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BMJ Open. 2017 Jan 11;7(1):e011580. doi: 10.1136/bmjopen-2016-011580.

Predicting patient 'cost blooms' in Denmark: a longitudinal population-based study.

Author information

1
Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
2
Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark.
3
Department of Statistics, Stanford University, Stanford, California, USA.

Abstract

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-that 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 (2010-2011) 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-that 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-especially for predicting future cost bloomers.

KEYWORDS:

high-cost patients; predictive analytics

PMID:
28077408
PMCID:
PMC5253526
DOI:
10.1136/bmjopen-2016-011580
[Indexed for MEDLINE]
Free PMC Article

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