Information Technology in Healthcare

The past several decades have ushered unprecedented advancements in body sensor networks, which include wearable, implantable and ingestible technologies to measure various aspects of health and wellness related to health and healthcare. The ubiquity of smartphones, smart watches and fitness tracking tools have provided vast amounts of digital physiologic and behavioral data that enable providers to provide personalized medicine based on each individual’s data. These new digital biomarkers of health—prosody of smartphone use, biometric data surrounding health events, geolocation and ambient environment conditions—provide an opportunity for social scientists, engineers and clinicians to advance health interventions antecedent to exacerbations of disease. Additionally, on a population health level, technologies that leverage public health infrastructure such as wastewater-based epidemiology and crowdsourced street maps are providing never before seen insights into the effects of disease in communities. The widespread adoption of electronic health records and application of machine learning to these vast health related data sets allow insight into public health information such as healthcare utilization, disease management, and early detection of preventable mortality.

This minitrack provides an opportunity for multidisciplinary collaboration and exploration of the various facets of health technologies and their ability to inform clinical decision-making, offer personalized medicine, and understand trends in public health related data. We encourage submissions that describe:

1. Innovative technological solutions and AI applications in disease detection and management at the intersection of medicine and public health
2. Pilot work around the real-world deployment and application of health technologies or generative AI at a personal and population health level
3. Research that serves to address health equity issues, promote diversity, and disseminate new technologies to resource-limited settings.
4. Qualitative research that informs behavioral health theory and acceptance models around personal health technology
5. Health law and ethical approaches to personal and population health disease surveillance
6. Early development and boundary condition exploration of technologies or AI to detect changes in disease or health status
7. Implementation Science strategies to bridge the gap between technological advances in research and real-world clinical deployment of these technologies

Minitrack Co-Chairs:

Charlotte Goldfine (Primary Contact)
Brigham and Women’s Hospital
cgoldfine@bwh.harvard.edu

Jasper Lee
Massachusetts General Hospital
jlee333@mgh.harvard.edu

Rachel Davis-Martin
UMass Chan Medical School
rachel.davis-martin@umassmed.edu

Healthcare processes (e.g. patient pathways) and services are often very complex and can involve various parties within an organization or between organizations such as hospitals and other caregivers, as well as the patients. The design of services is often different from traditional service design – as for many healthcare services patients receive care, but insurance companies pay for it. Implementing processes in this domain should result in providing faster, safer and more effective care, necessitating organizing and sharing information among all participants involved in patient care. While the need for well-defined healthcare processes is clear, there are many obstacles and opportunities for research, including technical, behavioral, and organizational topics. During and after the COVID-pandemic, many healthcare systems worldwide have been facing major challenges with demands constantly increasing, while suffering from financial pressure and significant staff shortages. Therefore, an efficient use of resources is crucial.

Operational Research approaches including mathematical programming and simulation modelling can help address and solve logistical challenges in designing and managing healthcare processes and services. While mathematical programming can give the optimal locations of ambulances or shift schedules for hospital doctors, for example, simulation approaches are a crucial tool to analyze different scenarios and model complex settings like emergency departments or operating rooms.

Decision support systems play a crucial role in healthcare, not only in the form of clinical decision support systems to assist physicians and other health professionals with medical decision making, but also to support logistical and organizational processes.

This minitrack will focus on the analysis, design and optimization of healthcare systems, the use of IT to support and improve care processes as well as discussions on integrated planning in healthcare and how research can improve its impact in practice. For that, we especially value submissions that use real-world data and input from healthcare practitioners and decision makers and would like authors to elaborate on the actual or potential impact their work had or will have on healthcare practice. We invite papers that focus on, but are not limited to:

