Escrever um comentário

Summary of Main Findings

This mixed-methods national baseline assessment evaluated HL7 FHIR adoption and interoperability maturity in Sri Lanka, combining desk review, literature review, 18 key informant interviews, a stakeholder survey (n=16), and structured maturity scoring using the HL7 FHIR Maturity Model (FMM) and the MEASURE Interoperability Maturity Toolkit. The study found that Sri Lanka has built substantial policy and governance foundations for FHIR adoption—including formal endorsement of FHIR R4, two national Implementation Guides (SL Core and NEHR), governance committees, and three national Connectathons demonstrating technical feasibility of data exchange. However, operational maturity lags significantly behind policy commitment: national FHIR artefacts remain at FMM Level 1 (profiles tested against only 37.5% of core data elements, versus an 80% threshold for Level 2), and overall interoperability maturity across governance, human resources, and technology domains was rated "nascent" (Level 1). Twenty-one barriers were identified, predominantly socio-technical (workforce shortages, absence of national testing infrastructure, weak vendor incentives, donor-dependent financing, fragmented governance) rather than purely technical. Benchmarking against the US, Australia, Singapore, and Israel confirmed Sri Lanka's earlier-stage position, though with notable regional leadership in training and community engagement.

This work advances the field by providing what the authors describe as the first framework-based, systematic national FHIR maturity assessment in a South Asian LMIC, offering a reproducible baseline methodology (combining FMM and MEASURE toolkit) and a transferable governance/roadmap model that other LMICs pursuing standards-based interoperability could adapt.

Major Issues

• Self-assessment and circularity of maturity scoring: The assessment team itself assigned FMM and MEASURE scores based on interviews/surveys largely drawn from stakeholders who were also the implementers being assessed—raising risk of optimism/social-desirability bias, which the authors acknowledge but do not mitigate through independent verification.

• Lack of independent technical validation: The paper explicitly states that production-level metrics (message volumes, payload quality, downstream clinical impact) were not independently verified, and no live production testing against national profiles was conducted. This significantly limits how much weight can be placed on the "37.5% of core data elements" figure and FMM Level 1 designation.

• Small, non-random, and non-representative sample: 18 purposively selected key informants and 16 survey respondents is a very small base for a "national baseline assessment." Private-sector, provincial, and frontline clinician/patient perspectives were explicitly excluded, likely biasing findings toward a more mature/optimistic picture than ecosystem-wide reality.

• Non-comparable benchmarking: The comparison against the US, Australia, Israel, and Singapore uses an adapted survey instrument not administered under the same conditions as the original HL7–Firely survey, and the authors themselves caution these are not statistically comparable—yet the comparison is presented prominently in a results table and discussed as if broadly informative. Comparator countries were also selected for being mature ecosystems, not for being contextually similar (income level, health system structure), which limits the value of the benchmarking exercise.

• No cost, cost-effectiveness, or clinical outcome data: Given the paper's stated aim of informing investment and policy priorities, the complete absence of cost data is a substantial gap for actionable decision-making.

Minor Issues

• Two consecutive sections are both numbered "2.3" (Rapid literature review and Key Informant Interviews)—a formatting/labelling error.

• The "+" notation used in maturity scoring (e.g., Level 3+) is somewhat ambiguously defined; readers would benefit from a clearer operational definition of what evidence threshold triggers a "+" versus a full level increase.

• The abstract and results state interoperability maturity was "nascent (Level 1) across leadership and governance, human resources, and technology domains," but Table 1 shows several subdomains scoring 2+ and 3+; the conservative "lowest common denominator" scoring rule is explained but could be foregrounded earlier to avoid an apparent contradiction for readers skimming the abstract.

• The introduction and discussion sections repeat several near-identical sentences about the "gap between policy and operational maturity"—tightening this repetition would improve readability.

• Table 4's "primary horizon" categories (e.g., "short to medium") are somewhat imprecise given the paper elsewhere defines discrete short/medium/long-term bands (6–12 months, 1–3 years, 3–5 years); clarifying which recommendations fall in which precise band would aid implementation planning.

• The Discussion section reiterates content from the Results/Interpretation subsections almost verbatim in places (e.g., barriers being "socio-technical rather than purely technical" is stated at least three times); consolidation would improve flow.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.