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Medical education and training
Original research
Exploring the relationship between simulation-based team training and sick leave among healthcare professionals: a cohort study across multiple hospital sites
  1. Anders Schram1,
  2. Hanne Irene Jensen2,3,
  3. Maria Gamborg1,4,
  4. Morten Lindhard4,5,
  5. Jan Rölfing1,6,
  6. Gunhild Kjaergaard-Andersen3,7,
  7. Magnus Bie1,
  8. Rune Dall Jensen1
  1. 1MidtSim, Central Denmark Region, Aarhus N, Denmark
  2. 2Department of Anaesthesiology and Intensive Care, Lillebaelt Hospital-University Hospital of Southern Denmark, Kolding, Denmark
  3. 3Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
  4. 4Department of Clinical Medicine, Aarhus Universitet, Aarhus, Denmark
  5. 5Department of Paediatrics, Randers Regional Hospital, Randers, Denmark
  6. 6Department of Orthopaedics, Aarhus Universitet, Aarhus, Denmark
  7. 7Department of Anaesthesiology and Intensive Care, Lillebaelt Hospital-University Hospital of Southern Denmark, Vejle, Denmark
  1. Correspondence to Anders Schram; anders.schram{at}rm.dk

Abstract

Objective Burnout and mental illness are frequent among healthcare professionals, leading to increased sick leave. Simulation-based team training has been shown to improve job satisfaction and mental health among healthcare professionals. This study seeks to investigate the relationship between simulation-based team training and sick leave.

Design Cohort study.

Setting and intervention Five Danish hospitals.

Participants A total of 15 751 individuals were screened for eligibility. To meet the eligibility criteria, individuals had to be employed in the same group (intervention or control) for the whole study period. A total of 14 872 individuals were eligible for analysis in the study.

Intervention From 2017 to 2019, a simulation-based team training intervention was implemented at two hospital sites. Three hospital sites served as the control group.

Outcome measures Data on sick leave from 2015 to 2020 covered five hospital sites. Using a difference-in-difference analysis, the rate of sick leave was compared across hospital sites (intervention vs control) and time periods (before vs after intervention).

Results Significant alterations in sick leave were evident when comparing the intervention and control groups. When comparing groups over time, the increase in sick leave was −0.3% (95% CI −0.6% to −0.0%) lower in the intervention group than in the control group. The difference-in-difference for the complete case analysis showed that this trend remained consistent, with analysis indicating a comparable lower increase in sick leave by −0.7% (95% CI −1.3% to −0.1%) in the intervention group.

Conclusion The increase in sick leave rate was statistically significantly lower in the intervention group, implying that simulation-based team training could serve as a protective factor against sick leave. However, when investigating this simulation intervention over 5 years, other potential factors may have influenced sick leave, so caution is required when interpreting the results.

  • MEDICAL EDUCATION & TRAINING
  • Health Education
  • EPIDEMIOLOGY

Data availability statement

All data relevant to the study are included in the article or uploaded as supplemental information. No additional data available.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2023-076163

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    STRENGTHS AND LIMITATIONS OF THIS STUDY

    • A controlled cohort design was used to investigate the relationship between simulation-based team training and sick leave rates among healthcare professionals.

    • A large sample size of 14 872 eligible individuals enhances statistical power and generalisability.

    • Difference-in-difference analysis was used to compare sick leave rates between intervention and control groups across time periods.

    • There are potential confounding factors beyond training that could have influenced sick leave over the 5-year study period.

    • We have limited understanding of the extent to which the control group performed simulation-based team training.

    Introduction

    Healthcare systems are under increasing pressure, causing an impact on patient safety.1–3 Among healthcare professionals, the consequences are associated with a decline in working conditions, leading to an increase in sick leave and staff turnover.4–7 Work environments in hospitals are currently characterised by high demands and changing working hours, and in some professions, a low salary.8 9 Consequently, stress-related sick leave among healthcare professionals is increasing, and there is a general belief that burnout and lack of employee engagement contribute to a higher employee turnover.4 5 Overall, high levels of sick leave among healthcare professionals contribute to decreased workforce capacity and increased healthcare costs.10–12 To improve the work environment, educational interventions should aim to improve working conditions, provide organisational support and increase employees’ teamwork skills.13 While some work-related issues such as changing work hours and salary are not targetable by education, others such as management of demands and stress, and self-efficacy are possible to improve through educational initiatives.14

