID: 39159148 PMCID: PMC11332917 DOI: 10.1371/journal.pone.0309077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 08/05/2024] [Indexed: 08/21/2024] Open
Abstract
BACKGROUND Hospital-at-home (HAH) is increasingly becoming an alternative for in-hospital stay in selected clinical scenarios. Nevertheless, there is still a question whether HAH could be a viable option for acutely ill patients, otherwise hospitalized in departments of general-internal medicine. METHODS This was a retrospective matched study, conducted at a telemedicine controlled HAH department, being part of a tertiary medical center. The objective was to compare clinical outcomes of acutely ill patients (both COVID-19 and non-COVID) admitted to either in-hospital or HAH. Non-COVID patients had one of three acute infectious diseases: urinary tract infections (UTI, either lower or upper), pneumonia, or cellulitis. RESULTS The analysis involved 159 HAH patients (64 COVID-19 and 95 non-COVID) who were compared to a matched sample of in-hospital patients (192 COVID-19 and 285 non-COVID). The median length-of-hospital stay (LOS) was 2 days shorter in the HAH for both COVID-19 patients (95% CI: 1-3; p = 0.008) and non-COVID patients (95% CI; 1-3; p < 0.001). The readmission rates within 30 days were not significantly different for both COVID-19 patients (Odds Ratio (OR) = 1; 95% CI: 0.49-2.04; p = 1) and non-COVID patients (OR = 0.7; 95% CI; 0.39-1.28; p = 0.25). The differences remained insignificant within one year. The risk of death within 30 days was significantly lower in the HAH group for COVID-19 patients (OR = 0.34; 95% CI: 0.11-0.86; p = 0.018) and non-COVID patients (OR = 0.38; 95% CI: 0.14-0.9; p = 0.019). For one year survival period, the differences were significant for COVID-19 patients (OR = 0.5; 95% CI: 0.31-0.9; p = 0.044) and insignificant for non-COVID patients (OR = 0.63; 95% CI: 0.4-1; p = 0.052). CONCLUSIONS Care for acutely ill patients in the setting of telemedicine-based hospital at home has the potential to reduce hospitalization length without increasing readmission risk and to reduce both 30 days and one-year mortality rates.
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Affiliation(s)
- Noa Zychlinski
- Faculty of Data and Decision Sciences, Technion–Israel Institute of Technology, Haifa, Israel
| | - Ronen Fluss
- Biostatistics and Biomathematics Unit, Gertner Institute of Epidemiology and Health Policy Research, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Yair Goldberg
- Faculty of Data and Decision Sciences, Technion–Israel Institute of Technology, Haifa, Israel
| | - Daniel Zubli
- Sheba Beyond Virtual Hospital, Chaim Sheba Medical Center, Ramat Gan, Israel
| | - Galia Barkai
- Sheba Beyond Virtual Hospital, Chaim Sheba Medical Center, Ramat Gan, Israel
| | - Eyal Zimlichman
- Management Wing, Chaim Sheba Medical Center, Ramat Gan, Israel
| | - Gad Segal
- Sheba Beyond Virtual Hospital, Chaim Sheba Medical Center, Ramat Gan, Israel
- Education Authority, Chaim Sheba Medical Center, Ramat Gan, Israel
- Faculty of Healthcare and Medicine, Tel Aviv University, Tel-Aviv, Israel
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17
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Lau ECH, Rajput VK, Hunter I, Florez-Arango JF, Ranatunga P, Veil KD, Kulatunga G, Gogia S, Kuziemsky C, Ito M, Iqbal U, John S, Iyengar S, Ramachandran A, Basu A. Telehealth and Precision Prevention: Bridging the Gap for Individualised Health Strategies. Yearb Med Inform 2024; 33:64-69. [PMID: 40199290 PMCID: PMC12020635 DOI: 10.1055/s-0044-1800720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2025] Open
Abstract
INTRODUCTION Precision prevention has shown an upsurge in popularity among epidemiologists in both developed and developing countries in the past decade. OBJECTIVES Initially practiced in oncology, this approach is increasingly adopted in public health to guard against other common non-communicable diseases (NCDs), such as diabetes and cardiovascular diseases. It aims to tailor preventive measures according to each individual's unique characteristics, such as genomic data, socio-demographic features, environmental factors, and cultural background. METHODS Healthcare information technologies, including telehealth and artificial intelligence (AI), have served as a vital catalyst in the expansion of this field in the past decade. Under this framework, real-time contemporaneous clinical data is collected via a wide range of digital health devices, such as telehealth monitors, wearables, etc., and then analyzed by AI or non-AI prediction models, which then generate preventive recommendations. RESULTS The utilization of telehealth technologies in the precision prevention of cardiovascular diseases (CVDs) is a very illustrative application. This paper explores these topics as well as certain limitations and unintended consequences (UICs) and outlines telehealth as a core enabler of precision prevention as well as public health.
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Affiliation(s)
| | | | | | | | - Prasad Ranatunga
- Provincial Department of Health Services, North-Western Province, Sri Lanka
| | | | | | - Shashi Gogia
- Society for Administration of Telemedicine and Health Care Informatics
| | | | - Marcia Ito
- Unidade de Pós-Graduação, Extensão e Pesquisa do Centro Paula Souza
| | - Usman Iqbal
- School of Population Health, Faculty of Medicine and Health, University Of New South Wales, Sydney, Australia
| | - Sheila John
- Teleophthalmology and E-learning departments, Sankara Nethralaya, Chennai, India
| | - Sriram Iyengar
- Department of Internal Medicine, University of Arizona College of Medicine
| | - Anandhi Ramachandran
- Department of Health Information Technology, International Institute of Health Management Research, Delhi, India
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18
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Gomez C, Smith BL, Zayas A, Unberath M, Canares T. Explainable AI decision support improves accuracy during telehealth strep throat screening. COMMUNICATIONS MEDICINE 2024; 4:149. [PMID: 39048726 PMCID: PMC11269612 DOI: 10.1038/s43856-024-00568-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 07/04/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Artificial intelligence-based (AI) clinical decision support systems (CDSS) using unconventional data, like smartphone-acquired images, promise transformational opportunities for telehealth; including remote diagnosis. Although such solutions' potential remains largely untapped, providers' trust and understanding are vital for effective adoption. This study examines how different human-AI interaction paradigms affect clinicians' responses to an emerging AI CDSS for streptococcal pharyngitis (strep throat) detection from smartphone throat images. METHODS In a randomized experiment, we tested explainable AI strategies using three AI-based CDSS prototypes for strep throat prediction. Participants received clinical vignettes via an online survey to predict the disease state and offer clinical recommendations. The first set included a validated CDSS prediction (Modified Centor Score) and the second introduced an explainable AI prototype randomly. We used linear models to assess explainable AI's effect on clinicians' accuracy, confirmatory testing rates, and perceived trust and understanding of the CDSS. RESULTS The study, involving 121 telehealth providers, shows that compared to using the Centor Score, AI-based CDSS can improve clinicians' predictions. Despite higher agreement with AI, participants report lower trust in its advice than in the Centor Score, leading to more requests for in-person confirmatory testing. CONCLUSIONS Effectively integrating AI is crucial in the telehealth-based diagnosis of infectious diseases, given the implications of antibiotic over-prescriptions. We demonstrate that AI-based CDSS can improve the accuracy of remote strep throat screening yet underscores the necessity to enhance human-machine collaboration, particularly in trust and intelligibility. This ensures providers and patients can capitalize on AI interventions and smartphones for virtual healthcare.
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Affiliation(s)
- Catalina Gomez
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | | | - Alisa Zayas
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mathias Unberath
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
- Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Therese Canares
- Division of Pediatric Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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19
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Khan M, Banerjee S, Muskawad S, Maity R, Chowdhury SR, Ejaz R, Kuuzie E, Satnarine T. The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment. Curr Allergy Asthma Rep 2024; 24:361-372. [PMID: 38954325 DOI: 10.1007/s11882-024-01152-y] [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] [Accepted: 05/19/2024] [Indexed: 07/04/2024]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI), be it neuronal networks, machine learning or deep learning, has numerous beneficial effects on healthcare systems; however, its potential applications and diagnostic capabilities for immunologic diseases have yet to be explored. Understanding AI systems can help healthcare workers better assimilate artificial intelligence into their practice and unravel its potential in diagnostics, clinical research, and disease management. RECENT FINDINGS We reviewed recent advancements in AI systems and their integration in healthcare systems, along with their potential benefits in the diagnosis and management of diseases. We explored machine learning as employed in allergy diagnosis and its learning patterns from patient datasets, as well as the possible advantages of using AI in the field of research related to allergic reactions and even remote monitoring. Considering the ethical challenges and privacy concerns raised by clinicians and patients with regard to integrating AI in healthcare, we explored the new guidelines adapted by regulatory bodies. Despite these challenges, AI appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers. Artificial intelligence offers new hope in the field of immunologic disease diagnosis, monitoring, and management and thus has the potential to revolutionize healthcare systems.
