Article type
Year
Abstract
Background: Every year, volunteers of the Belgian Red Cross (BRC) provide preventive medical aid at more than 8000 mass gatherings and smaller events. For optimal use of resources (personnel, materials, and money), it is important to be able to predict patient load and health care needs at these events. The BRC’s Medical Triage and Registration Informatics System (MedTRIS), containing data on more than 200,000 interventions at mass gatherings during the last 10 years, is a valuable source of information to build a predictive model on.
Objectives: To develop and validate a prediction model of patient presentation rate (PPR) and transfer to hospital rate (TTHR) at mass gatherings in Belgium, based on the 3 pillars of evidence-based practice (EBP): scientific evidence, in-house expertise and experience of health professionals and volunteer representatives, and preferences and resources of the target group (BRC volunteers).
Methods: We conducted a systematic review to identify potential environmental and event-specific predictors of PPR and TTHR at mass gatherings. Subsequently, we developed a nonlinear prediction model, containing these variables and using regression trees, with a subset of 28 mass gatherings in MedTRIS, and validated the model with another subset. Throughout the project, we regularly met with experienced volunteer representatives (e.g. first aid responders) and health professionals (e.g. emergency physicians), and made field visits to specific mass gatherings to better understand the practice of preventive first aid, peculiarities of the data, and preliminary findings of the model.
Results: We selected 12 potential predictor variables for our model from 16 studies identified by the systematic review. Five of these variables were predictive for PPR in the regression tree: number of days and type of the event, number and age distribution of attendants, and temperature. Internal validation of the model revealed an R² of 0.69. External validation indicated limited predictive value for some mass gathering types (R²=0.30). We obtained similar results for TTHR. The meetings and field visits helped identifying strengths and weaknesses in the underlying database and resulted in recommendations to further optimize data collection and analysis, which will improve the predictive power of the model.
Conclusions: Following EBP principles, we were able to develop and validate an evidence-based prediction model, which was further finetuned by consulting different stakeholders. Implementation of the prediction model will ultimately lead to a better use of resources at preventive aid actions by the BRC.
Patient or healthcare consumer involvement: This prediction model was developed in collaboration with the Relief Service at the Belgian Red Cross, which coordinates the preventive aid campaigns at mass gatherings in Flanders (Belgium). Patient and healthcare needs at mass gatherings were summarized by BRC volunteer representatives, specialized health professionals, and volunteering first aid responders we met during the field visits.
Objectives: To develop and validate a prediction model of patient presentation rate (PPR) and transfer to hospital rate (TTHR) at mass gatherings in Belgium, based on the 3 pillars of evidence-based practice (EBP): scientific evidence, in-house expertise and experience of health professionals and volunteer representatives, and preferences and resources of the target group (BRC volunteers).
Methods: We conducted a systematic review to identify potential environmental and event-specific predictors of PPR and TTHR at mass gatherings. Subsequently, we developed a nonlinear prediction model, containing these variables and using regression trees, with a subset of 28 mass gatherings in MedTRIS, and validated the model with another subset. Throughout the project, we regularly met with experienced volunteer representatives (e.g. first aid responders) and health professionals (e.g. emergency physicians), and made field visits to specific mass gatherings to better understand the practice of preventive first aid, peculiarities of the data, and preliminary findings of the model.
Results: We selected 12 potential predictor variables for our model from 16 studies identified by the systematic review. Five of these variables were predictive for PPR in the regression tree: number of days and type of the event, number and age distribution of attendants, and temperature. Internal validation of the model revealed an R² of 0.69. External validation indicated limited predictive value for some mass gathering types (R²=0.30). We obtained similar results for TTHR. The meetings and field visits helped identifying strengths and weaknesses in the underlying database and resulted in recommendations to further optimize data collection and analysis, which will improve the predictive power of the model.
Conclusions: Following EBP principles, we were able to develop and validate an evidence-based prediction model, which was further finetuned by consulting different stakeholders. Implementation of the prediction model will ultimately lead to a better use of resources at preventive aid actions by the BRC.
Patient or healthcare consumer involvement: This prediction model was developed in collaboration with the Relief Service at the Belgian Red Cross, which coordinates the preventive aid campaigns at mass gatherings in Flanders (Belgium). Patient and healthcare needs at mass gatherings were summarized by BRC volunteer representatives, specialized health professionals, and volunteering first aid responders we met during the field visits.