    In this context, research has indicated that social factors, such as the quality of relationships with attending physicians, tend to provide greater support to nurses during transitions compared with resilience.15 Additionally, an investigation into interprofessional teams in intensive care revealed that simulation-based team training led to an enhanced perception of collaboration quality among healthcare professionals. This improvement was subsequently associated with staff turnover reduction and a decrease in sick leave in the intervention group.16 In a more recent study, it was suggested that simulation-based training might have a preventative impact on sick leave.17 Consequently, the utilisation of simulation-based team training emerges as a promising approach to mitigating sick leave within the healthcare professional community.18

    Simulation-based team training can be used to imitate real-life scenarios from clinical settings, in which a scene of conditions is created to mirror authentic situations.19 20 Additionally, simulation can help healthcare professionals become conscious of their abilities and develop skills in a safe learning environment.19 21

    It is well-known that healthcare professionals who engage in simulation-based team training improve their teamwork skills.22 23 Such training fosters collaboration, coordination and effective communication among team members. Enhanced teamwork reduces errors, enhances efficiency and has been associated with decreased sick leave.16 19 21 Furthermore, simulation-based training provides healthcare professionals with opportunities to practise and improve their communication skills through effective information exchange, clear instructions and active listening.24–27 Improved communication is argued to minimise misunderstandings, prevent medical errors and foster a positive work environment, thereby potentially reducing sick leave.24–27 However, to our knowledge, no research has investigated if simulation-based team training could be related to long-term sick leave in larger populations of healthcare professionals. During a 5-year period, the present study aimed to investigate how simulation-based team training is related to sick leave.

    Methods

    Study design and setting

    Between April 2017 and December 2018, two Danish hospitals in the Region of Southern Denmark underwent a simulation-based team training intervention, designating them as the intervention group.28 The three remaining Danish hospitals in the same region were designated as the control group. Throughout the study duration, the control group did not have any established policy for simulation training. Discussions with stakeholders revealed that all hospitals in the control group conducted minimal simulation activities. Nevertheless, the extent of potential simulation training provided remained uncertain. Details about the study group characteristics, such as the number of hospital beds and employees, are provided in online supplemental appendix 1.

    Data on sick leave were collected covering 5 years (2015–2020), before, during and after the intervention. Data included detailed registration of individual rates of sick leave and were divided into three time periods, defined as time periods 1, 2 and 3:

    • Time period 1 (before intervention): January 2015–December 2016.

    • Time period 2 (during intervention): January 2017–December 2018.

    • Time period 3 (after intervention): January 2019–December 2019.

    To avoid the influence of COVID-19, time period 3 only covered 12 months. Online supplemental appendix 2 illustrates the time periods of intervention and when data were gathered.

    Intervention

    The allocation of hospitals to the intervention and control groups was determined by the willingness of hospital management to implement the simulation-based team training. Specifically, the two hospitals included in the intervention group were selected based on a commitment to incorporating this educational intervention. Simulation-based team training in the intervention group was introduced using a train-the-trainer approach. By completing a 4-day course, 54 healthcare professionals from a wide range of medical specialties (see online supplemental appendix 3) were trained as simulation facilitators. The hospital management selected the facilitators based on participants’ motivation to participate. The train-the-trainer approach involved three steps. The first step consisted of a 3-day workshop using classroom teaching and practice sessions to teach facilitators basic skills in the briefing, facilitation and debriefing of simulation-based team training. The second step was a 6-week training period in which all facilitators facilitated simulation-based training in their departments. In step three, a 1-day follow-up workshop was arranged for all facilitators to share experiences during the training period, receive feedback and discuss challenges. A detailed description of the simulation facilitator course can be seen in online supplemental appendix 4.

    To elucidate the underlying mechanism and potential connection between the intervention and the rate of sick leave, a logic model was formulated (refer to figure 1).29 Aligned with the study’s objective, figure 1 portrays the inputs that encompass planned resources allocated during the intervention. These inputs encompass the number of simulation sessions performed by healthcare professionals in the intervention group. Activities define the intervention’s substance, encompassing simulation-based team training for healthcare professionals. Outputs encapsulate the services and products offered within the intervention, quantifying the simulation sessions performed within the intervention group. Intermediate outcomes pertain to the results derived from the stated objective, thereby examining the sick leave rate among healthcare professionals. Finally, rooted in simulation-based team training, distal outcomes are defined as long term and encompassing broader potentials. The overall process can potentially be influenced by contextual factors.

    Figure 1
    Figure 1

    Logic model.