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Affiliation(s)
- Maham Khan
- Fatima Jinnah Medical University, Lahore, Pakistan.
| | | | | | - Rick Maity
- Institute of Post Graduate Medical Education and Research, Kolkata, West Bengal, India
| | | | - Rida Ejaz
- Shifa College of Medicine, Islamabad, Pakistan
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20
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Sezgin E, McKay I. Behavioral health and generative AI: a perspective on future of therapies and patient care. NPJ MENTAL HEALTH RESEARCH 2024; 3:25. [PMID: 38849499 PMCID: PMC11161641 DOI: 10.1038/s44184-024-00067-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 04/06/2024] [Indexed: 06/09/2024]
Affiliation(s)
- Emre Sezgin
- The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.
- The Ohio State University College of Medicine, Columbus, OH, USA.
| | - Ian McKay
- The Ohio State University College of Medicine, Columbus, OH, USA
- Department of Psychiatry and Behavioral Health, Nationwide Children's Hospital, Columbus, OH, USA
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Imam SN, Braun UK, Garcia MA, Jackson LK. Evolution of Telehealth-Its Impact on Palliative Care and Medication Management. PHARMACY 2024; 12:61. [PMID: 38668087 PMCID: PMC11054863 DOI: 10.3390/pharmacy12020061] [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: 12/29/2023] [Revised: 03/15/2024] [Accepted: 03/26/2024] [Indexed: 04/29/2024] Open
Abstract
Palliative care plays a crucial role in enhancing the quality of life for individuals facing serious illnesses, aiming to alleviate suffering and provide holistic support. With the advent of telehealth, there is a growing interest in leveraging technology to extend the reach and effectiveness of palliative care services. This article provides a comprehensive review of the evolution of telehealth, the current state of telemedicine in palliative care, and the role of telepharmacy and medication management. Herein we highlight the potential benefits, challenges, and future directions of palliative telemedicine. As the field continues to advance, the article proposes key considerations for future research, policy development, and clinical implementation, aiming to maximize the advantages of telehealth in assisting individuals and their families throughout the palliative care journey. The comprehensive analysis presented herein contributes to a deeper understanding of the role of telehealth in palliative care and serves as a guide for shaping its future trajectory.
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Affiliation(s)
- Syed N. Imam
- Office of Connected Care, Michael E. DeBakey Veteran Affairs Medical Center, Houston, TX 77030, USA
- Department of Medicine, Section of Geriatric and Palliative Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Rehabilitation & Extended Care Line, Section of Palliative Medicine, Michael E. DeBakey Veteran Affairs Medical Center, Houston, TX 77030, USA
| | - Ursula K. Braun
- Department of Medicine, Section of Geriatric and Palliative Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Rehabilitation & Extended Care Line, Section of Palliative Medicine, Michael E. DeBakey Veteran Affairs Medical Center, Houston, TX 77030, USA
| | - Mary A. Garcia
- Department of Medicine, Section of Geriatric and Palliative Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Rehabilitation & Extended Care Line, Section of Palliative Medicine, Michael E. DeBakey Veteran Affairs Medical Center, Houston, TX 77030, USA
| | - Leanne K. Jackson
- Department of Medicine, Section of Geriatric and Palliative Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Rehabilitation & Extended Care Line, Section of Palliative Medicine, Michael E. DeBakey Veteran Affairs Medical Center, Houston, TX 77030, USA
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22
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Kumar P, Puri O, Unnithan VB, Reddy AP, Aswath S, Pathania M. Preparedness of diabetic patients for receiving telemedical health care: A cross-sectional study. J Family Med Prim Care 2024; 13:1004-1011. [PMID: 38736819 PMCID: PMC11086785 DOI: 10.4103/jfmpc.jfmpc_1024_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 11/09/2023] [Indexed: 05/14/2024] Open
Abstract
Introduction This study evaluates feasibility of telemedicine to deliver diabetic care among different regions of the country. Materials and Methods Medical interns affiliated with Rotaract Club of Medicrew (RCM) organized a Free Diabetes Screening Camp called "Diab-at-ease" at multiple sites across the country. Of all beneficiaries of the camp >18 years of age, patients previously diagnosed with diabetes and undiagnosed patients with a random blood sugar level of more than 200 mg/dL were interviewed regarding their knowledge, attitude, and practice regarding diabetes care and preparedness and vigilance to receiving care through telemedicine. Random blood sugar, height, weight, and waist circumference were also documented. Results About 51.1% (N = 223) of female patients aged 57.57 ± 13.84 years (>18 years) with body mass index (BMI) =26.11 ± 4.63 were the beneficiaries of the health camps. About 75.3% (n = 168) of them were on oral hypoglycemic agents (OHAs), 15.7% (n = 35) were on insulin preparations, and 59.6% (n = 156) and 88.5% (n = 31) of which were highly compliant with treatment, respectively. About 35% (n = 78) and 43.9% (n = 98) of them were unaware of their frequency of hypoglycemic and hyperglycemic episodes, respectively. About 64.6% (n = 144) of the patients were equipped for receiving teleconsultation. Glucometer was only possessed by 51.6% (115) of which only 46.95% (n = 54) can operate it independently. Only 80 patients (35.9%) were aware of the correct value of blood glucose levels. Conclusion While a majority of the population is compliant with treatment and aware about diabetes self-care, they lack adequate knowledge and resource equipment for the same leading to very limited utilization.
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Affiliation(s)
- Pratyush Kumar
- Intern, Dr. Baba Saheb Ambedkar Medical College and Hospital, Rohini, Delhi, India
| | - Oshin Puri
- Intern, All India Institute of Medical Sciences (AIIMS), Rishikesh, Uttarakhand, India
| | - Vishnu B. Unnithan
- Department of Nuclear Medicine, Seth GS Medical College and KEM Hospital, Mumbai, Maharashtra, India
| | - Asmitha P. Reddy
- Intern, Father Muller Medical College, Mangalore, Karnataka, India
| | - Shravya Aswath
- Intern, Vydehi Institute of Medical Sciences and Research Centre, Bengaluru, Karnataka, India
| | - Monika Pathania
- Department of Medicine, All India Institute of Medical Sciences (AIIMS), Rishikesh, Uttarakhand, India
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23
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Kelkar AH, Hantel A, Koranteng E, Cutler CS, Hammer MJ, Abel GA. Digital Health to Patient-Facing Artificial Intelligence: Ethical Implications and Threats to Dignity for Patients With Cancer. JCO Oncol Pract 2024; 20:314-317. [PMID: 37922435 DOI: 10.1200/op.23.00412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 08/22/2023] [Accepted: 10/09/2023] [Indexed: 11/05/2023] Open
Abstract
Ethical considerations for patient-facing AI for oncology: dignity, autonomy, safety, equity, inclusivity.
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Affiliation(s)
- Amar H Kelkar
- Division of Hematologic Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
| | - Andrew Hantel
- Division of Hematologic Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA
- Center for Bioethics, Harvard Medical School, Boston, MA
| | | | - Corey S Cutler
- Division of Hematologic Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
| | - Marilyn J Hammer
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA
- Department of Nursing and Patient Care Services, Dana-Farber Cancer Institute, Boston, MA
| | - Gregory A Abel
- Division of Hematologic Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA
- Center for Bioethics, Harvard Medical School, Boston, MA
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Avoke D, Elshafeey A, Weinstein R, Kim CH, Martin SS. Digital Health in Diabetes and Cardiovascular Disease. Endocr Res 2024; 49:124-136. [PMID: 38605594 PMCID: PMC11484505 DOI: 10.1080/07435800.2024.2341146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/11/2024] [Accepted: 04/04/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND Digital health technologies are rapidly evolving and transforming the care of diabetes and cardiovascular disease (CVD). PURPOSE OF THE REVIEW In this review, we discuss emerging approaches incorporating digital health technologies to improve patient outcomes through a more continuous, accessible, proactive, and patient-centered approach. We discuss various mechanisms of potential benefit ranging from early detection to enhanced physiologic monitoring over time to helping shape important management decisions and engaging patients in their care. Furthermore, we discuss the potential for better individualization of management, which is particularly important in diseases with heterogeneous and complex manifestations, such as diabetes and cardiovascular disease. This narrative review explores ways to leverage digital health technology to better extend the reach of clinicians beyond the physical hospital and clinic spaces to address disparities in the diagnosis, treatment, and prevention of diabetes and cardiovascular disease. CONCLUSION We are at the early stages of the shift to digital medicine, which holds substantial promise not only to improve patient outcomes but also to lower the costs of care. The review concludes by recognizing the challenges and limitations that need to be addressed for optimal implementation and impact. We present recommendations on how to navigate these challenges as well as goals and opportunities in utilizing digital health technology in the management of diabetes and prevention of adverse cardiovascular outcomes.
