T Gould

Using ‘nudge’ principles for order set design: a before and after evaluation of an electronic prescribing template in critical care

Objective Computerised order sets have the potential to reduce clinical variation and improve patient safety but the effect is variable. We sought to evaluate the impact of changes to the design of an order set on the delivery of chlorhexidine mouthwash and hydroxyethyl starch (HES) to patients in the intensive care unit. Methods The study was conducted at University Hospitals Bristol NHS Foundation Trust, UK. Our intensive care unit uses a clinical information system (CIS). All drugs and fluids are prescribed with the CIS and drug and fluid charts are stored within a database. Chlorhexidine mouthwash was added as a default prescription to the prescribing template in January 2010. HES was removed from the prescribing template in April 2009. Both interventions were available to prescribe manually throughout the study period. We conducted a database review of all patients eligible for each intervention before and after changes to the configuration of choices within the prescribing system. Results 2231 ventilated patients were identified as appropriate for treatment with chlorhexidine, 591 before the intervention and 1640 after. 55.3% were prescribed chlorhexidine before the change and 90.4% after (p<0.001). 6199 patients were considered in the HES intervention, 2177 before the intervention and 4022 after. The mean volume of HES infused per patient fell from 630 mL to 20 mL after the change (p<0.001) and the percentage of patients receiving HES fell from 54.1% to 3.1% (p<0.001). These results were well sustained with time. Conclusions 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.

Increasing compliance with low tidal volume ventilation in the ICU with two nudge-based interventions: evaluation through intervention time-series analyses

Objectives Low 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). Setting University Hospitals Bristol, a tertiary, mixed medical and surgical ICU with 20 beds, admitting over 1300 patients per year. Participants Data 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 measure TVe in mL/kg IBW. Findings TVe 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. Conclusions This 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.

A machine learning approach to intensive care discharge.

Objective The primary objective is to work towards a clinical decision support tool that can improve discharge practice on the intensive care unit. Design We used two datasets of routinely collected patient data to test and improve upon a set of previously proposed discharge criteria. Setting Bristol Royal Infirmary general intensive care unit (GICU). Patients Two cohorts derived from historical datasets: 1933 intensive care patients from GICU in Bristol, and 10658 from MIMIC-III (a publicly available intensive care dataset). Interventions None. Primary outcome measure None Results In both cohorts few successfully discharged patients met the of all the discharge criteria. Both a random forest and a logistic classifier, trained on MIMIC and cross validated on GICU, 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 NLD criteria according to feature importance from the logistic model we showed improved performance over the original NLD criteria, while retaining good interpretability. Conclusions Our findings constitute a proof of concept for a decision support tool to run alongside a clinical information system, and streamline the process of discharge from the ICU. Strengths and Limitations of this study This study applies machine learning techniques to the problem of classifying patients that are ready for discharge from intensive care. Two cohorts of historical data are used, allowing cross-validation and a comparison of results between healthcare contexts. Our approach represents the first step towards a decision support tool that would help clinicians identify dischargeable patients as early as possible.Abstract Objective The primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care. Design We used two datasets of routinely collected patient data to test and improve upon a set of previously proposed discharge criteria. Setting Bristol Royal Infirmary general intensive care unit (GICU). Patients Two cohorts derived from historical datasets: 1870 intensive care patients from GICU in Bristol, and 7592 from MIMIC-III (a publicly available intensive care dataset). Results In 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. Conclusions Our 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. Strengths and Limitations of this study Training data from multiple source domains is leveraged to produce general classifiers. The restrictive feature representation tested could be expanded to better exploit the richness of available data and boost performance. Our approach has the potential to streamline the discharge process in cases where patient physiology makes them clear candidates for a de-escalation of care. High-risk patients would require additional levels of decision support to facilitate complex discharge planning.

Descriptive study of differences in acute kidney injury progression patterns in General and Cardiac Intensive Care Units

Background Acute kidney injury is common in critically ill patients with detrimental effects on mortality, length of stay and post-discharge outcomes. The Acute Kidney Injury Network developed guidelines based on urine output and serum creatinine to classify patients into stages of acute kidney injury. Methods In this analysis we utilize the Acute Kidney Injury Network guidelines to evaluate the acute kidney injury stage in patients admitted to general and cardiac intensive care units over a period of 18 months. Acute kidney injury stage was calculated in real time hourly based on the guidelines and using these temporal stage scores calculated for the population; the prevalence and progression of acute kidney injury stage was compared between the two units. We hypothesized that the prevalence and progression of acute kidney injury stage between the two units may be different. Results More cardiac intensive care unit patients had no acute kidney injury (stage <1) during their intensive care unit stay but more cardiac intensive care unit patients developed acute kidney injury (stage >1), compared to the General Intensive Care Unit. Both at intensive care unit admission and discharge, more General Intensive Care Unit patients had acute kidney injury; however, the number of cardiac intensive care unit patients with acute kidney injury was three times higher at discharge than admission. Acute kidney injury developed in a different pattern in the two intensive care units over five days of intensive care unit stay. In the General Intensive Care Unit, acute kidney injury was most prevalent on second day of intensive care unit stay and in cardiac intensive care unit acute kidney injury was most prevalent on the third day of intensive care unit stay. We observed the biggest increase in new acute kidney injury in the first day of General Intensive Care Unit and second day of the cardiac intensive care unit stay. Conclusions The study demonstrates the different trends of acute kidney injury pattern in general and cardiac intensive care unit patient populations highlighting the earlier development of acute kidney injury on General Intensive Care Unit and more prevalence of acute kidney injury on discharge from cardiac intensive care unit.