    Data collection and analysis

    All simulation activities in the intervention group were registered by the simulation facilitators. No information on simulation activity was collected from the control group. Data on sick leave and sociodemographic characteristics were available from an ongoing administrative Human Resources database, covering all employment-related information in the Region of Southern Denmark. Sociodemographic characteristics included gender, age, profession and workplace (hospital and department). The rate of sick leave was determined by dividing the hours of sick leave for each staff member by their corresponding portion of employed hours and subsequently multiplying the quotient by 100. Thus, part-time employment or change in the workplace was taken into account. The rate of sick leave was analysed using the percentage rate of sick leave and SD.

    To explore the research questions concerning the relationship between simulation-based team training and sick leave, the main analysis included a difference-in-difference analysis, comparing changes in sick leave over time between the intervention group and the control group. A change in the percentage rate of sick leave indicated a higher or lower rate of sick leave.

    Sick leave was explored in two separate analyses. The main analysis aimed to investigate sick leave including all healthcare professionals. In a sensitivity analysis, sick leave was examined only for healthcare professionals, who had been employed in the same department during all three time periods, referred to as the ‘complete case analysis’. Paired and unpaired t-tests were applied to make comparisons across time periods and groups. Histograms illustrating the distribution of data are shown in online supplemental appendix 5. Furthermore, since sick leave was not normally distributed, a non-parametric Wilcoxon signed-rank test was used to compare sick leave in the complete case analysis (online supplemental appendix 5). Due to the large sample size, no rank sum test was used to compare sick leave among all participants.30

    To ensure that the groups were comparable, only identical specialties across the intervention group and control group were eligible (online supplemental appendix 3). Individuals employed in both the intervention group and control group were excluded (figure 2).

    Figure 2
    Figure 2

    Study flow chart.

    Finally, unpaired t-tests were applied to illustrate an overview of changes in sick leave at individual control hospitals, which is illustrated in online supplemental appendix 6. These tests were exclusively conducted within the control hospitals, as the intervention hospitals are encompassed as one unit, consequently preventing a distinct overview due to data limitations.

    Patient and public involvement

    None.

    Results

    In total, 15 751 healthcare professionals were screened for eligibility, of which 879 did not meet the inclusion criteria (figure 2). Thus, data from 14 872 individuals were available for analysis.

    Baseline characteristics for each designated time period and study group are comprehensively outlined in table 1. In evaluating the percentages across different groups and time frames, a notable similarity was observed in terms of gender distribution, age distribution and the distribution of professions.

    View this table:
    Table 1

    Baseline characteristics divided into time periods and study groups

    Online supplemental appendix 6 provides an overview of the change in sick leave at individual control hospitals. The variation in sick leave remains relatively consistent over time across the three hospitals. Across the three control hospital sites, there are negligible fluctuations in sick leave ranging from 0.3% to 0.6%, between time period 1 and time period 3.

    Simulation sessions

    The number of simulated sessions among staff members was logged in the intervention group, with a total of 210 individual simulation sessions, which were performed by the 54 educated simulation facilitators. A mean of 6.9 (min=2 and max=22) of healthcare professionals participated in the simulation-based training sessions. Participants had the opportunity to take part in multiple simulation sessions.

    The learning objectives in the simulation sessions performed among staff members were both technical and non-technical; for example, sepsis treatment, cardiac arrest treatment, ABCDE (Airway, Breathing, Circulation, Disability, Exposure) approach, ISBAR (Identify, Situation, Background, Assessment and Recommendation) communication, closed-loop communication and leadership, which is illustrated in online supplemental appendix 7.31–33 An overview of the characteristics of simulation sessions at each intervention hospital is illustrated in online supplemental appendix 7. No information on simulation activity was collected from the control group.

    Sick leave

    Sick leave was examined in two analyses including (1) all participants and (2) the complete case analysis, referring to healthcare professionals employed in the same department during all three time periods.

    All participants

    To investigate how simulation-based team training is related to sick leave, table 2 shows the rate of sick leave across study groups and time periods. The rate of sick leave was lower in the intervention group compared with the control group at all three time periods. From time period 1 to time period 3, sick leave increased statistically significantly by 0.4% in the control group, whereas only a difference of 0.1% was observed in the intervention group. The difference-in-difference analysis comparing sick leave across groups from time period 1 to time period 3 indicates that sick leave increased statistically significantly by −0.3% (95% CI −0.6%; −0.0%) lower in the intervention group. A calculated Hedges’ g value of 0.04 suggests a small effect size. Histograms showed a right-sided distribution of data on sick leave (online supplemental appendix 5).