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Affiliation(s)
- Dorothy Avoke
- Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | | | - Robert Weinstein
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Chang H Kim
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Seth S Martin
- Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Payne RD, Guha N, Mallick BK. A Bayesian survival treed hazards model using latent Gaussian processes. Biometrics 2024; 80:ujad009. [PMID: 38364805 DOI: 10.1093/biomtc/ujad009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/27/2023] [Accepted: 11/12/2023] [Indexed: 02/18/2024]
Abstract
Survival models are used to analyze time-to-event data in a variety of disciplines. Proportional hazard models provide interpretable parameter estimates, but proportional hazard assumptions are not always appropriate. Non-parametric models are more flexible but often lack a clear inferential framework. We propose a Bayesian treed hazards partition model that is both flexible and inferential. Inference is obtained through the posterior tree structure and flexibility is preserved by modeling the log-hazard function in each partition using a latent Gaussian process. An efficient reversible jump Markov chain Monte Carlo algorithm is accomplished by marginalizing the parameters in each partition element via a Laplace approximation. Consistency properties for the estimator are established. The method can be used to help determine subgroups as well as prognostic and/or predictive biomarkers in time-to-event data. The method is compared with some existing methods on simulated data and a liver cirrhosis dataset.
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Affiliation(s)
- Richard D Payne
- Eli Lilly & Company, Lilly Corporate Center, Indianapolis, IN, 46285, United States
| | - Nilabja Guha
- Department of Mathematical Sciences, University of Massachusetts Lowell, One University Avenue, Lowell, Massachusetts, 01852, United States
| | - Bani K Mallick
- Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX, 77843-3143, United States
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Zuhair V, Babar A, Ali R, Oduoye MO, Noor Z, Chris K, Okon II, Rehman LU. Exploring the Impact of Artificial Intelligence on Global Health and Enhancing Healthcare in Developing Nations. J Prim Care Community Health 2024; 15:21501319241245847. [PMID: 38605668 PMCID: PMC11010755 DOI: 10.1177/21501319241245847] [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: 10/19/2023] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI), which combines computer science with extensive datasets, seeks to mimic human-like intelligence. Subsets of AI are being applied in almost all fields of medicine and surgery. AIM This review focuses on the applications of AI in healthcare settings in developing countries, designed to underscore its significance by comprehensively outlining the advancements made thus far, the shortcomings encountered in AI applications, the present status of AI integration, persistent challenges, and innovative strategies to surmount them. METHODOLOGY Articles from PubMed, Google Scholar, and Cochrane were searched from 2000 to 2023 with keywords including AI and healthcare, focusing on multiple medical specialties. RESULTS The increasing role of AI in diagnosis, prognosis prediction, and patient management, as well as hospital management and community healthcare, has made the overall healthcare system more efficient, especially in the high patient load setups and resource-limited areas of developing countries where patient care is often compromised. However, challenges, including low adoption rates and the absence of standardized guidelines, high installation and maintenance costs of equipment, poor transportation and connectivvity issues hinder AI's full use in healthcare. CONCLUSION Despite these challenges, AI holds a promising future in healthcare. Adequate knowledge and expertise of healthcare professionals for the use of AI technology in healthcare is imperative in developing nations.
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Affiliation(s)
- Varisha Zuhair
- Jinnah Sindh Medical University, Karachi, Sindh, Pakistan
| | - Areesha Babar
- Jinnah Sindh Medical University, Karachi, Sindh, Pakistan
| | - Rabbiya Ali
- Jinnah Sindh Medical University, Karachi, Sindh, Pakistan
| | - Malik Olatunde Oduoye
- The Medical Research Circle, (MedReC), Gisenyi, Goma, Democratic Republic of the Congo
| | - Zainab Noor
- Institute of Dentistry CMH Lahore Medical College, Lahore, Punjab, Pakistan
| | - Kitumaini Chris
- The Medical Research Circle, (MedReC), Gisenyi, Goma, Democratic Republic of the Congo
- Université Libre des Pays des Grands-Lacs Goma, Noth-Kivu, Democratic Republic of the Congo
| | - Inibehe Ime Okon
- The Medical Research Circle, (MedReC), Gisenyi, Goma, Democratic Republic of the Congo
- NiMSA SCOPH, Uyo, Akwa-Ibom State, Nigeria
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Singh A, Schooley B, Patel N. Effects of User-Reported Risk Factors and Follow-Up Care Activities on Satisfaction With a COVID-19 Chatbot: Cross-Sectional Study. JMIR Mhealth Uhealth 2023; 11:e43105. [PMID: 38096007 PMCID: PMC10727483 DOI: 10.2196/43105] [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: 09/29/2022] [Revised: 06/19/2023] [Accepted: 11/03/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic influenced many to consider methods to reduce human contact and ease the burden placed on health care workers. Conversational agents or chatbots are a set of technologies that may aid with these challenges. They may provide useful interactions for users, potentially reducing the health care worker burden while increasing user satisfaction. Research aims to understand these potential impacts of chatbots and conversational recommender systems and their associated design features. OBJECTIVE The objective of this study was to evaluate user perceptions of the helpfulness of an artificial intelligence chatbot that was offered free to the public in response to COVID-19. The chatbot engaged patients and provided educational information and the opportunity to report symptoms, understand personal risks, and receive referrals for care. METHODS A cross-sectional study design was used to analyze 82,222 chats collected from patients in South Carolina seeking services from the Prisma Health system. Chi-square tests and multinomial logistic regression analyses were conducted to assess the relationship between reported risk factors and perceived chat helpfulness using chats started between April 24, 2020, and April 21, 2022. RESULTS A total of 82,222 chat series were started with at least one question or response on record; 53,805 symptom checker questions with at least one COVID-19-related activity series were completed, with 5191 individuals clicking further to receive a virtual video visit and 2215 clicking further to make an appointment with a local physician. Patients who were aged >65 years (P<.001), reported comorbidities (P<.001), had been in contact with a person with COVID-19 in the last 14 days (P<.001), and responded to symptom checker questions that placed them at a higher risk of COVID-19 (P<.001) were 1.8 times more likely to report the chat as helpful than those who reported lower risk factors. Users who engaged with the chatbot to conduct a series of activities were more likely to find the chat helpful (P<.001), including seeking COVID-19 information (3.97-4.07 times), in-person appointments (2.46-1.99 times), telehealth appointments with a nearby provider (2.48-1.9 times), or vaccination (2.9-3.85 times) compared with those who did not perform any of these activities. CONCLUSIONS Chatbots that are designed to target high-risk user groups and provide relevant actionable items may be perceived as a helpful approach to early contact with the health system for assessing communicable disease symptoms and follow-up care options at home before virtual or in-person contact with health care providers. The results identified and validated significant design factors for conversational recommender systems, including triangulating a high-risk target user population and providing relevant actionable items for users to choose from as part of user engagement.
