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Latest Submissions Open for Peer Review

JMIR has been a leader in applying openness, participation, collaboration and other "2.0" ideas to scholarly publishing, and since December 2009 offers open peer review articles, allowing JMIR users to sign themselves up as peer reviewers for specific articles currently considered by the Journal (in addition to author- and editor-selected reviewers).

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JMIR Submissions under Open Peer Review

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Titles/Abstracts of Articles Currently Open for Review:

  • The Paradigm Shift from Patient to Health Consumer: 25 Years of Value Assessment in Health

    Date Submitted: May 14, 2024
    Open Peer Review Period: May 17, 2024 - Jul 12, 2024

    While economic analyses and health technology assessment have come a long way in their multi-faceted assessment of the clinical, economic, ethical, legal, and societal perspectives that may be impacted by a new technologies and procedures, these approaches do not reflect underlying patient preferences that may be important in the assessment of “value” in the current value-based healthcare revolution. Also, the arbitrary nature of the threshold in these studies limit a value-based approach to measuring dollars in terms if an increase in the QALY gained. The major challenges that come with the transformation to a value-based healthcare system lead to questions such as: “how are economic analyses, often the basis for policy and reimbursement decisions, going to switch from a societal to an individual perspective?”; and “how do we assess (economic) value, then, taking into account individual preference heterogeneity as well as varying heuristics and decision rules?” These challenges, both related to including the individual perspective in cost effectiveness analysis, have been widely debated. The societal perspective measures cost-effectiveness of treatment in terms of costs and Quality-Adjusted-Life-Years (QALY), where QALYs assume a health state that is more desirable is more valuable and, therefore, value is equated with preference or desirability. This approach has major empirical and conceptual shortcomings such as inconsistencies among values obtained from the standard-gamble, time-trade-off, and visual-analog-scale elicitation formats and more importantly, the linearity assumptions that violate the assumption of diminishing marginal utility. This paper reviews 25 years of value assessment approaches in health. It first describes the foundation of value assessment in other fields, then in the second part discusses the application of these methods in health economics. In the third part, it explains why value assessment works differently in health and a one-to-one copy from other fields in not always appropriate. It will be challenging to take into account the complexities of individual preferences and behaviors, especially if they are not met at the societal level. The paper does conclude with suggestions and opportunities to further improve value assessment methods in health in the years to come.

  • Background: Background The telemedicine landscape has evolved profoundly in recent time, bridging the gap between healthcare providers and patients, particularly in the face of modern challenges such as the COVID-19 pandemic. Objective: Objective This study seeks to explore the Swedish telemedicine landscape in terms of primary patient symptoms for teleconsultation, the pattern of telemedicine use in the periods before COVID-19, during COVID-19, and post- COVID-19; including the primary care utilization dynamics with respect to the teleconsultations done. Methods: Methods Secondary data was used in the observational retrospective study, and the study population consisted of Swedish residents, who had online meeting consultations. Telemedicine consultations were divided by text and video consultations; the period of analysis ranged from November 2018 to June 2023. The statistical methods used for the data analysis included descriptive analysis, two-way cross tabulations, and the generalized linear model. Results: Results During the pandemic, the number of teleconsultations concerning general, unspecified symptoms increased in comparison to the rest of the analysed symptoms, signaling the change in care-seeking behaviour under epidemiological pressure. General health-related issues were the most pronounced symptom across all periods: 186.9/1000 consultations during (pre-COVID-19), 1264.6/1000 consultations (during COVID), and 319.2/1000 consultations (post-COVID-19). There was no significant main effect of COVID period on the number of telemedicine consultation meetings (F(2) = 1.653, p = .377). The interaction effect between meeting style and period was statistically significant (F(2) = 14.723, p =.000). Conclusions: Conclusions The findings support the idea that the COVID-19 pandemic had a significant impact on the use of telemedicine, increasing its role in general health consultations and acute conditions. This trend indicates a preference for personal engagements and an interactive mode of communication in patient care. Video consultations were more prominent because of the importance of bi-directional communication. The study suggests the transformation of patterns of demand for healthcare and the necessity for healthcare systems to respond to these changes. Clinical Trial: Not applicable

  • Advancing Preeclampsia Prediction: A Tailored Machine Learning Pipeline for Handling Imbalanced Medical Data

    Date Submitted: May 9, 2024
    Open Peer Review Period: May 16, 2024 - Jul 11, 2024

    Background: Preeclampsia represents a significant challenge in obstetrics. Effective early prediction is crucial for timely intervention, yet the development of predictive models is complicated by the class imbalances inherent in clinical data. Objective: This study aims to develop a robust pipeline that enhances the predictive performance of ensemble machine learning models for the early prediction of preeclampsia in an imbalanced dataset. Methods: We evaluated combinations of six ensemble machine learning algorithms and eight resampling techniques across a spectrum of minority-to-majority ratios. Using statistical methods, we systematically identified and optimized these configurations, focusing on key performance metrics such as Geometric Mean. Results: The strategic optimization of variable selection and settings proved crucial. The configuration using the Inverse Weighted Gaussian Mixture Model for resampling, followed by the Gradient Boosting Decision Trees algorithm, with an optimized minority-to-majority ratio of 0.09, was identified as the most effective, achieving a Geometric Mean of 0.6694. This configuration significantly outperformed the baseline across all evaluated metrics, demonstrating substantial improvements in model performance. Conclusions: This study establishes a robust pipeline that significantly enhances the predictive performance of models for preeclampsia within imbalanced datasets. Our findings underscore the importance of a strategic approach to variable optimization in medical diagnostics, offering potential for broad application in various medical contexts where class imbalance is a concern.

  • Background: In today's intricate socio-economic landscape, working parents confront challenges in continuously supervising their children's actions, frequently turning to screen devices as a convenient substitute to keep their offspring occupied. Evidence indicates that disproportionate screen time engagement during a very early stage of life (0-3 years) increases substantially with age leading to adverse influence on children's cognitive, linguistic, and academic success over time. In response to this matter, a personalized mHealth solution can appear as a practical proposition to help parents manage potential threats. Objective: The aim of this qualitative systematic analysis is to underscore the existing blind spots in parental ignorance concerning screen time management, explore the recommended effective strategies for redirecting children under 3 years of age from unwarranted screen contact and lastly, establish a realistic as well as a holistic framework that supports cognitive progression amongst younger children within a context of their domestic setting. Methods: A systematic search of academic databases including Google Scholar, PubMed, IEEE Xplore, and Elsevier was conducted. Qualitative studies pertaining to the recognition of parental decision-making factors, their repercussions, shortcomings, and proposed conquering strategies to alleviate screen media contact in infants and toddlers (aged 0-3 years) were included. Finally, this review paper will integrate the advocated perspectives and propose an actionable replacement tailored to permit families in promoting mindful digital engagement. Results: In total, our comprehensive review included 36 articles. Parents’ perceptions were grouped into 9 distinct categories. It was found that parents generally consider digital devices beneficial for numerous reasons. On the contrary, negative effects such as cognitive harm, dependence and social isolation were detected, however, parents are bound to depend on digital devices due to their modern lifestyle demands. Various authorities have identified difficulties and have developed countermeasures such as limitations on usage and co-viewing, but their implementation must be refined accounting for the challenges of modern parents. The proposed solution could leverage four pivotal features: (i) Screen time tracking and monitoring mechanism, (ii) A reservoir for parental training, (iii) An alternative activity advocator, finally (iv) an interactive artificial intelligence assistant. Conclusions: Overall, the majority of parents have a positive perspective towards the recommended intervention strategies and perceive them as an effective solution. However, they also recognize a reasonable gap in these approaches, due to the lack of appropriate tools, guidance, and sufficient time for implementation. The findings of this study could offer future investigators valuable insights into the design of an empathetic and practical mHealth application, aiming to manage their children’s screen time more efficiently, improve adherence to healthy screen habits, and foster a digital eco-system where technology itself serves as a promoter for progress and well-being, rather than a liability. Clinical Trial: N/A

  • A Social Network Analysis of Organ Donation Conversations on X: Developing the OrgReach Social Media Marketing Strategy for Organ Donation Awareness

    Date Submitted: May 7, 2024
    Open Peer Review Period: May 14, 2024 - Jul 9, 2024

    Background: The digital landscape has become a vital platform for public health discourse, particularly concerning important topics like organ donation. With a global rise in organ transplant needs, fostering public understanding and positive attitudes is critical. Objective: The goal is to develop insights into organ donation discussions on a popular social media platform (X) and understand the context in which users discussed the role of education. We investigate the influence of prominent profiles and meta-level accounts, including those seeking health information. We use credibility theory to explore the construction and impact of credibility within social media contexts in organ donation discussions. Methods: Data was retrieved from X between October 2023 and May 2024, covering a seven-month period. The posts were analyzed using social network analysis and qualitative thematic analysis. NodeXL Pro was used to retrieve and analyze the data, and a network visualization was created by drawing upon the Clauset-Newman-Moore cluster algorithm and the Harel-Koren Fast Multiscale layout algorithm. Results: Our analysis reveals an "elite tier" shaping the conversation, with themes reflecting existing societal sensitivities around organ donation. We demonstrate how prominent social media profiles act as information intermediaries, navigating the tension between open dialogue and negative perceptions. We use our findings, social credibility theory, and review of existing literature to develop the OrgReach Social Media Marketing Strategy for Organ Donation Awareness. Conclusions: The study highlights the crucial role of analyzing social media data by drawing upon social networks and topic analysis to understand influence and network communication patterns. By doing so, the study identifies strategies that can feed into the marketing strategies for organ donation outreach and awareness.

