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Journal of Medical Internet Research Mar 2024The digital transformation of health care is advancing rapidly. A well-accepted framework for health care improvement is the Quadruple Aim: improved clinician... (Review)
Review
BACKGROUND
The digital transformation of health care is advancing rapidly. A well-accepted framework for health care improvement is the Quadruple Aim: improved clinician experience, improved patient experience, improved population health, and reduced health care costs. Hospitals are attempting to improve care by using digital technologies, but the effectiveness of these technologies is often only measured against cost and quality indicators, and less is known about the clinician and patient experience.
OBJECTIVE
This study aims to conduct a systematic review and qualitative evidence synthesis to assess the clinician and patient experience of digital hospitals.
METHODS
The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and ENTREQ (Enhancing the Transparency in Reporting the Synthesis of Qualitative Research) guidelines were followed. The PubMed, Embase, Scopus, CINAHL, and PsycINFO databases were searched from January 2010 to June 2022. Studies that explored multidisciplinary clinician or adult inpatient experiences of digital hospitals (with a full electronic medical record) were included. Study quality was assessed using the Mixed Methods Appraisal Tool. Data synthesis was performed narratively for quantitative studies. Qualitative evidence synthesis was performed via (1) automated machine learning text analytics using Leximancer (Leximancer Pty Ltd) and (2) researcher-led inductive synthesis to generate themes.
RESULTS
A total of 61 studies (n=39, 64% quantitative; n=15, 25% qualitative; and n=7, 11% mixed methods) were included. Most studies (55/61, 90%) investigated clinician experiences, whereas few (10/61, 16%) investigated patient experiences. The study populations ranged from 8 to 3610 clinicians, 11 to 34,425 patients, and 5 to 2836 hospitals. Quantitative outcomes indicated that clinicians had a positive overall satisfaction (17/24, 71% of the studies) with digital hospitals, and most studies (11/19, 58%) reported a positive sentiment toward usability. Data accessibility was reported positively, whereas adaptation, clinician-patient interaction, and workload burnout were reported negatively. The effects of digital hospitals on patient safety and clinicians' ability to deliver patient care were mixed. The qualitative evidence synthesis of clinician experience studies (18/61, 30%) generated 7 themes: inefficient digital documentation, inconsistent data quality, disruptions to conventional health care relationships, acceptance, safety versus risk, reliance on hybrid (digital and paper) workflows, and patient data privacy. There was weak evidence of a positive association between digital hospitals and patient satisfaction scores.
CONCLUSIONS
Clinicians' experience of digital hospitals appears positive according to high-level indicators (eg, overall satisfaction and data accessibility), but the qualitative evidence synthesis revealed substantive tensions. There is insufficient evidence to draw a definitive conclusion on the patient experience within digital hospitals, but indications appear positive or agnostic. Future research must prioritize equitable investigation and definition of the digital clinician and patient experience to achieve the Quadruple Aim of health care.
Topics: Adult; Humans; Hospitals; Delivery of Health Care; Qualitative Research
PubMed: 38466978
DOI: 10.2196/47715 -
Social Science & Medicine (1982) Dec 2023Despite the proliferation of Artificial Intelligence (AI) technology over the last decade, clinician, patient, and public perceptions of its use in healthcare raise a... (Review)
Review
INTRODUCTION
Despite the proliferation of Artificial Intelligence (AI) technology over the last decade, clinician, patient, and public perceptions of its use in healthcare raise a number of ethical, legal and social questions. We systematically review the literature on attitudes towards the use of AI in healthcare from patients, the general public and health professionals' perspectives to understand these issues from multiple perspectives.
METHODOLOGY
A search for original research articles using qualitative, quantitative, and mixed methods published between 1 Jan 2001 to 24 Aug 2021 was conducted on six bibliographic databases. Data were extracted and classified into different themes representing views on: (i) knowledge and familiarity of AI, (ii) AI benefits, risks, and challenges, (iii) AI acceptability, (iv) AI development, (v) AI implementation, (vi) AI regulations, and (vii) Human - AI relationship.
