Evidence map›Paper›PMID 41194189›Full record

ArticleBMC health services research2025

Building an initial programme theory to explain how and why on-the-day surgery cancellations occur and how they might be reduced.

Buddhika S W Samarasinghe, Ross Millar, Mark Exworthy, Justin Aunger

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Article in BMC health services research, 2025. 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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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

4 authors.

Buddhika S W SamarasingheHealth Services Management Centre, School of Social Policy and Society, Park House, University of Birmingham, Birmingham, B15 2RT, UK. bss861@alumni.bham.ac.uk.
Ross MillarHealth Services Management Centre, School of Social Policy and Society, Park House, University of Birmingham, Birmingham, B15 2RT, UK.
Mark ExworthyHealth Services Management Centre, School of Social Policy and Society, Park House, University of Birmingham, Birmingham, B15 2RT, UK.
Justin AungerNIHR Midlands Patient Safety Research Collaboration, University of Birmingham, Birmingham, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOn-the-day surgery cancellations (OTDSCs) have been a longstanding global problem, bringing significant suffering to patients and carers, and substantial waste across healthcare systems. Any cancellation of a surgery that occurs for any reason on the day of the scheduled surgery is defined as an OTDSC. Despite the high prevalence of OTDSCs, little is known about why they happen and how to minimise them. This study aimed to develop Initial Program Theories (IPTs) and share valuable insights that can form the basis for future evaluation of OTDSCs.

methodWe conducted a study to address the questions, “How do OTDSCs occur, and in what contexts can they be minimised?“. We used a qualitative and multi-stage approach to developing IPTs. Data collection included OTDSC literature (n = 35) identified from a systematic search, including feedback sessions with administrators (n = 10) from eight NHS trusts, two feedback events with patient expert advisers (n = 6), and expert practitioners (n = 8).

resultsThe iterative analysis found that OTDSCs are a complex undesired outcome, influenced by many interconnected “variables” at macro-level (e.g., waiting-list policies, austerity measures and workforce shortages) and meso-level (e.g., workload, high emergency admissions and interruptions), as well as healthcare professionals’ (HCPs) and patients’ perceptions and behaviours. The study identified that failures in various aspects of individualised care (such as care planning, communication and resource allocation) in preparing for surgery before admission could also contribute to different types of OTDSCs.

conclusionAs a result of the complex and interconnected nature of OTDSCs and the wide variety of causes, it can be hard to reduce their occurrence. OTDSCs can be minimised by carefully considering various aspects of individualisation of care, such as clinical care planning, communication and resource allocation and delivery when preparing patients to undergo surgery. Providing favourable working conditions and creating effective knowledge transfer between the stakeholders initiating OTDSCs and HCPs who prepare patients for surgery can be critical to minimising most OTDSCs. The study developed a taxonomy and novel IPTs that have practical implications for policymakers and practitioners when designing interventions to minimise OTDSCs.

Indexed as

Appointments and SchedulesHumansQualitative ResearchState MedicineUnited KingdomMulti-methodsRealist evaluationRealist theory buildingSurgery cancellations

Identifiers

PMID41194189
PMCPMC12587745

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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.