%0 Journal Article
%T Predicting patient ¡®cost blooms¡¯ in Denmark: a longitudinal population-based study
%A Arnold Milstein
%A Henrik Toft S£¿rensen
%A Jean-Raymond Betterton
%A Lars Pedersen
%A Lester Mackey
%A Lucas Janson
%A Nigam Shah
%A Suzanne Tamang
%J -
%D 2017
%R 10.1136/bmjopen-2016-011580
%X Objectives To compare the ability of standard versus enhanced models to predict future high-cost patients, especially those who move from a lower to the upper decile of per capita healthcare expenditures within 1 year¡ªthat is, ¡®cost bloomers¡¯.

Design We developed alternative models to predict being in the upper decile of healthcare expenditures in year 2 of a sample, based on data from year 1. Our 6 alternative models ranged from a standard cost-prediction model with 4 variables (ie, traditional model features), to our largest enhanced model with 1053 non-traditional model features. To quantify any increases in predictive power that enhanced models achieved over standard tools, we compared the prospective predictive performance of each model.

Participants and Setting We used the population of Western Denmark between 2004 and 2011 (2£¿146£¿801 individuals) to predict future high-cost patients and characterise high-cost patient subgroups. Using the most recent 2-year period (2010¨C2011) for model evaluation, our whole-population model used a cohort of 1£¿557£¿950 individuals with a full year of active residency in year 1 (2010). Our cost-bloom model excluded the 155£¿795 individuals who were already high cost at the population level in year 1, resulting in 1£¿402£¿155 individuals for prediction of cost bloomers in year 2 (2011).

Primary outcome measures Using unseen data from a future year, we evaluated each model's prospective predictive performance by calculating the ratio of predicted high-cost patient expenditures to the actual high-cost patient expenditures in Year 2¡ªthat is, cost capture.

Results Our best enhanced model achieved a 21% and 30% improvement in cost capture over a standard diagnosis-based model for predicting population-level high-cost patients and cost bloomers, respectively.

Conclusions In combination with modern statistical learning methods for analysing large data sets, models enhanced with a large and diverse set of features led to better performance¡ªespecially for predicting future cost bloomers
%U https://bmjopen.bmj.com/content/7/1/e011580