Evidence map›Paper›PMID 42768351›Full record

ArticleBMC proceedings2026

Data standards and interoperability for health.

Barbara Tornimbene, Zoila Beatriz Leiva Rioja, Fenella Beynon, Jose Costa Teixeira, Agnes Kiragga, Gerard Krause, Michael Ochola, Sylvia Thun, Philippe Veltsos, Oliver Morgan

Abstract read
In one paragraph

Article in BMC proceedings, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Barbara TornimbeneWorld Health Organization Hub for Pandemic and Epidemic Intelligence, Berlin, Germany. tornimbeneb@who.int.
Zoila Beatriz Leiva RiojaCPC Analytics, Berlin, Germany.
Fenella BeynonSwiss Tropical and Public Health Institute, Basel, Switzerland.
Jose Costa TeixeiraDigital Square, PATH, Washington, DC, USA.
Agnes KiraggaAfrican Population and Health Research Center, Nairobi, Kenya.
Gerard KrauseWorld Health Organization Head Quarter, Geneva, Switzerland.
Michael OcholaAfrican Population and Health Research Center, Nairobi, Kenya.
Sylvia ThunBerlin Institute of Health of the University Hospital Charité, Berlin, Germany.
Philippe VeltsosDigital Square, PATH, Washington, DC, USA.
Oliver MorganWorld Health Organization Hub for Pandemic and Epidemic Intelligence, Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The need for improved pandemic and epidemic intelligence has underlined the significance of data standardisation in global health. This report examines the role of standardized frameworks in enabling seamless data exchange, facilitating real-time information sharing, and improving global coordination during public health emergencies. The COVID-19 pandemic underscored longstanding deficiencies in data interoperability that had already been identified in health systems and prior public health emergencies, both within and across countries. However, the pandemic brought unprecedented visibility and urgency to these issues, highlighting the critical need for global data standards such as HL7® FHIR® (Fast Healthcare Interoperability Resources), SNOMED CT, LOINC, and ICD. The adoption of these standards can enhance health systems' ability to exchange data promptly and accurately on an international scale, thereby improving preparedness and responsiveness.Established guidance like the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and existing specifications like the International Patient Summary (IPS), among others, have supported the development of interoperable global health systems, lowered research redundancy and enhanced data quality. The adoption of such guidance and standards can contribute to enhancing scalable digital health initiatives and promoting international cooperation.The report emphasizes the need for robust digital health infrastructure to address future pandemics. It underscores the continuous endeavours of organisations such as WHO (with the SMART guidelines initiative) and HL7 (with the standards like FHIR®) to standardise data sharing and enhance global health outcomes. Despite ongoing issues related to privacy and scalability, the implementation of global standards is essential for enhancing pandemic and epidemic intelligence and fortifying global health systems.

Indexed as

Data standardsDigital health infrastructureInteroperabilityPandemic intelligencePublic health surveillance

Identifiers

PMID42768351
PMCPMC13591670

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.