Empowering diversity: striving for inclusivity by leveraging the American Medical Informatics Association's "For Your Informatics" Podcast.

Importance: Starting in 2018, the 'Women in American Medical Informatics Association (AMIA) Podcast' was women-focused, in 2021 the podcast was rebranded and relaunched as the "For Your Informatics Podcast" (FYI) to expand the scope of the podcast to include other historically underrepresented groups. That expansion of the scope, together with a rebranding and marketing campaign, led to a larger audience and engagement of the AMIA community. Objectives: The goals of this case report are to characterize our rebranding and expanding decisions, and to assess how they impacted our listenership and engagement to achieve the Podcast goals of increasing diversity among the Podcast team, guests, audience, and improve audience engagement. Materials and Methods: This descriptive case study is focused on the FYI Podcast team's processes to develop a revised mission, vision, and values, increase the diversity of guests, augment listenership through social media, and track the reach through the number of followers, downloads, and impressions. Results: As of December 2023, 35 FYI Podcast episodes are available with 685 social media followers, over 20 000 downloads, and nearly 145 000 impressions. In addition to introductions to informatics and loyal listeners within AMIA, the FYI Podcast episodes have been used by students as teaching material in a graduate biomedical informatics curriculum, and as introductory material for student clubs and programs. Discussion: The Podcast relaunching led to 98% of guests from underrepresented groups and growth in listenership by 329% since May 2021. Conclusion: The FYI Podcast supports AMIA's diversity mission, and gives voices to underrepresented groups, engages the clinical informatics community in critical conversations on justice, equity, diversity and inclusion, and supports education.

Centering Digital Health Equity During Technology Innovation: Protocol for a Comprehensive Scoping Review of Evidence-Based Tools and Approaches.

BACKGROUND: In the rush to develop health technologies for the COVID-19 pandemic, the unintended consequence of digital health inequity or the inability of priority communities to access, use, and receive equal benefits from digital health technologies was not well examined. OBJECTIVE: This scoping review will examine tools and approaches that can be used during digital technology innovation to improve equitable inclusion of priority communities in the development of digital health technologies. The results from this study will provide actionable insights for professionals in health care, health informatics, digital health, and technology development to proactively center equity during innovation. METHODS: Based on the Arksey and O'Malley framework, this scoping review will consider priority communities' equitable involvement in digital technology innovation. Bibliographic databases in health, medicine, computing, and information sciences will be searched. Retrieved citations will be double screened against the inclusion and exclusion criteria using Covidence (Veritas Health Innovation). Data will be charted using a tailored extraction tool and mapped to a digital health innovation pathway defined by the Centre for eHealth Research roadmap for eHealth technologies. An accompanying narrative synthesis will describe the outcomes in relation to the review's objectives. RESULTS: This scoping review is currently in progress. The search of databases and other sources returned a total of 4868 records. After the initial screening of titles and abstracts, 426 studies are undergoing dual full-text review. We are aiming to complete the full-text review stage by May 30, 2024, data extraction in October 2024, and subsequent synthesis in December 2024. Funding was received on October 1, 2023, from the Centre for Health Equity Incubator Grant Scheme, University of Melbourne, Australia. CONCLUSIONS: This paper will identify and recommend a series of validated tools and approaches that can be used by health care stakeholders and IT developers to produce equitable digital health technology across the Centre for eHealth Research roadmap. Identified evidence gaps, possible implications, and further research will be discussed. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/53855.

A Hybrid Physical-Digital Simulation Laboratory to Expedite Context-Aware Design and Usability Testing in Digital Health.

Fit within existing physical and digitalised workflows is a critical aspect of digital health software usability. Early, iterative exploration of contextual usability issues is complicated by barriers of access to healthcare settings. The Validitron SimLab is a new facility for digital health prototyping that augments immersive, realistic physical environments with a digital sandbox allowing new and existing software to be easily set up and tested in the physical space.

Clinical Experiences of Working with Patient-Generated Health Data in Primary Care.

This research aims to provide insight into the GP experience with patient-generated health data (PGHD) in a virtual care visit. Despite the prevalence of wearables, including smartwatches, the acceptability of generated data in primary care is understudied. The result of this study from mixed-method analysis showed the basic capabilities of PGHD to enhance clinical decision-making and positive impact on collaboration with the patient. The impact of PGHD on clinician satisfaction was not determined, highlighting the importance of rigorous methodology in future research.

Adapting an Environmental Scan for 'Insights Reporting': Learnings from an Online Brain Cancer Peer Support Platform.

When developing a digital health solution, product owners, healthcare professionals, researchers, IT teams, and consumers require timely, accurate contextual information to inform solution development. Insights Reporting can rapidly draw together information from literature, end users and existing technology to inform the development process. This was the case when creating an online brain cancer peer support platform where solution development was conducted in parallel with contextual information synthesis. This paper discusses the novel adaptation of an environmental scan methodology using codesign and multiple layers of qualitative rigor, to create Insights Reporting. This seven-step process can be completed in two months and results in salient points of knowledge that can rapidly inform the design of a solution, creating a shared understanding of a digital health phenomenon. Project members noted that Insights Reporting surfaces previously inaccessible knowledge, catalyzes decision-making and allows all stakeholders to influence the report agenda, affirming principles of digital health equity.

Analyzing the Spread of Informatics with PubMed.

We analyzed PubMed citations since 1988 to explore the dissemination of medical/health informatics concepts between countries and across medical domains. We extracted countries from the PubMed author affiliation field to identify and analyze the top 10 informatics publishing countries. We found that the informatics publications are becoming more similar over time and that the rate of exchange across countries has increased with the introduction of e-publishing. Nonetheless, with the exception of machine learning, the impact of core informatics concepts on mainstream medicine and radiology publications remains small.

Automatic Extraction of Skin and Soft Tissue Infection Status from Clinical Notes.

The reliable identification of skin and soft tissue infections (SSTIs) from electronic health records is important for a number of applications, including quality improvement, clinical guideline construction, and epidemiological analysis. However, in the United States, types of SSTIs (e.g. is the infection purulent or non-purulent?) are not captured reliably in structured clinical data. With this work, we trained and evaluated a rule-based clinical natural language processing system using 6,576 manually annotated clinical notes derived from the United States Veterans Health Administration (VA) with the goal of automatically extracting and classifying SSTI subtypes from clinical notes. The trained system achieved mention- and document-level performance metrics of the range 0.39 to 0.80 for mention level classification and 0.49 to 0.98 for document level classification.

Designing an Interprofessional Online Course to Foster Learning Health Systems.

The Learning Health Systems (LHS) framework demonstrates the potential for iterative interrogation of health data in real time and implementation of insights into practice. Yet, the lack of appropriately skilled workforce results in an inability to leverage existing data to design innovative solutions. We developed a tailored professional development program to foster a skilled workforce. The short course is wholly online, for interdisciplinary professionals working in the digital health arena. To transform healthcare systems, the workforce needs an understanding of LHS principles, data driven approaches, and the need for diversly skilled learning communities that can tackle these complex problems together.

Virtual emergency care in Victoria: Stakeholder perspectives of strengths, weaknesses, and barriers and facilitators of service scale-up.

BACKGROUND: Virtual emergency services have been proposed as an alternative service model to conventional in-person emergency department attendance. METHODS: Twenty participants were interviewed: 10 emergency medicine physicians, 4 health care consumers, and 6 other health care professionals. Conventional content analysis was performed on the interview transcriptions to identify perceived strengths and weaknesses of the VED, and barriers and facilitators to scaling-up the VED. RESULTS: VEDs are perceived as a convenient approach to provide and receive emergency care while ensuring safety and quality of care, however some patients may still need to attend the ED in person for physical assessments. There is currently a lack of evidence, guidelines, and resources to support their implementation. Most of the potential and existing barriers and facilitators for scaling-up the VED were related to their effectiveness, reach and adoption. Broader public health contextual factors were viewed as barriers, while potential actions to address resources and costs could be facilitators. CONCLUSIONS: VEDs were viewed as a convenient service model to provide care, can not replace all in-person visits. Current policies and guidelines are insufficient for wider implementation. Most of the barriers and facilitators for its scaling-up were related to VED effectiveness and delivery.

