32 research outputs found
Effect of a hospital command centre on patient safety: an interrupted time series study
Background
Command centres have been piloted in some hospitals across the developed world in the last few years. Their impact on patient safety, however, has not been systematically studied. Hence, we aimed to investigate this.
Methods
This is a retrospective population-based cohort study. Participants were patients who visited Bradford Royal Infirmary Hospital and Calderdale & Huddersfield hospitals between 1 January 2018 and 31 August 2021. A five-phase, interrupted time series, linear regression analysis was used.
Results
After introduction of a Command Centre, while mortality and readmissions marginally improved, there was no statistically significant impact on postoperative sepsis. In the intervention hospital, when compared with the preintervention period, mortality decreased by 1.4% (95% CI 0.8% to 1.9%), 1.5% (95% CI 0.9% to 2.1%), 1.3% (95% CI 0.7% to 1.8%) and 2.5% (95% CI 1.7% to 3.4%) during successive phases of the command centre programme, including roll-in and activation of the technology and preparatory quality improvement work. However, in the control site, compared with the baseline, the weekly mortality also decreased by 2.0% (95% CI 0.9 to 3.1), 2.3% (95% CI 1.1 to 3.5), 1.3% (95% CI 0.2 to 2.4), 3.1% (95% CI 1.4 to 4.8) for the respective intervention phases. No impact on any of the indicators was observed when only the software technology part of the Command Centre was considered.
Conclusion
Implementation of a hospital Command Centre may have a marginal positive impact on patient safety when implemented as part of a broader hospital-wide improvement programme including colocation of operations and clinical leads in a central location. However, improvement in patient safety indicators was also observed for a comparable period in the control site. Further evaluative research into the impact of hospital command centres on a broader range of patient safety and other outcomes is warranted
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The impact of hospital command centre on patient flow and data quality: findings from the UK NHS
YesBackground: In the last six years, hospitals in developed countries have been trialling the use of command centres for improving organisational efficiency and patient care. However, the impact of these Command Centres has not been systematically studied in the past.
Methods: It is a retrospective population based study. Participants were patients who visited Bradford Royal Infirmary Hospital, accident and emergency (A&E) department, between Jan 01, 2018 and August 31, 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.
Results: 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 minutes (95% CI: 0.35 to 1.4), 3 minutes (95% CI: 2.4 to 3.5), 9.7 minutes (95% CI: 8.4 to 11.0) and 3.1 minutes (95% CI: 2.7 to 3.5) during âpatient flow programâ, âcommand centre display roll-inâ, âcommand centre activationâ and âhospital wide training programâ, respectively. However, the transition time from patient treatment until conclusion of consultation showed an increase of 11.5 minutes (95% CI: 9.2 to 13.9), 12.3 minutes (95% CI: 8.7 to 15.9), 53.4 minutes (95% CI: 48.1 to 58.7) and 50.2 minutes (95% CI: 47.5 to 52.9) for the respective four post-intervention periods. Further, length of stay was not significantly impacted; the change was -8.8hrs (95% CI: -17.6 to 0.08), -8.9hrs (95% CI: -18.6 to 0.65), -1.67hrs (95% CI: -10.3 to 6.9) and -0.54hrs (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.
Conclusion: The findings of the study suggest that a command centre package that includes process change and software technology does not appear to have 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.This project is funded by the National Institute for Health Research Health Service and Delivery Research Programme (NIHR129483)
Effect of a hospital command centre on patient safety: An interrupted time series study
Background Command centres have been piloted in some hospitals across the developed world in the last few years. Their impact on patient safety, however, has not been systematically studied. Hence, we aimed to investigate this.
Methods This is a retrospective population-based cohort study. Participants were patients who visited Bradford Royal Infirmary Hospital and Calderdale & Huddersfield hospitals between 1 January 2018 and 31 August 2021. A five-phase, interrupted time series, linear regression analysis was used.