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Affiliation(s)
- Akanksha Singh
- Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Benjamin Schooley
- IT & Cybersecurity, Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT, United States
| | - Nitin Patel
- Hackensack Meridian Health, Hackensack, NJ, United States
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Pagallo U, O’Sullivan S, Nevejans N, Holzinger A, Friebe M, Jeanquartier F, Jean-Quartier C, Miernik A. The underuse of AI in the health sector: Opportunity costs, success stories, risks and recommendations. HEALTH AND TECHNOLOGY 2023; 14:1-14. [PMID: 38229886 PMCID: PMC10788319 DOI: 10.1007/s12553-023-00806-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 11/16/2023] [Indexed: 01/18/2024]
Abstract
Purpose This contribution explores the underuse of artificial intelligence (AI) in the health sector, what this means for practice, and how much the underuse can cost. Attention is drawn to the relevance of an issue that the European Parliament has outlined as a "major threat" in 2020. At its heart is the risk that research and development on trusted AI systems for medicine and digital health will pile up in lab centers without generating further practical relevance. Our analysis highlights why researchers, practitioners and especially policymakers, should pay attention to this phenomenon. Methods The paper examines the ways in which governments and public agencies are addressing the underuse of AI. As governments and international organizations often acknowledge the limitations of their own initiatives, the contribution explores the causes of the current issues and suggests ways to improve initiatives for digital health. Results Recommendations address the development of standards, models of regulatory governance, assessment of the opportunity costs of underuse of technology, and the urgency of the problem. Conclusions The exponential pace of AI advances and innovations makes the risks of underuse of AI increasingly threatening. Graphical Abstract
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Affiliation(s)
- Ugo Pagallo
- Law School, University of Turin, Turin, Italy
| | - Shane O’Sullivan
- Department of Urology, Faculty of Medicine, University of Freiburg - Medical Centre, Freiburg im Breisgau, Germany
| | - Nathalie Nevejans
- Ethics and Procedures Center (CDEP), Faculty of Law of Douai, University of Artois, Arras, France
| | - Andreas Holzinger
- Human-Centered AI Lab, Medical University of Graz, Graz, Austria
- University of Natural Resources and Life Sciences Vienna, Human-Centered AI Lab, Vienna, Austria
| | - Michael Friebe
- Department of Measurements and Electronics, AGH University of Science and Technology, Krak’ow, Poland
- Faculty of Medicine, Otto-von-Guericke-University, Magdeburg, Germany
- Center for Innovation and Business Development, FOM University of Applied Sciences, Essen, Germany
| | | | | | - Arkadiusz Miernik
- Department of Urology, Faculty of Medicine, University of Freiburg - Medical Centre, Freiburg im Breisgau, Germany
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Narayanan S, Ramakrishnan R, Durairaj E, Das A. Artificial Intelligence Revolutionizing the Field of Medical Education. Cureus 2023; 15:e49604. [PMID: 38161821 PMCID: PMC10755136 DOI: 10.7759/cureus.49604] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2023] [Indexed: 01/03/2024] Open
Abstract
Medical education has ventured into a new arena of computer-assisted teaching powered by artificial intelligence (AI). In medical institutions, AI can serve as an intelligent tool facilitating the decision-making process effectively. AI can enhance teaching by assisting in developing new strategies for educators. Similarly, students also benefit from intelligent systems playing the role of competent teachers. Thus, AI-integrated medical education paves new opportunities for advanced teaching and learning experiences and improved outcomes. On the other hand, optical mark recognition and automated scoring are ways AI can also transform into a real-time assessor and evaluator in medical education. This review summarizes the AI tools and their application in medical teaching or learning, assessment, and administrative support. This article can aid medical institutes in planning and implementing AI according to the needs of the educators.
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Affiliation(s)
- Suresh Narayanan
- Department of Anatomy, All India Institute of Medical Sciences, Madurai, Madurai, IND
| | | | - Elantamilan Durairaj
- Department of Microbiology, All India Institute of Medical Sciences, Madurai, Madurai, IND
| | - Arghya Das
- Department of Microbiology, All India Institute of Medical Sciences, Madurai, Madurai, IND
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Swarnakar R, Yadav SL. Artificial intelligence and machine learning in motor recovery: A rehabilitation medicine perspective. World J Clin Cases 2023; 11:7258-7260. [PMID: 37946764 PMCID: PMC10631394 DOI: 10.12998/wjcc.v11.i29.7258] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 09/01/2023] [Accepted: 09/18/2023] [Indexed: 10/13/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) are powerful technologies with the potential to revolutionize motor recovery in rehabilitation medicine. This perspective explores how AI and ML are harnessed to assess, diagnose, and design personalized treatment plans for patients with motor impairments. The integration of wearable sensors, virtual reality, augmented reality, and robotic devices allows for precise movement analysis and adaptive neurorehabilitation approaches. Moreover, AI-driven telerehabilitation enables remote monitoring and consultation. Although these applications show promise, healthcare professionals must interpret AI-generated insights and ensure patient safety. While AI and ML are in their early stages, ongoing research will determine their effectiveness in rehabilitation medicine.
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Affiliation(s)
- Raktim Swarnakar
- Department of Physical Medicine and Rehabilitation, All India Institute of Medical Sciences, New Delhi 110029, Delhi, India
| | - Shiv Lal Yadav
- Department of Physical Medicine and Rehabilitation, All India Institute of Medical Sciences, New Delhi 110029, Delhi, India
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31
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Nieto-Martínez R, De Oliveira-Gomes D, Gonzalez-Rivas JP, Al-Rousan T, Mechanick JI, Danaei G. Telehealth and cardiometabolic-based chronic disease: optimizing preventive care in forcibly displaced migrant populations. JOURNAL OF HEALTH, POPULATION, AND NUTRITION 2023; 42:93. [PMID: 37667387 PMCID: PMC10478318 DOI: 10.1186/s41043-023-00418-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/15/2023] [Indexed: 09/06/2023]
Abstract
The number of migrants, which includes forcibly displaced refugees, asylum seekers, and undocumented persons, is increasing worldwide. The global migrant population is heterogeneous in terms of medical conditions and vulnerability resulting from non-optimal metabolic risk factors in the country of origin (e.g., abnormal adiposity, dysglycemia, hypertension, and dyslipidemia), adverse travel conditions and the resulting stress, poverty, and anxiety, and varying effects of acculturation and access to healthcare services in the country of destination. Therefore, many of these migrants develop a high risk for cardiovascular disease and face the significant challenge of overcoming economic and health system barriers to accessing quality healthcare. In the host countries, healthcare professionals experience difficulties providing care to migrants, including cultural and language barriers, and limited institutional capacities, especially for those with non-legal status. Telehealth is an effective strategy to mitigate cardiometabolic risk factors primarily by promoting healthy lifestyle changes and pharmacotherapeutic adjustments. In this descriptive review, the role of telehealth in preventing the development and progression of cardiometabolic disease is explored with a specific focus on type 2 diabetes and hypertension in forcibly displaced migrants. Until now, there are few studies showing that culturally adapted telehealth services can decrease the burden of T2D and HTN. Despite study limitations, telehealth outcomes are comparable to those of traditional health care with the advantages of having better accessibility for difficult-to-reach populations such as forcibly displaced migrants and reducing healthcare associated costs. More prospective studies implementing telemedicine strategies to treat cardiometabolic disease burden in migrant populations are needed.
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Affiliation(s)
- Ramfis Nieto-Martínez
- Precision Care Clinic Corp., Saint Cloud, FL, USA.
- Departments of Global Health and Population and Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.
- Foundation for Clinic, Public Health, and Epidemiology Research of Venezuela (FISPEVEN INC), Caracas, Venezuela.
| | - Diana De Oliveira-Gomes
- Foundation for Clinic, Public Health, and Epidemiology Research of Venezuela (FISPEVEN INC), Caracas, Venezuela
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Juan P Gonzalez-Rivas
- Departments of Global Health and Population and Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
- Foundation for Clinic, Public Health, and Epidemiology Research of Venezuela (FISPEVEN INC), Caracas, Venezuela
- International Clinical Research Centre (ICRC), St Anne's University Hospital Brno (FNUSA), Brno, Czech Republic
| | - Tala Al-Rousan
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Jeffrey I Mechanick
- The Marie-Josée and Henry R. Kravis Center for Clinical Cardiovascular Health at Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Goodarz Danaei
- Departments of Global Health and Population and Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
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Saif-Ur-Rahman KM, Islam MS, Alaboson J, Ola O, Hasan I, Islam N, Mainali S, Martina T, Silenga E, Muyangana M, Joarder T. Artificial intelligence and digital health in improving primary health care service delivery in LMICs: A systematic review. J Evid Based Med 2023; 16:303-320. [PMID: 37691394 DOI: 10.1111/jebm.12547] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 08/30/2023] [Indexed: 09/12/2023]
Abstract
AIM Technology including artificial intelligence (AI) may play a key role to strengthen primary health care services in resource-poor settings. This systematic review aims to explore the evidence on the use of AI and digital health in improving primary health care service delivery. METHODS Three electronic databases were searched using a comprehensive search strategy without providing any restriction in June 2023. Retrieved articles were screened independently using the "Rayyan" software. Data extraction and quality assessment were conducted independently by two review authors. A narrative synthesis of the included interventions was conducted. RESULTS A total of 4596 articles were screened, and finally, 48 articles were included from 21 different countries published between 2013 and 2021. The main focus of the included studies was noncommunicable diseases (n = 15), maternal and child health care (n = 11), primary care (n = 8), infectious diseases including tuberculosis, leprosy, and HIV (n = 7), and mental health (n = 6). Included studies considered interventions using AI, and digital health of which mobile-phone-based interventions were prominent. m-health interventions were well adopted and easy to use and improved the record-keeping, service deliver, and patient satisfaction. CONCLUSION AI and the application of digital technologies improve primary health care service delivery in resource-poor settings in various ways. However, in most of the cases, the application of AI and digital health is implemented through m-health. There is a great scope to conduct further research exploring the interventions on a large scale.
