BACKGROUND: With rising health spending, predicting costs is essential to identify patients for interventions. Many of the existing approaches have moderate predictive ability, which may result, in part, from not considering potentially meaningful changes in spending over time. Group-based trajectory modeling could be used to classify patients into dynamic long-term spending patterns. OBJECTIVES: To classify patients by their spending patterns over a 1-year period and to assess the ability of models to predict patients in the highest spending trajectory and the top 5% of annual spending using prior-year predictors. SUBJECTS: We identified all fully insured adult members enrolled in a large US nationwide insurer and used medical and prescription data from 2009 to 2011. RESEARCH DESIGN: Group-based trajectory modeling was used to classify patients by their spending patterns over a 1-year period. We assessed the predictive ability of models that categorized patients in the top fifth percentile of annual spending and in the highest spending trajectory, using logistic regression and split-sample validation. Models were estimated using investigator-specified variables and a proprietary risk-adjustment method. RESULTS: Among 998,651 patients, in the best-performing model, prediction was strong for patients in the highest trajectory group (C-statistic: 0.86; R: 0.47). The C-statistic of being in the top fifth percentile of spending in the best-performing model was 0.82 (R: 0.26). Approaches using nonproprietary investigator-specified methods performed almost as well as other risk-adjustment methods (C-statistic: 0.81 vs. 0.82). CONCLUSIONS: Trajectory modeling may be a useful way to predict costly patients that could be implementable by payers to improve cost-containment efforts.
BACKGROUND: With rising health spending, predicting costs is essential to identify patients for interventions. Many of the existing approaches have moderate predictive ability, which may result, in part, from not considering potentially meaningful changes in spending over time. Group-based trajectory modeling could be used to classify patients into dynamic long-term spending patterns. OBJECTIVES: To classify patients by their spending patterns over a 1-year period and to assess the ability of models to predict patients in the highest spending trajectory and the top 5% of annual spending using prior-year predictors. SUBJECTS: We identified all fully insured adult members enrolled in a large US nationwide insurer and used medical and prescription data from 2009 to 2011. RESEARCH DESIGN: Group-based trajectory modeling was used to classify patients by their spending patterns over a 1-year period. We assessed the predictive ability of models that categorized patients in the top fifth percentile of annual spending and in the highest spending trajectory, using logistic regression and split-sample validation. Models were estimated using investigator-specified variables and a proprietary risk-adjustment method. RESULTS: Among 998,651 patients, in the best-performing model, prediction was strong for patients in the highest trajectory group (C-statistic: 0.86; R: 0.47). The C-statistic of being in the top fifth percentile of spending in the best-performing model was 0.82 (R: 0.26). Approaches using nonproprietary investigator-specified methods performed almost as well as other risk-adjustment methods (C-statistic: 0.81 vs. 0.82). CONCLUSIONS: Trajectory modeling may be a useful way to predict costly patients that could be implementable by payers to improve cost-containment efforts.
Authors: Chandrasekar Gopalakrishnan; Rishi J Desai; Jessica M Franklin; Yinzhu Jin; Joyce Lii; Daniel H Solomon; Jeffrey N Katz; Yvonne C Lee; Patricia D Franklin; Seoyoung C Kim Journal: Arthritis Care Res (Hoboken) Date: 2022-04-22 Impact factor: 5.178
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Authors: Paul P Schneider; Xavier G L V Pouwels; Valéria Lima Passos; Bram L T Ramaekers; Sandra M E Geurts; Khava I E Ibragimova; Maaike de Boer; Frans Erdkamp; Birgit E P J Vriens; Agnes J van de Wouw; Marien O den Boer; Manon J Pepels; Vivianne C G Tjan-Heijnen; Manuela A Joore Journal: PLoS One Date: 2020-04-09 Impact factor: 3.240
Authors: Edwin S Wong; Jean Yoon; Rebecca I Piegari; Ann-Marie M Rosland; Stephan D Fihn; Evelyn T Chang Journal: J Gen Intern Med Date: 2018-09-17 Impact factor: 6.473
Authors: Kathryn Wiens; Laura C Rosella; Paul Kurdyak; Simon Chen; Tim Aubry; Vicky Stergiopoulos; Stephen W Hwang Journal: BMC Health Serv Res Date: 2021-06-06 Impact factor: 2.655