Over the last few years, increasing attention has been directed toward the problems inherent to measuring the quality of healthcare and implementing benchmarking strategies. Besides offering accreditation and certification processes, recent approaches measure the performance of healthcare institutions in order to evaluate their effectiveness, defined as the capacity to provide treatment that modifies and improves the patient's state of health. This paper, dealing with hospital effectiveness, focuses on research methods for effectiveness analyses within a strategy comparing different healthcare institutions. The paper, after having introduced readers to the principle debates on benchmarking strategies, which depend on the perspective and type of indicators used, focuses on the methodological problems related to performing consistent benchmarking analyses. Particularly, statistical methods suitable for controlling case-mix, analyzing aggregate data, rare events, and continuous outcomes measured with error are examined. Specific challenges of benchmarking strategies, such as the risk of risk adjustment (case-mix fallacy, underreporting, risk of comparing noncomparable hospitals), selection bias, and possible strategies for the development of consistent benchmarking analyses, are discussed. Finally, to demonstrate the feasibility of the illustrated benchmarking strategies, an application focused on determining regional benchmarks for patient satisfaction (using 2009 Lombardy Region Patient Satisfaction Questionnaire) is proposed.
Online job portals collecting web vacancies have become important media for job demand and supply matching. They also represent a growing research area for the application of analytical methods to study the labour market using innovative data sources. This paper analyses Italian web job vacancies scraped from several types of Italian web job portals between June and September 2015. After describing how the occupations associated with each web vacancy (classification up to level 4) were identified and the related skills retrieved in texts using mixed supervised and unsupervised text mining approaches, we focused on job vacancies related to ICT and statistical positions. The principal aim of this paper is to describe these jobs in terms of the required skills that have emerged in the labour market from a demand perspective and to identify those skills that best distinguish statisticians from other ICT occupations. Hence, several machine learning techniques were used to assess those skills that best distinguish occupation codes from other job groups. After quality control and removal of duplications, the scraping collected more than 110,000 job advertisements: nearly 6,200 were classified as ICT or statistical positions (largely dominated by software developers). The data indicate that high‐level statisticians have superior and heterogeneous professional backgrounds, linked to theoretical statistics, where analytic skills are more relevant than computing skills. Many soft and management‐oriented skills were also called for, which are missing among lower level statisticians, who are restricted to more technical jobs oriented towards general computing and informatics.
The HoNOS-12 does not measure a single, underlying construct of mental health status. Nevertheless, the instrument can be utilized in a reduced version (HoNOS-6), as a clinically acceptable outcome scale (measuring self-perceived clinical and social needs for community support, rather than global mental disorder) for routine use in a community setting population.
BackgroundThe purpose of the current study was the psychometric evaluation of the Health of the Nation Outcome Scales (HoNOS), an instrument developed to meet the necessity of a clinically acceptable outcome scale for routine use in mental illness services.MethodsThe study participants included 2,162 outpatients and residential inpatients (rated on the HoNOS on three occasions during the year 2000) with a range of mental illnesses in different diagnostic groups from ten Mental Health Departments, located in the area of Milan (Italy). Principal Component Analysis, Confirmatory Factor Analysis, Discriminant Analysis and Partial Credit Rasch Model were used to assess two sources of validity: the internal structure and the relationships with other variables.ResultsThe results of the 12-item HoNOS demonstrate a significant departure from uni-dimensionality, confirmed by the Rasch analysis (which identified three misfitting items). However, HoNOS scores demonstrate stability and precision of item difficulties over time. Discriminant analysis showed that HoNOS scores have an acceptable level of discriminatory power in predicting the severity of patients' conditions (as represented by setting).ConclusionsIt was concluded that the Italian version of the HoNOS does not measure a single, underlying construct of mental health status. The internal structure validity analysis recommends a note of caution to use a summary index of the HoNOS scores, given the presence of multidimensionality and misfit. Nonetheless, the finding that the instrument is more multidimensional than unidimensional does not preclude the use of the HoNOS as a clinically valid tool for routine outcome assessment. In fact, item scores have demonstrated sufficient reliability (over diagnostic groups and care settings) and high precision in time, indicating that HoNOS items can be utilized as valid measurement instruments in longitudinal analyses.
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