1. Analysis and optimization of healthcare processes and services (e.g. patient pathways, appointment planning, hospital logistics, emergency medical services)
2. Multi-criteria decision analysis of healthcare processes and services
3. Machine learning and artificial intelligence for healthcare processes and services (e.g. demand forecasts, medical decision making)
4. Design of decision support systems in healthcare
5. Design and analysis of healthcare processes and services, including the design of robust processes, e.g., with respect to pandemic or crisis preparedness
6. Simulation studies of healthcare processes and services (e.g. emergency departments, hospital logistics)
7. Simulation-optimization approaches for healthcare processes and services
8. Integrated decision making in healthcare, within or between care institutions and stakeholder (e.g. simultaneously scheduling surgeries and physicians, assigning emergency patients in ambulances to hospitals taking turnaround times as well as the current ED occupancy and bed availability into account)
9. The use of technology and medical devices to support the processes and services and analysis of necessary (organizational) changes
10. Impact-driven healthcare research and discussion how research can increase the impact in healthcare practice

Minitrack Co-Chairs:

Melanie Reuter-Oppermann (Primary Contact)
Maastricht University
melanie.reuter-oppermann@dgre.org

Cameron Walker
University of Auckland
cameron.walker@auckland.ac.nz

Nikolaus Furian
Graz University of Technology
nikolaus.furian@tugraz.at

Digital health ecosystems represent complex socio-technical systems that interconnect various stakeholders through information infrastructures, platforms, and services. The emergence of these ecosystems is driven by the need to improve population health outcomes, increase healthcare efficiency, and support healthcare workforce satisfaction while enabling better coordination of care at local, regional, and national levels.

The increasing complexity of health ecosystems necessitates sophisticated governance approaches that balance technical standardization with organizational processes and institutional practices. While regulatory frameworks provide essential governance mechanisms, successful implementation depends on trust between stakeholders, acceptance by healthcare professionals, and the effective enactment of governance practices within and across organizational contexts.

This minitrack focuses on the socio-technical dynamics of governance in digital health ecosystems. We are particularly interested in understanding how organizations manage tensions when implementing and maintaining information infrastructures, managing standardization processes, and navigating institutional boundaries while ensuring regulatory compliance. This includes examining the interplay between technical architectures, organizational practices, and institutional arrangements in the context of inter-organizational health records, health data spaces, and cross-organizational health information exchange.

We welcome empirical, design-oriented, and conceptual papers that investigate the challenges and opportunities in building, implementing, and maintaining effective governance in digital health ecosystems. We particularly appreciate submissions that provide cross-national perspectives or comparative analyses across different institutional contexts.

The minitrack is open to a broad variety of research approaches at various levels of analysis. Topics of interest include (but are not limited to):

1. Inter-organizational health record implementations
2. Health information exchange architectures
3. Health data space development
4. Cross-organizational standardization initiatives
5. Platform governance implementation approaches
6. Data privacy and compliance strategies
7. Security risk management practices
8. Secondary use of health data for research
9. Building trust in interconnected systems
10. Stakeholder coordination and collaboration
11. Process compliance and workarounds
12. Cultural influences on health ecosystems

Exceptional contributions will be invited for fast-track publication in Health Policy and Technology (HPT). HPT, the official journal of the Fellowship of Postgraduate Medicine (FPM), is a cross-disciplinary journal, which focuses on past, present and future health policy and the role of technology in clinical and non-clinical national and international health environments.

Minitrack Co-chairs:

Till J. Winkler (Primary Contact)
University of Hagen
till.winkler@fernuni-hagen.de

Elizabeth J. Davidson
University of Hawaii at Manoa
edavidso@hawaii.edu

Monica Chiarini Tremblay
William & Mary
monica.tremblay@mason.wm.edu

Juhee Kwon
City University of Hong Kong
juhee.kwon@cityu.edu.hk

Persuasive systems design can facilitate behavior change around an individual’s health, a population’s health or a healthcare organization or system. Whether to promote health management, prevent disease, or make decisions around seeking or managing care, persuasive systems can promote behavior change on a personal or societal level.
This minitrack focuses on how persuasive systems and services created to influence health behaviors are designed, developed, implemented and evaluated. These Health Behavior Change Support Systems (HBCSS) can assist behavior change through various techniques and tools, such as providing information, education or motivation through an intentional system. The minitrack highlights how persuasive theories and models can be used to develop and evaluate efficient and effective HBCSSs for different contexts, including healthcare.

