Wisdom of patients: predicting the quality of care using aggregated patient feedback

Background The Care Quality Commission (CQC) is responsible for ensuring the quality of healthcare in England. To that end, CQC has developed statistical surveillance tools that periodically aggregate large numbers of quantitative performance measures to identify risks to the quality of care and prioritise its limited inspection resource. These tools have, however, failed to successfully identify poor-quality providers. Facing continued budget cuts, CQC is now further reliant on an ‘intelligence-driven’, risk-based approach to prioritising inspections and a new effective tool is required. Objective To determine whether the near real-time, automated collection and aggregation of multiple sources of patient feedback can provide a collective judgement that effectively identifies risks to the quality of care, and hence can be used to help prioritise inspections. Methods Our Patient Voice Tracking System combines patient feedback from NHS Choices, Patient Opinion, Facebook and Twitter to form a near real-time collective judgement score for acute hospitals and trusts on any given date. The predictive ability of the collective judgement score is evaluated through a logistic regression analysis of the relationship between the collective judgement score on the start date of 456 hospital and trust-level inspections, and the subsequent inspection outcomes. Results Aggregating patient feedback increases the volume and diversity of patient-centred insights into the quality of care. There is a positive association between the resulting collective judgement score and subsequent inspection outcomes (OR for being rated ‘Inadequate’ compared with ‘Requires improvement’ 0.35 (95% CI 0.16 to 0.76), Requires improvement/Good OR 0.23 (95% CI 0.12 to 0.44), and Good/Outstanding OR 0.13 (95% CI 0.02 to 0.84), with p<0.05 for all). Conclusions The collective judgement score can successfully identify a high-risk group of organisations for inspection, is available in near real time and is available at a more granular level than the majority of existing data sets. The collective judgement score could therefore be used to help prioritise inspections

Human factors in financial trading: an investigation of error, non-technical skills and culture

The financial crisis of 2008 was the most severe crisis since the great depression: millions of jobs and billions of pounds of household income were lost, resulting in pervasive unemployment, inequality and a rise in suicide rates (Barr et al., 2012). The failure exhibited complex organisational properties, such as tight coupling (e.g. the bankruptcy of Lehman Brothers triggered the collapse of other key organisations), the prioritisation of production over safety (e.g. profit over the wellbeing of clients) and a collective inaction to heed early warning signs (e.g. surrounding credit derivative swaps). Yet, research in the financial sector has failed to capture critical information on how the behaviours and practises (e.g. systemic rate rigging) within the industry eroded risk management processes, and led to organisational failure (Power, Ashby, & Palermo, 2013; Ring, et al., 2014). This thesis draws on human factors theory and methodology that have successfully been applied in other high-risk domains (e.g. aviation) and applies them to a financial trading organisation to investigate whether human factors approaches help understand error in the financial trading domain. To achieve this, four articles and three additional chapters have been developed for this thesis. Chapter 1 (introduction) conceptualises financial trading as a high-risk organisation and considers the implications of this for the domain, and the field of human factors. Chapter 2 (Article 1, published in Journal of Risk Research) conducts a systematic literature review of 19 studies in financial trading in order to establish the relevance of non-technical skills theory to the domain. Chapter 3 reports on the development of a methodology for capturing operational incidents within a financial trading firm: the Financial Incident Analysis System (FINANS). Chapter 4 (Article 2, published in Human Factors) uses FINANS to analyse 1,000 incidents and reveals the human factors issues that underlie operational incidents (e.g. 1% of trades are erroneous and the most common causes are slip/lapse and problems in situation awareness and teamwork). Chapter 5 (Article 3, under review at Human Factors) analyses a further 1,042 operational incidents and establishes the role of human skills for capturing error and indicates financial traders to be the ‘last-line of defence’ for preventing incidents. Chapter 6 (Article 4, published in the Journal of Business Ethics) analyses ten high-profile trading mishaps in the UK, and shows safety culture problems in each as underlying the failures. Chapter 7 reviews each study and discusses the findings, implications and limitations of each. Chapter 8 concludes that the application of human factors concepts in financial trading generates meaningful insight into how risk is managed in this domain, and extends human factors research into a previously unexplored environmen

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570