RESULTS
The final search identified 7,490 different records of which 105 publications were selected based on predefined inclusion/exclusion criteria. While the majority of patients, the general public and health professionals generally had a positive attitude towards the use of AI in healthcare, all groups indicated some perceived risks and challenges. Commonly perceived risks included data privacy; reduced professional autonomy; algorithmic bias; healthcare inequities; and greater burnout to acquire AI-related skills. While patients had mixed opinions on whether healthcare workers suffer from job loss due to the use of AI, health professionals strongly indicated that AI would not be able to completely replace them in their professions. Both groups shared similar doubts about AI's ability to deliver empathic care. The need for AI validation, transparency, explainability, and patient and clinical involvement in the development of AI was emphasised. To help successfully implement AI in health care, most participants envisioned that an investment in training and education campaigns was necessary, especially for health professionals. Lack of familiarity, lack of trust, and regulatory uncertainties were identified as factors hindering AI implementation. Regarding AI regulations, key themes included data access and data privacy. While the general public and patients exhibited a willingness to share anonymised data for AI development, there remained concerns about sharing data with insurance or technology companies. One key domain under this theme was the question of who should be held accountable in the case of adverse events arising from using AI.
CONCLUSIONS
While overall positivity persists in attitudes and preferences toward AI use in healthcare, some prevalent problems require more attention. There is a need to go beyond addressing algorithm-related issues to look at the translation of legislation and guidelines into practice to ensure fairness, accountability, transparency, and ethics in AI.
Topics: Humans; Artificial Intelligence; Algorithms; Educational Status; Emotions; Empathy
PubMed: 37949020
DOI: 10.1016/j.socscimed.2023.116357 -
Healthcare (Basel, Switzerland) Jan 2024One measure national governments took to react to the acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic was mobile applications (apps). This study aims... (Review)
Review
BACKGROUND
One measure national governments took to react to the acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic was mobile applications (apps). This study aims to provide a high-level overview of published reviews of mobile apps used in association with coronavirus disease 19 (COVID-19), examine factors that contributed to the success of these apps, and provide data for further research into this topic.
METHODS
We conducted a systematic review of reviews (also referred to as an umbrella review) and searched two databases, Medline and Embase, for peer-reviewed reviews of COVID-19 mobile apps that were written in English and published between January 1st 2020 and April 25th 2022.
RESULTS
Out of the initial 17,611 studies, 24 studies were eligible for the analysis. Publication dates ranged from May 2020 to January 2022. In total, 54% ( = 13) of the studies were published in 2021, and 33% ( = 8) were published in 2020. Most reviews included in our review of reviews analyzed apps from the USA, the UK, and India. Apps from most of the African and Middle and South American countries were not analyzed in the reviews included in our study. Categorization resulted in four clusters (app overview, privacy and security, MARS rating, and miscellaneous).
CONCLUSIONS
Our study provides a high-level overview of 24 reviews of apps for COVID-19, identifies factors that contributed to the success of these apps, and identifies a gap in the current literature. The study provides data for further analyses and further research.
PubMed: 38255029
DOI: 10.3390/healthcare12020139 -
Nurse Education Today Jan 2024Social media usage has been ubiquitous and extensively integrated into the daily lives of student nurses. However, there exists a paucity of understanding regarding the... (Review)
Review
BACKGROUND
Social media usage has been ubiquitous and extensively integrated into the daily lives of student nurses. However, there exists a paucity of understanding regarding the influence of social media on student nurses' personal and professional development.
OBJECTIVE
To examine the influence of social media on student nurses' personal and professional values.
DESIGN
A systematic mixed-studies review.
METHODS
English language published studies were sourced from hand searches and seven electronic databases (PubMed, CINAHL, Embase, PsycINFO, ProQuest Dissertation and Theses Global, Scopus, and Web of Science) from the inception of each database to January 2023.
