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  • Title: Where to focus efforts to improve overall ratings of care and willingness to return: the case of Tuscan emergency departments.
    Author: Seghieri C, Sandoval GA, Brown AD, Nuti S.
    Journal: Acad Emerg Med; 2009 Feb; 16(2):136-44. PubMed ID: 19120049.
    Abstract:
    OBJECTIVES: Both regression and optimization models were used to identify an efficient combination of aspects of care (e.g., comfort of waiting room) necessary to improve global emergency department (ED) patient satisfaction. The approach, based on patient survey data, tends to favor aspects of care with large regression coefficients and those whose current performance is low, because improvements produce a greater effect on global satisfaction. METHODS: The authors used ED patient satisfaction survey data collected between September and October 2007 from a random sample of 5,277 adult patients who visited 43 EDs in Tuscany, Italy. Ordinal logistic regression models were run to predict overall ratings of care and willingness to return using 20 independent variables (i.e., aspects of care). An optimization model was run to increase these two global items to a maximum of 15%. This model minimizes the total combined percentage increase of the aspects of care. Models using all cases (n = 5,277), cases from local hospitals (n = 4,264), and cases from teaching hospitals (n = 1,013) were run. RESULTS: Four aspects selected by the optimization algorithm were in all models: "satisfaction with waiting time,""comfort of the waiting room,""professionalism of physicians" (technical skills), and "level of collaboration between physicians and nursing staff." Most aspects needed a 15% increase to comply with the percentage increases set for the global satisfaction items. The model found that to increase overall ratings of care by 1, 2, or 8%, hospitals would need to focus only on one aspect: "level of collaboration between physicians and nursing staff." The total number of variables increased to six when the improvement in overall ratings of care was set at 15%. To increase 3 or 5% willingness to return, the optimization algorithm found that 6 or 14 aspects, respectively, are needed. An increase of 6% or more was unfeasible. CONCLUSIONS: This approach is only somewhat efficient, as a cost structure is absent. The optimization model assumes that the cost to increase each aspect by 1% is equivalent. By applying this modeling technique we have demonstrated that, at least, two elements are important to consider when developing efficient improvement strategies to increase global satisfaction: 1) the current level of satisfaction of the aspects of care and 2) the importance ascribed to the aspects of care. A third element, the cost to increase the aspects of care, might also be important. However, the impact of this element on the optimal solution is currently unknown.
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