As all HBCSSs are persuasive systems, designed with the intent to influence user behavior, recognition of their applicability to consumer health management is growing. Designing HBCSS interventions with, and building technology platforms for, the consumers as end-users is a major area of research to combat health challenges and achieve the outcomes desired. Some example topics are persuasive systems created for self-care, games designed to support chronic care, how end-users can be involved in designing a system, and evaluation methods to assess the impact of a system on health behaviors.

Our minitrack presents a wide range of cutting-edge research addressing the challenges to technology use for health behavior change. The emphasis in the minitrack will be placed on both the design and development of the HBCSSs, implementation and evaluation, or appraisal of their effects or artifacts. The primary focus is not purely on methodologies or technologies, but also on their impact on a population or problem. Work to further develop design protocols and rigorous, long-term research of impact for BCSS is encouraged. The topics of interest include, but are not limited to:

1. Design and development
   • Create digital health interventions for behavior change using stakeholders’ perspectives (users and experts)
   • HBCSS development that incorporates users early on in order to tailor systems with user profiles, characteristics or preferences
   • Persuasive strategies for social support
   • Design of mobile technologies for health and mobile approaches to HBCSSs
   • Persuasive prompts to create engagement and involvement in serious game interventions
   • Creation and testing of user profiles to identify which persuasive strategies matter most for whom
   • Discussion or evaluation of design approaches for developing HBCSSs, including considerations for personalization, privacy and security
   • Ethical perspectives on health behavior change support systems
   • Utilization of behavior change techniques and persuasive technology for health conditions
   • Applications of Augmented and Virtual Reality to promote health behavior change
2. Implementation and evaluation
   • Health behavior change through mobile technologies, teleconsultation and telemedicine
   • Consumer education, consumer empowerment, decision support tools for consumers, and remote monitoring
   • Evaluation of persuasiveness of different types of HBCSSs, moving towards a checklist for practice
   • Adequate design for measuring the effect of persuasive strategies on task adherence during usage and long-term effects
   • Frameworks and methodologies to measure A/B/C-Changes (attitude change, behavior change, or an act of compliance)
   • Profiling personalities and matching them with persuasive strategies
   • Multimodal cues and measurement of the effects on adherence and outcomes
   • Advanced analytics to predict adherence, and to identify usage patterns and the effects on adherence

Minitrack Co-Chairs:

Elena Vlahu-Gjorgievska (Primary Contact)
University of Wollonging
elenavg@uow.edu.au

Sriram Iyengar
University of Arizona
msiyengar@arizona.edu

Khin Than Win
University of Wollongong
win@uow.edu.au

Harri Oinas-Kukkonen
University of Oulu
Harri.Oinas-Kukkonen@oulu.fi

The insights gained from this mini-track will help drive the development of more reliable, secure, and human centered wearable devices for healthcare and research needs. This aligns with the mission of HICSS to advance the understanding of information technologies and their impact on individuals, organizations, and society. By fostering collaboration among academics, practitioners, and policymakers, this mini-track will ensure that next-generation wearable medical devices are not only functional safe, reliable, and resilient in the face of emerging challenges, but also designed with human-centered approaches to promote large scale population adoption. Topics include but are not limited to:

1. Designing Human-Centered Security Frameworks for Medical Wearables
2. Mitigating Cognitive Load in Medical Wearable Device Interfaces
3. Human Error and Security Vulnerabilities in Medical Wearable Devices
4. Adoption Challenges and Solutions for Medical Wearables in Healthcare Settings
5. The Role of AI and Automation in Enhancing Wearable Device Security and Usability
6. Privacy and Ethical Considerations in Patient-Centered Wearable Healthcare Technologies
7. Developing Effective Training and Awareness Programs for Medical Wearable Users
8. Human-Centered Designs in Medical Wearable Devices for Low-Resource Settings
9. Evaluating User Trust and Acceptance of Security Features in Wearable Medical Devices
10. Lessons Learned from Large-Scale Implementation of Secure Medical Wearables

We welcome a broad spectrum of contributions, including:

1. Examining how usability and security can be balanced in the development of wearable medical devices
2. Strategies for designing intuitive user interfaces that minimize cognitive strain for clinicians, patients, and caregivers
3. Investigating how human factors contribute to security risks and developing strategies to reduce errors in device use
4. Identifying key barriers to adoption from patient, clinician, and regulatory perspectives and proposing solutions
5. Exploring how AI-driven solutions can improve security while maintaining a user-friendly experience
6. Addressing ethical dilemmas, informed consent, and data privacy concerns in medical wearables
7. Best practices for educating healthcare professionals, patients, and caregivers on secure and effective device use
8. Adapting wearable security strategies to diverse healthcare environments with limited infrastructure
9. Methods for assessing and improving trust in security mechanisms without compromising usability
10. Case studies highlighting best practices and challenges from real-world deployments of medical wearable technologies

Minitrack Co-Chairs:

Carmen Quatman (Primary Contact)
Ohio State University
carmen.quatman@osumc.edu

Ryan Karl
Software Engineering Institute, Carnegie Mellon University
rmkarl@sei.cmu.edu

Digital technologies are transforming the way healthcare services are delivered and how individuals manage their physical, mental, and social well-being. From smartphone applications and wearable devices to telehealth platforms and AI-driven interventions, these innovations empower people to adopt more proactive and personalized approaches to health. Notably, such technologies hold particular promise for supporting vulnerable groups, whether due to age, socio-economic status, health conditions, geography, or other factors, by expanding access to care and enabling greater autonomy.

Through this minitrack, we aim to showcase cutting-edge research that informs how digital health innovations can improve lives, particularly among those most in need of accessible, user-centered solutions. This minitrack encourages submissions that investigate how digital tools, platforms, and services can address health disparities, promote well-being, and improve quality of life. We invite conceptual, empirical, theoretical, and design-oriented research with diverse methodological approaches (qualitative, quantitative, mixed methods, design science, etc.) that advances our understanding of digital innovations in health and well-being across a variety of contexts and populations. Submissions may address (but are not limited to) the following areas:

1. Factors influencing the adoption and use of health- and well-being-related technology by diverse populations
2. Technology-based interventions for vulnerable or underserved groups
3. Online platforms and virtual communities supporting health and well-being
4. Innovative solutions to address loneliness or social isolation across all age groups
5. The influence of technology on well-being-related tourism
6. Comparative studies of technology use among different demographic or cultural groups
7. Cross-national comparisons in the uptake and impact of digital health tools
8. Impact of training efforts on the adoption and sustained use of health and well-being technologies
9. User interface design, usability, and accessibility considerations
10. Effective strategies for involving (vulnerable) users in co-design and development processes
11. Technostress and unintended consequences of health and well-being technologies
12. Success factors, barriers, and risks associated with implementing digital health solutions
13. Trust and distrust in digital health environments
14. Personality traits, behavior change, and the long-term adoption of well-being technologies

Minitrack Co-Chairs:

Karoly Bozan (Primary Contact)
Duquesne University
bozank@duq.edu

Heiko Gewald
Neu-Ulm University of Applied Sciences
heiko.gewald@hs-neu-ulm.de

This minitrack explores the adoption, implementation, diffusion, and evaluation of healthcare technologies, with a particular focus on how these factors interact at various levels to influence healthcare system success. These successes or failures can be on individual, group, organizational, national, and international levels. We welcome research examining these dynamics across a wide range of healthcare technologies, including but not limited to telemedicine, PACS, electronic medical records, mobile health, and online health solutions. This minitrack is open to various established methodologies including, but not limited to, case studies (business/information systems oriented), surveys, experimental design, workflow and other forms of business process modeling, interview, content analysis, conceptual papers, and the various forms of quantitative analysis. Additionally, we encourage innovative research on adoption, implementation, use, and evaluation in healthcare. Work should be in the mature (data collected and some analysis performed), though not necessarily in the final stage. Completed, high-quality research and studies that spark scholarly or practitioner conversations will receive special consideration. Topics include but are not limited to:

1. Application of adoption, implementation, and diffusion theories, models, and constructs to the healthcare context
   • Unified Technology Adoption & Use Theory (UTAUT)
   • Technology Acceptance Model and Social Learning Theory
   • Diffusion of Innovation Theory
   • Information Systems Success models
   • Theory of Planned Behavior
   • Organizing Vision and Organizational Adoption Models
   • Information Assurance constructs of confidentiality, integrity, and availability IT
2. Adoption at the individual, project, organizational, or system level
   • Stakeholder analysis
   • User characteristics
   • Organizational or project structure and/or strategies
   • Regional Healthcare Initiatives and Global Development
   • Interaction among individual, organizational, project, and/or system-level
   • Role/impact of regulatory structures
   • Adoption of Lifestyle Apps in health
   • E-Health strategies
3. IT Implementation
   • Effective implementation strategies
   • Electronic Medical Records and Personal Health Records
   • Health IT project management
   • Participation of professionals in e-health projects
   • The influence of the local system
   • Workflow analysis
   • Serious Gaming in health
4. IT Use
   • Factors and models of continued use
   • Human-Computer Interaction
   • Models to measure or predict Use of IT (USE IT or PRIMA)
   • Emergence of standards and process controls
   • E-Health and mobile health
   • Meaningful Use
5. IT Evaluation
   • Evidence-based support of emerging healthcare technologies
   • Measures for Evaluating Healthcare Technologies
   • Level of IT capabilities
   • Health IS success factors
   • Feasibility Analysis
   • Allied Health Professions
   • Business modeling and business cases
   • Digital innovation in healthcare

Minitrack Co-Chairs:

Karoly Bozan (Primary Contact)
Duquesne University
bozank@duq.edu

Arnold Kamis
Brandeis University
akamis@brandeis.edu

Manuel Schmidt-Kraepelin
Karlsruhe Institute of Technology
manuel.schmidt-kraepelin@kit.edu

Scott Thiebes
Technical University of Munich
scott.thiebes@tum.de

We are currently witnessing the digital transformation of healthcare delivery. The rapid rise of a myriad of smart technology solutions are enabling more individualized health management that is both personalized and precise. Our minitrack serves to unpack critical aspects around harnessing the power and potential of artificial intelligence and analytics to support superior person health management to support prevention and wellness. We welcome research in progress or completed research papers that address technological aspects, human factors, medical challenges, socio-technical issues as well as applications, use cases, theories, and models including but not limited to:

1. Living Assistance and Preventive Care
   • Smart home technologies and assisted living.
   • Self-quantifying technologies to assist fitness and wellness promotion.
   • Mobile technologies and software solutions for preventive health management and fitness.
   • Mobile Apps and wearables to support health and fitness monitoring and/or rehabilitation.
2. Artificial Intelligence and Advanced Analytics in Personal Health Management
   • Methodologies, models, and frameworks to support individual care.
   • Machine learning, deep learning, and other smart analytics of individual health data.
   • Generative and conversational artificial intelligence to assist personal health management.
   • Development of mobile solutions by incorporating co-design and design science research.
3. Legal and Ethical Considerations
   • Regulatory, privacy, and security issues of personal health data.
   • Empowering and respecting patients’ choice in their health journey
   • Potentials and limits of value based individual care
4. Interdisciplinary and Innovative Approaches
   • Integrating game design and behavioral psychology into personal health management.
   • Exploring the integration of various therapeutic approaches such as music, visual arts, and movement therapies with smart health applications

