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Introduction: Adverse events in health care affect 8% to 12% of patients admitted to hospitals in the European Union (EU), with surgical adverse events being the most common types reported. Aim: SAFEST project aims to enhance perioperative care quality and patient safety by establishing and implementing widely supported evidence-based perioperative patient safety practices to reduce surgical adverse events. Methods: We will conduct a mixed-methods hybrid type III implementation study supporting the development and adoption of evidence-based practices through a Quality Improvement Learning Collaborative (QILC) in co-creation with stakeholders. The project will be conducted in 10 hospitals and related healthcare facilities of 5 European countries. We will assess the level of adherence to the standardised practices, as well as surgical complications incidence, patient-reported outcomes, contextual factors influencing the implementation of the patient safety practices, and sustainability. The project will consist of six components: 1) Development of patient safety standardised practices in perioperative care; 2) Guided self-evaluation of the standardised practices; 3) Identification of priorities and actions plans; 4) Implementation of a QILC strategy; 5) Evaluation of the strategy effectiveness; 6) Patient empowerment for patient safety. Sustainability of the project will be ensured by systematic assessment of sustainability factors and business plans. Towards the end of the project, a call for participation will be launched to allow other hospitals to conduct the self-evaluation of the standardized practices. Discussion: The SAFEST project will promote patient safety standardized practices in the continuum of care for adult patients undergoing surgery. This project will result in a broad implementation of evidence-based practices for perioperative care, spanning from the care provided before hospital admission to post-operative recovery at home or outpatient facilities. Different implementation challenges will be faced in the application of the evidence-based practices, which will be mitigated by developing context-specific implementation strategies. Results will be disseminated in peer-reviewed publications and will be available in an online platform. [ABSTRACT FROM AUTHOR]

Abstract Background Hospitals in European countries apply a wide range of quality improvement strategies. Knowledge of the effectiveness of these strategies, implemented as part of an overall hospital quality improvement system, is limited. Methods/Design We propose to study the relationships among organisational quality improvement systems, patient empowerment, organisational culture, professionals' involvement with the quality of hospital care, including clinical effectiveness, patient safety and patient involvement. We will employ a cross-sectional, multi-level study design in which patient-level measurements are nested in hospital departments, which are in turn nested in hospitals in different EU countries. Mixed methods will be used for data collection, measurement and analysis. Hospital/care pathway level constructs that will be assessed include external pressure, hospital governance, quality improvement system, patient empowerment in quality improvement, organisational culture and professional involvement. These constructs will be assessed using questionnaires. Patient-level constructs include clinical effectiveness, patient safety and patient involvement, and will be assessed using audit of patient records, routine data and patient surveys. For the assessment of hospital and pathway level constructs we will collect data from randomly selected hospitals in eight countries. For a sample of hospitals in each country we will carry out additional data collection at patient-level related to four conditions (stroke, acute myocardial infarction, hip fracture and delivery). In addition, structural components of quality improvement systems will be assessed using visits by experienced external assessors. Data analysis will include descriptive statistics and graphical representations and methods for data reduction, classification techniques and psychometric analysis, before moving to bi-variate and multivariate analysis. The latter will be conducted at hospital and multilevel. In addition, we will apply sophisticated methodological elements such as the use of causal diagrams, outcome modelling, double robust estimation and detailed sensitivity analysis or multiple bias analyses to assess the impact of the various sources of bias. Discussion Products of the project will include a catalogue of instruments and tools that can be used to build departmental or hospital quality and safety programme and an appraisal scheme to assess the maturity of the quality improvement system for use by hospitals and by purchasers to contract hospitals.

Self-management interventions (SMIs) offer a promising approach to actively engage patients in the management of their chronic diseases. Within the scope of the COMPAR-EU project, our goal is to provide evidence-based recommendations for the utilisation and implementation of SMIs in the care of adult individuals with type 2 diabetes mellitus (T2DM). A multidisciplinary panel of experts, utilising a core outcome set (COS), identified critical outcomes and established effect thresholds for each outcome. The panel formulated recommendations using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach, a transparent and rigorous framework for developing and presenting the best available evidence for the formulation of recommendations. All recommendations are based on systematic reviews (SR) of the effects and of values and preferences, a contextual analysis, and a cost-effectiveness analysis. The COMPAR-EU panel is in favour of using SMIs rather than usual care (UC) alone (conditional, very low certainty of the evidence). Furthermore, the panel specifically is in favour of using ten selected SMIs, rather than UC alone (conditional, low certainty of the evidence), mostly encompassing education, self-monitoring, and behavioural techniques. The panel acknowledges that, for most SMIs, moderate resource requirements exist, and cost-effectiveness analyses do not distinctly favour either the SMI or UC. Additionally, it recognises that SMIs are likely to enhance equity, deeming them acceptable and feasible for implementation. [ABSTRACT FROM AUTHOR]