  • Ethics of Conversational Artificial Intelligence in Mental Health: A Scoping Review

    Date Submitted: May 10, 2024
    Open Peer Review Period: May 10, 2024 - Jul 5, 2024

    Background: Conversational artificial intelligence (CAI) emerges as a promising new digital technology for mental healthcare. CAI applications, like psychotherapeutic chatbots, are already available in app stores. Objective: This scoping review aims to provide a comprehensive overview of the ethical considerations surrounding the use of CAI as a therapist for individuals with mental health disorders. The secondary aim is to delineate future research directions in this evolving field. Methods: We conducted a systematic search in PubMed, Embase, APA PsycINFO, Web of Science, Scopus, The Philosopher’s Index, and ACM Digital Library. Our search comprised three elements concerning embodied AI, ethics, and mental health, separated by AND commands. We defined CAI as a conversational agent that interacts with a person and uses NLP to formulate output. We included articles discussing ethical challenges related to AI-driven conversational agents that are aimed at functioning as a therapist for individuals with mental health issues. We added additional articles through snowball searching. We only included articles in English or Dutch. Additionally, all types of articles were considered except abstracts of symposia . Screening for eligibility was done by two independent researchers (MRM and TS). An initial charting form was made based on the expected considerations and further revised and complemented during the charting process. The ethical challenges were divided into different themes. When a certain concern occurred in more than two articles, we identified it as a distinct theme. Results: We included 73 articles, of which 90% were published in 2018 or later. Most were reviews (27%) followed by articles that used empirical data collection methods such as surveys or other qualitative methods (14%). The following 10 themes were distinguished: (1) Harm (reduction) and safety (discussed in 52% of articles), the most common topics within this theme were suicidality and crisis management, harmful or wrong suggestions, and the risk of dependency on CAI; (2) Explicability, transparency, and trust (25%), including topics such as the effects of “black-box” algorithms on trust; (3) Responsibility and accountability (26%); (4) Empathy and humanness (21%); (5) Justice (33%), including themes such as health inequalities due to differences in digital literacy; (6) Anthropomorphisation and deception (18%); (7) Autonomy (11%); (8) Effectiveness (30%); (9) Privacy and confidentiality (64%); and (10) Concerns for healthcare workers’ jobs (12%). Other themes were discussed in 14% of articles. Conclusions: Our scoping review has comprehensively covered various ethical aspects of CAI in mental healthcare. However, certain themes, including the climate impact of AI, the responsibility gap, and especially the nuanced examination of therapeutic processes, are less explored . Additionally, the scarcity of qualitative studies and underrepresentation of key stakeholders highlight areas for future research to deepen our understanding of the ethical implications of CAI in mental health.

  • Background: There is a dearth of communication skills training (CST) studies that specifically focus on guiding healthcare professionals (HCPs) to communicate with pediatric cancer patients. Few studies have developed and tested innovative interventions regarding pediatric cancer truth-telling for sick children and their parents. Objective: This study aimed to develop and evaluate the effectiveness of an online pediatric CST (PedCST) program and an interactive e-picture book application. Methods: This experimental study enrolled 43 HCPs from pediatric cancer wards and 29 sick children and their parents. The study included an online PedCST designed for HCPs and an interactive e-picture book application tailored for children with leukemia and their parents. Repeated measures analysis of variance and paired t-test were used for data analysis. Results: Online PedCST effectively enhanced the HCPs’ self-confidence and communication skills when communicating with sick children and their parents. These positive effects lasted for three months after the intervention (P<0.001, η2=0.668–0.137). Although the interventions had a limited impact on improving parents’ quality-of-life and emotional distress (P>0.05), they showed a medium-to-large effect on enhancing sick children’s quality-of-life (P<0.001, d=1.217) and symptom distress (P<0.001, d=0.577–0.872). Conclusions: The online PedCST offered substantial benefits to HCPs in conducting truth-telling to sick children and their parents. The interactive e-picture book application proved valuable not only in improving sick children’s quality-of-life and symptoms/emotional distress but also enhanced parents’ satisfaction with the communication process. These findings suggest the adoption of both interventions in clinical practice to enhance the processes and experiences of pediatric cancer truth-telling.

  • Background: Longitudinal cohort studies have traditionally relied on clinic-based recruitment models, which limit cohort diversity and the generalizability of research outcomes. Digital platforms can be used to increase participant access, improve study engagement, streamline data collection, and increase data quality; however, the efficacy and sustainability of digitally enabled studies rely heavily on the design, implementation, and management of the digital platform being used. Objective: The National Institutes of Health’s (NIH) All of Us Research Program (AOU) is an ongoing national, multiyear study aimed at building a large research cohort that reflects the diversity of the United States, including minority, health disparate, and other populations underrepresented in biomedical research (UBR). We sought to design and build a highly secure, privacy-preserving, validated, participant-centric digital research platform to recruit, enroll, and engage AOU participants from diverse backgrounds. Methods: AOU applied digital research methods to facilitate multi-site, hybrid, and remote study participation and multimodal data collection. We collaborated with community members, healthcare provider organizations, and NIH leadership to design, build, and validate a secure, feature-rich digital research platform based upon the core values of AOU. Participants were recruited by many methods, including in-person, print, and online digital campaigns. Participants accessed a secure digital research platform via web and mobile applications, either independently or with research staff support. The participant-facing tool facilitated electronic consent, multi-source data collection, including surveys, genomic results, wearables, electronic health records, and ongoing participant engagement. We also built tools for study staff and researchers to provide remote participant support, study workflow management, participant tracking, data analytics, data harmonization, and data management tools. Results: We built a secure, participant-centric digital research platform with engaging functionality used to recruit, engage, and collect data from diverse participants throughout the United States. As of April 2024, 87% of participants enrolled via the platform are from UBR groups, including racial and ethnic minorities (46%), rural dwelling individuals (8%), those over the age of 65 (31%), and individuals with low socioeconomic status (20%). Conclusions: This digital research platform demonstrated successful use among diverse participants. We built a user-friendly, participant-centric digital platform with tools to enable engagement with individuals from different racial, ethnic, socioeconomic, and other UBR groups. These findings could be used as best practices for effective use of digital platforms to build and sustain cohorts of various study designs to increase engagement with diverse populations in health research. Clinical Trial: N/A

  • Evaluating GPT-4 as a Clinical Decision Support Tool in Ischemic Stroke Management

    Date Submitted: May 9, 2024
    Open Peer Review Period: May 9, 2024 - Jul 4, 2024

    Background: Cerebrovascular diseases are the second most common cause of death worldwide and one of the major causes of disability burden. Advancements in artificial intelligence (AI) have the potential to revolutionize healthcare delivery, particularly in critical decision-making scenarios such as ischemic stroke management. Objective: Here, we evaluated the effectiveness of GPT-4 in providing clinical support for emergency room neurologists comparing its recommendations with expert opinions and real-world outcomes. Methods: A cohort of 100 patients with acute stroke symptoms was retrospectively reviewed. Data used for decision-making included patients’ history, clinical evaluation, imaging study results, and other relevant details. Each case was independently presented to GPT-4, which provided a scaled recommendation (1-7) regarding the appropriateness of treatment, the use of tissue plasminogen activator, and the need for endovascular thrombectomy. Additionally, GPT-4 estimated the 90-day mortality probability for each patient and elucidated its reasoning for each recommendation. The recommendations were then compared with a stroke specialist and actual treatment decision. Results: Agreement of GPT-4’s recommendations with the expert opinion yielded an AUC of 0.85 [95% CI: 0.77-0.93], and with real-world treatment decisions, an AUC of 0.80 [0.69-0.91]. Mortality prediction, GPT-4 accurately identified 10 out of 13 within its top 25 high-risk predictions (AUC = 0.89 [95% CI: 0.8077-0.9739]; HR: 6.98 [95% CI: 2.88-16.9]), surpassing supervised machine-learning models. Conclusions: This study demonstrates the potential of GPT-4 as a viable clinical decision-support tool in the management of acute stroke. Its ability to provide explainable recommendations without requiring structured data input aligns well with the routine workflows of treating physicians. Future studies should focus on prospective validations and exploring the integration of such AI tools into clinical practice.