Minitrack Co-Chairs:

Freimut Bodendorf (Primary Contact)
University of Erlangen-Nürnberg
freimut.bodendorf@fau.de

Nilmini Wickramasinghe
La Trobe University
n.wickramasinghe@latrobe.edu.au

Elliot Sloane
Villanova University
elliot.sloane@villanova.edu

Pavlina Kroeckel
Community Hospital Nuremberg and University of Erlangen-Nuremberg
pavlina.kroeckel@fau.de

According to the U.S. National Center for Health Statistics, a disease is chronic when its course lasts for more than three months. Chronic diseases and conditions, persist an entire lifetime and generally cannot be prevented by vaccines or cured by medication. This minitrack characterizes Chronic Diseases and Conditions very broadly to include illnesses (such as diabetes, Alzheimer asthma), conditions (such as physical, sensory, mental, and cognitive disabilities, post-traumatic stress disorder, attention deficit hyperactivity disorder, autistic spectrum, Tourette syndrome, old age-related conditions). Recurrent illnesses and conditions caused by chronic diseases, if not managed carefully, can not only diminish quality of life and ability to work, but can also result in health emergencies, complications, and even death. According to the World Health Organization (WHO), chronic diseases are the leading cause of mortality worldwide, and 80% of chronic disease deaths occur in low- and middle-income countries.

Advancing patients’ ability to engage in self-managed health through information and communication technologies (ICTs), such as mobile technologies and machine learning, is increasingly a top priority. Effective self-management is a proven way of improving the lives of individuals suffering from chronic diseases. Self-management refers to a care management approach in which patients actively take responsibility for treating their chronic diseases. It is a self-regulating, dynamic, continuous, interactive process. Despite technological advances in healthcare ICTs that improve care and reduce costs, patients often avoid using them. Although, ICTs have improved the health in healthcare services in terms of the delivery of high-quality patient care at low cost, but the development of ICTs that focus chiefly on patient-centered care is still in its infancy.

With that in mind, we are looking for papers taking a variety of approaches to answering research questions related to the design, development, and use of ICTs on patient-centered care. Such approaches might be described as experiments or quasi-experiments, design science, case studies, surveys, action research, psychometrics, and ethnography. We invite papers that use a variety of advanced technologies such as Virtual Reality (VR), Augmented Reality (AR), Artificial Intelligence (AI), GenAI (Generative AI), agentic and robotic self-management automations, or Machine Learning (ML). We call for papers that investigate the use of ICTs for patients with chronic physical and psychological conditions, from diabetes and asthma to obesity and fitness SM programs, to autism, dementia, bipolar disorders, and depression. Studies that investigate technologies that help patients with chronic diseases improve their health and wellness can also be submitted to this minitrack.

Authors are invited to submit papers that address issues related to the design, development, and implementation of ICTs in self-management of chronic diseases and conditions. Potential issues and topics include, but are not limited to:

1. Learning self-management regimen, skills, and strategies (e.g., Monitoring and managing symptoms, side effects, and body responses, adjusting treatment regimen to manage symptoms and side effects, Managing/taking medications, Goal setting, decision making, problem solving, planning, prioritizing and pacing in the self-management process)
2. Managing lifestyle changes (e.g. modifying diet, nutrition, smoking, and physical activity, changing behaviors to minimize disease impact, Balancing living life with health needs, Managing disruptions in school, work, family, and social activities)
3. Managing psychological aspects of chronic diseases and conditions (e.g. Developing confidence and self-efficacy, reducing stress caused by the chronic disease, Identifying and benefiting from psychological resources drawing on intrinsic resources, e.g., creativity, strength and wisdom from past experiences, maintaining positive outlook, hope, and self-worth, Dealing with shock of diagnosis, self-blame, and guilt)
4. Managing relationships with healthcare providers (e.g. Creating and maintaining relationships with healthcare providers)
5. Managing and sustaining relationships with family, friends, relatives, and peers (e.g. Creating a community of peers with similar experiences, Obtaining and managing social support from family and friends)
6. Seeking resources, such as financial assistance (e.g., prescription subsidies), environmental support (e.g., assistive devices), and community resources (e.g., transportation)
   Making sense of the chronic disease (e.g. Finding meaning in work, relationships, activities, and spirituality)