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Affiliation(s)
- K M Saif-Ur-Rahman
- College of Medicine, Nursing and Health Sciences, University of Galway, Galway, Ireland
- Evidence Synthesis Ireland and Cochrane Ireland, University of Galway, Galway, Ireland
- Health Systems and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Md Shariful Islam
- School of Public Health, University of Queensland, Brisbane, Australia
| | - Joan Alaboson
- Department of Psychology, Maynooth University, Kildare, Ireland
| | - Oluwadara Ola
- Sacred Heart Hospital, Abeokuta, Ogun State, Nigeria
| | - Imran Hasan
- Laboratory of Gut-Brain Signaling, Laboratory Sciences and Services Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Nazmul Islam
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Shristi Mainali
- Department of Operations, Marie Stopes International, Kathmandu, Nepal
| | - Tina Martina
- General Hospital of Haji Padjonga, South Sulawesi, Indonesia
| | - Eva Silenga
- Department of Mother and Child Health, Ministry of Health, Lusaka, Zambia
| | - Mubita Muyangana
- Lewanika School of Nursing and Midwifery, Ministry of Health, Mongu, Zambia
| | - Taufique Joarder
- SingHealth Duke-NUS Global Health Institute, National University of Singapore, Singapore
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Krishnan G, Singh S, Pathania M, Gosavi S, Abhishek S, Parchani A, Dhar M. Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm. Front Artif Intell 2023; 6:1227091. [PMID: 37705603 PMCID: PMC10497111 DOI: 10.3389/frai.2023.1227091] [Citation(s) in RCA: 78] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/09/2023] [Indexed: 09/15/2023] Open
Abstract
As the demand for quality healthcare increases, healthcare systems worldwide are grappling with time constraints and excessive workloads, which can compromise the quality of patient care. Artificial intelligence (AI) has emerged as a powerful tool in clinical medicine, revolutionizing various aspects of patient care and medical research. The integration of AI in clinical medicine has not only improved diagnostic accuracy and treatment outcomes, but also contributed to more efficient healthcare delivery, reduced costs, and facilitated better patient experiences. This review article provides an extensive overview of AI applications in history taking, clinical examination, imaging, therapeutics, prognosis and research. Furthermore, it highlights the critical role AI has played in transforming healthcare in developing nations.
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Affiliation(s)
- Gokul Krishnan
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Shiana Singh
- Department of Emergency Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Monika Pathania
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Siddharth Gosavi
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Shuchi Abhishek
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Ashwin Parchani
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Minakshi Dhar
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
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Martinez-Ortigosa A, Martinez-Granados A, Gil-Hernández E, Rodriguez-Arrastia M, Ropero-Padilla C, Roman P. Applications of Artificial Intelligence in Nursing Care: A Systematic Review. J Nurs Manag 2023; 2023:3219127. [PMID: 40225652 PMCID: PMC11919018 DOI: 10.1155/2023/3219127] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 07/12/2023] [Accepted: 07/17/2023] [Indexed: 04/15/2025]
Abstract
Aim To synthesise the available evidence on the applicability of artificial intelligence in nursing care. Background Artificial intelligence involves the replication of human cognitive abilities in machines, allowing to perform tasks that conventionally necessitate human cognition. However, its application in health sciences is a recent one, and its use is currently limited to supporting the diagnosis and prognosis of hospitalised patients, among others. Evaluation. A systematic review was conducted in the PubMed-Medline, Scopus, CINAHL, Web of Science, and Nursing & Allied Health databases until September 2022, following the PRISMA guidelines. Key Issues. A total of 21 articles were selected for the review. The different applications of artificial intelligence in nursing identified comprised (i) advances in early disease detection and clinical decision making; (ii) artificial intelligence-based support systems in nursing for patient monitoring and workflow optimisation; and (iii) artificial intelligence insights for nursing training and education. Conclusion Artificial intelligence-based systems demonstrated increased autonomy of patients and professionals in care processes such as wound management through guided instructions, improved workflows, and efficiency in terms of time, materials, and human resources. Implications for Nursing Management. Artificial intelligence applied to nursing practice can be a very useful resource for professionals, managers, and supervisors. It has the potential to change current working flow systems and may serve as a down-to-earth resource to support nursing professionals in their decision-making process that ensures high quality and patient safety care.
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Affiliation(s)
- Adrian Martinez-Ortigosa
- Emergency Department, San Cecilio University Hospital, Granada, Spain
- Faculty of Health Sciences, Department of Nursing Science, Physiotherapy and Medicine, University of Almeria, Almeria, Spain
| | - Alejandro Martinez-Granados
- Faculty of Health Sciences, Department of Nursing Science, Physiotherapy and Medicine, University of Almeria, Almeria, Spain
| | | | - Miguel Rodriguez-Arrastia
- Faculty of Health Sciences, Department of Nursing Science, Physiotherapy and Medicine, University of Almeria, Almeria, Spain
- Research Group CTS-1114 Health Advances and Innovation, University of Almeria, Spain
| | - Carmen Ropero-Padilla
- Faculty of Health Sciences, Department of Nursing Science, Physiotherapy and Medicine, University of Almeria, Almeria, Spain
- Research Group CTS-1114 Health Advances and Innovation, University of Almeria, Spain
| | - Pablo Roman
- Faculty of Health Sciences, Department of Nursing Science, Physiotherapy and Medicine, University of Almeria, Almeria, Spain
- Research Group CTS-1114 Health Advances and Innovation, University of Almeria, Spain
- Health Research Centre, University of Almeria, Almeria, Spain
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Gurupur VP, Shelleh M, Leone C, Schupp-Omid D, Azevedo R, Dubey S. THNN - A Neural Network Model for Telehealth Data Incompleteness Prediction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083566 DOI: 10.1109/embc40787.2023.10340989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
In modern-day medical practices, practitioners and physicians are adapting to new technologies and utilizing new methods of communication with patients. Telemedicine, or telehealth, is one of the newest innovations in medical technology, enabling practitioners to communicate with their patients over the phone, video conferencing, or chat. However, clinical data and sentiments/attitudes are often not reflected in the practitioner's analysis and diagnosis of the patients they serve. As a solution to the problem of data incompleteness in telehealth, THNN allows medical practices to accommodate for possible missing or incomplete data and provide a greater quality of care overall. Through an ensemble of Natural Language Processing (NLP) and AI-enabled systems, THNN produces sentiment and incompleteness mapping to provide seamless results.Clinical relevance- The method presented utilizes telehealth natural language data to process the sentiments of patients and the incompleteness found in the conversations, increasing the possibility of improved healthcare outcomes.
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Tiribelli S, Monnot A, Shah SFH, Arora A, Toong PJ, Kong S. Ethics Principles for Artificial Intelligence-Based Telemedicine for Public Health. Am J Public Health 2023; 113:577-584. [PMID: 36893365 PMCID: PMC10088937 DOI: 10.2105/ajph.2023.307225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/02/2023] [Indexed: 03/11/2023]
Abstract
The use of artificial intelligence (AI) in the field of telemedicine has grown exponentially over the past decade, along with the adoption of AI-based telemedicine to support public health systems. Although AI-based telemedicine can open up novel opportunities for the delivery of clinical health and care and become a strong aid to public health systems worldwide, it also comes with ethical risks that should be detected, prevented, or mitigated for the responsible use of AI-based telemedicine in and for public health. However, despite the current proliferation of AI ethics frameworks, thus far, none have been developed for the design of AI-based telemedicine, especially for the adoption of AI-based telemedicine in and for public health. We aimed to fill this gap by mapping the most relevant AI ethics principles for AI-based telemedicine for public health and by showing the need to revise them via major ethical themes emerging from bioethics, medical ethics, and public health ethics toward the definition of a unified set of 6 AI ethics principles for the implementation of AI-based telemedicine. (Am J Public Health. 2023;113(5):577-584. https://doi.org/10.2105/AJPH.2023.307225).
