Urooj Raza Khan

Background: Electronic medical records (EMRs) are widely implemented to improve information access, patient safety and care coordination. However, less is known about how EMRs are used in routine clinical processes such as nursing handover. Understanding how EMRs influence handover practices is important, given the central role of handover in continuity of care and patient safety. Method: An exploratory mixed-methods comparative case study using a cross-sectional survey design was conducted within a large multi-site public healthcare organisation in Victoria, Australia. An organisation-wide survey captured quantitative data on EMR use during preparation for and delivery of nursing handover, alongside qualitative free-text responses exploring perceived impacts, barriers and facilitators. Responses were compared between EMR-enabled clinical areas and predominantly paper-based or hybrid environments. Results: A total of 385 nurses completed the survey. EMR use during handover was substantially higher in EMR-enabled environments than in paper-based or hybrid settings. Qualitative analysis identified six key areas influencing handover: access to and visibility of clinical information; handover quality, safety and cognitive focus; usability and fit with nursing practice; system implementation and enabling conditions; impact on nursing workflows and time and bedside handover and patient engagement. EMR not only supported information access and verification during handover but also introduced cognitive and workflow challenges, particularly where usability, infrastructure or implementation maturity were limited. Conclusion: EMR use can support safer and more informed nursing handover by improving access to legible, real-time clinical information. However, its effectiveness is contingent on usability, infrastructure and alignment with nursing workflows, consistent with the principles of task-technology fit. Hybrid documentation environments and system access constraints limit the consistent realisation of benefits. Continued reliance on printed summaries, handwritten notes and personalised tools suggests that digital systems may complement, rather than completely replace, existing cognitive supports. Optimising EMR-supported handover requires organisational attention to implementation conditions, workflow integration and the practical realities of clinical work. Implications for health information management practice: Health information management professionals can contribute to EMR optimisation by supporting the accessibility, organisation and effective use of clinical information. Evaluation of information usability, documentation practices and ongoing reliance on paper-based workarounds can help identify opportunities to better support clinical information needs.

Background: Digital health technologies have become integral to modern health care, yet consumer adoption remains uneven. These technologies span a range of consumer-facing tools, including remote care services, mobile apps, wearable devices, conversational agents, and digital medication services, with uptake varying considerably across tool types and by gender. An earlier analysis of this dataset revealed a gender paradox: women reported higher digital literacy compared to men yet demonstrated lower adoption and curiosity toward digital health tools. The Capability, Opportunity, Motivation – Behavior (COM-B) model proposes that behavior occurs when individuals have the capability (skills and confidence), opportunity (access, affordability, and contextual support), and motivation (trust, perceived usefulness, and willingness) to act. Objective: This study aimed to examine associations between digital literacy, self-reported COM-B domains, gender, and adoption across digital health technologies and to explore whether these associations differ by gender. Methods: This cross-sectional quantitative study with an integrated qualitative component enrolled 416 adults and used multivariable logistic regression to examine associations between COM-B domains and adoption across five consumer-facing digital health technologies. Exploratory gender-by-predictor interaction and gender-stratified analyses examined potential gender-specific patterns, while self-reported reasons for nonuse were mapped to COM-B domains using directed content analysis. Results: Women had lower odds of chatbot/virtual assistant use (adjusted odds ratio [aOR] 0.32, 95% CI 0.18-0.59), marginally lower odds of e-pharmacy use (aOR 0.48, 95% CI 0.22-1.01), and higher odds of mobile health (mHealth) app use (aOR 2.59, 95% CI 1.28-5.25). Of 20 exploratory interaction tests, chatbot digital literacy-by-gender (P=.021) and capability-by-gender (P=.044) interactions were nominally significant. In directed barrier analysis, the gender distribution differed significantly for mHealth apps (P=.009), whereas the wearable result showed a trend (P=.079); motivation-related barriers were most frequent for most technologies. Conclusions: Behavioral determinants of digital health technology adoption varied by technology type. Women used several technologies less often than men despite reporting higher digital literacy, capability, and motivation, and opportunity emerged as the most consistent factor across the models examined. Exploratory analyses suggested these patterns may differ by gender for selected technologies but did not support a single gender-differentiated pathway. These findings are exploratory and require confirmation in larger studies.

