Evidence map›Paper›PMID 41819594›Full record

ArticleBMJ health & care informatics2026

Impact of the Federated Data Platform's digital surgery scheduling system on elective theatre utilisation at an NHS Trust: an interrupted time series analysis.

Elena Lammila-Escalera, Gabriele Kerr, Geva Greenfield, Benedict Hayhoe, Natalie Brewer, Carla Hearsum, Grazia Antonacci, Natasha Dsouza, Azeem Majeed, Ana Luisa Neves

Abstract read
In one paragraph

Article in BMJ health & care informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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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

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Elena Lammila-EscaleraDepartment of Primary Care and Public Health, Imperial College London, London, UK ekl220@ic.ac.uk.ORCID http://orcid.org/0000-0001-6805-5241
Gabriele KerrDepartment of Primary Care and Public Health, Imperial College London, London, UK.ORCID http://orcid.org/0000-0003-3170-3211
Geva GreenfieldDepartment of Primary Care and Public Health, Imperial College London, London, UK.
Benedict HayhoeDepartment of Primary Care and Public Health, Imperial College London, London, UK.ORCID http://orcid.org/0000-0002-2645-6191
Natalie BrewerChelsea and Westminster Hospital NHS Foundation Trust, London, UK.
Carla HearsumChelsea and Westminster Hospital NHS Foundation Trust, London, UK.
Grazia AntonacciDepartment of Primary Care and Public Health, Imperial College London, London, UK.
Natasha DsouzaDepartment of Primary Care and Public Health, Imperial College London, London, UK.
Azeem MajeedDepartment of Primary Care and Public Health, Imperial College London, London, UK.ORCID http://orcid.org/0000-0002-2357-9858
Ana Luisa NevesDepartment of Primary Care and Public Health, Imperial College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo evaluate the National Health Service (NHS) Federated Data Platform (FDP) Inpatient (IP) Care Coordination Solution (CCS) digital scheduling tool on elective theatre utilisation.

methodsAn interrupted time series assessed changes in theatre utilisation and cancellations following tool adoption (January 2022). Weekly data spanned 90 weeks (April 2021-December 2023). Outcomes included weekly median theatre utilisation (actual, booked and bookings per session) and the percentage of cancelled bookings. Models incorporated a 5-week lag and estimated level (step-change) and trend (slope) effects.

resultsPostintervention level and trend increases were observed for booked (β=4.40, p=0.045; β=0.26, p=0.002) and actual (β=3.98, p=0.064; β=0.23, p=0.006) utilisation. Bookings per session showed a significant level increase (β=0.34, p=0.002) with no trend change (β=0.00, p=0.790). Across the postintervention period, compared with counterfactual estimates, booked and actual utilisation were 15.0% (95% CI 13.4% to 16.5%, p<0.0001) and 12.2% (95% CI 10.8% to 13.5%, p<0.0001) higher, while bookings per session were 10.9% (95% CI 9.5% to 12.4%, p<0.0001) higher. Significant positive effects were observed for urology, general surgery, gynaecology, plastic surgery and ophthalmology. A significant upward trend in cancellation rates was associated with the introduction of the tool (β=2.1, p=0.001). DISCUSSION: Findings suggest that centralised digital scheduling tools can improve theatre capacity by enabling more efficient use of existing capacity through improved scheduling visibility. Future research should explore differences in specialty-level usage and long-term sustainability of gains.

conclusionThe introduction of the NHS FDP IP CCS product was associated with improved elective theatre utilisation.

Indexed as

Appointments and SchedulesElective Surgical ProceduresOperating RoomsHumansInterrupted Time Series AnalysisState MedicineUnited KingdomHealth Services AdministrationInformation ManagementInformation Technology

Identifiers

PMID41819594
PMCPMC12983765

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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.