Predictive modeling to identify potential participants of a disease management program hypertension.
Predictive modeling
disease management programs
hospitalization
hypertension
risk assessment
Journal
Expert review of pharmacoeconomics & outcomes research
ISSN: 1744-8379
Titre abrégé: Expert Rev Pharmacoecon Outcomes Res
Pays: England
ID NLM: 101132257
Informations de publication
Date de publication:
Apr 2021
Apr 2021
Historique:
pubmed:
1
7
2020
medline:
29
4
2021
entrez:
1
7
2020
Statut:
ppublish
Résumé
Based on the premise of limited health-care resources, decision-makers pursue to allocate disease management programs (DMP) more targeted. Based on routine data from a private health insurance company, a prediction model was developed to estimate the individual risk for future in-patient stays of patients eligible for a DMP Hypertension. The database included anonymous claims data of 38,284 policyholders with a diagnosis in the year 2013. A cutoff point of ≥70% was used for selecting candidates with a risk for future hospitalization. Using a logistic regression model, we estimated the model's prognostic power, the occurrence of clinical events, and the resource use. Overall, the final model shows acceptable prognostic power (detection rate = 64.3%; sensitivity = 68.7%; positive predictive value (PPV) = 64.1%, area under the curve (AUC) = 0.72). The comparison between the selected hypothetical DMP-group with a predicted (LOH) ≥70% showed additional costs of about 69% for the DMP-group compared to insure with a LOH <70%. The predictive analytical approach may identify potential DMP participants with a high risk of increased health services utilization and in-patient stays.
Sections du résumé
BACKGROUND
BACKGROUND
Based on the premise of limited health-care resources, decision-makers pursue to allocate disease management programs (DMP) more targeted.
METHODS
METHODS
Based on routine data from a private health insurance company, a prediction model was developed to estimate the individual risk for future in-patient stays of patients eligible for a DMP Hypertension. The database included anonymous claims data of 38,284 policyholders with a diagnosis in the year 2013. A cutoff point of ≥70% was used for selecting candidates with a risk for future hospitalization. Using a logistic regression model, we estimated the model's prognostic power, the occurrence of clinical events, and the resource use.
RESULTS
RESULTS
Overall, the final model shows acceptable prognostic power (detection rate = 64.3%; sensitivity = 68.7%; positive predictive value (PPV) = 64.1%, area under the curve (AUC) = 0.72). The comparison between the selected hypothetical DMP-group with a predicted (LOH) ≥70% showed additional costs of about 69% for the DMP-group compared to insure with a LOH <70%.
CONCLUSION
CONCLUSIONS
The predictive analytical approach may identify potential DMP participants with a high risk of increased health services utilization and in-patient stays.
Identifiants
pubmed: 32600073
doi: 10.1080/14737167.2020.1780919
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM