Background

It is now widely accepted that the mixed effect and success rates of strategies to improve quality and safety in health care are in part due to the different contexts in which the interventions are planned and implemented. The objectives of this study were to (i) describe the reporting of contextual factors in the literature on the effectiveness of quality improvement strategies, (ii) assess the relationship between effectiveness and contextual factors, and (iii) analyse the importance of contextual factors.

Methods

We conducted an umbrella review of systematic reviews searching the following databases: PubMed, Cochrane Database of Systematic Reviews, Embase and CINAHL. The search focused on quality improvement strategies included in the Cochrane Effective Practice and Organisation of Care Group taxonomy. We extracted data on quality improvement effectiveness and context factors. The latter were categorized according to the Model for Understanding Success in Quality tool.

Results

We included 56 systematic reviews in this study of which only 35 described contextual factors related with the effectiveness of quality improvement interventions. The most frequently reported contextual factors were: quality improvement team (n = 12), quality improvement support and capacity (n = 11), organization (n = 9), micro-system (n = 8), and external environment (n = 4). Overall, context factors were poorly reported. Where they were reported, they seem to explain differences in quality improvement effectiveness; however, publication bias may contribute to the observed differences.

Conclusions

Contextual factors may influence the effectiveness of quality improvement interventions, in particular at the level of the clinical micro-system. Future research on the implementation and effectiveness of quality improvement interventions should emphasize formative evaluation to elicit information on context factors and report on them in a more systematic way in order to better appreciate their relative importance.

Background

Given the amount of time and resources invested in implementing quality programs in hospitals, few studies have investigated their clinical impact and what strategies could be recommended to enhance its effectiveness.

Objective

To assess variations in clinical practice and explore associations with hospital- and department-level quality management systems.

Design

Multicenter, multilevel cross-sectional study.

Setting and participants

Seventy-three acute care hospitals with 276 departments managing acute myocardial infarction, deliveries, hip fracture, and stroke in seven countries.

Intervention

None.

Measures

Predictor variables included 3 hospital- and 4 department-level quality measures. Six measures were collected through direct observation by an external surveyor and one was assessed through a questionnaire completed by hospital quality managers. Dependent variables included 24 clinical practice indicators based on case note reviews covering the 4 conditions (acute myocardial infarction, deliveries, hip fracture and stroke). A directed acyclic graph was used to encode relationships between predictors, outcomes, and covariates and to guide the choice of covariates to control for confounding.

Results and limitations

Data were provided on 9021 clinical records by 276 departments in 73 hospitals. There were substantial variations in compliance with the 24 clinical practice indicators. Weak associations were observed between hospital quality systems and 4 of the 24 indicators, but on analyzing department-level quality systems, strong associations were observed for 8 of the 11 indicators for acute myocardial infarction and stroke. Clinical indicators supported by higher levels of evidence were more frequently associated with quality systems and activities.

Conclusions

There are significant gaps between recommended standards of care and clinical practice in a large sample of hospitals. Implementation of department-level quality strategies was significantly associated with good clinical practice. Further research should aim to develop clinically relevant quality standards for hospital departments, which appear to be more effective than generic hospital-wide quality systems.

Objective

This study aimed to investigate the associations of quality management systems with teamwork and safety climate, and to describe and compare differences in perceptions of teamwork climate and safety climate among clinical leaders and frontline clinicians.

Method

We used a multi-method, cross-sectional approach to collect survey data of quality management systems and perceived teamwork and safety climate. Our data analyses included descriptive and multilevel regression methods.

Setting and participants

Data on implementation of quality management system from seven European countries were evaluated including patient safety culture surveys from 3622 clinical leaders and 4903 frontline clinicians.

Main outcome measures

Perceived teamwork and safety climate.

Results

Teamwork climate was reported as positive by 67% of clinical leaders and 43% of frontline clinicians. Safety climate was perceived as positive by 54% of clinical leaders and 32% of frontline clinicians. We found positive associations between implementation of quality management systems and teamwork and safety climate.

Conclusions

Our findings, which should be placed in a broader clinical quality improvement context, point to the importance of quality management systems as a supportive structural feature for promoting teamwork and safety climate. To gain a deeper understanding of this association, further qualitative and quantitative studies using longitudinally collected data are recommended. The study also confirms that more clinical leaders than frontline clinicians have a positive perception of teamwork and safety climate. Such differences should be accounted for in daily clinical practice and when tailoring initiatives to improve teamwork and safety climate.

Objective

To explore how European hospitals have implemented patient safety strategies (PSS) and evidence-based organization of care pathway (EBOP) recommendations and examine the extent to which implementation varies between countries and hospitals.

Design

Mixed-method multilevel cross-sectional design in seven countries as part of the European Union-funded project 'Deepening our Understanding of Quality improvement in Europe' (DUQuE).

Setting and participants

Seventy-four acute care hospitals with 292 departments managing acute myocardial infarction (AMI), hip fracture, stroke, and obstetric deliveries. Main outcome measure Five multi-item composite measures-one generic measure for PSS and four pathway-specific measures for EBOP.

Results

Potassium chloride had only been removed from general medication stocks in 9.4-30.5% of different pathways wards and patients were adequately identified with wristband in 43.0-59.7%. Although 86.3% of areas treating AMI patients had immediate access to a specialist physician, only 56.0% had arrangements for patients to receive thrombolysis within 30 min of arrival at the hospital. A substantial amount of the total variance observed was due to between-hospital differences in the same country for PSS (65.9%). In EBOP, between-country differences play also an important role (10.1% in AMI to 57.1% in hip fracture).

Conclusions

There were substantial gaps between evidence and practice of PSS and EBOP in a sample of European hospitals and variations due to country differences are more important in EBOP than in PSS, but less important than within-country variations. Agencies supporting the implementation of PSS and EBOP should closely re-examine the effectiveness of their current strategies.

Background

Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS.

Methods

In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable.

Findings

The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90-0·95) in EARLI and 0·88 (0·84-0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81-0·94] vs 0·92 [0·88-0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group).

Interpretation

Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated.

Funding

US National Institutes of Health and European Society of Intensive Care Medicine.