Telehealth in Cystic Fibrosis. A systematic review incorporating a novel scoring system and expert weighting to identify a 'top 10 manuscripts' to inform future best practices implementation.

The ongoing development and integration of telehealth within CF care has been accelerated in response to the Covid-19 pandemic, with many centres publishing their experiences. Now, as the restrictions of the pandemic ease, the use of telehealth appears to be waning, with many centres returning to routine traditional face-to-face services. For most, telehealth is not integrated into clinical care models, and there is a lack of guidance on how to integrate such a service into clinical care. The aims of this systematic review were to first identify manuscripts which may inform best CF telehealth practices, and second, to analyse these finding to determine how the CF community may use telehealth to improve care for patients, families, and Multidisciplinary Teams into the future. To achieve this, the PRISMA review methodology was utilised, in combination with a modified novel scoring system that consolidates expert weighting from key CF stakeholders, allowing for the manuscripts to be placed in a hierarchy in accordance with their scientific robustness. From the 39 found manuscripts, the top ten are presented and further analysed. The top ten manuscripts are exemplars of where telehealth is used effectively within CF care at this time, and demonstrate specific use cases of its potential best practices. However, there is a lack of guidance for implementation and clinical decision making, which remains an area for improvement. Thus, it is suggested that further work explores and provides guidance for standardised implementation into CF clinical practice.

Virtual monitoring in CF - the importance of continuous monitoring in a multi-organ chronic condition.

Cystic Fibrosis (CF) is a chronic life-limiting condition that affects multiple organs within the body. Patients must adhere to strict medication regimens, physiotherapy, diet, and attend regular clinic appointments to manage their condition effectively. This necessary but burdensome requirement has prompted investigations into how different digital health technologies can enhance current care by providing the opportunity to virtually monitor patients. This review explores how virtual monitoring has been harnessed for assessment or performance of physiotherapy/exercise, diet/nutrition, symptom monitoring, medication adherence, and wellbeing/mental-health in people with CF. This review will also briefly discuss the potential future of CF virtual monitoring and some common barriers to its current adoption and implementation within CF. Due to the multifaceted nature of CF, it is anticipated that this review will be relevant to not only the CF community, but also those investigating and developing digital health solutions for the management of other chronic diseases.

Identifying Public Healthcare Priorities in Virtual Care for Older Adults: A Participatory Research Study.

There has been increasing adoption and implementation of virtual healthcare in recent years, especially with COVID-19 impacting the world. As a result, virtual care initiatives may not undergo stringent quality control processes to ensure that they are appropriate to their context and meet sector needs. The two objectives of this study were to identify virtual care initiatives for older adults currently in use in Victoria and virtual care challenges that could be prioritised for further investigation and scale-up and to understand why certain virtual care initiatives and challenges are prioritised over others for investigation and scale-up. METHODS: This project used an Emerging Design approach. A survey of public health services in the state of Victoria in Australia was first carried out, followed by the co-production of research and healthcare priorities with key stakeholders in the areas of primary care, hospital care, consumer representation, research, and government. The survey was used to gather existing virtual care initiatives for older adults and any associated challenges. Co-production processes consisted of individual ratings of initiatives and group-based discussions to identify priority virtual care initiatives and challenges to be addressed for future scale-up. Stakeholders nominated their top three virtual initiatives following discussions. RESULTS: Telehealth was nominated as the highest priority initiative type for scaling up, with virtual emergency department models of care nominated as the highest priority within this category. Remote monitoring was voted as a top priority for further investigations. The top virtual care challenge was data sharing across services and settings, and the user-friendliness of virtual care platforms was nominated as the top priority for further investigation. CONCLUSIONS: Stakeholders prioritised public health virtual care initiatives that are easy to adopt and address needs that are perceived to be more immediate (acute more so than chronic care). Virtual care initiatives that incorporate more technology and integrated elements are valued, but more information is needed to inform their potential scale-up.

