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

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.

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.

Machine Learning for Risk Prediction of Oesophago-Gastric Cancer in Primary Care: Comparison with Existing Risk-Assessment Tools.

Oesophago-gastric cancer is difficult to diagnose in the early stages given its typical non-specific initial manifestation. We hypothesise that machine learning can improve upon the diagnostic performance of current primary care risk-assessment tools by using advanced analytical techniques to exploit the wealth of evidence available in the electronic health record. We used a primary care electronic health record dataset derived from the UK General Practice Research Database (7471 cases; 32,877 controls) and developed five probabilistic machine learning classifiers: Support Vector Machine, Random Forest, Logistic Regression, Naïve Bayes, and Extreme Gradient Boosted Decision Trees. Features included basic demographics, symptoms, and lab test results. The Logistic Regression, Support Vector Machine, and Extreme Gradient Boosted Decision Tree models achieved the highest performance in terms of accuracy and AUROC (0.89 accuracy, 0.87 AUROC), outperforming a current UK oesophago-gastric cancer risk-assessment tool (ogRAT). Machine learning also identified more cancer patients than the ogRAT: 11.0% more with little to no effect on false positives, or up to 25.0% more with a slight increase in false positives (for Logistic Regression, results threshold-dependent). Feature contribution estimates and individual prediction explanations indicated clinical relevance. We conclude that machine learning could improve primary care cancer risk-assessment tools, potentially helping clinicians to identify additional cancer cases earlier. This could, in turn, improve survival outcomes.

Process Mining of Disease Trajectories: A Literature Review.

Disease trajectories model patterns of disease over time and can be mined by extracting diagnosis codes from electronic health records (EHR). Process mining provides a mature set of methods and tools that has been used to mine care pathways using event data from EHRs and could be applied to disease trajectories. This paper presents a literature review on process mining related to mining disease trajectories using EHRs. Our review identified 156 papers of potential interest but only four papers which directly applied process mining to disease trajectory modelling. These four papers are presented in detail covering data source, size, selection criteria, selections of the process mining algorithms, trajectory definition strategies, model visualisations, and the methods of evaluation. The literature review lays the foundations for further research leveraging the established benefits of process mining for the emerging data mining of disease trajectories.

Are Regulations Safe? Reflections From Developing a Digital Cancer Decision-Support Tool.

PURPOSE: Informatics solutions to early diagnosis of cancer in primary care are increasingly prevalent, but it is not clear whether existing and planned standards and regulations sufficiently address patients' safety nor whether these standards are fit for purpose. We use a patient safety perspective to reflect on the development of a computerized cancer risk assessment tool embedded within a UK primary care electronic health record system. METHODS: We developed a computerized version of the CAncer Prevention in ExetER studies risk assessment tool, in compliance with the European Union's Medical Device Regulations. The process of building this tool afforded an opportunity to reflect on clinical concerns and whether current regulations for medical devices are fit for purpose. We identified concerns for patient safety and developed nine practical recommendations to mitigate these concerns. RESULTS: We noted that medical device regulations (1) were initially created for hardware devices rather than software, (2) offer one-shot approval rather than supporting iterative innovation and learning, (3) are biased toward loss-transfer approaches that attempt to manage the fallout of harm instead of mitigating hazards becoming harmful, and (4) are biased toward known hazards, despite unknown hazards being an expected consequence of health care as a complex adaptive system. Our nine recommendations focus on embedding less-reductionist and stronger system perspectives into regulations and standards. CONCLUSION: Our intention is to share our experience to support research-led collaborative development of health informatics solutions in cancer. We argue that regulations in the European Union do not sufficiently address the complexity of healthcare information systems with consequences for patient safety. Future standards and regulations should continue to follow a system-based approach to risk, safety, and accident avoidance.