RESULTS
Twenty-six studies were included. Two main themes and eight subthemes were derived through thematic synthesis. The first main theme, Shaping Student Nurses into Nurses, included four subthemes: 1.1) Personal Development, 1.2) Professional Development, 1.3) Advocacy, and 1.4) Networking. The second main theme, Repercussions of Social Media Usage, included four subthemes: 2.1) Frustrations, 2.2) Discriminative Feelings, 2.3) Compulsive feelings, and 2.4) Consequences of Inappropriate Usage.
CONCLUSION
The ubiquitous utilization of social media among the current generation of student nurses, for personal, educational, and professional purposes, has precipitated transformative effects conducive to their holistic development. Notwithstanding the potential perils associated with privacy violation and inappropriate usage, educational institutions can develop pedagogical strategies and guidelines in collaboration with healthcare institutions and professionals, aimed at the incorporation of social media within the educational curricula and the prospective workplace environments of student nurses.
Topics: Humans; Delivery of Health Care; Prospective Studies; Social Media; Students, Nursing; Working Conditions
PubMed: 37871496
DOI: 10.1016/j.nedt.2023.106000 -
The Lancet. Public Health Sep 2023Syphilis is causing epidemics in many countries. Syphilis self-testing (SST) has potential to increase testing and treatment coverage in the same manner as documented... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Syphilis is causing epidemics in many countries. Syphilis self-testing (SST) has potential to increase testing and treatment coverage in the same manner as documented for self-testing of, for example, HIV, hepatitis C virus, and COVID-19. We aimed to synthesise current evidence on the utility of SST.
METHODS
We conducted a systematic review and, where possible, meta-analysis. We searched MEDLINE, Embase, CINAHL, Scopus, and Web of Science for publications published from Jan 1, 2000, to Oct 13, 2022. We included publications with original data on any syphilis rapid tests, including dual HIV-syphilis tests. Study populations were not restricted. We used random-effects meta-analysis to calculate the pooled proportion of people offered SST who undertook the test. The systematic review was registered in PROSPERO (CRD42022302129).
FINDINGS
In total, 40 499 citations were identified. 11 publications from seven studies from the USA, Zimbabwe, and China met eligibility criteria. Of those, four studies reported data from men who have sex with men and five studies used dual HIV-SST. Using data from one randomised controlled trial and three observational studies, the pooled proportion of people who received SST kits who undertook the test was 88% (95% CI 85-91). No studies provided data on the sensitivity or specificity of SST. Overall, user and provider preference for SST was high, with participants reporting convenience, privacy, rapid results, autonomy, trust in blood-based tests, decreased facility contact, and time savings, with individuals being able to correctly self-test. Publications from China reported that SST had lower costs per person tested than existing facility-based testing options.
INTERPRETATION
Our review builds on the literature for self-testing across different disease areas and demonstrates that SST has the potential to reach underserved populations. As this review found that SST use was acceptable and feasible to implement, SST can be used as an additional syphilis testing approach. Since no data on the sensitivity and specificity of SST were found, further implementation research will be required to guide the best strategies for SST service delivery and future scale-up.
FUNDING
WHO, Australian National Health and Medical Research Council, and Unitaid.
Topics: Male; Humans; Syphilis; Self-Testing; Homosexuality, Male; Sexual and Gender Minorities; COVID-19; Australia; HIV Infections
PubMed: 37482070
DOI: 10.1016/S2468-2667(23)00128-7 -
Npj Mental Health Research Sep 2023Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing... (Review)
Review
Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing accurate and timely diagnoses. Machine learning (ML) techniques have been utilized for early assessments and outcome prediction to address these challenges. This paper aims to conduct a systematic review to investigate if ML is a promising approach for PTSD diagnosis. In this review, statistical methods were employed to synthesize the outcomes of the included research and provide guidance on critical considerations for ML task implementation. These included (a) selection of the most appropriate ML model for the available dataset, (b) identification of optimal ML features based on the chosen diagnostic method, (c) determination of appropriate sample size based on the distribution of the data, and (d) implementation of suitable validation tools to assess the performance of the selected ML models. We screened 3186 studies and included 41 articles based on eligibility criteria in the final synthesis. Here we report that the analysis of the included studies highlights the potential of artificial intelligence (AI) in PTSD diagnosis. However, implementing AI-based diagnostic systems in real clinical settings requires addressing several limitations, including appropriate regulation, ethical considerations, and protection of patient privacy.