  • Background: Patients with long-term needs for health services are often expected to participate actively in specialized outpatient care, regardless of their condition or digital skills. Health literacy and digital literacy are seen as requisites for active participation to exploit the potential of digital outpatient services. However, associations between participation in a digital outpatient care service and health literacy remain unclear. Objective: The objective of the current study was to evaluate whether digital outpatient care for 6 months resulted in improved health literacy, health-related quality of life (HRQL), digital/eHealth literacy and utilization of healthcare services compared with usual care. Methods: We conducted a multicenter nonrandomized trial with one intervention arm and one control arm. Patients were allocated 1:2 in favor of the intervention arm. Eligible patients were aged 18 years or older and receiving outpatient care in the pain, lung, neurology, or cancer departments at two Norwegian university hospitals. Patients in the intervention arm received digital outpatient care utilizing a tailored combination of patient reported outcome (PRO) measures, self-monitoring, and chats for timely contact with the outpatient clinic. Patient responses were assessed by healthcare workers, via a dashboard that included a traffic light system to draw attention to the most urgent patient reports. The control group received care as usual. The data were collected at enrollment/baseline and after 3 and 6 months. The primary outcome was the change in health literacy according to the Health Literacy Questionnaire (HLQ) domain “Understanding health information well enough to know what to do” at 6 months. The secondary outcomes were four additional domains from HLQ, seven domains of digital/eHealth literacy, HRQL, acceptability of the digital intervention, and health service use. The data were analyzed using SPSS, with univariate methods. Results: A total of 162 patients were recruited, with 55 allocated to the control arm and 107 to the intervention arm. After 6 months of follow up, data were available for 135 individuals (attrition rate 17.3%). There was no statistically significant change in the primary outcome, “Understanding health information well enough to know what to do” at 6 months. After 3 months, the health literacy domains “Actively managing my own health”, and “Understanding health information well enough to know what to do,” as well as both physical and mental HRQL, improved in the digital outpatient intervention group compared with the control group. Overall, the participants in digital outpatient care had a high satisfaction rate when evaluating the digital outpatient care platform. Conclusions: The present study explored digital outpatient care comprising PRO measures, asynchronous messaging, and remote monitoring on clinical indications for patients with chronic pain, ILD, epilepsy, or cancer. Although no significant differences were observed in patients’ health literacy regarding their understanding of health information after 6 months, our data indicate an improvement in health literacy domains and HRQL at 3 months. Despite our mixed results, the participants reported high satisfaction with the digital outpatient care intervention, and our findings highlight the potential of digital interventions in outpatient care. Clinical Trial: NCT05068869 https://clinicaltrials.gov/ct2/show/NCT05068869 International Registered Report Identifier (IRRID): DERR1-10.2196/46649

  • Background: Fueled by innovations in technology and health interventions to promote, restore, and maintain health, and safeguard well-being, the field of eHealth yielded significant scholarly output. Objective: To understand eHealth research trends and multidisciplinary contributions to eHealth, we obtained evidence from three corpora: 10,022 OpenAlex documents with eHealth in title, 5,000 most relevant eHealth articles according to the Web of Science (WoS) algorithm, and all available (n=1,885) WoS eHealth reviews. Methods: In VOSviewer, we built keyword and concept co-occurrence networks. The scholarship on eHealth was synthesized by analyzing clusters and adding custom overlays that linked technologies to stakeholders and their needs. A co-citation map of sources referenced in WoS reviews demonstrated scientific fields supporting eHealth. Multidisciplinary contributions were also analyzed as co-occurring hierarchical concepts used by OpenAlex to tag eHealth articles. Results: Common research directions included eHealth studies on 1) self-management and interventions; 2) telemedicine, telehealth and technology acceptance; 3) privacy, security, and design; 4) health information consumers’ literacy; 5) health promotion and prevention of disease through active lifestyle choices; 6) mHealth and digital health; 7) HIV prevention. Researchers studied mental health and health literacy of young people; physical activity and lifestyle changes to prevent obesity, hypertension, cardiovascular disease and diabetes in adults and older adults; chronic disease, dementia, and pain management and medication adherence in older adults; cancer survivors and caregivers’ needs; as well as providers and health leaders. Echoing chronological developments in eHealth research, keywords internet (2017 mean publication year), telemedicine (2018), telehealth (2018), mHealth (2019), mobile health (2020), and digital health (2021) were strongly linked to literatures indexed with eHealth (2019) and e-Health (2017) keywords. Mean publication year was 2018.77 for eHealth articles and 2019.80 for eHealth reviews, a time lag of about 12 months. Given the volume of articles, review authors were more likely to focus on interventions and less likely to systematize research on eHealth and health literacy. Review authors cited a wide range of medical journals and journals specific to eHealth technologies, as well as journals in psychology, psychiatry, public health, epidemiology, health services, policy, education, health communication, and other fields. The Journal of Medical Internet Research stood out as the most cited source in eHealth reviews. An OpenAlex concept map confirmed these findings while also displaying a prominent role of political science and law, economics, nursing, business, and knowledge management. Conclusions: Drawing upon contributions from many disciplines, the field of eHealth has evolved from studies of internet-enabled communication, telemedicine, and telehealth to research on mobile health and emerging digital health technologies.

  • Background: Health information technology has revolutionized health care in the United States. Interoperable clinical care data exchange, e-prescribing, electronic public health reporting, and electronic patient access to health information have improved care and outcomes. Objective: This objective of this analysis is to examine progress and the Office of the National Coordinator for Health IT’s (ONC’s) mission to enhance health care through data access and exchange. Methods: This analysis leverages data on end-users of health IT to capture trends in engagement in interoperable clinical care data exchange (ability to find, send, receive, and integrate information from outside organizations), e-prescribing, electronic public health reporting, and capabilities to enable patient access to electronic health information. Data were primarily sourced from the American Hospital Association Annual Survey Information Technology Supplement (2008 to 2023), Surescripts e-prescribing utilization data (2008 to 2023), the National Cancer Institute's Health Information National Trends Survey (2014 to 2022), and the National Center for Health Statistics' National Electronic Health Records Survey (2009 to 2023). Results: Since 2009, there has been a remarkable 10-fold increase in EHR use among hospitals and 5-fold increase among physicians. This rapid digitization enabled the interoperable exchange of electronic health information, electronic prescribing, electronic public health data exchange, and the means for patients and their caregivers to access crucial personal health information digitally. Now, 70% of hospitals are interoperable, with many providers seamlessly integrated within EHR systems. Notably, nearly all pharmacies and 92% of prescribers possess e-prescribing capabilities, marking an 85-percentage point increase since 2008. In 2013, 40% of hospitals and a third of physicians allowed patients to view their online medical records. Patient empowerment has increased, with 97% of hospitals and 65% of physicians possessing EHRs that enable patients to access their online medical records. As of 2022, three-quarters of individuals report being offered online access to portals, and over half (57%) report actively engaging with their health information through their patient portal. Electronic public health reporting has also had an uptick, with most hospitals and physicians actively engaged in key reporting types. Conclusions: Federal incentives have served as catalysts for the widespread adoption of electronic health records (EHRs) and the rapid digitization in health care. We found tremendous growth in health IT capabilities. Interoperability initiatives have gained considerable momentum and have fostered collaboration across health care entities. However, challenges persist in achieving nationwide interoperability, stemming from technical, organizational, and policy challenges and optimizing the benefits of these technologies. Enhanced data standardization, governance structures, and the establishment of robust health information exchange networks are crucial steps forward. Interoperable clinical care data exchange, e-prescribing, electronic public health reporting, and patient access to health information have grown substantially over the past quarter-century and have been shown to improve health care outcomes. However, interoperability hurdles, usability issues, data security, and equitable patient access persist. Addressing these demands will require collaborative efforts among stakeholders, refining standards, and enhancing policy frameworks.

  • Background: The prompt and accurate identification of Mild Cognitive Impairment (MCI) is crucial for preventing its progression into more severe neurodegenerative diseases. However, the early diagnosis of MCI is challenging due to the subtlety of its initial symptoms. Traditional diagnostic methods often prove costly, time-consuming, and invasive, hindering patient compliance and the accessibility of these tests. The integration of machine learning and metaheuristic optimization algorithms presents a promising avenue for enhancing the early detection and diagnostic processes of this condition. Objective: This study aims to develop a dynamic adaptive ensemble learning framework enhanced by harmony search improvement (DAELF-HSI) that adaptively integrates multimodal physiological data collected from wearable wristband and digital cognitive metrics recorded on tablet, thereby improving the accuracy and practicality of MCI detection. Methods: In this framework, we initially align the collected electrodermal activity, photoplethysmography, and digital cognitive parameters using cubic spline interpolation and timestamp sorting. Subsequently, we have developed a multi-stage signal artifact removal algorithm that includes artifact correction, signal decomposition, and overlapping sliding window processing. To comprehensively represent physiological signals, we introduce a multi-scale feature extraction method, calculating features across time, frequency, and non-linear domains. We then propose a dynamic adaptive feature selection optimization algorithm based on harmony search, which dynamically adjusts hyperparameters during the iterative process to generate an optimal feature subset suitable for most base classifiers with reduced dimensionality. Finally, by improving the fitness function values of the harmony vectors and the selection probability of the harmony memory, we optimize the stacking performance of the base learners. The framework was validated in a clinical setting at Shanxi Medical University First Hospital with 376 participants over 65. Data collection was facilitated by the clinically certified Empatica 4 wristband and tablet, measuring physiological and digital cognitive data. Results: The experimental results show that our proposed framework achieved an accuracy of 88.5%, precision of 0.891, recall of 0.887, and an F1 score of 0.889, outperforming all benchmark models. Furthermore, DAELF-HSI has elucidated discriminative features associated with MCI, including the decay time of the skin conductance response, the percentage of consecutive normal-to-normal intervals exceeding 50 milliseconds, and the ratio of low to high frequency components in heart rate variability, along with cognitive timing features, establishing its potential as a practical and accurate method for MCI detection. Conclusions: The developed DAELF-HSI framework has demonstrated significant potential as an effective and efficient tool for detecting MCI. It establishes a new benchmark for non-invasive, cost-effective early MCI detection that can be integrated into routine wearable and tablet-based assessments. This approach not only enhances accessibility but also provides a feasible solution for autonomous, at-home monitoring and early detection, making it a valuable tool in the ongoing fight against neurodegenerative diseases.