Selected papers from this minitrack will be recommended to the editors of Data Base for Advances in Information Systems for fast track review and publication.

Minitrack Co-Chairs:

Kourosh Dadgar (Primary Contact)
University of San Francisco
kdadgar@usfca.edu

Zuan Sun
Whitworth University
zsun@whitworth.edu

Social media is changing the way patients, medical practitioners, and healthcare organizations, interact. Patients use social media to create communities with similar medical concerns or diagnoses, to research health related issues, and to inform their health decisions, including selecting a doctor, researching courses of treatment and sharing experiences with online communities. Clinicians and researchers use social media to design and implement behavioral interventions for a variety of health conditions, and to study how social media shapes healthcare decisions. Physicians use it to network professionally with colleagues and share medical knowledge within the medical community. Healthcare providers and organizations use social media to serve their communities, patients, and physicians, while also building awareness and enhancing their brand. Each of these uses provide opportunities to consider how social media influences health care, and to consider what data derived from social media exchanges – and which analytical methodologies – best serves this goal.

This minitrack solicits submissions that discuss diverse uses relating to social media in healthcare including methodological, conceptual, and design issues. Papers will include research studies that: (1) describe social media communities and the mining of data from social media platforms to address health issues; (2) evaluate the design, development, and implementation of social media applications related to health; (3) assess the impact of these applications, including impacts on patients, healthcare providers, organizations, and society in general; and (4) develop theories and models to better understand the mechanisms by which social media produces impacts on healthcare and health behavior.

Research in this rapidly developing field must address key methodological questions, we therefore welcome submissions that critically engage the following:

1. How are health-related social media data appropriately accessed? What constitutes an appropriate social media data set?
2. What analytic strategies or theories, both quantitative and qualitative, are optimal for analyzing social media data?
3. How to best measure effectiveness, acceptability and reach of social media-delivered health education?
4. What human subjects or ethical guidelines, inform the use of data obtained through social media?
5. Are existing social media applications designed to produce medical, psychological or public health impacts effective, safe and acceptable?

Minitrack Co-Chairs:

Beth Bock (Primary Contact)
Alpert Medical School, Brown University
Beth_Bock@Brown.edu

Jordan Braciszewski
Henry Ford Health
jbracis1@hfhs.org

Young Anna Argyris
Michigan State University
argyris@msu.edu

Artificial Intelligence, particularly deep learning architectures such as computer vision, large language model, has demonstrated significant potential in healthcare by assisting with disease diagnosis, treatment planning, and patient monitoring. However, training these models requires vast amounts of diverse, high-quality data. For example, AI-driven radiology applications require extensive labeled datasets of medical images, such as X-rays, MRIs, and CT scans, to develop accurate diagnostic models. Similarly, large language models in healthcare rely on vast amounts of structured and unstructured clinical notes, medical literature, and patient records to provide accurate recommendations and decision support. However, due to privacy regulations, institutional restrictions, and ethical concerns, acquiring such large and comprehensive datasets remains a major challenge, which limiting the ability to build robust AI models.

