Applying Machine Learning Algorithms to Segment High-Cost Patient Populations.
J Gen Intern Med
; 34(2): 211-217, 2019 02.
Article
em En
| MEDLINE
| ID: mdl-30543022
ABSTRACT
BACKGROUND:
Efforts to improve the value of care for high-cost patients may benefit from care management strategies targeted at clinically distinct subgroups of patients.OBJECTIVE:
To evaluate the performance of three different machine learning algorithms for identifying subgroups of high-cost patients.DESIGN:
We applied three different clustering algorithms-connectivity-based clustering using agglomerative hierarchical clustering, centroid-based clustering with the k-medoids algorithm, and density-based clustering with the OPTICS algorithm-to a clinical and administrative dataset. We then examined the extent to which each algorithm identified subgroups of patients that were (1) clinically distinct and (2) associated with meaningful differences in relevant utilization metrics.PARTICIPANTS:
Patients enrolled in a national Medicare Advantage plan, categorized in the top decile of spending (n = 6154). MAINMEASURES:
Post hoc discriminative models comparing the importance of variables for distinguishing observations in one cluster from the rest. Variance in utilization and spending measures. KEYRESULTS:
Connectivity-based, centroid-based, and density-based clustering identified eight, five, and ten subgroups of high-cost patients, respectively. Post hoc discriminative models indicated that density-based clustering subgroups were the most clinically distinct. The variance of utilization and spending measures was the greatest among the subgroups identified through density-based clustering.CONCLUSIONS:
Machine learning algorithms can be used to segment a high-cost patient population into subgroups of patients that are clinically distinct and associated with meaningful differences in utilization and spending measures. For these purposes, density-based clustering with the OPTICS algorithm outperformed connectivity-based and centroid-based clustering algorithms.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Custos de Cuidados de Saúde
/
Medicare Part C
/
Aprendizado de Máquina
Tipo de estudo:
Health_economic_evaluation
/
Prognostic_studies
Limite:
Aged
/
Aged80
/
Female
/
Humans
/
Male
País/Região como assunto:
America do norte
Idioma:
En
Revista:
J Gen Intern Med
Assunto da revista:
MEDICINA INTERNA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos