Evaluating the Effectiveness of the SPIKES Model to Break Bad News - A Systematic Review.
Meera MahendiranHerman YeungSamantha RossiHouman KhosravaniGiulia-Anna PerriPublished in: The American journal of hospice & palliative care (2023)
Introduction : Breaking bad news to patients and families can be challenging for healthcare providers. The present study conducted a systematic review of the literature to determine if formal communication training using the SPIKES protocol improves learner satisfaction, knowledge, performance, or system outcomes. Method : MEDLINE, Embase, CINAHL Plus (Nursing & Allied Health Sciences), and PsycINFO Databases were searched with keywords BAD NEWS and SPIKES. Studies were required to have an intervention using the SPIKES model and an outcome that addressed at least one of the four domains of the Kirkpatrick model for evaluating training effectiveness. The Cochrane Risk of Bias Tool was used to conduct a risk of bias assessment. Due to heterogeneity in the interventions and outcomes, meta-analysis was not undertaken and instead, a narrative synthesis was used with the information provided in the tables to summarise the main findings of the included studies. Results : Of 622 studies screened, 37 publications met the inclusion criteria. Interventions ranged from the use of didactic lecture, role play with standardised patients (SPs), video use, debriefing sessions, and computer simulations. Evaluation tools ranged from pre and post intervention questionnaires, OSCE performance with rating by independent raters and SPs, and reflective essay writing. Conclusions : Our systematic review demonstrated that the SPIKES protocol is associated with improved learner satisfaction, knowledge and performance. None of the studies in our review examined system outcomes. As such, further educational development and research is needed to evaluate the impact of patient outcomes, including the optimal components and length of intervention.
Keyphrases
- systematic review
- healthcare
- randomized controlled trial
- end stage renal disease
- case control
- meta analyses
- ejection fraction
- newly diagnosed
- chronic kidney disease
- physical activity
- prognostic factors
- peritoneal dialysis
- mental health
- public health
- type diabetes
- machine learning
- skeletal muscle
- deep learning
- social media
- molecular dynamics
- patient reported
- big data
- health promotion
- patient satisfaction