The Journal of Medical Internet Research is pleased to announce a call for papers for the theme issue “Navigating AI-Enabled Uncertainty – Strategic Implications for Digital Health Management.” 

Health care administrators are increasingly facing ambiguous decisions as artificial intelligence (AI) permeates reimbursement, documentation, workforce planning, and vendor ecosystems. Recent reports show that major insurers use predictive technologies to deny post–acute care authorization, raising concerns that AI is substituting clinical judgment with financial calculations [1]. Hospitals migrating electronic health records to public clouds report gains in scalability and reliability but admit that internal cloud expertise is lacking; 75% of organizations rely on third-party consultants during planning and migration [2]. The ambient clinical-documentation market is racing toward commoditization; analysts warn that large incumbents such as Epic and Microsoft control much of the health care infrastructure, making it difficult for niche AI start-ups to thrive [3]. These shifts demand new governance frameworks and leadership strategies. A recent scoping review in the Journal of Medical Internet Research found that successful AI transformation requires multidimensional leadership that balances technological opportunities with stakeholder needs and adapts to evolving regulatory and organizational contexts [4]. Building on these insights, this call for papers will collect empirical evidence and case-based perspectives that together illuminate how health care executives can navigate AI-enabled uncertainty. Submissions should emphasize practical, data-driven guidance for senior management and administrators, illustrating how digital tools contribute to value-based and equitable care. Accepted papers will form part of a coordinated “bundle” culminating in an integrative summary article.

Papers submitted to the theme issue for consideration may explore, but are not limited to, the following topics (sample titles):

AI-Enabled Reimbursement and Financial Uncertainty

• Evaluations of AI-enabled prior-authorization and claims-adjudication systems. Analyses should examine fairness, transparency, and clinical impact. Given evidence that AI models used by leading Medicare Advantage insurers expedited denials and led to higher denial rates [1], studies assessing strategies to manage such uncertainty (eg, human oversight, appeals processes, algorithmic auditing) are encouraged.
• Viewpoints or Tutorials that share organizational case studies and approaches on how executives negotiate reimbursement when AI determines eligibility. Contributions should link financial practices to patient-centered outcomes and value-based care. 

Cloud Migration and Talent Realignment in Health Care IT

• Empirical analyses of public cloud migration projects, detailing cost structures, reliability, and operational impacts. Surveys suggest that Amazon Web Services (AWS) and Microsoft Azure both support large health system workloads and that most organizations begin with disaster recovery before moving to full production environments [2]. However, a lack of internal expertise forces 75% of organizations to use external consultants [2]. Papers should explore how such dependencies reshape workforce planning, training, and vendor relationships.
• Scenario analyses describing how large vendors (Epic, Microsoft, Amazon) reshape roles traditionally filled by internal teams. Studies may investigate when outsourcing improves resilience versus when it erodes institutional knowledge.

Vendor Dynamics in AI-Enabled Health Care

• Comparative studies of competition between large incumbents and niche start-ups in areas such as ambient clinical documentation. The commoditization of single-use AI solutions has led to a “race to the bottom” in pricing and challenges for small vendors [3], while incumbents wield significant influence over data and workflow [3]. Manuscripts may include market analyses, network effects, or predictive models identifying conditions under which small vendors can achieve dominance.
• Policy analyses examining how interoperability, standards, and regulatory oversight affect vendor competition and innovation. Such articles would be considered Viewpoint articles if there is limited original data presented. 

Strategic Decision-Making Under AI-Related Uncertainty

• Methodological frameworks that help health care executives navigate rapid technological shifts, including risk assessment tools, scenario planning, and adaptive governance. Contributors may draw on leadership theories; evidence from JMIR suggests that AI transformation is an adaptive challenge requiring multidimensional leadership that balances technology with empathy and agility [4].
• Best practices for digital health governance, including data stewardship, transparency, stakeholder engagement, and continuous learning. For example, submissions may use mixed methods approaches to capture evolving behaviors and socio-technical factors.

Learning Health Systems in the AI Era

• Trust frameworks for AI-enabled learning health systems can be examined through empirical or policy analyses that explore how shared commitments (eg, engagement, equity, transparency, accountability) are operationalized to build trust when integrating AI into continuous learning environments [5].
• Embedding continuous learning across levels of care involves studies that show how AI-enabled feedback loops and digital infrastructure support learning health system principles at multiple scales, from N-of-1 microexperiments and team-based improvements to population health management and national learning networks [5]. 