Results After introduction of a Command Centre, while mortality and readmissions marginally improved, there was no statistically significant impact on postoperative sepsis. In the intervention hospital, when compared with the preintervention period, mortality decreased by 1.4% (95% CI 0.8% to 1.9%), 1.5% (95% CI 0.9% to 2.1%), 1.3% (95% CI 0.7% to 1.8%) and 2.5% (95% CI 1.7% to 3.4%) during successive phases of the command centre programme, including roll-in and activation of the technology and preparatory quality improvement work. However, in the control site, compared with the baseline, the weekly mortality also decreased by 2.0% (95% CI 0.9 to 3.1), 2.3% (95% CI 1.1 to 3.5), 1.3% (95% CI 0.2 to 2.4), 3.1% (95% CI 1.4 to 4.8) for the respective intervention phases. No impact on any of the indicators was observed when only the software technology part of the Command Centre was considered.
Conclusion Implementation of a hospital Command Centre may have a marginal positive impact on patient safety when implemented as part of a broader hospital-wide improvement programme including colocation of operations and clinical leads in a central location. However, improvement in patient safety indicators was also observed for a comparable period in the control site. Further evaluative research into the impact of hospital command centres on a broader range of patient safety and other outcomes is warranted
Formal Model-Based Assurance Cases in Isabelle/SACM : An Autonomous Underwater Vehicle Case Study
Isabelle/SACM is a tool for automated construction of model-based assurance cases with integrated formal methods, based on the Isabelle proof assistant. Assurance cases show how a system is safe to operate, through a human comprehensible argument demonstrating that the requirements are satisfied, using evidence of various provenances. They are usually required for certification of critical systems, often with evidence that originates from formal methods. Automating assurance cases increases rigour, and helps with maintenance and evolution. In this paper we apply Isabelle/SACM to a fragment of the assurance case for an autonomous underwater vehicle demonstrator. We encode the metric unit system (SI) in Isabelle, to allow modelling requirements and state spaces using physical units. We develop a behavioural model in the graphical RoboChart state machine language, embed the artifacts into Isabelle/SACM, and use it to demonstrate satisfaction of the requirements
Arabin cervical pessary for prevention of preterm birth in cases of twin-to-twin transfusion syndrome treated by fetoscopic LASER coagulation: the PECEP LASER randomised controlled trial
Abstract Background Fetoscopic LASER coagulation of the placental anastomoses has changed the prognosis of twin-twin transfusion syndrome. However, the prematurity rate in this cohort remains very high. To date, strategies proposed to decrease the prematurity rate have shown inconclusive, if not unfavourable results. Methods This is a randomised controlled trial to investigate whether a prophylactic cervical pessary will lower the incidence of preterm delivery in cases of twin-twin transfusion syndrome requiring fetoscopic LASER coagulation. Women eligible for the study will be randomised after surgery and allocated to either pessary or expectant management. The pessary will be left in place until 37 completed weeks or earlier if delivery occurs. The primary outcome is delivery before 32 completed weeks. Secondary outcomes are a composite of adverse neonatal outcome, fetal and neonatal death, maternal complications, preterm rupture of membranes and hospitalisation for threatened preterm labour. 352 women will be included in order to decrease the rate of preterm delivery before 32Â weeksâ gestation from 40% to 26% with an alpha-error of 0.05 and 80% power. Discussion The trial aims at clarifying whether the cervical pessary prolongs the pregnancy in cases of twin-twin transfusion syndrome regardless of cervical length at the time of fetoscopy. Trial registration ClinicalTrials.gov Identifier: NCT01334489 . Registered 04 December 2011
Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI systemâs actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings
Perspectives on Assurance Case Development for Retinal Disease Diagnosis Using Deep Learning
We report our experience with developing an assurance case for a deep learning system used for retinal disease diagnosis and referral. We investigate how an assurance case could clarify the scope and structure of the primary argument and identify sources of uncertainty. We also explore the need for an assurance argument pattern that could provide developers with a reusable template for communicating and structuring the different claims and evidence and clarifying the clinical context rather than merely focusing on meeting or exceeding performance measures