A global analysis of national cardiovascular disease control plans using a multi-agent artificial intelligence model

Hugh Pearson, Caleb J Kumar, Che L Reddy, Estella Rose LeBlanc, Rifat Atun and With the CVD Control Collaborative

PLOS Digital Health, 2026, vol. 5, issue 6, 1-100

Abstract: Cardiovascular diseases cause nearly one-third of global deaths, yet standalone National Cardiovascular Disease Control Plans remain uncommon and inconsistently structured. We assessed the comprehensiveness of recent national plans using a validated health-systems framework and a multi-agent artificial intelligence model. We identified the most recent official plan for 45 countries from World Health Organisation and World Heart Federation repositories and government sources. We adapted a health-systems planning framework for cardiovascular disease and validated it through a two-stage expert consensus process involving 42 specialists from 28 countries, resulting in 11 elements and 69 sub-elements with standardised definitions and scoring criteria. Plans were analysed using a three-stage artificial intelligence pipeline that ingested documents, applied framework-based scoring, and performed automated validation checks. Sub-elements were scored on a 0–5 scale and summarised by element, World Health Organisation region, and World Bank income group. Overall comprehensiveness was low (median 1.20/5). Plans most consistently addressed strategic direction (median 2.80) and governance arrangements (2.14). Contextual assessment was deficient — threats (0.12) and opportunities (0.29) — as were performance specification elements, including objectives (0.50) and health system outcomes (0.67). The Western Pacific region scored highest (median 1.71) and Africa lowest (0.90), though scores remained below moderate levels across all regions. Income group pairwise comparisons were non-significant across all groups; given the small LIC sample (n = 2), no inferential conclusions about income group differences are drawn. Validation against blinded human review across six countries showed 43.7%exact agreement and 68.0%agreement within one point; ordinal agreement statistics were uniformly weak and non-significant, indicating the approach is validated for structural benchmarking rather than fine-grained qualitative judgement. Most national cardiovascular disease plans articulate vision without sufficient operational detail, particularly for contextual analysis, measurement, and integrated financing. Standardised planning templates and artificial intelligence–supported benchmarking, complemented by expert review, could strengthen national planning quality and enable scalable global comparisons.Author summary: Cardiovascular disease remains the world’s leading cause of death, yet the national strategies designed to fight it often lag behind those for other major illnesses like cancer. In this study, we set out to understand the quality of these strategies by analysing 45 national control plans from around the globe. Using a specialised artificial intelligence tool validated against independent human review, we evaluated how comprehensive these plans truly are against a standard health-system framework. We found that while most countries are effective at setting high-level goals and identifying leadership structures, they frequently fail to include the practical details necessary for implementation, such as specific budgets, local risk analysis, and clear methods to measure progress. Plan quality did not differ significantly across income groups in this sample, suggesting that the global deficit in planning may reflect the absence of standardised methodology more than resource constraints alone. Our findings highlight an urgent need for evidence-based planning guides to help governments transition from political promises to practical action. We also demonstrate that while artificial intelligence can efficiently analyse large volumes of health policy, it serves best as a partner to human judgment rather than a replacement.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0001447

DOI: 10.1371/journal.pdig.0001447

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