The types of studies can include, but are not limited to:

• Empirical studies involving quantitative or qualitative data, including case studies,
• cross-sectional analyses, implementation evaluations, and predictive modeling
• Experiential case reports authored by hospital executives, digital health officers, or
• administrators detailing lessons learned and transferable strategies. Such articles will be considered as Viewpoints or, alternatively, adhere to iCHECK-DH (Guidelines and Checklist for the Reporting on Digital Health Implementations) reporting guidelines [6] for implementation reports.
• Mixed methods papers combining surveys, interviews, and data analytics to illuminate organizational phenomena
• Framework or methodology articles offering analytic tools or governance models

How to submit:

Please submit to the Journal of Medical Internet Research by selecting “Navigating AI-Enabled Uncertainty – Strategic Implications for Digital Health Management” in the “Section” drop-down list. See the article How do I submit to a theme issue? in our Knowledge Base and consult our Instructions for Authors for more information. 

Submission Guidelines: 

All submissions will undergo a rigorous peer-review process, and accepted articles will be published as part of a special issue “Navigating AI-Enabled Uncertainty – Strategic Implications for Digital Health Management.”

Submission Deadline: June 30, 2026 

Submit Now

Submissions not reviewed or accepted for publication in this theme issue in the Journal of Medical Internet Research may be offered cascading peer review or transfer to other JMIR Publications journals, according to standard publisher policies. For example, early-stage formative work or research may better fit the scope for JMIR Formative Research. Authors are encouraged to submit study protocols or grant proposals to JMIR Research Protocols before data acquisition to preregister the study (Registered Reports—subsequent acceptance in one of the JMIR Publications journals is then guaranteed). 

All articles submitted to this theme issue will be shared and published rapidly through the following mechanisms:

• All peer-reviewed articles in this theme issue will be immediately and permanently made open access. This is the standard for all titles within the JMIR Publications portfolio.
• Articles can be made immediately available in JMIR Preprints (with a DOI) after submission if authors select the preprint option at submission to enable this service.

Guest Editors

Dong-Gil Ko, PhD

Guest Advisors

Umberto Tachinardi, MD

Brett M Kissela, MD

Contact information for questions and presubmission enquiries:

Dr Dong-Gil Ko, PhD (kodg@ucmail.uc.edu) 

References

1. Shaw M. Insurers’ AI denials of postacute care face senate scrutiny. AJMC. Oct 28, 2024. URL: https://www.ajmc.com/view/insurers-ai-denials-of-postacute-care-face-senate-scrutiny [Accessed 2025-10-16]

2. Dyrda L. Amazon vs. Microsoft cloud with Epic: 6 notes. Becker’s Hospital Review. Nov 8, 2024.URL: https://www.beckershospitalreview.com/healthcare-informationtechnology/ehrs/amazon-vs-microsoft-cloud-with-epic-6-notes/ [Accessed 2025-10-16]

3. D’Amico LC. Beyond documentation: building platform moats in healthcare AI. MedCity News. March 13, 2025. URL: https://medcitynews.com/2025/03/beyond-documentation-building-platform-moats-in-healthcare-ai/ [Accessed 2025-10-16]

4. Sriharan A, Sekercioglu N, Mitchell C, et al. Leadership for AI transformation in health care organization: scoping review. J Med Internet Res. Aug 14, 2024;26:e54556. [doi: 10.2196/54556] [Medline: 39009038]

5. Madara JL, Miyamoto S, Dowdy SC, Greene SM, Tarrant J, Margolis PA. Transforming health care - shared commitments for a learning health system. N Engl J Med. Jul 10, 2025;393(2):192-197. [doi: 10.1056/NEJMsb2507600] [Medline: 40561191]

6. Perrin Franck C, Babington-Ashaye A, Dietrich D, et al. iCHECK-DH: Guidelines and Checklist for the Reporting on Digital Health Implementations. J Med Internet Res. May 10, 2023;25:e46694. [doi: 10.2196/46694] [Medline: 37163336]