UK Efficacy and Mechanism Evaluation Programme and National Institutes of Health.

The presentation of choices within an electronic prescribing system influenced the delivery of evidence-based interventions in a predictable way and the effect was well sustained. This approach has the potential to enhance the effectiveness of computerised order sets.

ObjectivesLow tidal volume (TVe) ventilation improves outcomes for ventilated patients, and the majority of clinicians state they implement it. Unfortunately, most patients never receive low TVes. ‘Nudges’ influence decision-making with subtle cognitive mechanisms and are effective in many contexts. There have been few studies examining their impact on clinical decision-making. We investigated the impact of 2 interventions designed using principles from behavioural science on the deployment of low TVe ventilation in the intensive care unit (ICU).SettingUniversity Hospitals Bristol, a tertiary, mixed medical and surgical ICU with 20 beds, admitting over 1300 patients per year.ParticipantsData were collected from 2144 consecutive patients receiving controlled mechanical ventilation for more than 1 hour between October 2010 and September 2014. Patients on controlled mechanical ventilation for more than 20 hours were included in the final analysis.Interventions(1) Default ventilator settings were adjusted to comply with low TVe targets from the initiation of ventilation unless actively changed by a clinician. (2) A large dashboard was deployed displaying TVes in the format mL/kg ideal body weight (IBW) with alerts when TVes were excessive.Primary outcome measureTVe in mL/kg IBW.FindingsTVe was significantly lower in the defaults group. In the dashboard intervention, TVe fell more quickly and by a greater amount after a TVe of 8 mL/kg IBW was breached when compared with controls. This effect improved in each subsequent year for 3 years.ConclusionsThis study has demonstrated that adjustment of default ventilator settings and a dashboard with alerts for excessive TVe can significantly influence clinical decision-making. This offers a promising strategy to improve compliance with low TVe ventilation, and suggests that using insights from behavioural science has potential to improve the translation of evidence into practice.

ObjectiveThe primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care.DesignWe used two datasets of routinely collected patient data to test and improve on a set of previously proposed discharge criteria.SettingBristol Royal Infirmary general intensive care unit (GICU).PatientsTwo cohorts derived from historical datasets: 1870 intensive care patients from GICU in Bristol, and 7592 from Medical Information Mart for Intensive Care (MIMIC)-III.ResultsIn both cohorts few successfully discharged patients met all of the discharge criteria. Both a random forest and a logistic classifier, trained using multiple-source cross-validation, demonstrated improved performance over the original criteria and generalised well between the cohorts. The classifiers showed good agreement on which features were most predictive of readiness-for-discharge, and these were generally consistent with clinical experience. By weighting the discharge criteria according to feature importance from the logistic model we showed improved performance over the original criteria, while retaining good interpretability.ConclusionsOur findings indicate the feasibility of the proposed approach to ready-for-discharge classification, which could complement other risk models of specific adverse outcomes in a future decision support system. Avenues for improvement to produce a clinically useful tool are identified.

Background Critical care transfers between hospitals are time critical high-risk episodes for unstable patients who often require urgent lifesaving intervention. This study aimed to establish the scale, nature and safety of current transfer practice in the South West Critical Care Network (SWCCN) in England. Methods The SWCCN database contains prospectively collected data in accordance with national guidelines. It was interrogated for all adult (>15 years of age) patients from January 2012 to November 2017. Results A total of 1124 inter-hospital transfers were recorded, with the majority (935, 83.2%) made for specialist treatment. The transferring team included a doctor in 998 (88.8%) and nurse in 935 (93.7%) transfers. In 204 (18.1%) transfers, delays occurred, with the commonest cause being availability of transport. Critical incidents occurred in 77 (6.9%). Conclusions This is the first published data on the transfer activity of a UK adult critical care network. It demonstrates that current ad-hoc provision is not meeting the longstanding expectations of national guidelines in terms of training, clinical experience and timeliness. The authors hope that this study may inform national conversation regarding the development of National Health Service commissioned inter-hospital transfer services for adult patients in England.