Predicting inhospital admission at the emergency department: a systematic review.

BACKGROUND: ED crowding has potential detrimental consequences for both patient care and staff. Advancing disposition can reduce crowding. This may be achieved by using prediction models for admission. This systematic review aims to present an overview of prediction models for admission at the ED. Furthermore, we aimed to identify the best prediction tool based on its performance, validation, calibration and clinical usability. METHODS: We included observational studies published in Embase.com, Medline Ovid, Cochrane CENTRAL, Web of Science Core Collection or Google scholar, in which admission models were developed or validated in a general medical population in European EDs including the UK. We used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist to assess quality of model development. Model performance was presented as discrimination and calibration. The search was performed on 11 October 2020. RESULTS: In total, 18 539 articles were identified. We included 11 studies, describing 16 different models, comprising the development of 9 models and 12 external validations of 11 models. The risk of bias of the development studies was considered low to medium. Discrimination, as represented by the area under the curve ranged from 0.630 to 0.878. Calibration was assessed in seven models and was strong. The best performing models are the models of Lucke et al and Cameron et al. These models combine clinical applicability, by inclusion of readily available parameters, and appropriate discrimination, calibration and validation. CONCLUSION: None of the models are yet implemented in EDs. Further research is needed to assess the applicability and implementation of the best performing models in the ED. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42017057975.

Do patients' disruptive behaviours influence the accuracy of a doctor's diagnosis? A randomised experiment.

BACKGROUND: Literature suggests that patients who display disruptive behaviours in the consulting room fuel negative emotions in doctors. These emotions, in turn, are said to cause diagnostic errors. Evidence substantiating this claim is however lacking. The purpose of the present experiment was to study the effect of such difficult patients' behaviours on doctors' diagnostic performance. METHODS: We created six vignettes in which patients were depicted as difficult (displaying distressing behaviours) or neutral. Three clinical cases were deemed to be diagnostically simple and three deemed diagnostically complex. Sixty-three family practice residents were asked to evaluate the vignettes and make the patient's diagnosis quickly and then through deliberate reflection. In addition, amount of time needed to arrive at a diagnosis was measured. Finally, the participants rated the patient's likability. RESULTS: Mean diagnostic accuracy scores (range 0-1) were significantly lower for difficult than for neutral patients (0.54 vs 0.64; p=0.017). Overall diagnostic accuracy was higher for simple than for complex cases. Deliberate reflection upon the case improved initial diagnostic, regardless of case complexity and of patient behaviours (0.60 vs 0.68, p=0.002). Amount of time needed to diagnose the case was similar regardless of the patient's behaviour. Finally, average likability ratings were lower for difficult than for neutral-patient cases. CONCLUSIONS: Disruptive behaviours displayed by patients seem to induce doctors to make diagnostic errors. Interestingly, the confrontation with difficult patients does however not cause the doctor to spend less time on such case. Time can therefore not be considered an intermediary between the way the patient is perceived, his or her likability and diagnostic performance.