Pilot Study for a Model for the Collection of Waiting Lists Data.

The analysis of data on waiting lists in Italy is regulated by the PNGLA (National Plan for the Governance of Waiting Lists). However, the Plan does not specify the characteristics of the data to be returned by the Regions for the purposes of monitoring, with the result that it is frequently either in aggregate form, unreadable, or incomplete, and therefore cannot be analysed in any meaningful way. Fondazione the Bridge and AGENAS, with the University of Genoa and the University of Pavia, conducted a pilot study on a methodological model for the collection of waiting lists data. The model proved to be effective and replicable, also providing a more valuable opportunity to analyse waiting lists data.

Standardization Proposal for the Transmission of Waiting List Data in Italy.

Each Italian region is required to manage and disclose data relating to waiting times for healthcare services which are provided by both public and private hospitals and local health units accredited to the Sistema Sanitario Nazionale (SSN - in English, National Healthcare System). The current law governing data relating to waiting times and their sharing is the Piano Nazionale di Governo delle Liste di Attesa (PNGLA - in English National Government Plan for Waiting Lists). However, this plan does not propose a standard to monitor such data, but only provides a few guidelines that the Italian regions are required to follow. The lack of a specific technical standard for managing sharing of waiting list data and the lack of precise and binding information in the PNGLA make the management and transmission of such data problematic, reducing the interoperability necessary to have an effective and efficient monitoring of the phenomenon. The proposal for a new standard for the transmission of waiting list data derives from these shortcomings. This proposed standard promotes greater interoperability, is easy to create with an implementation guide, and has sufficient degrees of freedom to assist the document author.

Healthcare Insights: Evaluating the Access to the Italian Healthcare System.

The Italian health system is organised on a regional basis and services are provided by both public and private operators, affecting the planning of services, access to services by citizens and their health rights. The creation of an observatory monitoring the methods and times of access to healthcare services has been pursued. The preliminary phase of the project is presented, which will lead to the comparison of the data obtained from 2019, with an eye on the Covid-19 pandemic impact.

Pinocchio testing in the forensic analysis of waiting lists: using public waiting list data from Finland and Spain for testing Newcomb-Benford's Law.

OBJECTIVE: Newcomb-Benford's Law (NBL) proposes a regular distribution for first digits, second digits and digit combinations applicable to many different naturally occurring sources of data. Testing deviations from NBL is used in many datasets as a screening tool for identifying data trustworthiness problems. This study aims to compare public available waiting lists (WL) data from Finland and Spain for testing NBL as an instrument to flag up potential manipulation in WLs. DESIGN: Analysis of the frequency of Finnish and Spanish WLs first digits to determine if their distribution is similar to the pattern documented by NBL. Deviations from the expected first digit frequency were analysed using Pearson's χ2, mean absolute deviation and Kuiper tests. SETTING/PARTICIPANTS: Publicly available WL data from Finland and Spain, two countries with universal health insurance and National Health Systems but characterised by different levels of transparency and good governance standards. MAIN OUTCOME MEASURES: Adjustment of the observed distribution of the numbers reported in Finnish and Spanish WL data to the expected distribution according to NBL. RESULTS: WL data reported by the Finnish health system fits first digit NBL according to all statistical tests used (p=0.6519 in χ2 test). For Spanish data, this hypothesis was rejected in all tests (p<0.0001 in χ2 test). CONCLUSIONS: Testing deviations from NBL distribution can be a useful tool to identify problems with WL data trustworthiness and signalling the need for further testing.