title: Health Data Entanglement and artificial intelligence-based analysis: a brand new methodology to improve the effectiveness of healthcare services.
abstract: Healthcare expenses will be the most relevant policy issue for most governments in the EU and in the USA. This expenditure can be associated with two major key categories: demographic and economic drivers. Factors driving healthcare expenditure were rarely recognised, measured and comprehended. An improvement of health data generation and analysis is mandatory, and in order to tackle healthcare spending growth, it may be useful to design and implement an effective, advanced system to generate and analyse these data. A methodological approach relied upon the Health Data Entanglement (HDE) can be a suitable option. By definition, in the HDE a large amount of data sets having several sources are functionally interconnected and computed through learning machines that generate patterns of highly probable future health conditions of a population. Entanglement concept is borrowed from quantum physics and means that multiple particles (information) are linked together in a way such that the measurement of one particle's quantum state (individual health conditions and related economic requirements) determines the possible quantum states of other particles (population health forecasts to predict their impact). The value created by the HDE is based on the combined evaluation of clinical, economic and social effects generated by health interventions. To predict the future health conditions of a population, analyses of data are performed using self-learning AI, in which sequential decisions are based on Bayesian algorithmic probabilities. HDE and AI-based analysis can be adopted to improve the effectiveness of the health governance system in ways that also lead to better quality of care.
authors.affiliation: Institute for Leadership and Management in Health - Kingston University London, London, UK - Economic Evaluation and HTA (CEIS- EEHTA) - IGF Department, Faculty of Economics, University of Rome Tor Vergata, Rome, Italy.
authors.forename: A
authors.initials: A
authors.lastname: Capone
authors.affiliation: Department of Business Administration, Catholic University of Sacred Heart, Rome, Italy.
authors.forename: A
authors.initials: A
authors.lastname: Cicchetti
authors.affiliation: Institute for Leadership and Management in Health - Kingston University London, London, UK - Economic Evaluation and HTA (CEIS- EEHTA) - IGF Department, Faculty of Economics, University of Rome Tor Vergata, Rome, Italy.
authors.forename: F S
authors.initials: FS
authors.lastname: Mennini
authors.affiliation: Economic Evaluation and HTA (CEIS- EEHTA) - IGF Department, Faculty of Economics, University of Rome Tor Vergata, Rome, Italy - Department of Demography, University of Rome Sapienza, Rome, Italy.
authors.forename: A
authors.initials: A
authors.lastname: Marcellusi
authors.affiliation: Department of Statistical Science, University College London, London, UK.
authors.forename: G
authors.initials: G
authors.lastname: Baio
authors.affiliation: Institute for Leadership and Management in Health - Kingston University London, London, UK.
authors.forename: G
authors.initials: G
authors.lastname: Favato
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D003695:Delivery of Health Care / Q000191:economics / Q000379:methods*; D006801:Humans
keywords: Data entanglement; GDP; Health governance; Healthcare spending; Self-learning artificial intelligence