How to direct patients to high-volume hospitals: exploring the influencing drivers.

BACKGROUND: During the last decade, planning concentration policies have been applied in healthcare systems. Among them, attention has been given to guiding patients towards high-volume hospitals that perform better, acccording to the volume-outcome association. This paper analyses which factors drive patients to choose big or small hospitals (with respect to the international standards of volumes of activity). METHODS: We examined colon cancer surgeries performed in Piedmont (Italy) between 2004 and 2018. We categorised the patient choice of the hospital as big/small, and we used this outcome as main dependent variable of descriptive statistics, tests and logistic regression models. As independent variables, we included (i) patient characteristics, (ii) characteristics of the closest big hospital (which should be perceived as the most immediate to be chosen), and (iii) territorial characteristics (i.e., characteristics of the set of hospitals among which the patient can choose). We also considered interactions among variables to examine which factors influence all or a subset of patients. RESULTS: Our results confirm that patient personal characteristics (such as age) and hospital characteristics (such as distance) play a primary role in the patient decision process. The findings seem to support the importance of closing small hospitals when they are close to big hospitals, although differences emerge between rural and urban areas. Other interesting insights are provided by examining the interactions between factors, e.g., patients affected by comorbidities are more responsive to hospital quality even though they are distant. CONCLUSIONS: Reorganising healthcare services to concentrate them in high-volume hospitals emerged as a crucial issue more than forty years ago. Evidence suggests that concentration strategies guarantee better clinical performance. However, in healthcare systems in which patients are free to choose where to be treated, understanding patients' behaviour and what drives them towards the most effective choice is of paramount importance. Our study builds on previous research that has already analysed factors influencing patients' choices, and takes a step further to enlighten which factors drive patients to choose between a small or a big hospital (in terms of volume). The results could be used by decision makers to design the best concentration strategy.

Hospital volume allocation: integrating decision maker and patient perspectives.

Planning problems in healthcare systems have received greater attention in the last decade, especially because of the concerns recently raised about the scattering of surgical interventions among a wide number of different facilities that can undermine the quality of the outcome due to the volume-outcome association. In this paper, an approach to plan the amount of surgical interventions that a facility has to perform to assure a low adjusted mortality rate is proposed. The approach explicitly takes into account the existing interaction among patients' choices and decision makers' planning decisions. The first objective of the proposed approach is to find a solution able to reach quality in health outcomes and patients' adherence. The second objective is to investigate the difference among solutions that are identified as optimal by either only one of the actors' perspective, i.e., decision makers and patients, or by considering both the perspectives simultaneously. Following these objectives, the proposed approach is applied to a case study on Italian colon cancer interventions performed in 2014. Results confirm a variation in the hospital planned volumes when considering patients' behaviour together with the policy maker plan, especially due to personal preferences and lack of information about hospital quality.

Time-frequency analysis of the endocavitarian signal in paroxysmal atrial fibrillation.

We apply the time-frequency analysis to the endocavitarian signal of patients suffering from paroxysmal atrial fibrillation. The time-frequency spectrum reveals the components of the endocavitarian signal. These components are located in the regions of the time-frequency domain that differ for in-rhythm and in-atrial fibrillation signals. By using experimental data, we perform a statistical study of these regions, and we obtain their average value. The difference in the shape of these regions is caused by the re-entry circuits that characterize atrial fibrillation. We propose a propagation model for atrial fibrillation based on the re-entry circuits, which explains the shape of the time-frequency spectrum.