Table 1. Example of KPI and PHP conceptual structure .

ITEM	Description
KPI	Rate of adults identified as obese. The pravelance of obesity in adults is known to be around 15% of the population However, there is only around 1.5% of diagnostics of obesity reported using ICD-9 codes, which implies that obesity condition Is not being taken into account when reporting and storing secondary diagnosis of the patients. The goal of this KPI would be to report an Increase In the prevalence of the obesity condition In the codification of the morbidities of each patient, promoting thus a systematic detection of this condition.
FORMULA	Numerator: Number of adult population property identified as obese (ICD9/1CD10) Denominator Number of adult population
DATA	The data for computing this KPI and report its evolution should be obtained from the datamarts of the Health Department La Fe including primary and secondary care information about morbidity.
stakeholderS	People Interested In the evolution of this KPI and who may be reported are Hospital Managers, the Head of the Endocrinology Service, and Experts on Public Health, among others.
GOAL	To sensitize healthcare professionals to boost the systematic detection of overweight and obesity In the population.
PHP	Strategy for physical activity, nutrition and prevention of obesity

ITEM	Description
ACTORS	People that may be affected by this PHP are citizens that are obese but they have not been correctly diagnosed and whose diagnosis has not been correctly stored. Also, endocrinologists may be affected as their work may be improved by this systematic detection of obesity.
MODELS	A model can be used to help detect people that are likely to be in the obesity profile, This is the risk stratification model proposed by UPV in Deliverable 5.2 based on a semi-supervised learning approach.
DATA FOR MODELS	The data to be used for the models will be determined during the development and validation of the semi-supervised model.