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Affiliation(s)
- Simona Tiribelli
- Simona Tiribelli is with the Department of Political Sciences, Communication, and International Relations, University of Macerata, Macerata, Italy, and the Institute for Technology and Global Health, Cambridge, MA. Annabelle Monnot is with Polygeia, Global Health Think Tank, Cambridge, UK. Syed F. H. Shah and Anmol Arora are with the School of Clinical Medicine, University of Cambridge, Cambridge. Ping J. Toong is with the Department of Pathology, University of Cambridge. Sokanha Kong is with the Department of Medical Genetics, University of Cambridge
| | - Annabelle Monnot
- Simona Tiribelli is with the Department of Political Sciences, Communication, and International Relations, University of Macerata, Macerata, Italy, and the Institute for Technology and Global Health, Cambridge, MA. Annabelle Monnot is with Polygeia, Global Health Think Tank, Cambridge, UK. Syed F. H. Shah and Anmol Arora are with the School of Clinical Medicine, University of Cambridge, Cambridge. Ping J. Toong is with the Department of Pathology, University of Cambridge. Sokanha Kong is with the Department of Medical Genetics, University of Cambridge
| | - Syed F H Shah
- Simona Tiribelli is with the Department of Political Sciences, Communication, and International Relations, University of Macerata, Macerata, Italy, and the Institute for Technology and Global Health, Cambridge, MA. Annabelle Monnot is with Polygeia, Global Health Think Tank, Cambridge, UK. Syed F. H. Shah and Anmol Arora are with the School of Clinical Medicine, University of Cambridge, Cambridge. Ping J. Toong is with the Department of Pathology, University of Cambridge. Sokanha Kong is with the Department of Medical Genetics, University of Cambridge
| | - Anmol Arora
- Simona Tiribelli is with the Department of Political Sciences, Communication, and International Relations, University of Macerata, Macerata, Italy, and the Institute for Technology and Global Health, Cambridge, MA. Annabelle Monnot is with Polygeia, Global Health Think Tank, Cambridge, UK. Syed F. H. Shah and Anmol Arora are with the School of Clinical Medicine, University of Cambridge, Cambridge. Ping J. Toong is with the Department of Pathology, University of Cambridge. Sokanha Kong is with the Department of Medical Genetics, University of Cambridge
| | - Ping J Toong
- Simona Tiribelli is with the Department of Political Sciences, Communication, and International Relations, University of Macerata, Macerata, Italy, and the Institute for Technology and Global Health, Cambridge, MA. Annabelle Monnot is with Polygeia, Global Health Think Tank, Cambridge, UK. Syed F. H. Shah and Anmol Arora are with the School of Clinical Medicine, University of Cambridge, Cambridge. Ping J. Toong is with the Department of Pathology, University of Cambridge. Sokanha Kong is with the Department of Medical Genetics, University of Cambridge
| | - Sokanha Kong
- Simona Tiribelli is with the Department of Political Sciences, Communication, and International Relations, University of Macerata, Macerata, Italy, and the Institute for Technology and Global Health, Cambridge, MA. Annabelle Monnot is with Polygeia, Global Health Think Tank, Cambridge, UK. Syed F. H. Shah and Anmol Arora are with the School of Clinical Medicine, University of Cambridge, Cambridge. Ping J. Toong is with the Department of Pathology, University of Cambridge. Sokanha Kong is with the Department of Medical Genetics, University of Cambridge
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Hartmann KV, Primc N, Rubeis G. Lost in translation? Conceptions of privacy and independence in the technical development of AI-based AAL. MEDICINE, HEALTH CARE, AND PHILOSOPHY 2023; 26:99-110. [PMID: 36348209 PMCID: PMC9984520 DOI: 10.1007/s11019-022-10126-8] [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] [Accepted: 10/27/2022] [Indexed: 06/16/2023]
Abstract
AAL encompasses smart home technologies that are installed in the personal living environment in order to support older, disabled, as well as chronically ill people with the goal of delaying or reducing their need for nursing care in a care facility. Artificial intelligence (AI) is seen as an important tool for assisting the target group in their daily lives. A literature search and qualitative content analysis of 255 articles from computer science and engineering was conducted to explore the usage of ethical concepts. From an ethical point of view, the concept of independence and self-determination on the one hand and the possible loss of privacy on the other hand are widely discussed in the context of AAL. These concepts are adopted by the technical discourse in the sense that independence, self-determination and privacy are recognized as important values. Nevertheless, our research shows that these concepts have different usages and meanings in the ethical and the technical discourses. In the paper, we aim to map the different meanings of independence, self-determination and privacy as they can be found in the context of technological research on AI-based AAL systems. It investigates the interpretation of these ethical and social concepts which technicians try to build into AAL systems. In a second step, these interpretations are contextualized with concepts from the ethical discourse on AI-based assistive technologies.
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Affiliation(s)
- Kris Vera Hartmann
- Institute for History and Ethics of Medicine, Faculty of Medicine, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany.
| | - Nadia Primc
- Institute for History and Ethics of Medicine, Faculty of Medicine, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany
| | - Giovanni Rubeis
- Department of General Health Studies, Division Biomedical and Public Health Ethics, Karl Landsteiner Private University for Health Sciences, Krems, Austria
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Strengthening capacities of multidisciplinary professionals to apply data science in public health: Experience of an international graduate diploma program in Peru. Int J Med Inform 2023; 169:104913. [PMID: 36410127 DOI: 10.1016/j.ijmedinf.2022.104913] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 10/28/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022]
Abstract
Nowadays it is necessary to strengthen health information systems and data-based solutions. However, there are few graduate training programs in Peru to use tools and methods of data science applied in public health. This article describes the development process and the initial assessment regarding the experience of the participants in an international multidisciplinary diploma in data intelligence for pandemics and epidemics preparedness, which was carried out from January to May 2021. The diploma was structured in 7 modules and 40 Peruvian professionals participated, of which 11 (27.5%) were women, and 16 (40%) came from regions outside of Lima and Callao. We discussed the need to strengthen institutional and health professionals' capacity to adequately manage large volumes of data, information, and knowledge through the application of emerging technologies to optimize data management processes to improve decision-making in health.
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Teleconsultation in respiratory medicine - A position paper of the Portuguese Pulmonology Society. Pulmonology 2023; 29:65-76. [PMID: 35705437 PMCID: PMC9188666 DOI: 10.1016/j.pulmoe.2022.04.007] [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: 12/23/2021] [Revised: 04/06/2022] [Accepted: 04/06/2022] [Indexed: 01/06/2023] Open
Abstract
The COVID-19 pandemic crisis, among so many social, economic and health problems, also brought new opportunities. The potential of telemedicine to improve health outcomes had already been recognised in the last decades, but the pandemic crisis has accelerated the digital revolution. In 2020, a rapid increase in the use of remote consultations occurred due to the need to reduce attendance and overcrowding in outpatient clinics. However, the benefit of their use extends beyond the pandemic crisis, as an important tool to improve both the efficiency and capacity of future healthcare systems. This article reviews the literature regarding telemedicine and teleconsultation standards and recommendations, collects opinions of Portuguese experts in respiratory medicine and provides guidance in teleconsultation practices for Pulmonologists.
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40
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A Multi-Industry Analysis of the Future Use of AI Chatbots. HUMAN BEHAVIOR AND EMERGING TECHNOLOGIES 2022. [DOI: 10.1155/2022/2552099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Artificial intelligence (AI) chatbots are set to be the defining technology of the next decade due to their ability to increase human capability at a low cost. However, more research is required to assess individuals’ behavioural intentions to use this technology when it becomes publicly available. This study applied an extended Technology Acceptance Model (TAM), with additional predictors of trust and privacy concerns, to assess individuals’ behavioural intentions to use AI chatbots across three industries: mental health care, online shopping, and online banking. These services were selected due to the current popularity of regular chatbots in these fields. Participants (
, 202 females) aged between 17 and 85 years (
,
) completed a 71-item online, cross-sectional survey. As hypothesised, perceived usefulness and trust were significant positive predictors of behavioural intentions across all three behaviours. However, the influence of the perceived ease of use and privacy concerns on behavioural intentions differed across the three behaviours. These findings highlight that the combination of predictors within the extended TAM have different influences on behavioural intentions to use AI chatbots for mental health care, online shopping, and online banking. This research contributes to the literature by demonstrating that the influence of the variables in one field cannot be generalised across all uses of AI chatbots.