BACKGROUND Health care’s digital transformation is increasing demand for a specialist workforce able to connect clinical practice, information systems, and organizational change. Postgraduate digital health education offers a pathway for professionals seeking to enter or advance within this workforce. However, limited evidence explains how graduates experience these programs, which educational conditions support capability and professional identity formation, and how perceived workforce readiness translates into specialist employment. The Community of Inquiry framework, comprising teaching, social, and cognitive presence, may provide a useful explanatory lens. OBJECTIVE This study aimed to explore graduates’ experiences of an Australian postgraduate digital health education program and examine how those experiences contributed to perceived capability, professional identity, workforce readiness, and transition into digital health work. It also examined the usefulness of the Community of Inquiry framework for explaining workforce-oriented educational outcomes. METHODS We conducted a qualitative interpretivist study involving graduates of an Australian postgraduate digital health education program. Fifteen graduates participated in individual semi structured interviews of approximately 60 minutes, conducted by videoconference. Interviews explored enrollment motivations, curriculum and teaching experiences, peer relationships, application of learning, professional identity, career development, and barriers to workforce transition. Data were analyzed using hybrid thematic analysis. Coding combined inductive engagement with participants’ accounts and Community of Inquiry concepts as sensitizing rather than predetermined categories. Reporting was guided by the Consolidated Criteria for Reporting Qualitative Research checklist. RESULTS Five themes were developed: bridging clinical practice and digital capability; negotiating an accelerated and technically demanding program; becoming a digital health professional through community and identity formation; translating learning into work; and experiencing education as integrated infrastructure for workforce transformation. Graduates described relevant content, responsive facilitation, peer community, and authentic application as interconnected influences rather than discrete program features. Teaching, social, and cognitive presence operated as mutually reinforcing conditions that helped participants manage complexity, connect new knowledge with prior experience, and develop belonging within digital health. Participants attributed increased confidence, expanded responsibilities, career movement, and emerging professional identities to the program. However, perceived capability did not consistently translate into access to specialist positions. Experience-based recruitment expectations and limited entry pathways constrained some transitions despite relevant postgraduate preparation. CONCLUSIONS Postgraduate DHE can support specialist workforce development not only through curriculum content but through the integrated educational experience produced by responsive facilitation, designed community, and authentic application. Translating this educational capability into employment requires complementary system-level mechanisms, particularly structured work-integrated learning and clearer transition pathways that address experience-based hiring norms. The CoI framework, traditionally applied to online learning processes, offers a transferable explanatory lens for designing workforce-oriented postgraduate health professions education. CLINICALTRIAL digital health education; workforce readiness; postgraduate education; Community of Inquiry; qualitative research; health informatics; employability

BACKGROUND Digital health technologies have become integral to modern healthcare, yet consumer adoption remains uneven. These technologies span a range of consumer facing tools, including remote care services, mobile applications, wearable devices, conversational agents, and digital medication services, with uptake varying considerably across tool types and by gender. An earlier analysis of this dataset revealed a gender paradox: women reported higher digital literacy compared to men yet demonstrated lower adoption and curiosity toward digital health tools. To explore this paradox, this study applies the Capability, Opportunity, Motivation – Behaviour (COM-B) model of behaviour, which proposes that behaviour occurs when individuals have the capability (skills and confidence), opportunity (access, affordability, and contextual support), and motivation (trust, perceived usefulness, and willingness) to act. OBJECTIVE Using the COM-B model of behaviour, this study examines behavioural determinants of digital health adoption and tests gender-specific pathways across five technologies: telehealth, mobile health apps, wearable devices, chatbots or virtual assistants, and e-pharmacies. METHODS A cross-sectional mixed-methods study of 416 adults combined logistic regression, gender-by-predictor interaction analyses, exploratory nested model comparisons, and qualitative mapping of self-reported reasons for non-use. RESULTS Women reported higher digital literacy, capability, and motivation, yet lower adoption of wearables (64% vs 89%), chatbots (53% vs 87%), and e-pharmacies (75% vs 93%). Opportunity emerged as the strongest overall predictor of adoption for both genders (adjusted OR 2.7–7.0, p < 0.01). However, gender moderated the underlying pathways: capability played a greater role in men’s adoption, particularly for chatbots (p = 0.044), while qualitative findings reinforced that women’s non-use centred on opportunity-related barriers such as access, affordability, and contextual support. CONCLUSIONS While opportunity is central to digital health adoption for all users, women’s engagement is more heavily shaped by opportunity-related barriers, whereas men’s engagement is more influenced by capability. These differentiated pathways highlight the need for digital health interventions that prioritise equitable access and supportive conditions. The Behaviour Change Wheel framework provides a structured approach for designing targeted interventions to address the identified opportunity barriers.