Virtual Care Initiatives for Older Adults in Australia: Scoping Review.

BACKGROUND: There has been a rapid shift toward the adoption of virtual health care services in Australia. It is unknown how widely virtual care has been implemented or evaluated for the care of older adults in Australia. OBJECTIVE: We aimed to review the literature evaluating virtual care initiatives for older adults across a wide range of health conditions and modalities and identify key challenges and opportunities for wider adoption at both patient and system levels in Australia. METHODS: A scoping review of the literature was conducted. We searched MEDLINE, Embase, PsycINFO, CINAHL, AgeLine, and gray literature (January 1, 2011, to March 8, 2021) to identify virtual care initiatives for older Australians (aged ≥65 years). The results were reported according to the World Health Organization's digital health evaluation framework. RESULTS: Among the 6296 documents in the search results, we identified 94 that reported 80 unique virtual care initiatives. Most (69/80, 89%) were at the pilot stage and targeted community-dwelling older adults (64/79, 81%) with chronic diseases (52/80, 65%). The modes of delivery included videoconference, telephone, apps, device or monitoring systems, and web-based technologies. Most initiatives showed either similar or better health and behavioral outcomes compared with in-person care. The key barriers for wider adoption were physical, cognitive, or sensory impairment in older adults and staffing issues, legislative issues, and a lack of motivation among providers. CONCLUSIONS: Virtual care is a viable model of care to address a wide range of health conditions among older adults in Australia. More embedded and integrative evaluations are needed to ensure that virtually enabled care can be used more widely by older Australians and health care providers.

Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions.

The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, responsible for 5%-10% of acute care hospital admissions worldwide. Spontaneous reporting of ADRs has long been the standard method of reporting, however this approach is known to have high rates of under-reporting, a problem that limits pharmacovigilance efforts. Automated ADR reporting presents an alternative pathway to increase reporting rates, although this may be limited by over-reporting of other drug-related adverse events. We developed a deep learning natural language processing algorithm to identify ADRs in discharge summaries at a single academic hospital centre. Our model was developed in two stages: first, a pre-trained model (DeBERTa) was further pre-trained on 1.1 million unlabelled clinical documents; secondly, this model was fine-tuned to detect ADR mentions in a corpus of 861 annotated discharge summaries. This model was compared to a version without the pre-training step, and a previously published RoBERTa model pretrained on MIMIC III, which has demonstrated strong performance on other pharmacovigilance tasks. To ensure that our algorithm could differentiate ADRs from other drug-related adverse events, the annotated corpus was enriched for both validated ADR reports and confounding drug-related adverse events using. The final model demonstrated good performance with a ROC-AUC of 0.955 (95% CI 0.933 - 0.978) for the task of identifying discharge summaries containing ADR mentions, significantly outperforming the two comparator models.

Fostering the use of Learning Health Systems through a fellowship program for interprofessional clinicians.

Introduction: To address Australian workforce needs, we developed a Learning Healthcare System (LHS) Academy fellowship program for clinicians. In the Academy, fellows complete foundational coursework, an LHS project, and other professional development deliverables to foster their future as digital health champions within their organizations. In this paper, we describe the 11-month-long program, as well as our evaluation results from the first 2 months of the program. Methods: In the first week of the program, we sent all fellows an open-ended survey asking fellows to describe their digital health professional identities and what they expected to achieve from the fellowship program. At 2 months, we sent a follow-up open-ended survey that captured identical measures, their perceived barriers to participation in the program, perceived use of topics in the workplace and to their projects, and recommendations for program improvement. We analyzed the open text responses using qualitative content analysis, to identify categories of responses. Results: Overall, 2 months into the program, it was evident that participants were finding the teaching model engaging, useful, valuable, and applicable to their work and projects. Fellows perceived barriers to engagement in the program as balancing other commitments, lacking technical expertise, and having difficulty seeing themselves as leaders. Fellows expected that the program will allow them to implement new models of care, provide them with enough expertise to become leaders and champions in digital health, and become mentors for future generations. As far as changes in their professional identity, there was a notable increase in the number of fellows perceiving themselves as leaders. Conclusion: Fellowship programs are one promising means of developing the healthcare workforce in LHS capabilities. Future studies should describe and evaluate LHS programs, to provide insights and recommendations for other educators interested in implementing similar programs of work within their own institutions.

