Section: Application Domains

Health

• Care pathways analysis for supporting pharmaco-epidemiological studies. Pharmaco-epidemiology applies the methodologies developed in general epidemiology to answer to questions about the uses and effects of health products, drugs [20], [19] or medical devices [17], on population. In classical pharmaco-epidemiology studies, people who share common characteristics are recruited to build a dedicated prospective cohort. Then, meaningful data (drug exposures, diseases, etc.) are collected from the cohort within a defined period of time. Finally, a statistical analysis highlights the links (or the lack of links) between drug exposures and outcomes (e.g. adverse effects). The main drawback of prospective cohort studies is the time required to collect the data and to integrate it. Indeed, in some cases of health product safety, health authorities have to answer quickly to pharmaco-epidemiology questions.

  New approaches of pharmaco-epidemiology consist in using large EHR (Electronic Health Records) databases to investigate the effects and uses (or misuses) of drugs in real conditions. The objective is to benefit from nationwide available data to answer accurately and in a short time pharmaco-epidemiological queries for national public health institutions. Despite the potential availability of the data, their size and complexity make their analysis long and tremendous. The challenge we tackle is the conception of a generic digital toolbox to support the efficient design of a broad range of pharmaco-epidemiology studies from EHR databases.

  We propose to use pattern mining algorithm and reasoning techniques to analyse the typical care pathways of specific groups of patients.

  To be able to answer the broad range of pharmaco-epidemiological queries from national public health institutions, the PEPS (PEPS: Pharmaco-Epidémiologie et Produits de Santé – Pharmacoepidemiology of health products) platform exploits, in secondary use, the French health cross-schemes insurance system, called SNIIRAM. The SNIIRAM covers most of the French population with a sliding period of 3 past years. The main characteristics of this data warehouse are described in [18]. Contrary to local hospital EHR or even with other national initiatives, the SNIIRAM data warehouse covers a huge population. It makes possible studies on unfrequent drugs or diseases in real conditions of use. To tackle the volume and the diversity of the SNIIRAM data warehouse, a research program has been established to design an innovative toolbox. This research program is focused first on the modeling of care pathways from the SNIIRAM database and, second, on the design of tools supporting meaningful insights extraction about massive and complex care pathways by clinicians. In such database a care pathway is an individual sequence of drugs exposures, medical procedures and hospitalizations.