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.

Cross-sectional study using routinely collected organisational data.

A tertiary-care academic hospital in the Netherlands.

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 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.

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.

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.

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.

Systematic review following the preferred reporting items for systematic reviews and meta-analyses 2020 guidelines.

Web of Science, PubMed, Embase, Scopus, PsycINFO, Dimensions and Lens were searched up to December 2025.

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.

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.

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.

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.

CRD42024592858.

To compare the quality and time efficiency of physician-written summaries with customised large language model (LLM)-generated medical summaries integrated into the electronic health record (EHR) in a non-English clinical environment.

Cross-sectional non-inferiority validation study.

Tertiary academic hospital.

52 physicians from 8 specialties at a large Dutch academic hospital participated, either in writing summaries (n=42) or evaluating them (n=10).

Physician writers wrote summaries of 50 patient records. LLM-generated summaries were created for the same records using an EHR-integrated LLM. An independent, blinded panel of physician evaluators compared physician-written summaries to LLM-generated summaries.

Primary outcome measures were completeness, correctness and conciseness (on a 5-point Likert scale). Secondary outcomes were preference and trust, and time to generate either the physician-written or LLM-generated summary.

The completeness and correctness of LLM-generated summaries did not differ significantly from physician-written summaries. However, LLM summaries were less concise (3.0 vs 3.5, p=0.001). Overall evaluation scores were similar (3.4 vs 3.3, p=0.373), with 57% of evaluators preferring LLM-generated summaries. Trust in both summary types was comparable, and interobserver variability showed excellent reliability (intraclass correlation coefficient 0.975). Physicians took an average of 7 min per summary, while LLMs completed the same task in just 15.7 s.

LLM-generated summaries are comparable to physician-written summaries in completeness and correctness, although slightly less concise. With a clear time-saving benefit, LLMs could help reduce clinicians’ administrative burden without compromising summary quality.

Maintaining a healthy workforce is crucial for safe, high-quality care. To enhance well-being and engagement in Dutch university medical centres (UMCs), an overview of staff well-being and job perceptions is needed first. Surveys are widely used to improve working conditions, but varying questionnaires hinder a comprehensive view. This study aimed to evaluate the content of employee surveys currently used in UMCs in the Netherlands from a well-being perspective and to analyse the survey results at a national level.

All seven UMCs were approached to participate in the study and share employee survey data. The primary outcome of interest is work experience; a secondary analysis was conducted. Items were categorised following the Job Demands-Resources model. Descriptive statistics were presented as percentages, means and medians with IQRs.

Two UMCs participated and 31 862 completed surveys were included. Variation in survey items (eg, 15–18 subcategories, 21–33 question items), response options (eg, 1–5, 1–10), frequency (1–3 times per year) and timing were found. Scores on the following outcomes are presented: work overload, coworker support, job control, organisational justice, participation in decision-making, performance feedback, possibilities for learning and development, recognition, task variety, team atmosphere, team effectiveness, trust in leadership, other job resources, connecting/inspiring leadership, self-efficacy, goal-directiveness, boredom, burnout, job satisfaction, work engagement, other employee well-being, commitment organisation/team and work ability. Results should be interpreted with caution, and solely found for hospital A, for certain job control items, median scores of 2 or 3 were observed, whereas the majority of other question items revealed a median score of 4.

There is a significant lack of cohesion across employee surveys. As it stands, employee surveys in Dutch UMCs are not effective tools for monitoring the work experience or well-being of the healthcare workforce. While these surveys may support management decisions, this support is not reflected in interventions related to work and the work environment.