    View this table:
    Table 2

    Rate of sick leave before, during and after intervention among all participants

    Complete case analysis

    Table 3 presents the complete case analysis. This table further elucidates that a relation between simulation-based team training and sick leave seems to occur. When comparing groups, sick leave was similar at time period 1 and time period 2, though statistically significantly higher in the control group at time period 3. The rate of sick leave increased statistically significantly across time periods in both groups. The difference-in-difference analysis from time period 1 to time period 3 revealed a significantly lower increase in sick leave by −0.7% (95% CI −1.3%; −0.1%) in the intervention group. A calculated Hedges’ g value of 0.07 suggests a small effect size. Histograms showed a right-sided distribution of data on sick leave, whereas the non-parametric Wilcoxon signed-rank test supported a significant difference (p<0.0001) in sick leave from time period 2 to time period 3 (online supplemental appendix 5).

    View this table:
    Table 3

    Complete case analysis of rate of sick leave before, during and after intervention

    Discussion

    The current study examines the relationship between simulation-based team training and sick leave over a 5-year span encompassing five hospital sites. The primary investigation employed a difference-in-difference analysis, contrasting alterations in sick leave between the intervention and control groups. Notably, during time periods 1–3, findings reveal a lower statistically significant increase in the rate of sick leave within the intervention group, when compared with the control group. This outcome emerged following 210 simulations performed by healthcare professionals in the intervention group, potentially suggesting a protective effect of simulation-based team training. To define the effect size, Hedges’ g value was calculated among all participants (Hedges’ g=0.04) and in the complete case analysis (Hedges’ g=0.07). This indicates that the observed difference between the variables is quite subtle. The small effect size indicates that the practical significance of the difference may be constrained, and the association between simulation-based team training and sick leave might be limited. To determine tangible gains, the current data indicate that an average healthcare professional works 1743 hours over a year. A reduction of 0.3% in sick leave would translate to a gain of 5.2 working hours within the same 1-year time frame. Among the approximately 2000 healthcare professionals employed at the intervention hospitals, this would translate into more than 10 000 working hours.

    Sick leave was measured in two separate analyses. One analysis (table 2) included all participants, whereas another analysis (table 3) only included individuals who had been employed during all three time periods, characterised as a complete case analysis. Several studies indicate that elements such as job satisfaction, working conditions and safety climate differ when comparing all participants and healthcare professionals, who had been employed in the same department during all three time periods (complete case analysis).22 23 34 Thus, it was of interest to investigate the rate of sick leave among healthcare professionals, who were employed throughout the whole study period. However, the findings were similar when comparing these two analyses.

    The rate of sick leave is likely to be dependent on a variety of elements, including simulation-based team training. Although simulation training might have a role in this context, the observed rise in sick leave in the control group and among all participants highlights the intricate nature of sick leave, being dependent on a myriad of factors. While simulation-based team training can contribute to alleviating specific work-related triggers for sick leave, its scope is limited in addressing the comprehensive spectrum of underlying issues. Notably, variables like group dynamics, stress levels, pre-existing health conditions and external environmental factors could all be related to sick leave.4–7 35 36

    When exploring the significance of simulation-based team training in the empirical landscape though, available evidence suggests that simulation-based team training contributes positively to enhancing teamwork dynamics and potentially improving the quality of patient care.25 37–39 Additionally, prior research showcases its positive contributions to fostering professional relationships and enhancing the psychological well-being of healthcare practitioners.25 40 While the current study’s scope does not encompass relational dynamics, it does shed light on the quantifiable relation between simulation-based team training and sick leave. This suggests that interpersonal factors might contribute to the observed sick leave outcomes. Moreover, these findings strengthen the proposition that simulation-based team training can effectively curtail sick leave occurrences, with strong colleague connections and a supportive work environment acting as vital buffers against chronic stress.24 41 Nevertheless, there remains considerable ambiguity regarding the specific ways in which these elements relate to the dynamics of sick leave and stress, as well as the extent to which they interconnect with organisational and individual characteristics.

    Strengths and limitations

    The present study used data on the rate of sick leave among 14 872 healthcare professionals. The study included a control group, which is considered rare among studies investigating the advantages of simulation-based team training.42–47 Although this strengthens the study, there are several limitations.

