Evaluating organizational change in health care: the patient-centered hospital model.

BACKGROUND: An increasing number of hospitals react to recent demographic, epidemiological and managerial challenges moving from a traditional organizational model to a Patient-Centered (PC) hospital model. Although the theoretical managerial literature on the PC hospital model is vast, quantitative evaluations of the performance of hospitals that moved from the traditional to the PC organizational structure is scarce. However, quantitative analysis of effects of managerial changes is important and can provide additional argument in support of innovation. METHODS: We take advantage of a quasi-experimental setting and of a unique administrative data set on the population of hospital discharge charts (HDCs) over a period of 9 years of Lombardy, the richest and one of the most populated region of Italy. During this period three important hospitals switched to the PC model in 2010, whereas all the others remained with the functional organizational model. This allowed us to develop a difference-in-difference analysis of some selected measures of efficiency and effectiveness for PC hospitals focusing on the "between-variability" of the 25 major diagnostic categories (MDCs) in each hospital and estimating a difference-in-difference model. RESULTS: We contribute to the literature that addresses the evaluation of healthcare and hospital change by providing a quantitative estimation of efficiency and effectiveness changes following to the implementation of the PC hospital model. Results show that both efficiency and effectiveness have significantly increased in the average MDC of PC hospitals, thus confirming the need for policy makers to invest in new organizational models close to the principles of PC hospital structures. CONCLUSIONS: Although an organizational change towards the PC model can be a costly process, implying a rebalancing of responsibilities and power among hospital personnel (e.g. medical and nursing staff), our results suggest that changing towards a PC model can be worthwhile in terms of both efficacy and efficiency. This evidence can be used to inform and sustain hospital managers and policy makers in their hospital design efforts and to communicate the innovation advantages within the hospital organizations, among the personnel and in the public debate.

The redistributive effects of copayment in outpatient prescriptions: evidence from Lombardy.

BACKGROUND: In Italy, copayment has changed its nature and it can no longer be simply considered a system to curb inappropriate expenditure. It has become an important form of revenue for public health care provision, but it might also become a source of distortions in income and health benefits redistribution. METHODS: We use a rich administrative dataset gathering information on patients demand (whose records have been matched to income declared for tax purposes) to study the effects of an additional copayment (the so called "superticket" introduced by the Italian government in 2012) in Lombardy, the biggest Italian Region whose socio-economic dimension is comparable to that of many European countries (e.g., the Netherlands, Switzerland, etc.). RESULTS: Our analysis shows that at the aggregate level the non-uniform superticket schedule adopted in Lombardy is slightly pro-poor, but this result coexists with evidences pointing towards possible cases of restriction to access caused by the additional copayment. CONCLUSIONS: The introduction of the superticket and the ensuing increase in the out-of pocket payment for health care raises questions about the distribution of the burden among patients, and the sustainability of the extra revenue through time. This issue needs to be further investigated by combining health status data with the information in this dataset.

How have casemix, cost and hospital stay of inpatients in the last year of life changed over the past decade? Evidence from Italy.

Healthcare utilisation and expenditure are highly concentrated in hospital inpatient services, in particular in end-of-life care with the peak occurring in the very last year of life, regardless of patient age. Few scientific studies have investigated hospital costs and stays of patients at the end of life, and even fewer studies have analysed their evolution over time. In this paper, we exploit hospitalisation data for the Lombardy region of Italy with the aim of studying the evolution of hospital casemix, costs and stays of chronic patients, and compare the last year of life of two cohorts of patients who died in 2005 and 2014. Despite an overall three-year increase in the age at death, the results showed a significant decrease in hospital costs and use due to reduced interventions and length of hospital stays. However, this was not associated with an increase in quality of life/conditions (as indicated by clinical casemix as a proxy) for end-of-life patients; patients' casemix characteristics and clinical condition, as measured by the number of comorbidities, disease severity, prevalence of pulmonary disease and heart failure diagnosis, significantly worsened over the decade. This gives rise to important health policy concerns on how to identify effective policies and possible changes in healthcare system organisation to move from hospital-centred care to a community-centred approach whose value has been demonstrated during the COVID-19 pandemic.

The impact of COVID-19 pandemic on AMI and stroke mortality in Lombardy: Evidence from the epicenter of the pandemic.

BACKGROUND: The first Covid-19 epidemic outbreak has enormously impacted the delivery of clinical healthcare and hospital management practices in most of the hospitals around the world. In this context, it is important to assess whether the clinical management of non-Covid patients has not been compromised. Among non-Covid cases, patients with Acute Myocardial Infarction (AMI) and stroke need non-deferrable emergency care and are the natural candidates to be studied. Preliminary evidence suggests that the time from onset of symptoms to emergency department (ED) presentation has significantly increased in Covid-19 times as well as the 30-day mortality and in-hospital mortality. METHODS: We check, in a causal inference framework, the causal effect of the hospital's stress generated by Covid-19 pandemic on in-hospital mortality rates (primary end-point of the study) of AMI and stroke over several time-windows of 15-days around the implementation date of the State of Emergency restrictions for COVID-19 (March, 9th 2020) using two quasi-experimental approaches, regression-discontinuity design (RDD) and difference-in-regression-discontinuity (DRD) designs. Data are drawn from Spedali Civili of Brescia, one of the most hit provinces in Italy by Covid-19 during March and May 2020. FINDINGS: Despite the potential adverse effects on expected mortality due to a longer time to hospitalization and staff extra-burden generated by the first wave of Covid-19, the AMI and stroke mortality rates are overall not statistically different during the first wave of Covid-19 than before the first peak. The obtained results provided by RDD models are robust also when we account for seasonality and unobserved factors with DRD models. INTERPRETATION: The non-statistically significant impact on mortality rates for AMI and stroke patients provides evidence of the hospital ability to manage -with the implementation of a dual track organization- the simultaneous delivery of high-quality cares to both Covid and non-Covid patients.

A bivariate prediction approach for adapting the health care system response to the spread of COVID-19.

The spread of COVID-19 implied a large and fast increase of demand for intensive care services. To face this increase in demand, health care systems need to adapt their response by increasing hospital beds, intensive care unit (ICU) capacity and by (re-)deploying doctors and other personnel. This paper proposes a forecast approach based on the Vector Error Correction model for the daily counts of hospitalized patients with symptoms and of patients in ICU, using publicly available data on the current COVID-19 outbreak in Italy, Switzerland and Spain. The level of analysis is the local government managing the health care system response, which corresponds to regions for Italy. The one-week-ahead forecasts are validated with out-of-sample data over successive weeks; they are found to provide timely and robust prediction of ICU capacity needs in Lombardy, the most-affected Italian region, starting from the sample of the first 2 weeks of data. The same methodology is successfully validated on other Italian regions, Switzerland and Spain. This approach may be used in other countries/regions/provinces to help adapt the health care system response to COVID-19 (or other similar disease); for this purpose, the open-source software code to produce the forecasts is provided with the paper.