PP26 Convey or not convey? Does crew skill level predict hospital conveyance rate in a UK regional NHS ambulance service trust?. Issue 1 (14th January 2019)
- Record Type:
- Journal Article
- Title:
- PP26 Convey or not convey? Does crew skill level predict hospital conveyance rate in a UK regional NHS ambulance service trust?. Issue 1 (14th January 2019)
- Main Title:
- PP26 Convey or not convey? Does crew skill level predict hospital conveyance rate in a UK regional NHS ambulance service trust?
- Authors:
- Black, Sarah
Frampton, Ian - Abstract:
- Abstract : Background: This study aimed to explore how demographic, temporal and patient characteristics influenced the decision to convey in a large dataset of nearly half a million consecutive calls to an NHS Ambulance Service Trust in the course of a single year. Methods: The retrospective dataset combined information from patient clinical records and Control system data. Thirty variables were examined using a variety of techniques, including Pearson χ 2, Mantel-Haenszel test for trend and Binomial logistic regression. Challenges and benefits to combining large datasets from different sources were explored. Results: The binomial logistic regression model was statistically significant, χ 2 (21)=90409, p<0.0005, and showed that crew skill level independently predicted decision to convey. The model explained 24.3% (Nagelkerke R 2 ) of the variance in conveyance and correctly classified 68.2% of cases. Sensitivity was 86.4%, specificity was 44.4%, positive predictive value was 33.1% and negative predictive value was 71.6%. All five predictor variables were statistically significant. Controlling for all the other variables; increasing crew skill level was independently associated with a significantly reduced likelihood of being conveyed. Conclusions: The potential implications of this finding for ambulance services, Emergency Departments and the wider NHS are profound. Investment in community services in more rural areas may reduce ambulance conveyance, resulting in fewerAbstract : Background: This study aimed to explore how demographic, temporal and patient characteristics influenced the decision to convey in a large dataset of nearly half a million consecutive calls to an NHS Ambulance Service Trust in the course of a single year. Methods: The retrospective dataset combined information from patient clinical records and Control system data. Thirty variables were examined using a variety of techniques, including Pearson χ 2, Mantel-Haenszel test for trend and Binomial logistic regression. Challenges and benefits to combining large datasets from different sources were explored. Results: The binomial logistic regression model was statistically significant, χ 2 (21)=90409, p<0.0005, and showed that crew skill level independently predicted decision to convey. The model explained 24.3% (Nagelkerke R 2 ) of the variance in conveyance and correctly classified 68.2% of cases. Sensitivity was 86.4%, specificity was 44.4%, positive predictive value was 33.1% and negative predictive value was 71.6%. All five predictor variables were statistically significant. Controlling for all the other variables; increasing crew skill level was independently associated with a significantly reduced likelihood of being conveyed. Conclusions: The potential implications of this finding for ambulance services, Emergency Departments and the wider NHS are profound. Investment in community services in more rural areas may reduce ambulance conveyance, resulting in fewer avoidable admissions and easing pressure on the system. A striking difference in conveyance rates due to the time of day the 999 call was made is evident. During the 'in hours' period ambulance clinicians were able to discharge more patients at scene without the requirement for an ED conveyance. Conversely access to alternative health and social care providers is limited 'out of hours'; making these services available over a longer operating period may similarly reduce ambulance conveyance rates during the evening and overnight. … (more)
- Is Part Of:
- Emergency medicine journal. Volume 36:Issue 1(2019)
- Journal:
- Emergency medicine journal
- Issue:
- Volume 36:Issue 1(2019)
- Issue Display:
- Volume 36, Issue 1 (2019)
- Year:
- 2019
- Volume:
- 36
- Issue:
- 1
- Issue Sort Value:
- 2019-0036-0001-0000
- Page Start:
- e10
- Page End:
- e11
- Publication Date:
- 2019-01-14
- Subjects:
- Emergency medicine -- Periodicals
616.02505 - Journal URLs:
- http://www.bmj.com/archive ↗
https://emj.bmj.com/ ↗ - DOI:
- 10.1136/emermed-2019-999.26 ↗
- Languages:
- English
- ISSNs:
- 1472-0205
- Deposit Type:
- Legaldeposit
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 18107.xml