  • Background: Artificial Intelligence (AI) chatbots like ChatGPT are expected to impact vision healthcare significantly. Their potential to optimize the consultation process and diagnostic capabilities across range of ophthalmic sub-specialties remain to be fully explored. Objective: To investigate the performance of AI chatbots in recommending ophthalmic outpatient registration and in diagnosing eye diseases within clinical case profiles. Methods: This cross-sectional study utilized clinical cases from the Chinese Standardized Resident Training (SRT) - Ophthalmology (2nd Edition). For each case, two profiles were created: “Patient with History” (Hx) and “Patient with History and Examination” (Hx and Ex). These profiles served as independent queries for ChatGPT-3.5 and 4.0 (accessed from March 5-18, 2024). Similarly, three ophthalmic residents were posed the same profiles in a questionnaire format. The accuracy of recommending ophthalmic sub-specialty registration was primarily evaluated using “Hx” profiles. The accuracy of the top-ranked diagnosis, and the accuracy of diagnosis within the top three suggestions (do-not-miss diagnosis), were assessed using “Hx and Ex” profiles. The gold standard for judgment was the published official diagnosis. Characteristics of incorrect diagnoses by ChatGPT were also analyzed. Results: A total of 208 clinical profiles from 12 ophthalmic sub-specialties were analyzed (104 “Hx” and 104 “Hx + Ex”). For “Hx” cases, GPT-3.5, GPT-4.0, and residents showed comparable accuracy in registration suggestions (63.5%, 77.9%, and 69.2%, respectively, P = 0.073), with ocular trauma, retinal diseases, and strabismus & amblyopia achieving the top three accuracy. For “Hx + Ex” cases, both GPT-4.0 and residents demonstrated higher diagnostic accuracy than GPT-3.5 (59.6% and 60.6% vs. 39.4%, P = 0.003 and P = 0.001). Accuracy for “do-not-miss” diagnoses also improved (76.0% and 65.4% vs. 49.0%, P < 0.001 and P = 0.015). The highest diagnostic accuracy were observed in glaucoma, lens diseases, and eyelid/lacrimal/orbital diseases. GPT-4.0 recorded fewer incorrect top-3 diagnosis (59.5% vs. 84.1%, P = 0.005) and more partially correct diagnosis (50% vs. 11.1%, P < 0.001) than GPT-3.5, while GPT-3.5 had more completely incorrect (42.9% vs. 16.7%, P = 0.005) and less precise diagnosis (34.9% vs. 11.9%, P = 0.009). Conclusions: GPT-3.5 and GPT-4.0 showed intermediate performance in recommending ophthalmic sub-specialties for registration. While GPT-3.5 under-performed, GPT-4.0 approached and numerically surpassed residents in differential diagnosis. AI chatbots show promise in facilitating ophthalmic patient registration. However, their integration into diagnostic decision-making requires more validation.

  • From Doubt to Confidence: How We Overcame Fraudulent Survey Submissions from Bots and Other Survey Takers of a Web-based Survey

    Date Submitted: May 3, 2024
    Open Peer Review Period: May 3, 2024 - Jun 28, 2024

    In 2019, we launched a web-based longitudinal survey of people who frequently use e-cigarettes, called the Vaping and Patterns of E-cigarette Use Research (VAPER) Study. The initial attempt to collect survey data failed due to fraudulent survey submissions, likely submitted by survey bots and other survey takers. Many lessons were learned, effective risk mitigation strategies were identified and implemented, and, ultimately, we completed 5 waves of data collection with reasonable confidence in the integrity of the data. This paper aims to share our experiences with challenges and mitigation strategies with researchers building and utilizing their own web-based samples, particularly samples that target lower prevalence populations.

  • Use of video consultations in outpatient medical care in Germany and characteristics of their user groups: analysis of claims data

    Date Submitted: May 3, 2024
    Open Peer Review Period: May 3, 2024 - Jun 28, 2024

    Background: Supplementing outpatient medical care with the use of video consultations could, among other benefits, improve access, especially in structurally disadvantaged areas. Objective: This claims data analysis, carried out as part of the German research project "Preference-based use of video consultation in urban and rural regions", aims to analyze the use of video consultations and the characteristics of its user groups. Methods: Claims data from three Statutory Health Insurance Funds (SHIFs) and four Associations of Statutory Health Insurance Physicians (ASHIPs) from the period April 2017 to the end of 2020 were used. A sample of around six million insured and 33,100 physicians / psychotherapists was analyzed. In addition to data on the use of video consultations, patient data on sociodemographic characteristics, diagnoses and place of residence were included. To analyze the physicians’ perspective, specialty groups, demographic characteristics and the type of practice location were also included. Descriptive analyses were performed according to different subgroups. Results: From 2017 to 2019, video consultations had almost no relevance in outpatient care in the German health care system. Although this changed significantly with the start of the Covid 19 pandemic, there was also a clear decline in the use of video consultations as the number of infections flattened out. Video consultations are mainly used in psychotherapeutic care. Younger age groups and those located in urban areas use video consultations more frequently; this applies to both patients and service providers. Conclusions: The widespread and lasting use of video consultations will only succeed if the potential user groups accept this form of service provision and recognize its advantages. Further analyses should therefore investigate the preferences of user groups for the use of video consultations.

  • DTx-based cardio-oncology rehabilitation for lung cancer survivors: a randomized controlled trial

    Date Submitted: May 2, 2024
    Open Peer Review Period: May 2, 2024 - May 22, 2024

    Background: For lung cancer survivors, cardiopulmonary fitness is a strong independent predictor of survival. Home-based cardiac telerehabilitation through wearable devices and mobile apps is a substitution for traditional center-based rehabilitation with equal efficacy and a higher completion rate. However, it has not been widely used in clinical practice. Objective: Early-stage non-small cell lung cancer survivors aged 18-70 years. All the participants received surgery 1-2 months before enrollment and did not require further antitumor therapy. Methods: Participants were randomized to receive cardiac telerehabilitation or usual care for 5 months. AI-driven exercise prescription with video guide & real-time HR monitoring was generated based on cardiopulmonary exercise testing and optimized dynamically. Outcome measurements included cardiopulmonary fitness, lung function, cardiac function, tumor marker, safety, compliance, and scales assessing symptoms, psychology, sleep, fatigue, and quality of life. Results: Forty of 47 patients (85%) finished the trial. The average prescription compliance rate of patients in the telerehabilitation group reached 101.2%, with an average exercise duration of 151.4 min per week and an average effective exercise duration of 92.3 min per week. The cardiac telerehabilitation was associated with higher improvement of VO2peak (3.66±3.23 mL/Kg/min vs 1.09±3.23 mL/Kg/min, p=0.02) and global health status/QOL (16.25±23.02 vs 1.04±13.90, p=0.03) compared with usual care. Better alleviation of affective interference (-0.88±1.50 vs 0.21±1.22, p=0.048), fatigue (-8.89±15.96 vs 1.39±12.09, p=0.02), anxiety (-0.31±0.44 vs -0.05±0.29, p=0.048), and daytime dysfunction (-0.55±0.69 vs 0.00±0.52, p=0.02) were also observed in the telerehabilitation group. No exercise-related adverse events were identified during the intervention period. Conclusions: Cardio-oncology telerehabilitation improved cardiorespiratory fitness and quality of life in lung cancer survivors with good compliance and safety. Clinical Trial: chictr.org.cn ChiCTR2200064000

  • Background: Technology-mediated medication adherence interventions have proven useful, yet implementation in clinical practice is low. The ENABLE COST Action (CA19132) online repository of medication adherence technologies (MATech) aims to provide an open access, searchable knowledge management platform to facilitate innovation and support medication adherence management across health systems. To provide a solid foundation for optimal use and collaboration, the repository requires a shared interdisciplinary terminology. Objective: We consulted stakeholders on their views and level of agreement on the terminology proposed to inform the ENABLE repository structure. Methods: A real-time online Delphi study was conducted with stakeholders from 39 countries, active in research, clinical practice, patient representation, policy making, and technology development. Participants rated terms and definitions of MATech and of 21 attribute clusters on product and provider information, medication adherence descriptors, and evaluation and implementation. Criteria of relevance, clarity and completeness were rated on 9-point scales, and free-text comments provided interactively. Participants had the possibility to reconsider their ratings based on real-time aggregated feedback and revisit the survey throughout the study period. We quantified agreement and process indicators for the complete sample and per stakeholder group, and performed content analysis on comments. Consensus was considered reached for ratings with disagreement index (DI) below 1. Median ratings guided decisions on whether attributes were considered mandatory, optional or not relevant. We used results to improve the terminology and repository structure. Results: Of 250 stakeholders invited, 117 rated the MATech definition, of which 83 rated all attributes. Consensus was reached for all items. The definition was considered appropriate and clear (median ratings 7.02 and 7.26, respectively). Most attributes were considered relevant and mandatory, and sufficiently clear to remain unchanged, except ISO certification (considered optional, median relevance rating 6.34), and medication adherence phase, medication adherence measurement, and medication adherence intervention (candidates for optional changes, median clarity ratings 6.07, 6.37, and 5.67, respectively). Subgroup analyses found several attribute clusters considered moderately clear by some stakeholder groups. Results were consistent across stakeholder groups and across time, yet response variation was found within some stakeholder groups for selected clusters, suggesting targets for further discussion. Comments highlighted issues for further debate and provided suggestions which informed modifications to improve comprehensiveness, relevance, and clarity. Conclusions: By reaching agreement on a comprehensive MATech terminology developed following state-of-the-art methodology, this study represents a key step in the ENABLE initiative to develop an information architecture that has the potential to structure and facilitate the development and implementation of MATech in health systems across Europe. The debates and challenges highlighted in stakeholders’ comments outline a potential roadmap for further development of the terminology and the ENABLE repository.

