Interoperability as a dynamic capability for artificial inteligence-augmented and sustainable health systems
DOI:
https://doi.org/10.62741/ahrj.v3i3.271Keywords:
Health information Interoperability, Health information exchange, Artificial intelligence, Electronic Health Records, Sustainable developmentAbstract
Introduction: Healthcare systems face growing pressure to integrate artificial intelligence into clinical workflows, yet a fundamental governance gap constrains these tools' potential: a lack of interoperability across health information systems, institutions, and care levels.
Objectives: This study examines how interoperability functions as a dynamic capability that enables synergistic collaboration between artificial and human intelligence in healthcare systems, and identifies governance, workforce, and implementation implications for health administrators at the national, regional, and institutional levels.
Methodology: A systematic literature review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement analyzed 40 peer-reviewed studies published between 2020 and 2025, retrieved from Scopus, Web of Science, PubMed, and IEEE Xplore. Quality assessment by two independent reviewers using the Mixed Methods Appraisal Tool achieved 89% inter-rater agreement; thematic synthesis inter-coder reliability reached a Cohen's kappa of 0.78.
Results: Three interconnected themes emerged: interoperability as a meta-governance mechanism, in which governance-first design is an empirical predictor of artificial intelligence implementation success; an artificial intelligence and human intelligence augmentation cycle, in which interoperable data foundations elevate clinical roles and generate new workforce competencies; and sustainable development goal pathways, with mental health data integration identified as a consequential and underexplored governance frontier. Complementary empirical evidence from a Brazilian reference hospital documents annual losses exceeding four million United States dollars attributable to interoperability failure.
Conclusion: Interoperability must be conceptualized as strategic dynamic capability-building, not information technology infrastructure. Governance-first implementation, legal clarity, workforce development, and multilevel coordination are preconditions for equitable, artificial intelligence-enabled health systems.
References
Char DS, Shah NH, Magnus D. Implementing machine learning in health care. N Engl J Med. 2020;378(11):981-983. doi:10.1056/NEJMp1714229
World Health Organization. Global Strategy on Digital Health 2020–2025. WHO; 2021.
Teece DJ, Pisano G, Shuen A. Dynamic capabilities and strategic management. Strateg Manag J. 1997;18(7):509-533. doi:10.1002/smj.4250180904
Teece DJ. Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strateg Manag J. 2007;28(13):1319-1350. doi:10.1002/smj.640
Trist EL, Bamforth KW. Some social and psychological consequences of the longwall method of coal-getting. Hum Relat. 1951;4(1):3-38. doi:10.1177/001872675100400101
Pasmore WA. Designing Effective Organizations: The Socio-Technical Systems Perspective. Wiley; 1988.
Eisenhardt KM, Martin JA. Dynamic capabilities: what are they? Strateg Manag J. 2000;21(10-11):1105-1121. doi:10.1002/smj.127
Shneiderman B. Human-centered artificial intelligence: three fresh ideas. AIS Trans Hum Comput Interact. 2020;12(3):109-124. doi:10.17705/1thci.00131
Lebovitz S, Levina N, Lifshitz-Assaf H. Is AI ground truth really true? The dangers of training and evaluating AI tools based on experts' know-what. MIS Q. 2022;46(3):1501-1526. doi:10.25300/MISQ/2022/16564
Ostrom E. Crossing the great divide: coproduction, synergy, and development. World Dev. 1996;24(6):1073-1087. doi:10.1016/0305-750X(96)00023-X
Bovaird T. Beyond engagement and participation: user and community coproduction of public services. Public Adm Rev. 2007;67(5):846-860. doi:10.1111/j.1540-6210.2007.00773.x
Gerke S, Minssen T, Cohen G. Ethical and legal challenges of AI-driven healthcare. In: Bohr A, Memarzadeh K, eds. Artificial Intelligence in Healthcare. Elsevier; 2020:295-336.
