My research is focused on developing innovative technologies that empower communities of healthcare professionals and patients to collaborate on a worldwide basis. The development of these collaborative care platforms is being used to investigate what factors can promote and sustain online communities of healthcare providers and patients.

I lead the development new web platform architecture called Alicanto Social Learning platform that can be used for training and care communication between health professionals and patients. The system is being designed to have seamless data interchange between the learning system, collaboration system, consultation system, and clinical data repository. Alicanto has been used for the OPENPediatrics web platform (OpenPediatrics.org) by Boston Children’s Hospital for global pediatric education and the MADCaP Global network (madcapnetowork.org) to support prostate cancer research and education.  A new implementation of the system will be used to connect women, midwives, pediatricians and other care providers in a mobile phone-enabled network.  The system will be used to train health professionals (pediatricians, neonatologists, nurses, mid-wives).  The system is not disease-specific so that it can be adapted for use in other conditions and diseases such as cancer, diabetes and mental health, and coordination of care of elderly.

AI Research

The 2025 DCI Network conference and workshop laid the groundwork for a coordinated, multidisciplinary effort to define responsible frameworks for AI in healthcare. These four resulting papers, authored by over 50 leaders from academia, industry, regulatory bodies, and patient advocacy, offer complementary perspectives on critical domains: real-world data (RWD), clinical decision support (CDS), consumer health, and cross-cutting governance structures. Together, they reflect months of consensus-building and analysis, advancing the field from broad ethical principles toward domain-specific action frameworks that balance innovation with safety, efficacy, equity, and trust (SEET).

The paper “Towards Responsible AI in Healthcare – Getting Real About Real-World Data and Evidence” (JAMIA, in press) examines the challenges of using real-world data (RWD) to power AI applications in healthcare. It identifies urgent concerns around bias, insufficient data documentation, privacy, and the lack of accountability in how data is sourced and used. The authors recommend actionable strategies including standardized metadata practices, instructional “nutrition labels” for AI models, cross-disciplinary training programs, and processes for continuous model monitoring. The paper stresses that without addressing foundational data quality and governance, the promise of AI in real-world settings cannot be safely realized.

In “Toward a Responsible Future: Recommendations for AI-Enabled Clinical Decision Support” (JAMIA, 2024), the authors focus on the unique demands of AI in clinical decision support systems (AI-CDS). Drawing on workshops and expert input, the paper proposes a practical framework to ensure safety and trust in AI-CDS through pre-deployment validation, post-market surveillance, national safety reporting systems, and clinician training. The consensus process emphasized that the dynamic and evolving nature of AI models, especially those using large language models, necessitates adaptive governance mechanisms that include ongoing oversight and real-time error monitoring to prevent harm and bias in clinical settings.

The paper “Towards a Multi-Stakeholder Process for Developing Responsible AI Governance in Consumer Health” (IJMI, 2025) highlights the distinct challenges of consumer-facing AI tools, such as mobile apps and wearables. Recognizing that traditional healthcare regulations are often too slow or broad, the paper proposes the creation of the Health AI Consumer Consortium (HAIC2) to empower patient representation. It introduces the “SAM-I-AM” stakeholder framework to align incentives and recommends “nutrition label”-style disclosures for AI tools, post-market feedback mechanisms, and rapid-cycle governance through prototype evaluation. The approach balances innovation with real-world accountability in a domain where consumer risk is rising.

Finally, “Toward Responsible AI Governance: Balancing Multi-Stakeholder Perspectives on AI in Healthcare” (IJMI, 2025) synthesizes findings from across the DCI Network deliberations to propose a cognitive framework for AI governance grounded in tradeoffs between speed, scope, and capability. It outlines three intermediate-level governance models tailored to CDS, RWD, and consumer health, emphasizing voluntary certification, participatory oversight, and risk-based stratification of governance needs. The paper underscores that responsible AI in healthcare demands flexible, stakeholder-aligned governance structures that adapt as the technology and its societal implications evolve.

Together, these papers represent a significant contribution to the practical governance of healthcare AI. They move the field beyond aspirational principles toward implementable actions rooted in interdisciplinary collaboration and informed by real-world application domains. The upcoming 2025 DCI Network AI Conference will build on these foundations to chart the next phase of responsible innovation.

Learn more at the AI and Healthcare Page.

Patient Centered Digital Health

I am part of the InfoSAGE elder care network project that is investigating the impact of an online platform to connect elders over 75 with their family members for information sharing and care coordination. This study investigates the information needs of elders and their families in order to better understand the challenges for families of communicating and coordinating using online and mobile technologies. We have also developed a medication manager tool for patients and their families and thereby to improve medication management and safety.

Digital Mental Health Research

With colleagues at Homewood Research Institute in Canada, I have developed new methods to evaluate digital mental health systems using evidence-based approaches.

Data and Research Platforms

I am also investigating the challenges in building global health informatics platforms that can link clinical systems, home care systems, public health systems, community outreach systems to create online coordinated care communities, and to facilitate the collection of data from multiple care providers. I am investigating the challenges of data interchange among countries and patterns of collaboration between clinicians and basic science researchers.

Serious Games for Health

With international collaborators, I am investigating the application of serious games for health. Serious games for health are interactive gaming applications that have the goal to educate and motivate users for behavior change. Applications include patient education, physiotherapy, chronic disease management, simulations, and continuing professional education. I developed the first international course on Serious Games for Health and I have recently published a book on this topicThe course and book provide an overview of the design and evaluation of serious games for health, and the best implementations to date.

Pediatric Oncology

Previously, I led the development of the Cure4Kids program with a focus on pediatric cancer and collaboration tools to thousands of healthcare providers worldwide. The Cure4Kids Web site offers a digital research library, on-demand seminars with slides and audio in several languages, and online meeting rooms for international collaborations and communication.  Cure4Kids’ online live meeting rooms have enabled international clinical discussions, reduced travel costs, and improved collaborations. The design of Cure4Kids has been used as a model for collaborative platforms being developed by international agencies such as UICC, WHO, and IAEA. My publications have examined the design and evaluation of usage of these systems in low resource settings. I also led development of the Pediatric Oncology Network Database, a secure, web-based, multilingual pediatric hematology/oncology database created for use in countries with limited resources to meet various clinical data management needs including cancer registration, delivery of protocol-based care, outcome evaluation, and assessment of psychosocial support programs. POND serves as a tool for oncology units to store patient data for easy retrieval and analysis and to achieve uniform data collection to facilitate meaningful comparison of information among centers.