  • Background: Digital health interventions (DHIs) aim to support health-related knowledge transfer e.g., through websites or mobile applications (apps). They have the potential to either increase health inequalities due to the digital divide or to reduce health inequalities by making healthcare available to those who might not otherwise be able to access it, such as geographically remote populations. They can also overcome language barriers though translated content and enable people to access support and advocacy from family members or friends. However, public health programmes and patient-level healthcare delivered digitally need to consider ways to mitigate the digital divide through DHI design, deployment, and engagement mechanisms, to reach digitally excluded populations. Objective: The objective of this systematic scoping review was to identify the features of DHI design and deployment conducive to improving access to, and engagement with, DHIs by people from demographic groups likely to be affected by the digital divide. The review was conducted during the evolving Covid-19 pandemic, and its findings informed the rapid design, deployment, and evaluation of a post-Covid-19 rehabilitation DHI called ‘Living With Covid Recovery’ (LWCR). LWCR needed to be engaging and usable for patients with a wide range of demographic characteristics, to avoid exacerbating existing health inequalities as far as possible. LWCR was introduced as a service in 33 participating NHS hospital clinics from August 2020, was used by 7,679 patients, and the study ran until 20th December 2022. Methods: This systematic scoping review followed the methodology recommended by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Review (PRISMA-ScR) guidance. The following databases were searched for primary research studies published in English from 1 October 2011 to 1 October 2021: Cochrane Library, Epistemonikos, NICE Evidence, PROSPERO, PubMed (with MEDLINE and Europe PMC) and Trip. In addition, we used OpenGrey and Google Scholar to search for grey literature. We selected publications that met the following inclusion criteria: primary research papers that explored and/or evaluated features of DHI design and deployment intended to enable access to and engagement by adults from demographic groups likely to be affected by the digital divide (e.g., older age; minority ethnic groups; lower income/education level). The data from studies that met the review inclusion criteria were extracted, narratively synthesised, and thematically analyzed. Results: A total of 22 papers were included in the review. Inclusion criteria were met for 19 papers of 1245 hits retrieved by the search and three further papers were added from a search of publications included in relevant reviews. DHIs evaluated in the studies included: telehealth, virtual assistants, text message interventions, decision aids and e-health learning programs. The main themes resulting from analysis of extracted data relating to design considerations included: co-development with end-users and user testing for iterative design cycles to produce DHIs that help improve digital skills and digital health literacy through use; tailoring for low literacy levels through animations, pictures, videos and writing for a low reading age; use of virtual assistants to collect information from patients and guide use of a DHI. For deployment, themes revealed included: provide devices and data, if possible, otherwise use text messages or signpost to sources of cheap/free devices and free WiFi; provide ‘human support’ for implementation / onboarding and troubleshooting; provide tailored digital skills education as part of the intervention; and incorporate peer/family support. Conclusions: Taking these “universal precaution”’ can help reduce the digital divide. The results helped guide the iterative design and successful deployment of the LWCR DHI. They also have wider implications for practitioners, policy makers, and researchers, and will inform best practices in the design and delivery of DHIs for equitable health improvement

  • Shared Decision Making Tools Implemented in the Electronic Health Record: A Scoping Review

    Date Submitted: Apr 26, 2024
    Open Peer Review Period: May 1, 2024 - Jun 26, 2024

    Background: Shared decision making (SDM) is a model of patient-centered care that encourages patients and clinicians to work together to reach medical decisions by weighing the risks and benefits of various options within the context of the values and goals of the patient. Despite the interest in incorporating SDM into routine care, current research studies identify various obstacles that limit SDM adoption. These obstacles include technical integration issues, logistical and workflow challenges, and psychological impediments such as uncertainty and legacy belief systems, which continue to impede progress. Integrating SDM tools and processes into EHR systems is often a complex and challenging problem. Objective: We aimed to understand the integration and implementation characteristics of reported Shared Decision Making (SDM) interventions integrated into an electronic health record (EHR) system. Methods: We conducted a scoping review using Arksey and O’Malleys' methodologic framework with guidance from the Joanna Briggs Institute. Results: A total of 19 studies of 2153 were included in the final review. There is a high degree of variation across studies, including SDM definitions, standardized measures, technical integration, and implementation strategies. SDM tools that target established healthcare processes promoted use. Integrating SDM templates and tools into an EHR appeared to improve the outcomes for most studies. Most SDM interventions were designed for clinicians. Patient-specific goals and values were not included in several studies. The two most common study outcome measures were patient satisfaction and SDM tool use. Conclusions: Understanding the approaches for presenting SDM tools directly into a clinician’s workflow within the EHR is a logical approach to promoting SDM into routine clinical practice. This review contributes to the literature by illuminating features of SDM tools that have been integrated into an EHR system. Standardization of SDM tools and processes is needed for consistency across SDM studies. Targeting accepted clinical processes may enhance the adoption and use of SDM tools. Future studies designed as randomized control trials are needed to expand the quality of the evidence base. Keeping the goals and values of the patient at the center of shared decision making interactions is a key area for future studies.

  • On March 13, 2024, the Parliament sanctioned the inaugural comprehensive artificial intelligence (AI) statute globally. The European Union deems the governance of this technology as crucial, in light of potential infringements upon fundamental rights and public freedoms, detrimental impacts on the environment, and the integrity of democratic frameworks. Conversely, the undeniable advances in the medical domain necessitate the regulation of such technology to bolster investments and research, thereby providing the requisite legal certainty for users/patients, professionals, enterprises, and investors. The Regulation acknowledges the potential perils certain AI applications pose to fundamental rights, enacting prohibitions on practices such as biometric categorization, emotion recognition, and social scoring, among others. Nevertheless, the Regulation does not neglect the significance of medical research and, recognizing the imperative for support of new technologies and innovation, permits research with fewer constraints compared to the commercial use of AI algorithms in healthcare.

  • Ethical Considerations and Fundamental Principles of Large Language Models in Medical Education

    Date Submitted: Apr 30, 2024
    Open Peer Review Period: Apr 30, 2024 - Jun 25, 2024

    This viewpoint article explores the ethical challenges facing the future application of large language models (LLMs) in medical education. These challenges encompass academic integrity, privacy and data risks, bias, educational unfairness, and the notable absence of transparency and interpretability. Moreover, it addresses issues related to responsibility and copyright. In addressing these ethical challenges, the author suggests drawing upon ethical guidelines from artificial intelligence applications in other domains. They propose the establishment of a globally unified ethical framework for the integration of LLMs into medical education. This framework is underpinned by eight fundamental principles: privacy and data protection, transparency and interpretability, fairness and equal treatment, academic integrity and moral norms, quality control and supervision mechanisms, accountability and traceability mechanisms, protection and respect for intellectual property rights, and fostering educational research and innovation. Through the implementation of a unified ethical framework, safeguarding individual rights and privacy, enhancing educational fairness, improving educational quality and safety, promoting academic integrity and ethical standards, and advancing educational research and innovation, the proposed guidelines aim to facilitate the reasonable and effective application of LLMs in medical education.