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Dascalu A, Walker BN, Oron Y, David EO. Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms. J Cancer Res Clin Oncol 2022; 148:2497-2505. [PMID: 34546412 PMCID: PMC8453469 DOI: 10.1007/s00432-021-03809-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 09/15/2021] [Indexed: 11/18/2022]
Abstract
PURPOSE Non-melanoma skin cancer (NMSC) is the most frequent keratinocyte-origin skin tumor. It is confirmed that dermoscopy of NMSC confers a diagnostic advantage as compared to visual face-to-face assessment. COVID-19 restrictions diagnostics by telemedicine photos, which are analogous to visual inspection, displaced part of in-person visits. This study evaluated by a dual convolutional neural network (CNN) performance metrics in dermoscopic (DI) versus smartphone-captured images (SI) and tested if artificial intelligence narrows the proclaimed gap in diagnostic accuracy. METHODS A CNN that receives a raw image and predicts malignancy, overlaid by a second independent CNN which processes a sonification (image-to-sound mapping) of the original image, were combined into a unified malignancy classifier. All images were histopathology-verified in a comparison between NMSC and benign skin lesions excised as suspected NMSCs. Study criteria outcomes were sensitivity and specificity for the unified output. RESULTS Images acquired by DI (n = 132 NMSC, n = 33 benign) were compared to SI (n = 170 NMSC, n = 28 benign). DI and SI analysis metrics resulted in an area under the curve (AUC) of the receiver operator characteristic curve of 0.911 and 0.821, respectively. Accuracy was increased by DI (0.88; CI 81.9-92.4) as compared to SI (0.75; CI 68.1-80.6, p < 0.005). Sensitivity of DI was higher than SI (95.3%, CI 90.4-98.3 vs 75.3%, CI 68.1-81.6, p < 0.001), but not specificity (p = NS). CONCLUSION Telemedicine use of smartphone images might result in a substantial decrease in diagnostic performance as compared to dermoscopy, which needs to be considered by both healthcare providers and patients.
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Affiliation(s)
- A Dascalu
- Department of Physiology and Pharmacology, Sackler School of Medicine, Tel Aviv University, 6 Matmon Cohen Street, 6209406, Tel Aviv, Israel.
| | - B N Walker
- Sonification Lab, School of Psychology and School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, United States
| | - Y Oron
- Department of Physiology and Pharmacology, Sackler School of Medicine, Tel Aviv University, 6 Matmon Cohen Street, 6209406, Tel Aviv, Israel
| | - E O David
- Department of Computer Science, Bar-Ilan University, Ramat-Gan, Israel
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Kappel C, Rushton-Marovac M, Leong D, Dent S. Pursuing Connectivity in Cardio-Oncology Care-The Future of Telemedicine and Artificial Intelligence in Providing Equity and Access to Rural Communities. Front Cardiovasc Med 2022; 9:927769. [PMID: 35770225 PMCID: PMC9234696 DOI: 10.3389/fcvm.2022.927769] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 05/24/2022] [Indexed: 01/22/2023] Open
Abstract
The aim of this review is to discuss the current health disparities in rural communities and to explore the potential role of telehealth and artificial intelligence in providing cardio-oncology care to underserviced communities. With advancements in early detection and cancer treatment, survivorship has increased. The interplay between cancer and cardiovascular disease, which are the leading causes of morbidity and mortality in this population, has been increasingly recognized. Worldwide, cardio-oncology clinics (COCs) have emerged to deliver a multidisciplinary approach to the care of patients with cancer to mitigate cardiovascular risks while minimizing interruptions in cancer treatment. Despite the value of COCs, the accessibility gap between urban and rural communities in both oncology and cardio-oncology contributes to health care disparities and may be an underrecognized determinant of health globally. Telehealth and artificial intelligence offer opportunities to provide timely care irrespective of rurality. We therefore explore current developments within this sphere and propose a novel model of care to address the disparity in urban vs. rural cardio-oncology using the experience in Canada, a geographically large country with many rural communities.
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Affiliation(s)
- Coralea Kappel
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Moira Rushton-Marovac
- Division of Medical Oncology, The Ottawa Hospital Cancer Centre, University of Ottawa, Ottawa, ON, Canada
| | - Darryl Leong
- Department of Medicine, McMaster University, Hamilton, ON, Canada.,Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON, Canada.,The Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada
| | - Susan Dent
- Division of Medical Oncology, Duke Cancer Institute, Duke University, Durham, NC, United States
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Hah H, Goldin D. Moving toward AI-assisted decision-making: Observation on clinicians' management of multimedia patient information in synchronous and asynchronous telehealth contexts. Health Informatics J 2022; 28:14604582221077049. [PMID: 35225704 DOI: 10.1177/14604582221077049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Artificial intelligence (AI) intends to support clinicians' patient diagnosis decisions by processing and identifying insights from multimedia patient information. OBJECTIVE We explored clinicians' current decision-making patterns using multimedia patient information (MPI) provided by AI algorithms and identified areas where AI can support clinicians in diagnostic decision-making. DESIGN We recruited 87 advanced practice nursing (APN) students who had experience making diagnostic decisions using AI algorithms under various care contexts, including telehealth and other healthcare modalities. The participants described their diagnostic decision-making experiences using videos, images, and audio-based MPI. RESULTS Clinicians processed multimedia patient information differentially such that their focus, selection, and utilization of MPI influence diagnosis and satisfaction levels. CONCLUSIONS AND IMPLICATIONS To streamline collaboration between AI and clinicians across healthcare contexts, AI should understand clinicians' patterns of MPI processing under various care environments and provide them with interpretable analytic results for them. Furthermore, clinicians must be trained with the interface and contents of AI technology and analytic assistance.
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Affiliation(s)
- Hyeyoung Hah
- Department of Information Systems and Business Analytics, 5450Florida International University, FL, USA
| | - Deana Goldin
- Nicole Wertheim College of Nursing & Health Sciences, 5450Florida International University, FL, USA
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44
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Fernandes JG. Artificial Intelligence in Telemedicine. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_93] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Brkic A, Kim JG, Haugeberg G, Diamantopoulos AP. Decentralizing healthcare in Norway to improve patient-centered outpatient clinic management of rheumatoid arthritis - a conceptual model. BMC Rheumatol 2021; 5:43. [PMID: 34743757 PMCID: PMC8572582 DOI: 10.1186/s41927-021-00215-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 07/07/2021] [Indexed: 12/14/2022] Open
Abstract
A growing population of older adults and improved effective treatments for inflammatory rheumatic diseases will increase the demand for more healthcare resources that already struggle with staggering outpatient clinic waiting times. Transformative delivery care models that provide sustainable healthcare services are urgently needed to meet these challenges. In this mini-review article, a proposed Lifelong Treatment Model for a decentralized follow-up of outpatient clinic patients living with rheumatoid arthritis is presented and discussed.Our conceptual model follows four steps for a transformative care delivery model supported by an Integrated Practice Unit; (1) Diagnosis, (2) Treatment, (3) Patient Empowered Disease Management, and (4) Telehealth. Through an Integrated Practice Unit, a multidisciplinary team could collaborate with patients with rheumatoid arthritis to facilitate high-value care that addresses most important outcomes of the patients; (1) Early Remission, (2) Decentralization, (3) Improved Quality of Life, and (4) Lifelong Sustain Remission.The article also addresses the growing challenges for the healthcare delivery system today for patients with rheumatoid arthritis and proposes how to reduce outpatient clinic visits without compromising quality and safety.
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Affiliation(s)
- Alen Brkic
- Department of Research, Sorlandet Hospital, Service Box 416, Kristiansand, Norway.
