Identifying Consistent High-cost Users in a Health Plan: Comparison of Alternative Prediction Models.
BACKGROUND
High-cost users in a period may not incur high-cost utilization in the next period. Consistent high-cost users (CHUs) may be better targets for cost-saving interventions.


OBJECTIVE
To compare the characteristics of CHUs (patients with plan-specific top 20% medical costs in all 4 half-year periods across 2008 and 2009) and point high-cost users (PHUs) (top users in 2008 alone), and to build claims-based models to identify CHUs.


METHODS
This is a retrospective cohort study. Logistic regression was used to predict being CHUs. Independent variables were derived from 2007 claims; 5 models with different sets of independent variables (prior costs, medications, diagnoses, medications and diagnoses, medications and diagnoses and prior costs) were constructed.


METHODS
Three-year continuous enrollees aged from 18 to 62 years old from a large administrative database with $100 or more yearly costs (N=1,721,992).


METHODS
Correlation, overlap, and characteristics of top risk scorers derived from 5 CHUs models were presented. C-statistics, sensitivity, and positive predictive value were calculated.


RESULTS
CHUs were characterized by having increasing total and pharmacy costs over 2007-2009, and more baseline chronic and psychosocial conditions than PHUs. Individuals' risk scores derived from CHUs models were moderately correlated (∼0.6). The medication-only model performed better than the diagnosis-only model and the prior-cost model.


CONCLUSIONS
Five models identified different individuals as potential CHUs. The recurrent medication utilization and a high prevalence of chronic and psychosocial conditions are important in differentiating CHUs from PHUs. For cost-saving interventions with long-term impacts or focusing on medication, CHUs may be better targets.
*Department of Health Policy & Management †Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health ‡Department of Medicine, Division of Geriatric Medicine and Gerontology, School of Medicine, Johns Hopkins University §Center for Population Health Information Technology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Hsien-Yen
HY
Chang
Cynthia M
CM
Boyd
Bruce
B
Leff
Klaus W
KW
Lemke
David P
DP
Bodycombe
Jonathan P
JP
Weiner
D000293:Adolescent; D000328:Adult; D002908:Chronic Disease / Q000191:economics*; D016208:Databases, Factual; D005260:Female; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006801:Humans; D007348:Insurance, Health / Q000191:economics / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D001523:Mental Disorders / Q000191:economics*; D008875:Middle Aged; D015233:Models, Statistical; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data; D012189:Retrospective Studies; D055815:Young Adult