Synthetic data generation emerges as a powerful technique to bridge this data gap by creating realistic, privacy-preserving, and high-quality datasets. Advances in generative models, such as Generative Adversarial Networks (GANs) and diffusion models, enable the creation of synthetic medical images, speech, and textual data to supplement real-world datasets. The use of synthetic data in medical imaging has already shown promise. For instance, GANs can generate high-resolution synthetic MRI or CT scan images that closely mimic real scans, helping to train AI models while reducing reliance on sensitive patient data. Additionally, synthetic ECG and EEG data can be produced to aid in training models for cardiovascular and neurological disorder detection. Large-scale text-based medical datasets, which are crucial for training AI-powered clinical decision-support systems, can also be synthesized to protect patient confidentiality while maintaining the integrity of AI learning models. Beyond diagnostics, synthetic data is proving invaluable for AI models aimed at predictive analytics and personalized medicine. For example, synthetic patient records can be generated to train AI systems in forecasting disease progression, optimizing treatment plans, and improving hospital resource allocation.

As the field continues to evolve, novel techniques such as multimodal synthetic data generation—combining image, text, and speech synthesis—are enabling even more comprehensive AI model training. These methods allow AI to achieve a deeper understanding of patient data by integrating diverse inputs, leading to more accurate and holistic healthcare solutions.

This minitrack invites research contributions exploring the development, evaluation, and application of synthetic data in healthcare AI, including but not limited to:

1. Novel synthetic data generation techniques for medical images, text, and audio
2. Applications of synthetic data in disease diagnosis, medical imaging, and patient monitoring
3. Ethical considerations and regulatory compliance for synthetic healthcare data
4. Comparative analysis of real vs. synthetic data performance in AI models
5. Case studies of synthetic data implementation in clinical practice”

Minitrack Co-Chairs:

Siavash H. Khajavi (Primary Contact)
Aalto University
siavash.khajavi@aalto.fi

Zixuan Liu
Tulane University
zliu41@tulane.edu

Technology-based interventions have demonstrated efficacy for reducing mental health symptoms, suicide risk, cigarette smoking, and use of other various substances (e.g., alcohol, cannabis), as well as optimizing health for those without clinical levels of disorder (i.e., all levels of prevention). Digital solutions offer significant advantages over traditional approaches to care including perfect reproducibility of intervention content, continuous monitoring and engagement, and improved access for those unable to engage in traditional services. Technology can increase the likelihood of honest reporting on sensitive topics, which is often the case among mental health and substance use conditions. New technologies allow for a high degree of tailoring and personalization, features desired by both patients and providers, which increases intervention acceptability and effectiveness. Finally, screening and intervention can be completed in any setting – also requested by patients and providers – which improves scalability and reduces delays between problem development and treatment initiation. Increasingly, health care providers are being asked to do more (e.g., conditions, comorbidities, patients) with less (e.g., time, resources, staff, training). Technology-based approaches can help fill this gap while improving health and decreasing clinical workflow burden.

This minitrack aims to bring together innovative research that concerns the development, testing, and implementation of technology-based interventions (including preventive interventions) to address mental health and substance use. It calls for research that: (1) describes the development of novel digital interventions; (2) evaluates key patient and provider characteristics of intervention engagement; (3) assesses important patient outcomes as a result of using these strategies; and/or (4) addresses implementation factors that increase and/or inhibit generalization and scalability. Key areas of interest include, but are not limited to:

1. How can we design technology-based mental health and substance use interventions that are attractive and engaging for patients?
2. Once initiated, what are the key features of such interventions that result in continued engagement?
3. What are the important factors in the design of digital mental health and substance use interventions that optimize the likelihood of providers using patient data to improve care?
4. Are there barriers and facilitators of technology-based mental health and substance use intervention implementation that are generalizable across platforms and/or conditions?
5. What theories or mechanisms should guide the development and implementation of these approaches?”

Minitrack Co-Chairs:

Jordan Braciszewski (Primary Contact)
Henry Ford Health
jbracis1@hfhs.org

Beth Bock
Alpert Medical School, Brown University
Beth_Bock@Brown.edu