Background: Ineffective clinical handover has the potential to compromise patient safety and quality of care. Standardizing the handover process is a widely adopted improvement strategy intended to reduce failures of information transfer. By enabling real-time access to patient information, electronic medical records (EMRs) could address communication issues inherent to nursing handover. Objective: This case study sought to compare the quality of nursing handover occurring at EMR-enabled sites with that occurring at paper-based sites, within a single Australian public health service. Methods: A comparative case study design was used, using quantitative data collected from observational audits of 60 handovers and posthandover surveys conducted in EMR-enabled and paper-based ward environments. Handover quality was measured through compliance with the organization’s Clinical Handover Standard and staff-reported perceptions, enabling comparison between cohorts. Results: Compared with paper-based wards, EMR-enabled wards demonstrated more consistent communication of clinical alerts and risks and fewer interruptions, whereas paper-based wards showed higher rates of bedside handover and patient engagement. Conclusions: EMR implementation alone does not ensure high-quality nursing handover. EMR interface design and functionality may act as a barrier to bedside handover and patient engagement, and contribute to continued reliance on paper-based artifacts. Targeted EMR design and nursing informatics-led optimization are required to better support nursing handover as a complex and cognitively challenging communication process.

Background: Generative artificial intelligence (AI) tools, such as ChatGPT, are increasingly used in higher education and have raised significant concerns about assessment validity and academic integrity. In Digital Health and Health Information Management (DIGHIM) programs, assessments are designed to evaluate a mix of technical skills, contextual reasoning, and professional judgment that underpin medical and health practice. Understanding how generative AI performs across different assessment types is, therefore, critical to identifying which formats are most susceptible to AI-generated content and how assessments may be redesigned to remain authentic and educationally meaningful. Objective: This study aimed to evaluate ChatGPT’s performance across diverse assessment types in DIGHIM education by examining how task complexity influences AI-generated output quality, and develop recommendations for ethical and effective AI integration in assessments. Methods: A pilot quasi-experimental design compared ChatGPT-generated responses with deidentified student submissions across 5 assessment types: digital health solution design, business case analysis, reflective assessment, SQL health database programming, and a health classification quiz. For each task, multiple AI submissions were produced using different prompting strategies, including rubric integration and the use of ChatGPT (GPT-4 and o1 Preview model). Blinded academic markers evaluated all AI-generated submissions and previously submitted deidentified student assessments against standard rubrics, and descriptive statistics were used to compare performance. Results: ChatGPT’s performance varied considerably across assessment types. It achieved its highest accuracy scores in objective, rule-based tasks such as multiple-choice quiz items in health classification (mean 88%, SD 0%) and produced well-structured, coherent responses for reflective assessments (mean 69%, SD 12.8%), though these often lacked personalization and nuanced industry context. In descriptive analytical tasks, such as digital health business cases and solution designs, ChatGPT produced logically structured work with reasonable use of evidence but failed to provide deep contextualization, domain-specific insights, or visual elements expected in DIGHIM practice. Technical assessments revealed the greatest limitations: SQL programming tasks averaged 42% (SD 17.2%) with persistent schema errors, incomplete queries, and weak interpretation of health data outputs, while scenario-based clinical coding scored just 7% (SD 0%), reflecting a lack of precision in applying ICD-10-AM (International Classification of Diseases, Tenth Revision, Australian Modification) rules and coding conventions. Structured prompting and rubric integration improved results, particularly in descriptive and reflective tasks (up to 80%), but the advanced o1 Preview model did not consistently outperform earlier versions. Conclusions: While ChatGPT performs well in structured, rule-based, and reflective tasks, it remains limited in technical accuracy, contextual reasoning, and applied DIGHIM competencies. To support academic integrity and workforce readiness, assessment design should prioritize critical thinking, ethical reasoning, and scenario-based problem-solving aligned with health care practice. Using AI as a tool for critique and refinement, rather than a substitute for student work, may help educators prepare learners for responsible AI use in medical and health professional education.