This study, as part of the COMPAR-EU project, utilized a mixed-methods approach involving 37 individual, semi-structured interviews and one focus group with 7 participants to investigate the factors influencing the implementation and use of self-management interventions (SMIs) decision tools in clinical practice. The interviews and focus group discussions were guided by a tailored interview and focus group guideline developed based on the Tailored Implementation for Chronic Diseases (TICD) framework. The data were analyzed using a directed qualitative content analysis, with a deductive coding system based on the TICD framework and an inductive coding process. A rapid analysis technique was employed to summarize and synthesize the findings. The study identified five main dimensions and facilitators for implementation: decision tool factors, individual health professional factors, interaction factors, organizational factors, and social, political, and legal factors. The findings highlight the importance of structured implementation through SMI decision support tools, emphasizing the need to understand their benefits, secure organizational resources, and gain political support for sustainable implementation. Overall, this study employed a systematic approach, combining qualitative methods and comprehensive analysis, to gain insights into the factors influencing the implementation of SMIs' decision-support tools in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Cooperation is a core feature of integrated healthcare systems and an important link in their value-creating mechanism. The premise is that providers who cooperate can promote more efficient use of health services while improving health outcomes. We studied the performance of an integrated healthcare system in improving regional cooperation. Methods: Using claims data and social network analysis, we constructed the professional network from 2004 to 2017. Cooperation was studied by analyzing the evolution of network properties at network and physician practice (node) level. The impact of the integrated system was studied with a dynamic panel model that compared practices that participated in the integrated system versus nonparticipants. Results: The regional network evolved favourably towards cooperation. Network density increased 1.4% on average per year, while mean distance decreased 0.78%. At the same time, practices participating in the integrated system became more cooperative compared to other practices in the region: Degree (1.64e-03, p = 0.07), eigenvector (3.27e-03, p = 0.06) and betweenness (4.56e-03, p < 0.001) centrality increased more for participating practices. Discussion: Findings can be explained by the holistic approach to patients’ care needs and coordination efforts of integrated healthcare. The paper provides a valuable design for performance assessment of professional cooperation. Highlights •Using claims data and social network analysis, we identify a regional cooperation network and conduct a panel analysis to measure the impact of an integrated care initiative on enhancing professional cooperation. •Physician practices participating in the integrated system became more cooperative and improved their influence in the regional network more than non-participating practices. •Integrated healthcare systems effectively incentivize cooperation through a holistic approach to patient care needs and coordination efforts. [ABSTRACT FROM AUTHOR]

Our aim was to predict future high-cost patients with machine learning using healthcare claims data. We applied a random forest (RF), a gradient boosting machine (GBM), an artificial neural network (ANN) and a logistic regression (LR) to predict high-cost patients in the following year. Therefore, we exploited routinely collected sickness funds claims and cost data of the years 2016, 2017 and 2018. Various specifications of each algorithm were trained and cross-validated on training data (n = 20,984) with claims and cost data from 2016 and outcomes from 2017. The best performing specifications of each algorithm were selected based on validation dataset performance. For performance comparison, selected models were applied to unforeseen data with features of the year 2017 and outcomes of the year 2018 (n = 21,146). The RF was the best performing algorithm measured by the area under the receiver operating curve (AUC) with a value of 0.883 (95% confidence interval (CI): 0.872–0.893) on test data, followed by the GBM (AUC = 0.878; 95% CI: 0.867–0.889). The ANN (AUC = 0.846; 95% CI: 0.834–0.857) and LR (AUC = 0.839; 95% CI: 0.826–0.852) were significantly outperformed by the GBM and the RF. All ML algorithms and the LR performed ´good´ (i.e. 0.9 > AUC ≥ 0.8). We were able to develop machine learning models that predict high-cost patients with 'good' performance facilitating routinely collected sickness fund claims and cost data. We found that tree-based models performed best and outperformed the ANN and LR. [ABSTRACT FROM AUTHOR]