CONCLUSIONThe burden of healthcare costs will continue to grow unless and until the efficiency andefficacy of healthcare systems will be achieved. HDE and AI-based analyses can be adoptedto improve the effectiveness of health governance system in ways that also lead to betterquality of care. Indeed, the full integration of data and the use of the AI will allow theidentification of the most appropriate, effective and specific solutions pertaining to thegiven context. These health governance solutions take into account the evolution of healthconditions, distinctively predicted in that population.

RESULTSData can be generated, collected and stored by the means of several technologicalsystems (including innovative and smart devices, advanced web technologies,telemedicine or other). Then a comprehensive appraisal of clinical value, economicvalue, social value and system of governance of health interventions is carried outwith the self-learning artificial intelligence (AI). Sequential decisions are based onBayesian algorithmic probabilities using a direct approximation of AIXI* (i.e. aBayesian optimality notion for general reinforcement learning agents in unknown orpartially known environments) [2]. To find out proper corrective actions and achievea better health governance system, a Bayesian approach and Monte Carlosimulations must be included as a part of an artificial intelligence self-learning cycle(figure 3) [3]. The AIXI agent interacts with the environment (i.e. healthcare setting)in cycles. In each cycle, the agent executes an action and in turn receivesobservations and rewards. The HDE and AI-based assessment will provide an in-depth portrait of the national health condition performing a unique combination ofmacrodata (economic and social assessment) with micro or nanodata (clinicalanalysis). First the combination is accomplished considering individual andsubpopulation data then the AI predicts the key features of future conditions, healthneeds, funding and proper policy resolutions to maintain and improve the healthstatus of population.

*AIXI or super artificial intelligence is basically a simple concept. It combines a search strategy over all possiblefutures of an agent (a math operator using observations, rewards and actions) with a weight that favoursthriftiness (in the sense of the complexity of a program reaching this future, its sequence of actions andobservations).

METHODTo tackle the healthcare spending growth, the implementation of an effective, advanced systemto generate and analyze real and qualified health data such as the methodological approachrelied upon the Health Data Entanglement (HDE) is advised. Entanglement concept is borrowedfrom quantum physics and refers to the presence of correlations (i.e. inseparableinterconnections) between observable physical quantities (health variables). Entanglementmeans that multiple particles (or information) are linked together in a way such that themeasurement of one particle's quantum state (individual health conditions and relatedeconomic requirements) determines the possible quantum states of other particles (populationhealth forecasts exploited to predict the overall economic impact and drive policy decisions).

BACKGROUNDAlthough many countries are currently trying to constrain or stabilize it, over the last 2-3decades healthcare spending has been keeping on rising faster than national gross domesticproduct (GDP) itself. According to a medium and long-term perspective, healthcare expensewill be one of the most relevant policy issue for most governments in the European Unionand in the USA. The public healthcare expenditure can be associated with two major keycategories: demographic and economic drivers (figure 1) [1]. Demographic drivers aredirectly correlated with the dynamic variations of population structure (ageing process ofpopulation). In many countries a continuous increase of age will be observed. Of course, thegreater the life expectations the higher demand for healthcare. Considering thedeterioration in today’s economic environment, it’s worth mentioning the potentialrelationship between determinants of contribution to GDP and consumption of economicresources due to healthcare demand (figure 2). The collection of full integrated reliable datais critical in improving the health governance and the quality of healthcare servicesdelivered. Despite a plenty of health data (i.e. observational studies, claims databases,registries, PRO and other medical record linkage systems) all of them commonly show somecaveats, and factors driving the expenditure were rarely recognized, measured andcomprehended. The increase of healthcare budget in response to growing demand withoutits assessment and governance is no longer a valuable option.

HEALTH DATA ENTANGLEMENT AND ARTIFICIAL INTELLIGENCE-BASED ANALYSES TO IMPROVE THE EFFECTIVENESS OF SERVICES AND TACKLE THE HEALTHCARE SPENDING GROWTH

A. Capone1, FS. Mennini1,2, A. Cicchetti3, A. Marcellusi2,4, G. Baio5, R. Torlone6 and G. Favato1

1 Institute for Leadership and Management in Health - Kingston University London, London, UK. 2 Economic Evaluation and HTA (CEIS- EEHTA) - IGF Department, Faculty of Economics, University of Rome «Tor Vergata», Rome, Italy. 3 Department of Business Administration, Catholic University of Sacred Heart, Rome, Italy. 4 Department of Demography, University of Rome «Sapienza», Rome, Italy.5 Department of Statistical Science, University College

London, London, UK. 6 Department of Informatics and Automation, University «Roma 3», Rome, Italy

ISPOR 20th Annual International Meeting, May 16-20, 2015. Philadelphia Marriott Downtown, Philadelphia, USA. A.Capone@kingston.ac.uk

REFERENCES1. De La Maisonneuve C. and Oliveira Martins J. A projection method for public health and long-term care expenditures. Economics Department Working Papers No. 1048, OECD 2013.2.Hutter M. Universal Artificial Intelligence: Sequential Decisions Based on AlgorithmicProbability. Springer 20053. Veness J, Siong Ng K, Hutter M, Uther W, Silver D. A Monte-Carlo AIXI Approximation. Journalof Artificial Intelligence Research 2011; 40: 95-142.

Figure 2: Unbalanced power relationship between GDP contributors and economic resources absorbed by healthcare demand. GPD = Gross

Domestic Product

Figure 3: Self-learning cycle of artificial intelligence. MC = Monte Carlo simulation. AIXI = artificial intelligence agent. Modified from Veness J. et

al. [3].

Figure 1: Determinants of healthcare spending. Modified fromDe La Maisonneuve C. et al. [1].