Artificial Intelligence and Deep Learning for Rheumatologists.

Deep learning has emerged as the leading method in machine learning, spawning a rapidly growing field of academic research and commercial applications across medicine. Deep learning could have particular relevance to rheumatology if correctly utilized. The greatest benefits of deep learning methods are seen with unstructured data frequently found in rheumatology, such as images and text, where traditional machine learning methods have struggled to unlock the trove of information held within these data formats. The basis for this success comes from the ability of deep learning to learn the structure of the underlying data. It is no surprise that the first areas of medicine that have started to experience impact from deep learning heavily rely on interpreting visual data, such as triaging radiology workflows and computer-assisted colonoscopy. Applications in rheumatology are beginning to emerge, with recent successes in areas as diverse as detecting joint erosions on plain radiography, predicting future rheumatoid arthritis disease activity, and identifying halo sign on temporal artery ultrasound. Given the important role deep learning methods are likely to play in the future of rheumatology, it is imperative that rheumatologists understand the methods and assumptions that underlie the deep learning algorithms in widespread use today, their limitations and the landscape of deep learning research that will inform algorithm development, and clinical decision support tools of the future. The best applications of deep learning in rheumatology must be informed by the clinical experience of rheumatologists, so that algorithms can be developed to tackle the most relevant clinical problems.

The Rapid Development of Virtual Care Tools in Response to COVID-19: Case Studies in Three Australian Health Services.

BACKGROUND: News of the impact of COVID-19 around the world delivered a brief opportunity for Australian health services to plan new ways of delivering care to large numbers of people while maintaining staff safety through greater physical separation. The rapid pivot to telemedicine and virtual care provided immediate and longer term benefits; however, such rapid-cycle development also created risks. OBJECTIVE: The aim of this study was to understand the sociotechnical aspects of the rapid-cycle development of seven different COVID-19 virtual care tools, and to identify enablers, barriers, and risks at three health services in Victoria, Australia. METHODS: A qualitative, embedded, multiple case study design was adopted. Researchers from three health services collaborated with university researchers who were independent from those health services to gather and analyze structured interview data from key people involved in either clinical or technical aspects of designing and deploying seven different virtual care tools. RESULTS: The overall objectives of each health service reflected the international requirements for managing large numbers of patients safely but remotely and for protecting staff. However, the governance, digital maturity, and specific use cases at each institution shaped the methodology and specific outcomes required. Dependence on key individuals and their domain knowledge within an existing governance framework generally enabled rapid deployment, but sometimes posed barriers. Existing relationships with technical service developers enabled strong solutions, which in some cases were highly scalable. Conventional project methodologies such as steering committees, scope, budget control, tight functional specification, consumer engagement and codesign, universal accessibility, and postimplementation evaluation were ignored almost universally in this environment. CONCLUSIONS: These three health services took a variety of approaches to the rapid-cycle development of virtual care tools to meet their urgent needs for triaging and remote monitoring during the first year of the COVID-19 pandemic. Their experiences provided insights into many social and technical barriers and enablers to the development of virtual care tools. If these are addressed proactively, they will improve clinical governance and technical management of future virtual care. Some changes can be made within individual health services, while others entail health system policy reforms. Enhancing the environment for virtual care tool design and implementation now will yield returns not only during future health emergencies but also in many more routine care settings.