    The fluctuation of sick leave within the control group points to the multifaceted aspect of sick leave which is shown in previous research.4 5 Figure 1 illustrates how contextual factors can be related to the intervention process and subsequent outcomes. Recognising the context in intervention studies becomes crucial for interpreting evaluation findings and extrapolating them beyond the study scope.48

    When investigating sick leave over 5 years (2015–2020), it is impossible to isolate the exposure of simulation-based team training and thus avoid possible confounders. Changes in context can potentially be related to rates of sick leave across all healthcare professionals or specifically within the intervention or control groups. Factors like organisational modifications, resource constraints and shifts in responsibilities are shown to be significantly related to staff burnout and sick leave.10 49 Additionally, a relationship between management and sick leave rates is well documented.50 Within the study’s timeline, alterations in departmental management could have contributed to variations in sick leave rates. To address this, online supplemental appendix 6 demonstrates the changes in sick leave within individual control hospitals, revealing comparable trends across these hospitals and lessening the likelihood of confounding factors specific to certain settings.

    However, it is important to recognise that the management in the intervention group was committed to educational interventions. This commitment may reflect an organisational culture that encourages adopting such initiatives, potentially related to sick leave. In line with the present study, Lund et al examined sick leave among Danish employees and identified long-term sick leave associations with gender, age and educational level.51 The adjustment for sociodemographic characteristics was not emphasised in this study, given the similarity across sociodemographic characteristics. Yet, future research investigating the association between sick leave among healthcare professionals and sociodemographic characteristics is relevant. Notably, the present study encompassed five hospitals with hospital size and bed count discrepancies when comparing intervention and control groups (online supplemental appendix 1). The extent to which such differences might be related to sick leave rates remains uncertain.

    The present study only gathered data on the quantity of performed simulation sessions in the intervention group. It is unknown to what extent the control group performed simulation-based team training, which could lead to overestimating or underestimating findings in the study and thus impact both the internal and external validity. However, no known major reorganisations or major staff turnovers occurred in either the intervention or control hospitals during the study period.

    It is possible that the degree of exposure, as indicated by the proportion of completed simulation sessions, might not have reached the required threshold to significantly decrease the rate of sick leave. This level of exposure underscores the significance of evaluating the potency of an educational intervention when seeking to enhance sick leave outcomes. Data on sick leave were available from a central organisation in the Region of Southern Denmark, which enhances generalisability. Although the validity of sick leave data was not assessed, we expect the registration of sick leave to be accurate as these records are legal documents.52 While time period 1 (2015–2017) and time period 2 (2017–2019) covered 24 months, time period 3 (2019–2020) covered 12 months. Data from 2020 to 2021 were excluded due to COVID-19. As expected, these data showed an increase in the sick leave rate among all participants.

    By using mean values to describe sick leave rates, the polarised and nuanced distribution of the data may have been overlooked.53 Histograms in online supplemental appendix 5 covering data from tables 2 and 3 showed a right-sided distribution of data. This distribution of data was expected, since the rate of sick leave could not be less than 0%, meaning the proportion of healthcare professionals with a high rate of sick leave would influence the distribution of data.

    Conclusion

    The present study explored the relationship between simulation-based team training and sick leave among healthcare professionals. Based on the difference-in-difference analysis, the increase in sick leave rate was statistically significantly lower in the intervention group. These results imply that simulation-based team training may relate to the work environment and serve as a protective practice against sick leave among healthcare professionals. However, calculations for effect size indicated that the practical significance of the difference may be limited. When aiming to detect variations in complex factors like sick leave rates, this emphasises the importance of the extent of exposure in educational interventions. Furthermore, when investigating simulation interventions over several years, other potential factors may be related to sick leave, so caution is needed in interpreting and generalising these results.

    Data availability statement

    All data relevant to the study are included in the article or uploaded as supplemental information. No additional data available.

    Ethics statements

    Patient consent for publication

    Ethics approval

    This study was registered at the Regional Ethics Committee (no. 1-16-02-43-23). According to current legislation, participants were not required to provide informed consent. After also requesting if the study should be registered at the Central Denmark Region Committees on Health Research Ethics (request 88/2023), the committee decided that the study should not be notified to the committees.

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    Supplementary materials

    Footnotes

    • Contributors AS, HIJ, ML, JR, MG, GK-A, MB and RDJ contributed to the study conception, design, material preparation and data collection. AS conducted the analysis. AS, RDJ and MG wrote the first draft of the manuscript. All authors contributed to the subsequent versions and the final manuscript. All authors read and approved the final manuscript. AS was responsible for the overall content as guarantor.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.