This study explores Australian consumers’ digital literacy (DL), use of digital health technologies (DHTs), and curiosity toward emerging tools. A cross-sectional online survey (n = 416) examined DL levels, current usage of technologies such as telehealth, wearables, mHealth apps, e-pharmacy, and chatbots, and preferences for future innovations like smart glasses, virtual reality/augmented reality, medical drones, and robot companions. DL was highest in data and communication domains and varied by age, gender, education, and location. Despite women and younger adults reporting higher DL, technology adoption often hinged on perceived usefulness, usability, and trust. Telehealth was widely used (90%+) while emerging technologies attracted greater curiosity from men and the 30–39 age group. These findings suggest that curiosity – both diversive and specific – drives early exploration and continued engagement with DHTs. To support equitable adoption, digital health strategies should integrate DL-building interventions and curiosity-driven design, aligned with the Australian Digital Health Strategy’s goals for inclusive, consumer-centred innovation.

BACKGROUND Ineffective clinical handover has the potential to compromise patient safety and quality of care. Standardising the handover process is a widely adopted improvement strategy intended to reduce failures of information transfer. By enabling real-time access to patient information, Electronic Medical Records (EMRs) could address communication issues inherent to nursing handover. OBJECTIVE This case study sought to compare handover occurring at EMR-enabled sites with that occurring at paper-based sites in a single Australian public health service to examine differences in handover quality. METHODS A mixed-methods research design was utilised, incorporating an observational audit of 60 handovers and post-handover surveys conducted in both EMR-enabled and paper-based ward environments, allowing analysis between the two cohorts. Quality was measured as compliance against key principles of the organisation’s Clinical Handover Standard and ratings of perceived handover quality from handover recipients. RESULTS Overall compliance with the Clinical Handover Standard was comparable. Key differences between the two cohorts included improved bedside handover and patient engagement factors in the paper-based environments, while improved transfer of critical alerts and risks occurred in the EMR-enabled wards both more efficiently and with fewer interruptions. CONCLUSIONS The presence of an EMR, particularly the design and functionality of the EMR interface, may act as a barrier to bedside handover, patient engagement in the handover process and the delivery of patient centred care. These barriers require further investigation and targeted solutions. While reported benefits suggest a degree of user acceptance of the EMR, the ongoing reliance on paper-based handover forms indicates a gap where the EMR does not yet adequately support nursing handover. CLINICALTRIAL N/A