| | - Jung G Kim
- Kaiser Permanente Bernard J. Tyson School of Medicine, Department of Health Systems Science, Pasadena, CA, USA
| | - Glenn Haugeberg
- Division of Rheumatology, Department of medicine, Sorlandet Hospital, Kristiansand, Norway
| | - Andreas P Diamantopoulos
- Department of Rheumatology, Martina Hansens Hospital, Bærum (Oslo), Norway.,Division of Rheumatology, Department of Medicine, Akershus University Hospital, Kongsvinger, Norway
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Brehon K, Carriere J, Churchill K, Loyola-Sanchez A, O'Connell P, Papathanassoglou E, MacIsaac R, Tavakoli M, Ho C, Pohar Manhas K. Evaluating Community-Facing Virtual Modalities to Support Complex Neurological Populations During the COVID-19 Pandemic: Protocol for a Mixed Methods Study. JMIR Res Protoc 2021; 10:e28267. [PMID: 34101610 PMCID: PMC8315160 DOI: 10.2196/28267] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/26/2021] [Accepted: 06/04/2021] [Indexed: 01/19/2023] Open
Abstract
Background The COVID-19 pandemic and concomitant governmental responses have created the need for innovative and collaborative approaches to deliver services, especially for populations that have been inequitably affected. In Alberta, Canada, two novel approaches were created in Spring 2020 to remotely support patients with complex neurological conditions and rehabilitation needs. The first approach is a telehealth service that provides wayfinding and self-management advice to Albertans with physical concerns related to existing neurological or musculoskeletal conditions or post-COVID-19 recovery needs. The second approach is a webinar series aimed at supporting self-management and social connectedness of individuals living with spinal cord injury. Objective The study aims to evaluate the short- and long-term impacts and sustainability of two virtual modalities (telehealth initiative called Rehabilitation Advice Line [RAL] and webinar series called Alberta Spinal Cord Injury Community Interactive Learning Seminars [AB-SCILS]) aimed at advancing self-management, connectedness, and rehabilitation needs during the COVID-19 pandemic and beyond. Methods We will use a mixed-methods evaluation approach. Evaluation of the approaches will include one-on-one semistructured interviews and surveys. The evaluation of the telehealth initiative will include secondary data analyses and analysis of call data using artificial intelligence. The evaluation of the webinar series will include analysis of poll questions collected during the webinars and YouTube analytics data. Results The proposed study describes unique pandemic virtual modalities and our approaches to evaluating them to ensure effectiveness and sustainability. Implementing and evaluating these virtual modalities synchronously allows for the building of knowledge on the complementarity of these methods. At the time of submission, we have completed qualitative and quantitative data collection for the telehealth evaluation. For the webinar series, so far, we have distributed the evaluation survey following three webinars and have conducted five attendee interviews. Conclusions Understanding the impact and sustainability of the proposed telehealth modalities is important. The results of the evaluation will provide data that can be actioned and serve to improve other telehealth modalities in the future, since health systems need this information to make decisions on resource allocation, especially in an uncertain pandemic climate. Evaluating the RAL and AB-SCILS to ensure their effectiveness demonstrates that Alberta Health Services and the health system care about ensuring the best practice even after a shift to primarily virtual care. International Registered Report Identifier (IRRID) DERR1-10.2196/28267
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Affiliation(s)
- Katelyn Brehon
- School of Public Health, University of Alberta, Edmonton, AB, Canada
| | - Jay Carriere
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
| | - Katie Churchill
- Department of Occupational Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
| | - Adalberto Loyola-Sanchez
- Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB, Canada
| | - Petra O'Connell
- Obesity Diabetes Nutrition Strategic Clinical Network, Alberta Health Services, Calgary, AB, Canada.,Neurosciences, Rehabilitation & Vision Strategic Clinical Network, Alberta Health Services, Calgary, AB, Canada
| | - Elisavet Papathanassoglou
- Neurosciences, Rehabilitation & Vision Strategic Clinical Network, Alberta Health Services, Calgary, AB, Canada
| | - Rob MacIsaac
- Spinal Cord Injury Alberta, Edmonton, AB, Canada
| | - Mahdi Tavakoli
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
| | - Chester Ho
- Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB, Canada.,Neurosciences, Rehabilitation & Vision Strategic Clinical Network, Alberta Health Services, Calgary, AB, Canada
| | - Kiran Pohar Manhas
- Neurosciences, Rehabilitation & Vision Strategic Clinical Network, Alberta Health Services, Calgary, AB, Canada
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Lal S, Rehman SU, Shah JH, Meraj T, Rauf HT, Damaševičius R, Mohammed MA, Abdulkareem KH. Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition. SENSORS (BASEL, SWITZERLAND) 2021; 21:3922. [PMID: 34200216 PMCID: PMC8201392 DOI: 10.3390/s21113922] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 05/31/2021] [Accepted: 06/04/2021] [Indexed: 12/15/2022]
Abstract
Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the security and robustness of the deployed algorithms need to be guaranteed. The security susceptibility of the DL algorithms to adversarial examples has been widely acknowledged. The artificially created examples will lead to different instances negatively identified by the DL models that are humanly considered benign. Practical application in actual physical scenarios with adversarial threats shows their features. Thus, adversarial attacks and defense, including machine learning and its reliability, have drawn growing interest and, in recent years, has been a hot topic of research. We introduce a framework that provides a defensive model against the adversarial speckle-noise attack, the adversarial training, and a feature fusion strategy, which preserves the classification with correct labelling. We evaluate and analyze the adversarial attacks and defenses on the retinal fundus images for the Diabetic Retinopathy recognition problem, which is considered a state-of-the-art endeavor. Results obtained on the retinal fundus images, which are prone to adversarial attacks, are 99% accurate and prove that the proposed defensive model is robust.
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Affiliation(s)
- Sheeba Lal
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan; (S.L.); (S.U.R.); (J.H.S.); (T.M.)
| | - Saeed Ur Rehman
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan; (S.L.); (S.U.R.); (J.H.S.); (T.M.)
| | - Jamal Hussain Shah
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan; (S.L.); (S.U.R.); (J.H.S.); (T.M.)
| | - Talha Meraj
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan; (S.L.); (S.U.R.); (J.H.S.); (T.M.)
| | - Hafiz Tayyab Rauf
- Department of Computer Science, Faculty of Engineering & Informatics, University of Bradford, Bradford BD7 1DP, UK
| | - Robertas Damaševičius
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
| | - Mazin Abed Mohammed
- College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq;
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Shafi G, Desai S, Srinivasan K, Ramesh A, Chaturvedi R, Uttarwar M. Host-dependent molecular factors mediating SARS-CoV-2 infection to gain clinical insights for developing effective targeted therapy. Mol Genet Genomics 2021; 296:501-511. [PMID: 33743061 PMCID: PMC7980125 DOI: 10.1007/s00438-021-01774-1] [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: 10/14/2020] [Accepted: 03/09/2021] [Indexed: 11/08/2022]
Abstract
Coronavirus disease 2019 (COVID-19), a recent viral pandemic that first began in December 2019, in Hunan wildlife market, Wuhan, China. The infection is caused by a coronavirus, SARS-CoV-2 and clinically characterized by common symptoms including fever, dry cough, loss of taste/smell, myalgia and pneumonia in severe cases. With overwhelming spikes in infection and death, its pathogenesis yet remains elusive. Since the infection spread rapidly, its healthcare demands are overwhelming with uncontrollable emergencies. Although laboratory testing and analysis are developing at an enormous pace, the high momentum of severe cases demand more rapid strategies for initial screening and patient stratification. Several molecular biomarkers like C-reactive protein, interleukin-6 (IL6), eosinophils and cytokines, and artificial intelligence (AI) based screening approaches have been developed by various studies to assist this vast medical demand. This review is an attempt to collate the outcomes of such studies, thus highlighting the utility of AI in rapid screening of molecular markers along with chest X-rays and other COVID-19 symptoms to enable faster diagnosis and patient stratification. By doing so, we also found that molecular markers such as C-reactive protein, IL-6 eosinophils, etc. showed significant differences between severe and non-severe cases of COVID-19 patients. CT findings in the lungs also showed different patterns like lung consolidation significantly higher in patients with poor recovery and lung lesions and fibrosis being higher in patients with good recovery. Thus, from these evidences we perceive that an initial rapid screening using integrated AI approach could be a way forward in efficient patient stratification.
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Affiliation(s)
| | | | | | - Aarthi Ramesh
- School of Science, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500, Subang Jaya, Selangor, Malaysia
| | - Rupesh Chaturvedi
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India
- Nanofluidiks Pvt. Ltd. Jawaharlal Nehru University-Foundation for Innovation New Delhi, New Delhi, 110067, India
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He Q, Du F, Simonse LWL. A Patient Journey Map to Improve the Home Isolation Experience of Persons With Mild COVID-19: Design Research for Service Touchpoints of Artificial Intelligence in eHealth. JMIR Med Inform 2021; 9:e23238. [PMID: 33444156 PMCID: PMC8043148 DOI: 10.2196/23238] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 12/18/2020] [Accepted: 01/10/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND In the context of the COVID-19 outbreak, 80% of the persons who are infected have mild symptoms and are required to self-recover at home. They have a strong demand for remote health care that, despite the great potential of artificial intelligence (AI), is not met by the current services of eHealth. Understanding the real needs of these persons is lacking. OBJECTIVE The aim of this paper is to contribute a fine-grained understanding of the home isolation experience of persons with mild COVID-19 symptoms to enhance AI in eHealth services. METHODS A design research method with a qualitative approach was used to map the patient journey. Data on the home isolation experiences of persons with mild COVID-19 symptoms was collected from the top-viewed personal video stories on YouTube and their comment threads. For the analysis, this data was transcribed, coded, and mapped into the patient journey map. RESULTS The key findings on the home isolation experience of persons with mild COVID-19 symptoms concerned (1) an awareness period before testing positive, (2) less typical and more personal symptoms, (3) a negative mood experience curve, (5) inadequate home health care service support for patients, and (6) benefits and drawbacks of social media support. CONCLUSIONS The design of the patient journey map and underlying insights on the home isolation experience of persons with mild COVID-19 symptoms serves health and information technology professionals in more effectively applying AI technology into eHealth services, for which three main service concepts are proposed: (1) trustworthy public health information to relieve stress, (2) personal COVID-19 health monitoring, and (3) community support.
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Affiliation(s)
- Qian He
- Department of Design Organisation & Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Fei Du
- Department of Design Organisation & Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Lianne W L Simonse
- Department of Design Organisation & Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
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Artificial Intelligence in Telemedicine. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_93-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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