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Using computer decision support systems in NHS emergency and urgent care: ethnographic study using normalisation process theory
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BACKGROUND: Information and communication technologies (ICTs) are often proposed as 'technological fixes' for problems facing healthcare. They promise to deliver services more quickly and cheaply. Yet research on the implementation of ICTs reveals a litany of delays, compromises and failures. Case studies have established that these technologies are difficult to embed in everyday healthcare. METHODS: We undertook an ethnographic comparative analysis of a single computer decision support system in three different settings to understand the implementation and everyday use of this technology which is designed to deal with calls to emergency and urgent care services. We examined the deployment of this technology in an established 999 ambulance call-handling service, a new single point of access for urgent care and an established general practice out-of-hours service. We used Normalization Process Theory as a framework to enable systematic cross-case analysis. RESULTS: Our data comprise nearly 500 hours of observation, interviews with 64 call-handlers, and stakeholders and documents about the technology and settings. The technology has been implemented and is used distinctively in each setting reflecting important differences between work and contexts. Using Normalisation Process Theory we show how the work (collective action) of implementing the system and maintaining its routine use was enabled by a range of actors who established coherence for the technology, secured buy-in (cognitive participation) and engaged in on-going appraisal and adjustment (reflexive monitoring). CONCLUSIONS: Huge effort was expended and continues to be required to implement and keep this technology in use. This innovation must be understood both as a computer technology and as a set of practices related to that technology, kept in place by a network of actors in particular contexts. While technologies can be 'made to work' in different settings, successful implementation has been achieved, and will only be maintained, through the efforts of those involved in the specific settings and if the wider context continues to support the coherence, cognitive participation, and reflective monitoring processes that surround this collective action. Implementation is more than simply putting technologies in place - it requires new resources and considerable effort, perhaps on an on-going basis.
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“Technology implementation in hospital settings involves a large variety of stakeholders and organizational procedures, with strong routines and professional identities, as well as strict legal and regulatory standards [18–20].”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [18]
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Metadata
- Authors
- Pope, C., Halford, S., Turnbull, J., Prichard, J., Calestani, M., & May, C.
- Affiliations
- University of Southampton (GB)
- Corresponding author
- Pope, Catherine
- Venue
- BMC Health Services Research
- Published
- 2013-03-23
- Volume
- 13
- Issue
- 1
- Pages
- 111-111
- ISSN
- 1472-6963 (ISSN-L)
- PMC
- PMC3614561
- OpenAlex
- W2150880750
- Cited by
- 101
- References
- 72
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 8 · 2023: 8 · 2022: 9 · 2021: 10 · 2020: 12 · 2019: 9 · 2018: 7 · 2017: 11 · 2016: 9 · 2015: 5 · 2014: 5 · 2013: 2
- FWCI
- 22.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Health Services Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Explore Bristol Research (publishedVersion)
- Research Repository (Kingston University London) (acceptedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- Funders
- National Institute for Health and Care Research (08/1819/217)
- Health Services and Delivery Research Programme
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Keywords
- emergency care, urgent care, out-of-hours general practice, ICT implementation, Normalisation Process Theory, coherence, cognitive participation, reflexive monitoring, collective action, technology adoption, emergency department
- MeSH terms
- Anthropology, Cultural, Emergency Medical Services, Emergency Service, Hospital, England, Humans, State Medicine, Hotlines, Decision Support Systems, Clinical, Qualitative Research, After-Hours Care
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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