Mandl KD, Gottlieb D, Mandel JC. Integration of AI in healthcare requires an interoperable digital data ecosystem. Nat Med. 2024;30(3):631-634. doi:10.1038/s41591-024-02821-z
Rigas ES, Tsakalidis G, Vergidis K, Tsichlas K. Semantic interoperability for AI-based applications in smart hospitals using HL7 FHIR. J Syst Softw. 2024;215:112093. doi:10.1016/j.jss.2023.112093
Bates DW, Levine DM, Syrowatka A, et al. The potential of artificial intelligence to improve patient safety: a scoping review. NPJ Digit Med. 2021;4:54. doi:10.1038/s41746-021-00423-6
Williamson SM, Prybutok V. Balancing privacy and progress: a review of privacy challenges, systemic oversight, and patient perceptions in AI-driven healthcare. Appl Sci. 2024;14(2):675. doi:10.3390/app14020675
Cresswell K, Williams R, Sheik A. Using cloud technology in health care during the COVID-19 pandemic. Lancet Digit Health. 2021;3(1):e4-e5. doi:10.1016/s2589-7500(20)30291-0
Cresswell K, Sheik A, Williams R.' Managed convergence' in health system digitalization. J R Soc Med. 2022;115:284-285. doi:10.1177/01410768221098274
Mökander J, Morley J, Taddeo M, Floridi L. Ethics-based auditing of automated decision-making systems: nature, scope, and limitations. Sci Eng Ethics. 2021;27(4):44. doi:10.1007/s11948-021-00319-4
Moreira OJ, Teixeira SSMP, Rodrigues MVRy, Ramos Filho AdC. Knowledge management practices in a reference hospital — from diagnosis to results: a case study. Int J Health Sci. 2022;2(36). doi:10.22533/at.ed.15923622110710
Kulkov I, Kulkova J, Rohrbeck R, et al. Artificial intelligence-driven sustainable development: examining organizational, technical, and processing approaches to achieving global goals. Sustain Dev. 2023;32(3):2253-2267. doi:10.1002/sd.2560
Truby J. Governing artificial intelligence to benefit the UN Sustainable Development Goals. Sustain Dev. 2020;28(4):946-959. doi:10.1002/sd.2047
Van Zanten JA, van Tulder R. Towards nexus-based governance: defining interactions between the Sustainable Development Goals (SDGs). Int J Sustain Dev World Ecol. 2021;28(3):210-226. doi:10.1080/13504509.2020.1860697
Aanestad M, Vassilakopoulou P, Øvrelid E. The role of data infrastructure in healing the pandemic. In: Å Iden J, Bygstad B, Munkvold BE, eds. Digital Transformation and Public Services. Routledge; 2022:217-233.
World Health Organization. WHO Policy Brief: Climate Change and Health. WHO; 2023.
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi:10.1136/bmj.n71
Gilboa A. The Implementation of a National Health Information Exchange Platform in Israel. Inter-American Development Bank; 2022. doi:10.18235/0004272
Sedeeq FS, Arman P. Human-centric AI governance: balancing innovation and accountability in healthcare. J Law Med Ethics. 2025. Advance online publication.
Mökander J, Floridi L. Operationalizing AI governance through ethics-based auditing: an industry case study. AI Soc. 2022;38:1805-1822. doi:10.1007/s00146-022-01447-8
Lee G, Bach E, Yang E, et al. FHIR-AgentBench: Benchmarking LLM agents for realistic interoperable EHR question answering. Proc Mach Learn Res. 2026;297:1297-1315.
Kabak Y, Laleci Erturkmen GB, Gencturk M, et al. FHIR-RAG-MEDS: Integrating HL7 FHIR with retrieval-augmented large language models for enhanced medical decision support. AI. 2026;7. doi:10.3390/ai7010000
Reddy S. Generative AI in healthcare: an implementation science-informed translational path on application, integration and governance. Implement Sci. 2024;19(1):27. doi:10.1186/s13012-024-01357-9
Dixon BE, Holmgren AJ, Adler-Milstein J, Grannis SJ. Health information exchange and interoperability. In: Shortliffe EH, Cimino JJ, eds. Clinical Informatics Study Guide. Springer; 2022.
Adegoke K, Adeyinka O, Adeniran T, et al. Interoperability as a catalyst for digital health and therapeutics. Int J Environ Res Public Health. 2025;22(10):1535. doi:10.3390/ijerph22101535
Raisch S, Krakowski S. Artificial intelligence and management: the automation-augmentation paradox. Acad Manage Rev. 2021;46(1):192-210. doi:10.5465/amr.2018.0072
Catalogue of Mental Health Measures. DATAMIND and HDR UK; King’s College London. 2024. https://www.cataloguementalhealth.ac.uk
Buhari OM, Goodarzi Z, Holroyd-Leduc JM, et al. Using linked administrative data to identify older adults with dementia and their health service use in Ontario. BMC Geriatr. 2023;23(1):124. doi:10.1186/s12877-023-03837-3
Mugotitsa B, Bhattacharjee T, Ochola M, et al. Integrating longitudinal mental health data into a staging database: harnessing DDI-lifecycle and OMOP vocabularies within the INSPIRE Network Datahub. Front Big Data. 2024;7:1435510. doi:10.3389/fdata.2024.1435510
Helfat CE, Peteraf MA. Managerial cognitive capabilities and the microfoundations of dynamic capabilities. Strateg Manag J. 2015;36(6):831-850. doi:10.1002/smj.2247
Patiño GA, Gichuru K, Park YS, et al. The promise and perils of artificial intelligence in health professions education. Acad Med. 2024;99(5):477-481. doi:10.1097/ACM.0000000000005524
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