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: The use of artificial intelligence (AI) in health care has been steadily increasing for over 2 decades. Integrating AI into neonatal intensive care units (NICUs) has promise as it has the potential to reshape neonatal care and improve outcomes. However, challenges such as data quality, clinical interpretation, and ethical considerations may hinder AI’s practical implementation in NICUs. Objective: This study aims (1) to analyze the current AI research landscape for predicting clinical outcomes and length of stay in the NICU and (2) to explore the benefits and challenges of using AI in the NICU for these predictions. Methods: A systematic review was conducted across 6 databases—PubMed, Embase, CINAHL, Cochrane Library, Informit, and La Trobe Library—to identify English-language peer-reviewed articles published between January 2017 and March 2023 that focused on the use of AI for predicting length of stay and clinical outcomes for NICU patients. Eligibility criteria excluded studies outside the NICU context or lacking predictive focus. Both prospective and retrospective designs were included. A thematic analysis of AI applications in NICUs from the articles identified was conducted. Results: A total of 24 studies were included in the review, comprising 15 retrospective and 9 prospective designs. These studies primarily originated from the United States (13 studies), with others from Austria, Taiwan, and other countries. The studies evaluated AI applications in NICU settings to predict comorbidities (18/24), mortality (4/24), and length of stay (2/24). Sixteen studies were in the exploration stage, lacking cohesive AI strategies, while 8 demonstrated systematic exploration but no fully integrated solutions. The synthesis of results identified key applications of AI in NICU care, including data-driven insights and predictive models, advancements in medical imaging, improved risk stratification, and personalized neonatal care. AI showed promise in enhancing diagnostic accuracy and care planning, but significant challenges persist, such as data quality, model generalization, and ethical concerns. No studies reported a fully integrated AI ecosystem, highlighting the need for further research to bridge gaps and realize AI’s transformative potential in neonatal care. Conclusions: This review highlights the potential of AI in improving NICU care, particularly through predictive models, medical imaging, and personalized interventions. However, the evidence is limited by significant methodological variability, small sample sizes, risk of bias, and a lack of external validation in included studies. Many studies remain in exploratory phases without cohesive AI strategies or integration into clinical practice, limiting the practical applicability of findings. These results underscore the importance of addressing challenges such as data quality, model generalization, and ethical considerations to fully realize AI’s potential in neonatal care. Future research should focus on robust validation, comprehensive implementation strategies, and ethical frameworks to ensure AI's effective and responsible integration into NICU settings. Trial Registration:

BACKGROUND Generative artificial intelligence (AI) tools, such as ChatGPT, are increasingly used in higher education and have raised significant concerns about assessment validity and academic integrity. In Digital Health and Health Information Management (DIGHIM) programs, assessments are designed to evaluate a mix of technical skills, contextual reasoning, and professional judgment that underpin medical and health practice. Understanding how generative AI performs across different assessment types is, therefore, critical to identifying which formats are most susceptible to AI-generated content and how assessments may be redesigned to remain authentic and educationally meaningful. OBJECTIVE This study aimed to evaluate ChatGPT’s performance across diverse assessment types in DIGHIM education by examining how task complexity influences AI-generated output quality, and develop recommendations for ethical and effective AI integration in assessments. METHODS A pilot quasi-experimental design compared ChatGPT-generated responses with deidentified student submissions across 5 assessment types: digital health solution design, business case analysis, reflective assessment, SQL health database programming, and a health classification quiz. For each task, multiple AI submissions were produced using different prompting strategies, including rubric integration and the use of ChatGPT (GPT-4 and o1 Preview model). Blinded academic markers evaluated all AI-generated submissions and previously submitted deidentified student assessments against standard rubrics, and descriptive statistics were used to compare performance. RESULTS ChatGPT’s performance varied considerably across assessment types. It achieved its highest accuracy scores in objective, rule-based tasks such as multiple-choice quiz items in health classification (mean 88%, SD 0%) and produced well-structured, coherent responses for reflective assessments (mean 69%, SD 12.8%), though these often lacked personalization and nuanced industry context. In descriptive analytical tasks, such as digital health business cases and solution designs, ChatGPT produced logically structured work with reasonable use of evidence but failed to provide deep contextualization, domain-specific insights, or visual elements expected in DIGHIM practice. Technical assessments revealed the greatest limitations: SQL programming tasks averaged 42% (SD 17.2%) with persistent schema errors, incomplete queries, and weak interpretation of health data outputs, while scenario-based clinical coding scored just 7% (SD 0%), reflecting a lack of precision in applying ICD-10-AM (International Classification of Diseases, Tenth Revision, Australian Modification) rules and coding conventions. Structured prompting and rubric integration improved results, particularly in descriptive and reflective tasks (up to 80%), but the advanced o1 Preview model did not consistently outperform earlier versions. CONCLUSIONS While ChatGPT performs well in structured, rule-based, and reflective tasks, it remains limited in technical accuracy, contextual reasoning, and applied DIGHIM competencies. To support academic integrity and workforce readiness, assessment design should prioritize critical thinking, ethical reasoning, and scenario-based problem-solving aligned with health care practice. Using AI as a tool for critique and refinement, rather than a substitute for student work, may help educators prepare learners for responsible AI use in medical and health professional education.