Evidence map›Paper›PMID 41536905›Full record

ArticleFuture healthcare journal2026

A platform for generating clinical intelligence from routine health data in hospitals: PICTURE.

Stuart A Bowyer, Eleni Pissaridou, John Booth, Daniel Key, William A Bryant, Ewart Sheldon, Alexandros Zenonos, Rebecca Pope, Harry Hemingway, Andrew Taylor and 1 more

Abstract read
In one paragraph

Article in Future healthcare journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Let's talk about death and dying.Future healthcare journal · 2026
    Article
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

11 authors.

Stuart A BowyerData Research, Innovation and Virtual Environments (DRIVE) Unit, Great Ormond Street Hospital, London, UK.
Eleni PissaridouData Research, Innovation and Virtual Environments (DRIVE) Unit, Great Ormond Street Hospital, London, UK.
John BoothData Research, Innovation and Virtual Environments (DRIVE) Unit, Great Ormond Street Hospital, London, UK.
Daniel KeyData Research, Innovation and Virtual Environments (DRIVE) Unit, Great Ormond Street Hospital, London, UK.
William A BryantData Research, Innovation and Virtual Environments (DRIVE) Unit, Great Ormond Street Hospital, London, UK.
Ewart SheldonData Research, Innovation and Virtual Environments (DRIVE) Unit, Great Ormond Street Hospital, London, UK.
Alexandros ZenonosRoche Products Ltd., Hexagon Place, Shire Park, Falcon Way, Welwyn Garden City, UK.
Rebecca PopeRoche Products Ltd., Hexagon Place, Shire Park, Falcon Way, Welwyn Garden City, UK.
Harry HemingwayInstitute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK.
Andrew TaylorData Research, Innovation and Virtual Environments (DRIVE) Unit, Great Ormond Street Hospital, London, UK.
Neil J SebireData Research, Innovation and Virtual Environments (DRIVE) Unit, Great Ormond Street Hospital, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The proliferation of electronic health records has led to an increasing volume of routine data about patients, their conditions and clinical interventions. While these data are widely used by clinicians to inform treatment, there is little standardised aggregation for clinical support or operational insights. This research explores a possible approach to redressing this. We describe a scalable 'clinical intelligence tool' allowing researchers and managers to aggregate and analyse routine data in a generalised form, generating on-demand analyses for clinical planning and hospital operations. We demonstrate a proof-of-concept implementation, called PICTURE. In addition to detailing the architecture of this tool, we show how it can address analytics questions, such as providing an informatics consult to address clinicians' questions. We show that aggregating health record data in a clinical intelligence tool has the potential to improve clinical practice, healthcare provider operations and more. The key innovations are the generalisation and standardisation of analytical methods for reuse across a range of EHR data; the use of generic data models to support the reusability and composability of analyses; and the approach of developing analytic functionality with open-source languages and modern software development practices.

Identifiers

PMID41536905
PMCPMC12796585

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.