• Juan F Orueta, Arturo García-Alvarez, Juan J Aurrekoetxea, and Manuel García-Goñi.
• Centro de Salud de Astrabudua (Primary Health Care Center of Astrabudua), OSI Uribe, Osakidetza (Basque Health Service), Erandio, Spain.
• BMJ Open. 2018 May 31; 8 (5): e019830.

ObjectivePredictive statistical models used in population stratification programmes are complex and usually difficult to interpret for primary care professionals. We designed FINGER (Forming and Identifying New Groups of Expected Risks), a new model based on clinical criteria, easy to understand and implement by physicians. Our aim was to assess the ability of FINGER to predict costs and correctly identify patients with high resource use in the following year.DesignCross-sectional study with a 2-year follow-up.SettingThe Basque National Health System.ParticipantsAll the residents in the Basque Country (Spain) ≥14 years of age covered by the public healthcare service (n=1 946 884).MethodsWe developed an algorithm classifying diagnoses of long-term health problems into 27 chronic disease groups. The database was randomly divided into two data sets. With the calibration sample, we calculated a score for each chronic disease group and other variables (age, sex, inpatient admissions, emergency department visits and chronic dialysis). Each individual obtained a FINGER score for the year by summing their characteristics' scores. With the validation sample, we constructed regression models with the FINGER score for the first 12 months as the only explanatory variable.ResultsThe annual FINGER scores obtained by patients ranged from 0 to 57 points, with a mean of 2.06. The coefficient of determination for healthcare costs was 0.188 and the area under the receiver operating characteristic curve was 0.838 for identifying patients with high costs (>95th percentile); 0.875 for extremely high costs (>99th percentile); 0.802 for unscheduled admissions; 0.861 for prolonged hospitalisation (>15 days); and 0.896 for death.ConclusionFINGER presents a predictive power for high risks fairly close to other classification systems. Its simple and transparent architecture allows for immediate calculation by clinicians. Being easy to interpret, it might be considered for implementation in regions involved in population stratification programmes.© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

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