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 score's 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.

Demand, selection and patient outcomes in German acute care hospitals.

This paper examines the effects of variation in unexpected demand on patient outcomes in acute care German hospitals. Naturally, an unexpected surge in demand may negatively affect the quality of care and thus patient outcomes, such as in-hospital mortality. We estimate models explaining patient outcomes depending on demand, unobservable patient selection and seasonal factors, as well as patient-specific risk factors and unobservable hospital and department fixed-effects. The main message of this analysis is that hospitals are well prepared to deal with this unexpected volatility in demand, as by and large it does not negatively affect patient outcomes. Hospitals seem to deal with high unexpected workload by steering the patients' length of stay relating to their severity of illness. Elective patients are discharged earlier, while discharges of high-risk emergency patients are postponed.

Financial incentives for disease management programmes and integrated care in German social health insurance.

As a result of recent health care reforms sickness funds and health care providers in German social health insurance face increased financial incentives for implementing disease management and integrated care. Sickness funds receive higher payments form the risk adjustment system if they set up certified disease management programmes and induce patients to enrol. If health care providers establish integrated care projects they are able to receive extra-budgetary funding. As a consequence, the number of certified disease management programmes and the number of integrated care contracts is increasing rapidly. However, contracts about disease management programmes between sickness funds and health care providers are highly standardized. The overall share of health care expenses spent on integrated care still is very low. Existing integrated care is mostly initiated by hospitals, is based on only one indication and is not fully integrated. However, opportunity to invest in integrated care may open up innovative processes, which generate considerable productivity gains. What is more, integrated care may serve as gateway for the introduction of more widespread selective contracting.

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.