Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2014 Feb;9(3):68-79.

Predicting patients with high risk of becoming high-cost healthcare users in Ontario (Canada)

Yuriy Chechulin  1 , Amir Nazerian  2 , Saad Rais  1 , Kamil Malikov  3
Affiliations

Predicting patients with high risk of becoming high-cost healthcare users in Ontario (Canada)

Yuriy Chechulin et al. Healthc Policy. 2014 Feb.

Abstract
in English, French

Literature and original analysis of healthcare costs have shown that a small proportion of patients consume the majority of healthcare resources. A proactive approach is to target interventions towards those patients who are at risk of becoming high-cost users (HCUs). This approach requires identifying high-risk patients accurately before substantial avoidable costs have been incurred and health status has deteriorated further. We developed a predictive model to identify patients at risk of becoming HCUs in Ontario. HCUs were defined as the top 5% of patients incurring the highest costs. Information was collected on various demographic and utilization characteristics. The modelling technique used was logistic regression. If the top 5% of patients at risk of becoming HCUs are followed, the sensitivity is 42.2% and specificity is 97%. Alternatives for implementation of the model include collaboration between different levels of healthcare services for personalized healthcare interventions and interventions addressing needs of patient cohorts with high-cost conditions.

La littérature et l'analyse des coûts des services de santé démontrent qu'une petite portion de patients mobilise la majorité des ressources des services de santé. Une démarche proactive consiste à privilégier les interventions visant des patients qui présentent des risques de devenir des utilisateurs très coûteux (UTC). Cette démarche demande un recensement précis des patients à haut risque avant que des coûts substantiels ne soient engagés et que leur état de santé ne se soit détérioré davantage. Nous avons mis au point un modèle de prévision pour recenser les patients susceptibles de devenir des UTC en Ontario. Les UTC correspondent aux premiers 5 % d'utilisateurs qui génèrent les coûts les plus élevés. L'information a été recueillie selon diverses caractéristiques démographiques et modes d'utilisation. La régression logistique a été employée comme technique de modélisation. Si on effectue le suivi des premiers 5 % de patients à risque de devenir des UTC, le résultat de la sensibilité est de 42,2 % et celui de la spécificité s'élève à 97 %. Les choix pour l'application du modèle comprennent la collaboration entre divers niveaux de services de santé pour offrir des interventions personnalisées, ou encore la mise en place d'interventions qui répondent aux besoins de groupes de patients présentant des états de santé dont les coûts sont élevés.

PubMed Disclaimer

Figures

FIGURE 1.
FIGURE 1.
Receiver operating characteristic (ROC) plot of model performance on scored 2008 data
FIGURE 2.
FIGURE 2.
Goodness of fit (calibration) curve on scored 2008 data
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Ash A.S., Zhao Y., Ellis R.P., Kramer M.S. 2001. “Finding Future High-Cost Cases: Comparing Prior Cost versus Diagnosis-Based Methods.” Health Services Research 36(6 Part II): 194–206 - PMC - PubMed
    1. Berk M.L., Monheit A.C. 2001. “The Concentration of Health Care Expenditures, Revisited.” Health Affairs 20(2): 9–18 - PubMed
    1. Billings J., Dixon J., Mijanovich T., Wennberg D. 2006. “Case Finding for Patients at Risk of Readmission to Hospital: Development of Algorithm to Identify High-Risk Patients.” British Medical Journal 333(7563): 327. 10.1136/bmj.38870.657917.AE. - PMC - PubMed
    1. Billings J., Georghiou T., Blunt I., Bardsley M. 2013. “Choosing a Model to Predict Hospital Admission: An Observational Study of New Variants of Predictive Models for Case Finding.” BMJ Open 3: e003352. 10.1136/bmjopen-2013-003352. - PMC - PubMed
    1. Calver J., Brameld K.J., Preen D.B., Alexia S.J., Boldy D.P., McCaul K.A. 2006. “High-Cost Users of Hospital Beds in Western Australia: A Population-Based Record Linkage Study.” Medical Journal of Australia 184: 393–97 - PubMed

LinkOut - more resources