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30-day readmission following an acute myocardial infarction. Methods and Results Patients were enrolled into derivation and validation cohorts. The derivation cohort included inpatient discharges from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of acute myocardial infarction, who were discharged alive, and not transferred from another facility. The validation cohort included patients from Dartmouth-Hitchcock Health Center between April 2, 2011, and December 31, 2016, meeting the same eligibility criteria described above. Data from both sites were linked to Centers for Medicare & Medicaid Services administrative data to supplement 30-day hospital readmissions. Clinical notes from each cohort were extracted, and an NLP model was deployed, counting mentions of 7 social risk factors. Five machine learning models were run using clinical and NLP-derived variables. Model discrimination and calibration were assessed, and receiver operating characteristic comparison analyses were performed. The 30-day rehospitalization rates among the derivation (n=6165) and validation (n=4024) cohorts were 15.1% (n=934) and 10.2% (n=412), respectively. The derivation models demonstrated no statistical improvement in model performance with the addition of the selected NLP-derived social risk factors. Conclusions Social risk factors extracted using NLP did not significantly improve 30-day readmission prediction among hospitalized patients with acute myocardial infarction. Alternative methods are needed to capture social risk factors.

Resources for Improving Pediatric Readiness and Quality of Care in Rural Communities and Emergency Departments.

OBJECTIVES: To share the process and products of an 8-year, federally funded grant from the Health Resources and Services Administration Emergency Medical Services for Children program to increase pediatric emergency readiness and quality of care provided in rural communities located within 2 underserved local emergency medical services agencies (LEMSAs) in Northern California. METHODS: In 2 multicounty LEMSAs with 24 receiving hospital emergency departments, we conducted focus groups and interviews with patients and parents, first responders, receiving hospital personnel, and other community stakeholders. From this, we (a regional, urban children's hospital) provided a variety of resources for improving the regionalization and quality of pediatric emergency care provided by prehospital providers and healthcare staff at receiving hospitals in these rural LEMSAs. RESULTS: From this project, we provided resources that included regularly scheduled pediatric-specific training and education programs, pediatric-specific quality improvement initiatives, expansion of telemedicine services, and cultural competency training. We also enhanced community engagement and investment in pediatric readiness. CONCLUSIONS: The resources we provided from our regional, urban children's hospital to 2 rural LEMSAs facilitated improvements in a regionalized system of care for critically ill and injured children. Our shared resources framework can be adapted by other regional children's hospitals to increase readiness and quality of pediatric emergency care in rural and underserved communities and LEMSAs.

Implementability of healthcare interventions: an overview of reviews and development of a conceptual framework.

BACKGROUND: Implementation research may play an important role in reducing research waste by identifying strategies that support translation of evidence into practice. Implementation of healthcare interventions is influenced by multiple factors including the organisational context, implementation strategies and features of the intervention as perceived by people delivering and receiving the intervention. Recently, concepts relating to perceived features of interventions have been gaining traction in published literature, namely, acceptability, fidelity, feasibility, scalability and sustainability. These concepts may influence uptake of healthcare interventions, yet there seems to be little consensus about their nature and impact. The aim of this paper is to develop a testable conceptual framework of implementability of healthcare interventions that includes these five concepts. METHODS: A multifaceted approach was used to develop and refine a conceptual framework of implementability of healthcare interventions. An overview of reviews identified reviews published between January 2000 and March 2021 that focused on at least one of the five concepts in relation to a healthcare intervention. These findings informed the development of a preliminary framework of implementability of healthcare interventions which was presented to a panel of experts. A nominal group process was used to critique, refine and agree on a final framework. RESULTS: A total of 252 publications were included in the overview of reviews. Of these, 32% were found to be feasible, 4% reported sustainable changes in practice and 9% were scaled up to other populations and/or settings. The expert panel proposed that scalability and sustainability of a healthcare intervention are dependent on its acceptability, fidelity and feasibility. Furthermore, acceptability, fidelity and feasibility require re-evaluation over time and as the intervention is developed and then implemented in different settings or with different populations. The final agreed framework of implementability provides the basis for a chronological, iterative approach to planning for wide-scale, long-term implementation of healthcare interventions. CONCLUSIONS: We recommend that researchers consider the factors acceptability, fidelity and feasibility (proposed to influence sustainability and scalability) during the preliminary phases of intervention development, evaluation and implementation, and iteratively check these factors in different settings and over time.