PubMed: 38609504
DOI: 10.1038/s44184-023-00035-w -
MedRxiv : the Preprint Server For... Apr 2024The launch of the Chat Generative Pre-trained Transformer (ChatGPT) in November 2022 has attracted public attention and academic interest to large language models...
BACKGROUND
The launch of the Chat Generative Pre-trained Transformer (ChatGPT) in November 2022 has attracted public attention and academic interest to large language models (LLMs), facilitating the emergence of many other innovative LLMs. These LLMs have been applied in various fields, including healthcare. Numerous studies have since been conducted regarding how to employ state-of-the-art LLMs in health-related scenarios to assist patients, doctors, and public health administrators.
OBJECTIVE
This review aims to summarize the applications and concerns of applying conversational LLMs in healthcare and provide an agenda for future research on LLMs in healthcare.
METHODS
We utilized PubMed, ACM, and IEEE digital libraries as primary sources for this review. We followed the guidance of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRIMSA) to screen and select peer-reviewed research articles that (1) were related to both healthcare applications and conversational LLMs and (2) were published before September 1, 2023, the date when we started paper collection and screening. We investigated these papers and classified them according to their applications and concerns.
RESULTS
Our search initially identified 820 papers according to targeted keywords, out of which 65 papers met our criteria and were included in the review. The most popular conversational LLM was ChatGPT from OpenAI (60), followed by Bard from Google (1), Large Language Model Meta AI (LLaMA) from Meta (1), and other LLMs (5). These papers were classified into four categories in terms of their applications: 1) summarization, 2) medical knowledge inquiry, 3) prediction, and 4) administration, and four categories of concerns: 1) reliability, 2) bias, 3) privacy, and 4) public acceptability. There are 49 (75%) research papers using LLMs for summarization and/or medical knowledge inquiry, and 58 (89%) research papers expressing concerns about reliability and/or bias. We found that conversational LLMs exhibit promising results in summarization and providing medical knowledge to patients with a relatively high accuracy. However, conversational LLMs like ChatGPT are not able to provide reliable answers to complex health-related tasks that require specialized domain expertise. Additionally, no experiments in our reviewed papers have been conducted to thoughtfully examine how conversational LLMs lead to bias or privacy issues in healthcare research.
CONCLUSIONS
Future studies should focus on improving the reliability of LLM applications in complex health-related tasks, as well as investigating the mechanisms of how LLM applications brought bias and privacy issues. Considering the vast accessibility of LLMs, legal, social, and technical efforts are all needed to address concerns about LLMs to promote, improve, and regularize the application of LLMs in healthcare.
PubMed: 38712148
DOI: 10.1101/2024.04.26.24306390 -
Nature Communications Mar 2024Cloud-based personal health records increase globally. The GPOC series introduces the concept of a Global Patient co-Owned Cloud (GPOC) of personal health records. Here,... (Meta-Analysis)
Meta-Analysis
Cloud-based personal health records increase globally. The GPOC series introduces the concept of a Global Patient co-Owned Cloud (GPOC) of personal health records. Here, we present the GPOC series' Prospective Register of Systematic Reviews (PROSPERO) registered and Preferred Reporting Items Systematic and Meta-Analyses (PRISMA)-guided systematic review and meta-analysis. It examines cloud-based personal health records and factors such as data security, efficiency, privacy and cost-based measures. It is a meta-analysis of twelve relevant axes encompassing performance, cryptography and parameters based on efficiency (runtimes, key generation times), security (access policies, encryption, decryption) and cost (gas). This aims to generate a basis for further research, a GPOC sandbox model, and a possible construction of a global platform. This area lacks standard and shows marked heterogeneity. A consensus within this field would be beneficial to the development of a GPOC. A GPOC could spark the development and global dissemination of artificial intelligence in healthcare.