  • A Decade of Health Information Technology and the Impact on Health Care in the U.S.: Systematic Review

    Date Submitted: Apr 25, 2024
    Open Peer Review Period: Apr 30, 2024 - Jun 25, 2024

    Background: In 2004, the Office of the National Coordinator for Health Information Technology (ONC) was established to facilitate the nationwide adoption and use of health information technology (health IT). Since its inception, the health IT landscape has evolved with a diverse array of federal investments, programs, and policies to advance its use. Previous systematic reviews of literature related to health IT focused on assessing the adoption and use of technology. As health IT has evolved, research has pivoted from tracking adoption of specific health IT features to assessing the impact of these technologies and tools. Objective: This paper provides a comprehensive review of peer-reviewed publications published over the past decade to closely examine the impacts of health IT including the impact of federal policies, changing priorities, and how the expanded use of EHR features, and effect of health IT on people, processes, and outcomes. Methods: All health IT-related peer-reviewed published between January 2013 and June 2023 were evaluated to identify articles that focused on the impact of health IT. Articles and studies were extracted through a review of PubMed. A stepwise process was used to identify articles that met the inclusion criteria, focused on the impact of health IT, and demonstrated sufficient scientific rigor. Results: The resulting 408 articles were coded based on their primary focus (provider-facing or patient-facing technology), or based on topics that pertained to the systemwide use of health IT. Within each of these categories, articles were organized around key themes. Overwhelmingly, research studies reported that health IT generated a positive impact. More than half of all articles focused on provider-facing technology with a focus on measurable outcomes including quality, safety, and costs. A number of studies evaluated the increased use of patient portals and other tools to support engagement. Studies on interoperability highlighted the value of increased health information exchange. An emerging area of study included a focus on the role of health IT in advancing public and population health. Over three-quarters of the published literature concluded that health IT generated a positive, mixed, or neutral impact. These effects were consistent across the different categories of health IT that were examined whether provider-facing, patient-facing, or systemwide impact of health IT. Conclusions: Over the past decade, the focus of studies on the impact of health IT has evolved, transitioning from a concentration on health IT adoption to optimizing its potential. This includes assessing the effectiveness of EHR functions as well as increasing information exchange. As the landscape evolved with broader acceptance of health IT, the focus shifted with greater interest in technology’s impact on patient engagement, and opportunities to use data to advance health care including population and public health. Clinical Trial: N/A

  • Background: There is growing interest in the real-time assessment of physical activity and physiological variables. Acceleration, particularly those collected through wearable sensors, has been increasingly adopted as an objective measure of physical activity. However, sensor-based measures often pose challenges for large-scale studies due to their associated costs, inability to capture contextual information, and restricted user populations. Smartphone-delivered Ecological Momentary Assessment (EMA) offers an unobtrusive and undemanding means to measure physical activity to address these limitations. Objective: To evaluate the usability of EMA by comparing its measurement outcomes with two self-report assessments of physical activity: Global Physical Activity Questionnaire (GPAQ) and a modified version of Bouchard’s Physical Activity Record (BAR). Methods: 235 participants (137 females, 98 males, 94 repeated) participated in one or more 7-day study. Waist-worn sensors provided by Actigraph™ captured accelerometer data while participants completed three self-report measures of physical activity. The multilevel modeling method was used with EMA, GPAQ, and BAR as separate measures, with eight sub-domains of physiological activity (overall physical activity; overall excluding occupational; move; moderate and vigorous exercise; moderate and vigorous occupational; sedentary) to model accelerometer data. Results: Among the three measurement outcomes, EMA (β = .185, p = .005) and BAR (β = .270, p < .001) exhibited higher overall performance over GPAQ (β = .140, p = .019). EMA also showed a more balanced performance, compared to other measurement tools, in modeling various physical activity domains, including occupational, leisure, and sedentary behaviour. Conclusions: Multilevel modeling on three self-report assessments of physical activity indicates that smartphone-delivered EMA is a valid and efficient method for assessing physical activity.telemedicine; smartphone; wearable electronic devices; physical activity

  • Background: Despite the widespread adoption of Electronic Health Records (EHRs) in healthcare, their effective use in Spanish-speaking regions hinges on adequately assessing healthcare professionals' satisfaction. Existing instruments predominantly focus on English-speaking settings and lack rigorous validation for Spanish. This study addresses this significant gap by developing and validating a Spanish-language instrument tailored to measure satisfaction levels with EHR systems among healthcare professionals. Objective: To address the gap in validated Spanish-language instruments for measuring healthcare professionals' satisfaction with EHRs, this study aimed to develop, validate, and assess such an instrument, contributing to improved healthcare delivery and outcomes. Methods: A quantitative methodology was utilized, beginning with the Delphi method for content validity through expert and healthcare professionals' feedback over two rounds. The study involved 221 participants for data collection, with item reduction conducted via inter-item and item-total correlation, stability validated through test-retest, reliability measured by Cronbach's alpha, and factor structure determined through Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), leading to a structural equation model. Results: The instrument consists of 17 items across three latent factors: 'General data on EHR' (8 items), 'Accessibility and use of EHR' (6 items), and 'Drugs and patient education' (3 items), measured on a Likert scale. It showed excellent reliability (Cronbach's alpha = 0.92) and model fit (RMSEA = 0.073, 95% CI [0.061-0.086]), with all standardized beta values above 0.48. Conclusions: The validation process underscored the instrument's capability to comprehensively measure satisfaction with EHRs, highlighting its effectiveness across various dimensions of EHR usage. The newly developed instrument offers a validated, reliable measure for assessing healthcare professionals' satisfaction with EHRs in Spanish-speaking settings, poised to enhance the efficacy of EHR systems and healthcare quality. Clinical Trial: This study was carried out with human participants whose protocol was reviewed and approved with an ID reference number 07102022-CN-ENM1-CI by the Research and Ethics Committees respectively of Hospital Clinica Nova, in San Nicolas, Nuevo Leon, Mexico. The patients who agreed to participate in the study signed an informed consent letter.

  • Artificial Intelligence for Diagnosing Acute Stroke: A 25-Year Retrospective

    Date Submitted: Apr 20, 2024
    Open Peer Review Period: Apr 29, 2024 - Jun 24, 2024

    Background: Background: Stroke is a leading cause of death and disability in the world. Rapid and accurate diagnosis is crucial for minimizing brain damage and optimize treatment plans. Objective: Objective: This review aims to summarize the methods of artificial intelligence (AI) assisted diagnosis of acute stroke and the assessment of stroke prognosis over the past 25 years, providing an overview of common performance metrics and the development trends of algorithms. It also delves into existing issues and future prospects, intending to provide a comprehensive reference for clinical practice. Methods: Method: A total of 33 representative articles published between 1999 and 2024 on utilizing AI technology for acute stroke diagnosis were systematically selected and analyzed in detail. Results: Results: The segmentation of acute stroke lesions from 1999 to 2024 can be divided into three stages. Prior to 2012, research mainly focused on brain white matter segmentation using thresholding techniques. From 2012 to 2016, the focus shifted to stroke lesion segmentation based on machine learning (ML). After 2016, the emphasis was on deep learning (DL) based stroke lesion segmentation, with a significant improvement in accuracy observed. For the classification and prognosis assessment of strokes, both ML and DL have their advantages, achieving a high level of accuracy. Conclusions: Conclusion: Over the past 25 years, AI technology has shown promising performance in segmenting, classifying, and assessing the prognosis of acute stroke lesion.

  • The CeHRes Roadmap 2.0: an update of a holistic framework for development, implementation, and evaluation of eHealth technologies

    Date Submitted: Apr 17, 2024
    Open Peer Review Period: Apr 22, 2024 - Jun 17, 2024

    Background: To ensure that an eHealth technology fits with its intended users, other stakeholders, and the context within which it will be used, thorough development, implementation, and evaluation processes are necessary. The CeHRes (Centre for eHealth Research & Wellbeing) Roadmap is a framework that can help shape these processes. While it has been successfully used in research and practice, new developments and insights have arisen since the Roadmap’s first publication in 2011 – not just within the domain of eHealth, but also within the different disciplines in which the Roadmap is grounded. Because of these new developments and insights, a revision of the Roadmap was imperative. Objective: The objective of this viewpoint paper is to present the updated pillars and phases of the CeHRes Roadmap 2.0. Methods: The Roadmap was updated based on four types of sources: (1) experiences with its application in research, (2) literature reviews on eHealth development, implementation and evaluation, (3) discussions with eHealth researchers, and (4) new insights and updates from relevant frameworks and theories. Results: The updated pillars state that eHealth development, implementation and evaluation (1) are ongoing and intertwined processes, (2) have a holistic approach in which context, people, and technology are intertwined, (3) consist of continuous evaluation cycles, (4) require active stakeholder involvement from the start, and (5) are based on interdisciplinary collaboration. The CeHres Roadmap 2.0 consists of five interrelated phases, of which the first is the contextual inquiry, in which an overview of the involved stakeholders, the current situation, and points of improvement is created. The findings from the contextual inquiry are specified in the value specification, in which the foundation for the to-be-developed eHealth-technology is created by means of formulating values and requirements, preliminarily selecting behaviour change techniques and persuasive features, and initiating a business model. In de Design phase, the requirements are translated into several lo- and hi-fi prototypes that are iteratively tested with end-users and/or other stakeholders. A version of the technology is rolled out in the operationalization phase, using the business model and an implementation plan. In the summative evaluation phase, the impact, uptake and working mechanisms are evaluated using a multi-method approach. All phases are interrelated by continuous formative evaluation cycles that ensure coherence between outcomes of phases and alignment with stakeholder needs. Conclusions: While the CeHRes Roadmap 2.0 consists of the same phases as the first version, the objectives and pillars have been updated and adapted, reflecting the increased emphasis on behaviour change, implementation, and evaluation as a process. There is a need for more empirical studies that apply and reflect on the CeHRes Roadmap 2.0 to provide points of improvement, because just as any eHealth technology, the Roadmap has to be constantly improved based on input of its users.

