From 2020 to 2025, healthcare management research shifted from siloed, single-method studies to integrated, data-intensive and hybrid designs. Two engines drive this change. First, rapid diffusion of artificial intelligence and large-scale analytics is reconfiguring measurement and decision workflows in both clinical and administrative domains, with empirical syntheses documenting broadening use cases and performance gains (Santamato, Tricase, Faccilongo, Iacoviello, & Marengo, 2024). Adoption on the front line accelerated sharply; for example, a large U.S. physician survey reported use of health AI rising from 38 percent in 2023 to 66 percent in 2024 (Henry, 2025). Second, method integration matured: mixed-methods designs now combine quantitative panels with qualitative inquiry to capture complex organizational and patient-experience dynamics that single modalities miss (Rana & Chimoriya, 2023). Parallel scientometric work maps where the field is moving—quality and total quality management topics globalized, with rising output and collaboration networks in recent years (Hu et al., 2024).
What is ascendant are mixed-methods designs for explanatory depth, AI/ML pipelines for prediction and operational optimization, and bibliometric/quality-management analytics for strategic oversight (Rana & Chimoriya, 2023; Santamato et al., 2024; Hu et al., 2024). What is fading are monomethod studies that under-represent social and contextual mechanisms, rigid, target-fixated performance regimes associated with New Public Management, and linear, one-off change models that struggle in complex, adaptive hospital settings (PSAC, 2024; Lapsley & Miller, 2024; Wojciechowski, Murphy, Pearsall, & French, 2016).
Bottlenecks are well characterized. Change resistance remains the modal implementation risk across nursing and hospital settings, requiring early engagement, visible leadership sponsorship and staged capability building (Cheraghi, Ebrahimi, Kheibar, & Sahebihagh, 2023). Data-intensive projects encounter governance and ethics constraints that traditional research ethics committees were not designed to evaluate, necessitating updated guidance on privacy, transparency and risk assessment (Ferretti, Ienca, Rivas Velarde, Hurst, & Vayena, 2022). Policy and reimbursement lag the technology frontier; health technology assessment and regulatory processes can slow diffusion when evidence is nascent or cost–benefit is uncertain (Mundy, Forrest, Huang, & Maddern, 2024). Together, these frictions explain why proven methods may still scale unevenly despite favorable evidence syntheses (Santamato et al., 2024).
Implications for 2025 are direct. Portfolios should weight mixed-methods and AI-enabled studies that tie outcomes to value, equity and experience; measurement systems should de-emphasize one-size-fits-all targets in favor of flexible, context-sensitive indicators; change programs should use iterative, hybrid models rather than single-cycle approaches (Rana & Chimoriya, 2023; PSAC, 2024; Wojciechowski et al., 2016). Ethics-by-design, model transparency and staged evaluation pathways can shorten the distance between analytics and routine operations (Ferretti et al., 2022; Mundy et al., 2024).
Condensed table of leading methods and measurement approaches, 2020–2025
| Approach | Primary value proposition | Typical applications | Evidence base |
|---|---|---|---|
| Mixed-methods designs | Explanatory depth by integrating quantitative trends with qualitative mechanisms | Patient experience linked to operational or quality metrics; policy evaluation | Method guidance and exemplars in healthcare research (Rana & Chimoriya, 2023) |
| AI/ML for management analytics | Prediction, triage, and resource optimization at scale | Readmission risk, coding, workflow automation, decision support | Systematic review with ML augmentation; rising frontline adoption (Santamato et al., 2024; Henry, 2025) |
| Bibliometric and TQM analytics | Field-level foresight and performance learning | Mapping hotspots, collaboration networks, and quality themes | Global landscape analyses in healthcare TQM (Hu et al., 2024) |
| Hybrid change management | Iterative, participatory implementation suited to complex systems | Lean-enabled service redesign; staged culture change | Case evidence on limits of linear models and gains from hybrids (Wojciechowski et al., 2016) |
What is being de-emphasized and why
Monomethod studies underperform in complex service contexts because they miss cross-level mechanisms revealed by integrated designs (Rana & Chimoriya, 2023). Metric-heavy New Public Management frames face mounting critique for inducing perverse incentives and workforce strain; more trust-based and values-aligned governance models are explored as alternatives (PSAC, 2024; Lapsley & Miller, 2024). Linear freeze–change–refreeze models are ill-suited to volatile hospital environments, with hybrid, continuous-improvement approaches showing better fit (Wojciechowski et al., 2016).
Key bottlenecks to watch
Human factors lead the failure modes; addressing resistance requires communication, co-design and training pathways embedded from project inception (Cheraghi et al., 2023). Ethics and privacy expectations outpace legacy oversight; committees need data-science fluency and standardized risk heuristics for big-data protocols (Ferretti et al., 2022). Regulatory and HTA processes can slow adoption even when potential is high; adaptive, stage-gated evaluation and reimbursement can reduce inertia (Mundy et al., 2024). Finally, trustworthy AI depends on transparency and evaluation rigor throughout the lifecycle (Santamato et al., 2024).
References
Cheraghi, R., Ebrahimi, H., Kheibar, N., & Sahebihagh, M. H. (2023). Reasons for resistance to change in nursing: An integrative review. BMC Nursing, 22(1), 310. https://doi.org/10.1186/s12912-023-01460-0
Ferretti, A., Ienca, M., Rivas Velarde, M., Hurst, S., & Vayena, E. (2022). The challenges of big data for research ethics committees: A qualitative Swiss study. Journal of Empirical Research on Human Research Ethics, 17(1–2), 124–134. https://doi.org/10.1177/15562646211053538
Henry, T. A. (2025, February 26). 2 in 3 physicians are using health AI—up 78% from 2023. American Medical Association News. Retrieved August 16, 2025, from https://www.ama-assn.org/practice-management/digital-health/2-3-physicians-are-using-health-ai-78-2023
Hu, Z., Wang, R. S., Qin, X., Huang, Y. N., Li, L., Chiu, H. C., … & Wang, B. L. (2024). The global research landscape and future trends in healthcare Total Quality Management. Archives of Public Health, 82, Article 193. https://doi.org/10.1186/s13690-024-01420-3
Lapsley, I., & Miller, P. (Eds.). (2024). The resilience of New Public Management. Oxford University Press.
Mundy, L., Forrest, B., Huang, L. Y., & Maddern, G. (2024). Health technology assessment and innovation: Here to help or hinder? International Journal of Technology Assessment in Health Care, 40(1), e37. https://doi.org/10.1017/S026646232400059X
Public Service Alliance of Canada. (2024). Flawed frameworks: The harmful impact of New Public Management and lean production on public services. https://psacunion.ca/flawed-frameworks-harmful-impact-new-public
Rana, K., & Chimoriya, R. (2023). A guide to a mixed-methods approach to healthcare research. Encyclopedia, 5(2), 51. https://doi.org/10.3390/encyclopedia5020051
Santamato, V., Tricase, C., Faccilongo, N., Iacoviello, M., & Marengo, A. (2024). Exploring the impact of artificial intelligence on healthcare management: A combined systematic review and machine-learning approach. Applied Sciences, 14(22), 10144. https://doi.org/10.3390/app142210144
Wojciechowski, E., Murphy, P., Pearsall, T., & French, E. (2016). A case review: Integrating Lewin’s theory with Lean’s system approach for change. Online Journal of Issues in Nursing, 21(2), 4. https://doi.org/10.3912/OJIN.Vol21No02Man04