Topics: Humans; Artificial Intelligence; Health Records, Personal; Privacy; Computer Security
PubMed: 38467643
DOI: 10.1038/s41467-024-46503-5 -
BMC Medical Education Sep 2023The present review aimed to systematically identify and classify barriers and facilitators of conducting research with a team science approach.
BACKGROUND
The present review aimed to systematically identify and classify barriers and facilitators of conducting research with a team science approach.
METHODS
PubMed, EMBASE, PsycINFO, Scopus, Web of Science, Emerald, and ProQuest databases were searched for primary research studies conducted using quantitative, qualitative, or mixed methods. Studies examining barriers and facilitators of research with a team science approach were included in search. Two independent reviewers screened the texts, extracted and coded the data. Quality assessment was performed for all 35 included articles. The identified barriers and facilitators were categorized within Human, Organization, and Technology model.
RESULTS
A total of 35 studies from 9,381 articles met the inclusion criteria, from which 42 barriers and 148 facilitators were identified. Human barriers were characteristics of the researchers, teaming skills, and time. We consider Human facilitators across nine sub-themes as follows: characteristics of the researchers, roles, goals, communication, trust, conflict, disciplinary distances, academic rank, and collaboration experience. The barriers related to organization were institutional policies, team science integration, and funding. Organizational facilitators were as follows: team science skills training, institutional policies, and evaluation. Facilitators in the field of technology included virtual readiness and data management, and the technology barriers were complexity of techniques and privacy issues.
CONCLUSIONS
We identified major barriers and facilitators for conducting research with team science approach. The findings have important connotations for ongoing and future implementation of this intervention strategy in research. The analysis of this review provides evidence to inform policy-makers, funding providers, researchers, and students on the existing barriers and facilitators of team science research.
TRIAL REGISTRATION
This review was prospectively registered on PROSPERO database (PROSPERO 2021 CRD42021278704).
Topics: Humans; Interdisciplinary Research; Communication; Trust; Mental Processes; Administrative Personnel
PubMed: 37670349
DOI: 10.1186/s12909-023-04619-0 -
Sensors (Basel, Switzerland) Aug 2023This literature review highlights the emergence of the Internet of Things (IoT) and the proliferation of connected devices as the driving force behind the adoption of... (Review)
Review
This literature review highlights the emergence of the Internet of Things (IoT) and the proliferation of connected devices as the driving force behind the adoption of smart spaces. This review also discusses the various applications of smart spaces, including smart homes, smart cities, and smart healthcare: (1) Background: the aim of this research is to provide a comprehensive overview of the concept of smart spaces, including their key features, technologies, and applications in built environments and urban areas; (2) Methods: The study adopts a qualitative approach, drawing on secondary sources, such as academic journals, reports, and online sources; (3) Results: The findings suggest that smart spaces have the potential to transform the way people interact with their environment and each other. They could improve efficiency, safety, and quality of life. However, there are also concerns about privacy and security in relation to the collection and use of personal data; (4) Conclusions: The study concludes that smart spaces have significant theoretical and practical implications for various fields, including architecture, urban planning, and healthcare. The theoretical implications include the need for new models and frameworks to understand the complex relationships between technology, space, and society. The practical implications involve the development of new standards and regulations to ensure the responsible and ethical use of smart spaces.
Topics: Humans; Quality of Life; Built Environment; Cities; City Planning; Internet
PubMed: 37571721
DOI: 10.3390/s23156938