  • Background: Background: Governments and policymakers struggle to achieve a balance between hierarchical steering and horizontal governance in systems characterized by fragmented decision authority and multiple interests. To realize its “One Citizen – One Journal” eHealth policy vision, the Norwegian government established a special eHealth board of stakeholders to ensure eHealth policy development. The aim was to create an inclusive governance model that aligned stakeholders’ interests with government ambitions through coordination and consensus. Little empirical knowledge exists on how countries realize such governance models. Objective: The objective of this study was to investigate how the Norwegian inclusive eHealth governance model developed as a tool to align the government’s policy ambitions with stakeholders’ concerns from January 2012 to December 2022. Objective: Objective: The objective of this study was to investigate how the Norwegian inclusive eHealth governance model developed as a tool to align the government’s policy ambitions with stakeholders’ concerns from January 2012 to December 2022. Methods: Methods: In a longitudinal case study we analyzed 16 policy documents and 175 consultation documents issued between January 2012 and December 2022 related to the Norwegian “One Citizen – One Journal” policy implementation process. We used a qualitative approach and employed thematic analysis. Results: Results: (1) The national policy implementation process progressed through three phases, with changes in stakeholder inclusion and perceived influence on the decision-making process characterizing transitions from phase to phase. (2) Tension developed between two contrasting views regarding stakeholders’ autonomy and top-down government authority. Regional health trusts, municipalities, healthcare professional organizations, and industry actors became increasingly concerned about the model’s ability to balance stakeholders’ autonomy concerns with top-down government authority. On the other hand, patient organizations wanted a hierarchical model to ensure equal access to care and quality of care through coherent digital solutions. (3) Governmental insensitivity to participation, lack of transparency, and decreasing trust between the government and stakeholder groups challenged the legitimacy of the inclusive horizontal governance model. As a response, the government changed its approach and adjusted the model to an inclusive bottom-up network model that combined horizontal and hierarchical decision-making. Conclusions: Conclusions: We conclude that Norway’s “One citizen – one Journal” policy trajectory was characterized by a process that unfolded across three distinct phases. Furthermore, the process was characterized by two contrasting stakeholder perspectives: one concerning the extent of justifiable top-down governance to realize a national journal and the other regarding the impact of top-down governance on stakeholders’ autonomy and freedom to govern their own electronic health record implementation process. Finally, it was characterized by diminishing trust in the inclusive governance model. The National eHealth Governance Board faced challenges in establishing legitimacy as a top-down defined horizontal inclusive governance model, primarily attributed to its handling of dilemmas related to participation, transparency, and trust. These dilemmas represent significant obstacles to inclusive governance models and necessitate ongoing vigilance and responsiveness from governmental entities.

  • Background: The ageing population is experiencing more mobility limitations and functional impairments, prompting research into assistive technologies as solutions. These innovations aim to support the health, well-being, and independence of older adults and individuals with mobility challenges. Indoor mobility, vital for daily activities and independence, significantly impacts the lives of these individuals. However, restricted indoor mobility can negatively affect their quality of life and increase the risk of falls. Objective: This study aims to explore the influences of positive ageing perception, quality of life enhancement and social support on indoor assistive technology acceptance and readiness among older adults. Methods: This cross-sectional study was conducted at a gerontechnology laboratory. Participants were required to physically visit the laboratory. The session lasted approximately 60 minutes and consisted of participation in a demonstration of various indoor assistive technologies, as well as the completion of a questionnaire. The demonstrated assistive technologies included a wide range of devices. Participants' positive perceptions of ageing, quality of life enhancement, social support, technology acceptance, and technology readiness were assessed using validated scales. Data analysis was conducted using SPSS 26.0, including descriptive statistics, correlation analysis, and linear regression. Results: A total of 104 older adults aged 60 or above participated and completed the evaluations. The participants' mean age was 67.92 years. Regression analysis revealed that positive ageing perception was positively associated with attitudinal beliefs and gerontechnology confidence. Quality of life enhancement was positively associated with behavioural intention. However, social support showed negative associations with gerontechnology confidence and security. Notably, no significant relationships were found between positive ageing perception and control beliefs, behavioural intention, optimism, innovativeness, comfort, and security. Quality of life enhancement had no significant relationships with attitudinal beliefs, control beliefs, gerontechnology confidence, optimism, innovativeness, comfort, and security. Social support also had no significant associations with attitudinal beliefs, control beliefs, behavioural intention, optimism, innovativeness, and comfort. Conclusions: This study sheds light on the factors influencing older adults' acceptance and readiness to adopt assistive technologies in an indoor setting. The findings underscore the significance of cultivating positive ageing perceptions and emphasising quality of life enhancement through these technologies. It is crucial to address concerns related to gerontechnology confidence, security, and social support to foster greater acceptance and readiness for technology use among older adults. Further research is needed to delve into the underlying mechanisms and develop targeted interventions that promote successful technology adoption in this population. These insights provide valuable guidance for researchers and practitioners seeking to enhance older adults' well-being and quality of life in the digital age. Clinical Trial: N/A

  • Background: Internet gaming disorder (IGD) is a prevalent public health issue among adolescents. Few studies have, however, examined the relationships between IGD symptoms, low self-control, and meaning in life (MIL). Objective: The present study aimed to examine the mediating role of IGD symptoms in the relationships between low self-control and meaning in life and adolescents’ family and school functioning. Methods: A sample of 2,064 adolescents (46.9% females, mean age = 14.6 years) was recruited from five middle schools in Sichuan, China in 2022. Indirect effects of low self-control and MIL on family and school functioning via IGD symptoms were analyzed via structural equation modeling (SEM). Results: All scales showed satisfactory model fit and scalar measurement invariance by gender. Males showed significantly greater IGD symptoms and lower levels of self-control than females. Impulsivity, temper, search for meaning, and lower presence of meaning were significantly associated with greater IGD symptoms. There were significant indirect effects from impulsivity, temper, and presence of meaning to family and school functioning via IGD symptoms. Multigroup SEM across gender found that the positive association between search for meaning and IGD symptoms existed in males but not females. Presence of meaning significantly and negatively moderated the association between impulsivity and IGD symptoms. Conclusions: The findings support a mediating role of IGD symptoms in the relationships between low self-control and MIL and functioning and a buffering role of MIL on the associations between impulsivity and IGD symptoms among the ethnic minority adolescents. The results have implications for targeted interventions to help males with lower self-control and presence of meaning.

  • Background: The aging population in China is becoming increasingly severe, and there is a health inequality phenomenon among urban and rural elderly. With the development of ICT, eHealth has become one of the important factors affecting health. Urban elderly could more conveniently access health information and medical services, while rural elderly may have difficulty enjoying the digital dividends brought by eHealth, highlighting the phenomenon of the “digital health divide”. Objective: This study analyzes the digital health divide and determinants among urban and rural elderly from the perspective of capital theory. Methods: The model for analyzing the digital health divide among urban and rural elderly is constructed based on capital theory. Analysis of variance is used to verify the digital health divide among urban and rural elderly. Structural equation modeling is used to analyze the factors, and the Blinder-Oaxaca decomposition method is used to analyze the main causes. Results: There are three levels of digital health divide among urban and rural elderly, namely digital access divide (F=11.39, P<.01), digital usage divide (F=39.53, P<.001), and digital outcome divide (F=30.20, P<.001). The influence of different levels of divide is transmitted along the digital chain, the impact coefficient of digital access divide on digital usage divide is β=0.060 (P<.05), and digital usage divide on digital outcome divide is β=0.363 (P<.001). The digital usage divide is the most significant level, and cultural capital (β=0.221, P<.001), social support (β=0.361, P<.001), economic capital (β=0.111, P<.01), and habits (β=0.248, P<.001) are most dominant factors contributing to the rural-urban digital usage divide. The Blinder-Oaxaca decomposition results further indicate that cultural capital (33.9%) and social capital (22.5%) are the main factors influencing digital usage divide among urban and rural elderly. Conclusions: here exists digital health divide among urban and rural elderly, and the influence of three level of divide is transmitted along the digital chain. The digital usage divide is the main level, cultural capital and social capital are the main reasons for its formation. To against these divides among urban and rural elderly, interventions in policy, society, technology, and economics are recommended.

  • Background: Breast cancer is prevalent among women in the United States. Non-metastatic disease is treated by partial or complete mastectomy procedures. However, the rates of those procedures vary across practices. Generating real-world evidence on breast cancer surgery could lead to improved and consistent practices. Objective: The paper aims to determine whether All of Us data are fit for use in generating real-world evidence on mastectomy procedures. Methods: Our mastectomy phenotype consisted of adult female participants who had CPT4 or SNOMED codes for a partial or complete mastectomy procedure. We evaluated the phenotype with a novel data quality framework that consisted of five elements: conformance, completeness, concordance, plausibility, and temporality. Also, we used a previously developed adjectival rating matrix with categories of poor (providing little to no data), fair (using only internal EHR data), and good (using internal and external benchmark/data) to evaluate each data quality dimension (DQD). Our subgroup analysis compared partial to complete mastectomy procedure phenotypes. Results: There were 3,704 participants in the partial or complete mastectomy cohort. The geospatial distribution of our cohort varied substantially across states. For example, our cohort consisted of 817 (22.1%) participants from Massachusetts but fewer than 20 participants from multiple other states. We compared the sociodemographics of the partial (n = 2,445) and complete (n = 1,259) mastectomy subgroups. Those groups differed in the distribution of education (P = .02) and income (P < .001) levels using chi-square analysis. The DQD conformance was rated as good. A total of 3,216 (86.7%) participants in our cohort had CPT4 codes for a mastectomy that did not conform to a SNOMED standard. The DQD completeness was rated as fair. The prevalence of breast cancer related concepts was higher in our cohort compared to adult female participants who did not have a mastectomy procedure (P < .001). The DQD concordance was rated as fair. In both the partial and complete mastectomy subgroups, the correlations among concepts were consistent with the clinical management of breast cancer. The DQD plausibility was rated as fair. Although we did not have external benchmark comparisons, the distributions of concepts by age group and time were consistent with expectations. The DQD temporality was rated as fair. The median time between biopsy and mastectomy was seven weeks. Conclusions: Our data quality framework was implemented successfully on a mastectomy phenotype. Moreover, the framework allowed us to differentiate breast-conserving therapy and complete mastectomy subgroups in the All of Us data. The results of our analysis could be informative for future breast cancer studies with the OMOP CDM.

