Tom Bazuin

Background: Hospitals frequently seek to improve care by copying best practices from high-performing peers, yet comparable outcomes are often not achieved. While implementation science recognises context as a key determinant of success, existing frameworks are generic and unequipped to actively map and compare the unique contextual conditions of both the source and receiving organisations. To bridge this gap, we developed the implementation-oriented Two-Dimensional Practice Context (2DPC) framework capturing the context of best practices in hospital care to support their emulation in other settings. Methods: We developed the 2DPC-framework through a two-phase scoping review and evidence-mapping study. Phase 1 built the framework’s two axes. Searching MEDLINE for publications on the contextual factors that shape best practices in healthcare, we thematically synthesised these factors into distinct domains (Step 1a) and arrayed them against established levels of implementation context (Step 1b). Phase 2 populated the resulting grid. A second MEDLINE search identified empirical studies that implemented or evaluated best practices in hospital settings, and from each we extracted the reported contextual influences, mapped them onto the grid, and thematically analysed the contents of every cell. Results: Phase 1 included 13 publications and yielded six best-practice context domains (Support, Knowledge, Coordination, Resources, Quality monitoring, and Patient-centeredness), organised against established implementation context levels into the 2DPC-framework. Phase 2 included 173 empirical studies, from which 992 contextual extracts were identified, predominantly barriers ( n = 881), and a smaller fraction facilitators ( n = 108). The most populated implementation levels were healthcare professionals ( n = 261), organisational support ( n = 257), and the wider environment ( n = 147). The most populated domains were Knowledge ( n = 271), Resources ( n = 249), Coordination ( n = 199), and Support ( n = 129), while Quality monitoring ( n = 20) and Patient-centeredness ( n = 24) were less frequently represented. Conclusions: Rather than generic solutions, replicating best practices requires a granular alignment between the source and receiving hospital’s context. The 2DPC framework operationalises this, providing an actionable mapping tool for healthcare leaders to evaluate and compare implementation readiness across settings. Furthermore, by exposing a critical deficit in how best practices are documented, this framework challenges both practitioners and researchers to shift from tracking implementation barriers toward actively capturing the enabling conditions that drive success.

Objectives: Well-being of healthcare professionals (HCPs) is vital for care quality, staff retention and overall healthcare system effectiveness. This study aims to identify the organisational and workplace variables associated with sick leave and measures of engagement of HCPs on department level within a single Dutch academic hospital. Design: Cross-sectional study using routinely collected organisational data. Setting: A tertiary-care academic hospital in the Netherlands. Participants: 25 clinical departments were included. Department level variables were derived from routinely collected hospital databases. Availability of data varied across variables. Analysis included information on patient population, human resources, care processes, quality of care and employee and patient experiences to assess differences, correlations and predictors for sick leave and engagement. Primary and secondary outcome measures: Primary outcome measures were (1) sick leave (%) and (2) engagement, assessed through two staff-survey items (vitality and connectedness; 0–10 Numeric Rating Scale). Both outcomes were analysed at department level. Results: Employee population data showed the most consistent patterns across analyses. Departments with higher staffing capacity had higher sick leave and lower engagement in group comparisons (p=0.009, p=0.030, respectively). In multivariable models, higher staffing capacity remained associated with increased sick leave (B=1.38, 95% CI 0.53 to 2.23, p=0.003). Engagement was positively associated with higher inflow (B=0.92, 95% CI 0.06 to 1.77, p=0.037) and negatively associated with outflow (B = −1.36, 95% CI −2.08 to −0.63, p=0.001). No consistent associations were found with patient population and patient experience measures. Conclusions: Workforce-related factors, particularly staffing capacity and inflow and outflow, are strongly linked to sick leave and engagement. Routinely collected hospital data can be used to identify at-risk departments and inform targeted strategies for improving workforce sustainability. Future studies should explore more granular, team-level data to better support staff well-being and care quality.

Background: Digital health technologies (DHTs) are increasingly implemented to improve efficiency, quality, and patient experience. However, conditions that could influence the impact on well-being of healthcare professionals (HCPs) are rarely assessed. Objective: To develop a tool to support policymakers, hospital administrators, and implementation teams in considering workforce experience and well-being implications of DHTs on HCPs. Methods: A mixed-methods study was conducted in three Dutch university medical centres. First, we re-analysed a scoping review on DHT-implementation, applying criteria focusing on HCP-related determinants and thematically analysing these within a modified Work-System model and Quintuple Aim framework. Second, we conducted a prioritisation survey using the Reweighted Priority-Setting tool to rank themes related to Care-team well-being , one of the Quintuple Aim domains. Third, we explored the highest-ranked themes in focus-groups and interviews with HCPs and stakeholders. Finally, we synthesised findings into a tool. Results: From 73 eligible studies we extracted determinants, generating themes and subthemes, of which 44 mapped to Care-team well-being , and were used as survey items. Twelve respondents (eight HCPs and four policy-staff) prioritised these. Top-ranked themes included perceived benefits, acceptance, involvement in development and implementation, ‘champions’, training and skills, communication, and role clarity. Analysis of two focus-groups and three interviews yielded six domains: acceptance, champions, process and development, role of the professional, communication, and training. These informed a 14-item checklist for use during DHT-implementation. Conclusion: We developed a reflective checklist to support structured consideration of conditions shaping workforce experience in DHT-implementation. The tool offers a starting point for embedding workforce conditions in DHT-governance.

Objectives: The functional resonance analysis method (FRAM) is increasingly used to analyse healthcare processes. FRAM uses four steps to analyse a process and its potential variability. We systematically reviewed studies using FRAM in healthcare on how the four steps in FRAM are reported, defined and supported by data. Design: Systematic review following the preferred reporting items for systematic reviews and meta-analyses 2020 guidelines. Data sources: Web of Science, PubMed, Embase, Scopus, PsycINFO, Dimensions and Lens were searched up to December 2025. Eligibility criteria for selecting studies: All peer-reviewed studies using FRAM in a healthcare context that presented a FRAM visualisation were included. The papers had to be written in English. Data extraction and synthesis: Two independent reviewers screened titles and abstracts, and subsequently the full text of selected papers. Data was extracted reporting on the steps of FRAM, how functions were supported by data, and the functions and couplings of the visualisations. Results: Sixty-eight papers were included, of which 20 (29%) reported at least one aspect of all four steps in FRAM. While most studies (85%) described how functions were supported by data, the methods used varied widely. Terminology was interpreted differently concerning variability, the output of variability and the effect of combined variability. Conclusion: Most FRAM studies in healthcare do not report all steps of FRAM, and interpretations of key terms differ. FRAM studies should more clearly describe which steps of the method are conducted, and how data is collected and analysed. Refinement of FRAM guidelines, particularly on data use and terminology, would enhance consistency and comparability across studies. PROSPERO registration number: CRD42024592858.