Diagnostic accuracy of case-identification algorithms for heart failure in the general population using routinely collected health data: a systematic review.

Background: Heart failure (HF), affecting 1-4% of adults in industrialized countries, is a major public health priority. Several algorithms based on administrative health data (HAD) have been developed to detect patients with HF in a timely and inexpensive manner, in order to perform real-world studies at the population level. However, their reported diagnostic accuracy is highly variable.

Development and user-oriented visualization of health care composite indicators at district level: territorial health profiles.

Background: after the recent reform of territorial healthcare, districts have been designated within the Lombardy Regional Health System to coordinate and deliver territorial care. This entails the need of readily available information to measure the quality of provided healthcare, identify critical areas for improvement, monitor the balance between demand and supply of healthcare services.

Objectives: to present the development of a dynamic evaluation system of processes and outcomes resulting from the integration of territorial and hospital care, based on a set of composite indicators, called 'health profiles', and their visualization and release through a dedicated web platform.

Time-series cohort study to forecast emergency department visits in the city of Milan and predict high demand: a 2-day warning system.

Objectives: The emergency department (ED) is one of the most critical areas in any hospital. Recently, many countries have seen a rise in the number of ED visits, with an increase in length of stay and a detrimental effect on quality of care. Being able to forecast future demands would be a valuable support for hospitals to prevent high demand, particularly in a system with limited resources where use of ED services for non-urgent visits is an important issue.

Administrative healthcare data to predict performance status in lung cancer patients.

The dataset includes 4488 patients diagnosed with lung cancer (ICD-O 3[3], C33-C34) between 2010-2012 and 2016-2018 in the territory of the Agency for Health Protection (ATS) of Milan, Italy, and selected from its population cancer registry on the basis of availability of the following information: performance status (PS), age, sex, and stage at diagnosis. The dataset includes also the following variables, extracted from the health databases of the ATS and linked to the variables derived from the cancer registry through deterministic record linkage on a unique key (tax code): Charlson comorbidity index, presence of chronic obstructive pulmonary disease, number of hospitalizations, outpatient visits, emergency accesses and prescribed drugs in the previous year, and dispensed durable medical equipment in the previous three years. The dataset was used to develop a logistic prediction model for PS, dichotomized as 'poor' (ECOG, 3-5) and 'good' (ECOG, 0-2), on the basis of all other variables in the dataset.