Health AI research capacity and equity-adjusted patient value in 27 European Union countries: an ecological panel study, 2011-2024.

内容摘要

Investments in health artificial intelligence (AI) are accelerating across European Union member states, yet evidence linking national AI research capacity to population-level health outcomes remains scarce. Most available evaluations focus on the performance of individual-level algorithms rather than system-wide effects. This study examines whether countries with greater health AI research activity achieve better population-level health value, and under what systemic conditions this relationship emerges. We conducted an ecological panel study across all 27 EU member states for the period 2011-2024 (maximum 378 country-year observations). Two novel composite indices were constructed: the Equity-Adjusted Patient Value Index (EAPVI), aggregating self-perceived health, unmet medical needs, income-related equity gaps, and treatable mortality; and the Health AI Capacity Index (HAICI), capturing health AI publication volume, intensity, and international collaboration. Two-way fixed effects panel regressions with Arellano cluster-robust standard errors were estimated. Additional specifications included a HAICI&#xd7;OOP (out-of-pocket expenditure) interaction, a COVID-19-era structural break model (HAICI&#xd7;POST2020), Hansen threshold regression, and an event study design to validate parallel pre-trends. Fifteen robustness checks were conducted, including leave-one-country-out analysis, placebo tests, winsorization, and a spatial Durbin model. The Equity-Adjusted Patient Value Index (EAPVI) and the Health AI Capacity Index (HAICI) were not uniformly linked across the 27 European Union member states. Greater national health AI research capacity did not automatically translate into higher equity-adjusted patient value. The relationship between the two indices varied across financial-protection contexts. Marginal-effects analyses showed that higher HAICI was not consistently associated with more favourable EAPVI outcomes: the HAICI-EAPVI association was negative at lower OOP levels and became statistically indistinguishable from zero at higher OOP levels. This pattern is consistent with the multidimensional structure of EAPVI, in which component-level signals may move in opposite directions. For treatable mortality, a marked shift emerged after the onset of the COVID-19 pandemic (P < .001). Countries with greater health AI research capacity experienced substantially fewer treatable deaths after 2020 - approximately 0.925 fewer per 100,000 population per index unit - with effects twice as large in Eastern compared to Western European countries. A Hansen threshold specification identified a HAICI threshold of 37.9, above which the HAICI-treatable mortality association became negative, although this result should be interpreted descriptively rather than as evidence of a causal threshold. The direction of these associations was supported by most robustness checks; however, a first-difference specification that accounts for the non-stationarity of HAICI produced a coefficient of opposite sign, indicating that the reported magnitudes are sensitive to the stationarity assumption. Health AI research capacity does not automatically translate into improved population-level patient value. The observed associations were concentrated in country-years with lower out-of-pocket expenditure and were markedly stronger during the COVID-19 period than before it. This pattern is descriptive; the ecological design does not permit inferences about underlying causal mechanisms such as mobilization of research-side capacity during system stress. Because this is an ecological and correlational analysis, the findings do not identify specific policy levers. They indicate that in this panel the association between health AI research capacity and equity-adjusted population health outcomes was heterogeneous across financial-protection contexts and was most pronounced during the COVID-19 period, a pattern that merits further investigation with study designs capable of supporting causal inference.