Chronicity is the real challenge for public healthcare systems especially in relation to multi-morbidity. The growing demand for multidisciplinary care could be addressed by implementing integrated programs in the primary care field and facilitating other specific care only as necessary. Some models of long-term management have been suggested since the 2000s. The objective here is to propose the Individual Profile of Pathology (IPP) model as the preliminary step for identifying groups of population which shares health and social needs and for optimizing the management of chronicity, referring to the Kaiser Permanente Pyramid paradigm. The IPP model is able to inform a data feedback system for improving performances at the patient's individual level and for addressing and evaluating health policies. The stratification of needs comes out of the IPP algorithm. It works on patient information databases based on the logic of disease as a process that evolves over time and interacts with many factors unique to that patient. Individual patients' data used in this work refers to 138,859 subjects from a large area in Italy and concerns hospitalization, outpatient drug prescriptions, access to the emergency room and outpatient prescriptions for visits, laboratory/imaging tests, and medications. The IPP model allows to identify for each subject a complexity level, taking into account the weight of groups of pathologies, both in terms of absorption of resources and the level of severity. Costs and healthcare performances have been analyzed taking into account the complexity levels. The IPP model can be an efficient methodology for (a) improving performances at the patient's individual level (b) allowing standardized comparison among different geographical areas (c) supporting large population-focused surveillance programs and (d) providing knowledge to identify and fill the gaps in public health policies. Currently, the IPP algorithm is limited by data availability, restricted to the administrative databases processing, but the theoretical model is able to include more data dimensions providing the potential to identify homogeneous groups of subjects with a higher level of precision.
Keywords: algorithm; co-morbidity; complex needs; general population; health policy; long term conditions; segmentation.