  • The development of digital strategies for reducing sedentary behaviour in a hybrid office environment: a modified Delphi study.

    Date Submitted: Apr 24, 2024
    Open Peer Review Period: Apr 11, 2024 - Jun 6, 2024

    Background: Hybrid work has become the new modus operandi for many office workers causing higher levels of sedentary behaviour than working only in the office. Given the potential of digital interventions to reduce sedentary behaviour and the current lack of studies evaluating such interventions for home-office settings, it is crucial to develop digital interventions in such context involving all stakeholders. Objective: The aim of the current study was to reach experts’ consensus on the most feasible work strategies and the most usable digital elements as a delivery method to reduce sedentary behaviour in home-office context. Methods: A modified Delphi study, including 3-survey rounds and focus groups were held to achieve consensus. The first Delphi round consisted of two 9-point Likert scales for assessing the feasibility of work strategies and the potential usability of digital elements to deliver the strategies. The median and mean absolute deviation from the median (MAD-M) for each item were reported. The second round involved two ranking lists with the highly feasible strategies and highly useful digital elements based on round 1 responses to order the list according to experts’ preferences. The weighted average ranking for each item were calculated to determine the most highly ranked work strategy and digital element. The third round encompassed work strategies with a weight above the median from round 2 to be matched with the most useful digital elements to implement each strategy. Four focus groups were additionally conducted to gain a greater understanding of the findings from the Delphi phase. Focus groups were analysed using the principles of Thematic Analysis. Results: Twenty-seven international experts in the field of occupational health participated in the first round, with response rates of 86.2% (n= 25) and 65.5% (n= 19) in round 2 and 3, and 51.7% (n= 15) in the focus groups. Eighteen work strategies and 16 digital elements achieved consensus. Feedback on activity progress and goal achievement, create an action plan and standing while reading, answering phone calls, or performing videoconferences were the most feasible work strategies, while wrist-based activity trackers, combination of media, and application interface in smartphones were the most useful digital elements. Moreover, experts highlighted the requirement of combining multiple levels of strategies such as social support, physical environment, and individual strategies, to enhance their implementation and effectiveness in reducing sedentary behaviour when working from home. Conclusions: This expert consensus provide the foundation for digital interventions development to address sedentary behaviour in desk-based home-office workers. Ongoing interventions should enable evaluation of the feasible strategies delivered by useful digital elements in home-office or hybrid contexts.

  • How to embed a choice experiment in an online decision aid or tool: a scoping review

    Date Submitted: Apr 5, 2024
    Open Peer Review Period: Apr 9, 2024 - Jun 4, 2024

    Background: Decision aids empower patients to understand how treatment options match their preferences. Choice experiments, a valuable method to clarify values used within decision aids, present patients with hypothetical scenarios to reveal their preferences for treatment characteristics. Given the rise in research embedding choice experiments in decision tools and the emergence of novel developments in embedding methodology, a scoping review is warranted. Objective: This scoping review examines how choice experiments are embedded into decision tools and how these tools are evaluated, to identify best practices. Methods: This scoping review was conducted following best practices in line with the PRISMA extension for scoping reviews. The searchers were conducted on MEDLINE, PsycInfo, and Web of Science databases using key search terms. Data were extracted using data charting tables created in Excel. A narrative synthesis was used to summarize the data and illustrations were used to visualise the results using tables and figures. Results: Overall, 22 tools were included in the scoping review. The methodology, development and evaluation details of tools were extracted from 33 papers. These tools were developed for a variety of health conditions including musculoskeletal conditions, oncological conditions, and chronic conditions. Most tools (78%) originated in the USA. The primary purpose (91%) of these tools was to assist patients in comparing or choosing treatments. The most commonly included attributes in the choice tasks were efficacy and side effects. Adaptive conjoint analysis was the most frequent (10 tools) design approach. Conjoint analysis designs used a higher number of tasks (16 -20) while DCEs and adaptive conjoint analysis designs used low (6) to moderate (12) number of tasks. Sawtooth software was commonly used to embed choice tasks in the tools. After completing the choice tasks patients received tailored information in the form of attribute importance scores, highlighting which treatment characteristics mattered most to the patient based on their choices (16 tools), and/or a "best match" treatment recommendation aligned with the patient's preferences (5 tools). A high degree of heterogeneity was observed in the evaluation methodologies and outcome measures used to assess the decision tools. The decisional conflict scale emerged as the most frequently employed outcome measure. Conclusions: This study highlights several methodological challenges that require further investigation. Future research should focus on determining the most effective methods for embedding choice tasks in decision tools, presenting balanced information, and selecting suitable outcome measures to evaluate these tools.

  • 25 Years of Evolution and Hurdles in Electronic Health Records and Interoperability in Medical Research: A Comprehensive Review

    Date Submitted: Mar 31, 2024
    Open Peer Review Period: Apr 7, 2024 - Jun 2, 2024

    Background: Electronic Health Records (EHRs) have revolutionized the accessibility and sharing of patient data among healthcare providers, fostering a more coordinated and efficient delivery of care. Over the past 25 years, the evolution of EHRs has significantly contributed to scientific achievements in healthcare, improving the accuracy and efficiency of patient care and supporting better health outcomes. Despite their numerous benefits, EHRs face challenges including interoperability issues, common data models, system compatibility, privacy concerns, and data cleaning complexities. Objective: The objective of our study was to examine the evolution of EHRs over the past 25 years, focusing on their advancements in technology, interoperability, and the impact on healthcare delivery and research. We aimed to identify the challenges and limitations of EHRs in facilitating disease management and understanding, as well as their contribution to epidemiological studies, pragmatic clinical trials, and health economic studies. Methods: We conducted a comprehensive review of literature from PubMed database pertaining to the development and implementation of EHRs over the past quarter-century. Studies from January 2000 to February 2024 were included. Finally, 1,377 studies were selected for the analysis and presentation. Results: Studies that utilized EHR data were for various research purposes, including epidemiological studies, clinical trials, cost-effective studies, and policy studies. We highlighted significant advancements in EHR technology that facilitated improved management and understanding of diseases through comprehensive data collection and analysis over the past 25 years. However, challenges related to data interoperability, privacy, and inconsistencies were also identified. The studies underscored the importance of EHRs in creating more accurate representations of clinical practices and patient populations. We also saw great efforts in incorporating data from different sources and formats with the EHRs, as well as new analytic tools and platforms. Conclusions: EHRs have emerged as a pivotal component of modern healthcare systems, enhancing the efficiency and accuracy of patient care and supporting advanced clinical research. Despite facing interoperability and data management challenges, the benefits of EHRs in improving healthcare delivery and facilitating significant scientific achievements are undeniable. To maximize their potential, there is a critical need for improved resource sharing, collaborations among healthcare providers, and the development of consistent data formats and policies in healthcare networks. Clinical Trial: NA

  • Background: Conventional neuropsychological screening tools for mild cognitive impairment (MCI) have been threatened by their burdensomeness and inaccurate at detecting MCI. From a digital healthcare perspective, smartphone interaction, longitudinally and unobtrusively acquired behavior data in a non-clinical setting, alleviate these limitations. Objective: This study aimed to investigate the discriminant powers of digital biomarkers, drawn from smartphone-derived keystroke dynamics using the Neurokeys keyboard application. Methods: 64 healthy controls (HCs) and 47 patients with MCI producing 3,530 typing sessions within a month, performing the Korean version of the Montreal Cognitive Assessment (MoCA-K), were tested. A total of 2,740 were finally analyzed using the receiving operant curve analysis to investigate sensitivity and specificity. Results: Patients with MCI had significantly higher keystroke latency than controls. In particular, latency between key presses resulted in the highest sensitivity (97.9%) and specificity (96.9%). In addition, keystroke dynamics were significantly correlated with the MoCA-K (hold time: r=-.468, P<0.001; flight time: r=-0.497, P<0.001). Conclusions: The current findings shed new light on the potential of smartphone-derived keystroke dynamics as an ecological surrogate for a laboratory-based conventional screening tool. Clinical Trial: Thaiclinicaltrial.org TCTR20220415002, https:// https://www.thaiclinicaltrials.org/show/TCTR20220415002