Axel Focke

Multicriteria tour planning for mobile healthcare facilities in a developing country

A multiobjective combinatorial optimization (MOCO) formulation for the following location-routing problem in healthcare management is given: For a mobile healthcare facility, a closed tour with stops selected from a given set of population nodes has to be found. Tours are evaluated according to three criteria: (i) An economic efficiency criterion related to the tour length, (ii) the criterion of average distances to the nearest tour stops corresponding to p-median location problem formulations, and (iii) a coverage criterion measuring the percentage of the population unable to reach a tour stop within a predefined maximum distance. Three algorithms to compute approximations to the set of Pareto-efficient solutions of the described MOCO problem are developed. The first uses the P-ACO technique, and the second and the third use the VEGA and the MOGA variant of multiobjective genetic algorithms, respectively. Computational experiments for the Thies region in Senegal were carried out to evaluate the three approaches on real-world problem instances.

Determining location and size of medical departments in a hospital network: a multiobjective decision support approach.

Decisions on the location and size of medical departments in a given hospital network are prime examples of priority setting in health care, which is an issue of growing political importance. As such decisions are regularly characterized by multiple and often conflicting objectives in real-life, this paper integrates the fields of hospital planning and multiobjective decision support. The proposed two-phase solution procedure for our corresponding mathematical programming model does not require a priori preference information. Instead, it seeks efficient solutions by means of multiobjective tabu search in the first phase, while applying clustering in the second phase to allow the decision makers to interactively explore the solution space until the “best” configuration is determined. The real-world applicability of our approach is illustrated through a numerical example based on hospital data from Germany.

The Complexity Signature: Developing a Tool to Communicate Biopsychosocial Severity of Disease for Children with Chronic Neurological Complexity.

Aim For children with medical complexity, interdisciplinary treatment approaches are required to address the various aspects defined within the biopsychosocial model. Methods The present study identifies dimensions of the biopsychosocial model to generate a standardized visualized severity score for chronic neurological diseases in children. We demonstrate the scores applicability and usefulness in clinical practice among clinicians with and without pediatric board certification with the aid of illustrative patient cases. The results are compared by Spearman correlation coefficient. Results Nine dimensions were identified as the basis for the development of the score, which consists of five grades of severity for each of the selected neuropediatric subsections. All board-certified pediatricians would recommend the application of the severity score in clinical routine. Furthermore, a good correlation was revealed between direct and indirect (severity score) assessment. Interpretation The severity score developed in this study takes into account biopsychosocial aspects of chronic diseases while being comprehensible and easily applicable in clinical routine-a biopsychosocial signature serving as an excellent, striking communication basis within the interdisciplinary team. However, upcoming studies including more patient cases are needed for further refinement.