The impact of hospital command centre on patient flow and data quality: findings from the UK National Health Service

Author:

Mebrahtu Teumzghi F12ORCID,McInerney Ciaran D13ORCID,Benn Jonathan24ORCID,McCrorie Carolyn34,Granger Josh4,Lawton Tom5,Sheikh Naeem3,Habli Ibrahim6,Randell Rebecca78ORCID,Johnson Owen13ORCID

Affiliation:

1. School of Computing, University of Leeds , Leeds LS2 9JT, UK

2. Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford Royal Infirmary , Duckworth Ln, Bradford BD9 6RJ, UK

3. Yorkshire and Humber Patient Safety Translational Research Centre, Wolfson Centre for Applied Health Research, Bradford Royal Infirmary , Duckworth Ln, Bradford BD9 6RJ, UK

4. School of Psychology, University of Leeds , Woodhouse Lane, Leeds LS2 9JT, UK

5. Bradford Teaching Hospitals NHS Foundation Trust, Bradford Royal Infirmary , Duckworth Ln, Bradford BD9 6RJ, UK

6. Department of Computer Science, University of York , Heslington, York YO10 5DD, UK

7. Wolfson Centre for Applied Health Research, Bradford Royal Infirmary , Duckworth Ln, Bradford BD9 6RJ, UK

8. Faculty of Health Studies, University of Bradford , Richmond Rd, Bradford BD7 1DP, UK

Abstract

Abstract In the last 6 years, hospitals in developed countries have been trialling the use of command centres for improving organizational efficiency and patient care. However, the impact of these command centres has not been systematically studied in the past. It is a retrospective population-based study. Participants were patients who visited the Bradford Royal Infirmary hospital, Accident and Emergency (A&E) Department, between 1 January 2018 and 31 August 2021. Outcomes were patient flow (measured as A&E waiting time, length of stay, and clinician seen time) and data quality (measured by the proportion of missing treatment and assessment dates and valid transition between A&E care stages). Interrupted time-series segmented regression and process mining were used for analysis. A&E transition time from patient arrival to assessment by a clinician marginally improved during the intervention period; there was a decrease of 0.9 min [95% confidence interval (CI): 0.35–1.4], 3 min (95% CI: 2.4–3.5), 9.7 min (95% CI: 8.4–11.0), and 3.1 min (95% CI: 2.7–3.5) during ‘patient flow program’, ‘command centre display roll-in’, ‘command centre activation’, and ‘hospital wide training program’, respectively. However, the transition time from patient treatment until the conclusion of consultation showed an increase of 11.5 min (95% CI: 9.2–13.9), 12.3 min (95% CI: 8.7–15.9), 53.4 min (95% CI: 48.1–58.7), and 50.2 min (95% CI: 47.5–52.9) for the respective four post-intervention periods. Furthermore, the length of stay was not significantly impacted; the change was −8.8 h (95% CI: −17.6 to 0.08), −8.9 h (95% CI: −18.6 to 0.65), −1.67 h (95% CI: −10.3 to 6.9), and −0.54 h (95% CI: −13.9 to 12.8) during the four respective post-intervention periods. It was a similar pattern for the waiting and clinician seen times. Data quality as measured by the proportion of missing dates of records was generally poor (treatment date = 42.7% and clinician seen date = 23.4%) and did not significantly improve during the intervention periods. The findings of the study suggest that a command centre package that includes process change and software technology does not appear to have a consistent positive impact on patient safety and data quality based on the indicators and data we used. Therefore, hospitals considering introducing a command centre should not assume there will be benefits in patient flow and data quality.

Funder

National Institute for Health and Care Research

Publisher

Oxford University Press (OUP)

Subject

Public Health, Environmental and Occupational Health,Health Policy,General Medicine

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