Defining ‘actionable’ high- costhealth care use: results using the Canadian Institute for Health Information population grouping methodology

Abstract

BackgroundA small proportion of the population consumes the majority of health care resources. High-cost health care users are a heterogeneous group. We aim to segment a provincial population into relevant homogenous sub-groups to provide actionable information on risk factors associated with high-cost health care use within sub-populations.MethodsThe Canadian Institute for Health Information (CIHI) Population Grouping methodology was used to define mutually exclusive and clinically relevant health profile sub-groups. High-cost users (> = 90th percentile of health care spending) were defined within each sub-group. Univariate analyses explored demographic, socio-economic status, health status and health care utilization variables associated with high-cost use. Multivariable logistic regression models were constructed for the costliest health profile groups.ResultsFrom 2015 to 2017, 1,175,147 individuals were identified for study. High-cost users consumed 41% of total health care resources. Average annual health care spending for individuals not high-cost were $642; high-cost users were $16,316. The costliest health profile groups were ‘long-term care’, ‘palliative’, ‘major acute’, ‘major chronic’, ‘major cancer’, ‘major newborn’, ‘major mental health’ and ‘moderate chronic’. Both ‘major acute’ and ‘major cancer’ health profile groups were largely explained by measures of health care utilization and multi-morbidity. In the remaining costliest health profile groups modelled, ‘major chronic’, ‘moderate chronic’, ‘major newborn’ and ‘other mental health’, a measure of socio-economic status, low neighbourhood income, was statistically significantly associated with high-cost use.InterpretationModel results point to specific, actionable information within clinically meaningful subgroups to reduce high-cost health care use. Health equity, specifically low socio-economic status, was statistically significantly associated with high-cost use in the majority of health profile sub-groups. Population segmentation methods, and more specifically, the CIHI Population Grouping Methodology, provide specificity to high-cost health care use; informing interventions aimed at reducing health care costs and improving population health.

Identifying subgroups of adult high-cost health care users: a retrospective analysis

“…The studies of high-cost users in Canada 10 and of medically complex patients in northern California 11 both defined their populations for a single time period (i.e., episodic health care use) rather than for those who were continually high-cost users in multiple subsequent years, and who arguably had the greatest potential for targeted interventions and cost savings. 12 Much of the previous work in this area lacks granularity and neglects the high turnover in the high-cost population over time.…”

“…[5][6][7][8][9] A recent Canadian study of high-cost patients described the clinical and demographic profiles of the top 10% of individuals by cumulative cost accrued during a 2-year window. 10 They identified 7 high-cost subgroups: "long-term care," "palliative care," "major acute illness," "major chronic diseases," "major cancer," "major newborn" and "mental health. "…”

Validación predictiva de un método de clasificación funcional en adultos mayores

“…Hasta la fecha, según el conocimiento de los autores, no se han publicado modelos predictivos latinoamericanos que hayan incluido información relacionada con los diagnósticos previos, datos suministrados de forma directa por el paciente, mediciones objetivas del estado funcional y la presencia de fragilidad, para identificar individuos con necesidades en salud. La importancia de estas investigaciones radica en la posibilidad de identificar subgrupos de adultos mayores con un riesgo más elevado de desenlaces adversos (10,(20)(21)(22). Estas herramientas podrían contribuir en la implementación de intervenciones específicas estratificadas según el riesgo basal al interior de los programas de atención de los adultos mayores con ECNT.…”

Identifying patterns of clinical conditions among high-cost older adult health care users using claims data: a latent class approach

“…Health systems worldwide are facing the challenge of increased health care costs and sustainability [ 1 , 2 ]. Previous studies [ 3 , 4 ] have shown that a small proportion of individuals spend the majority of health care resources.…”

“…This population is referred to as high-cost health care users (also high-cost beneficiary, high user, high spender, super-utilizer) [ 5 ]. Studies have found that high-cost health care users are a heterogeneous population, and heterogeneity may contribute to the fact that few interventions have demonstrated evidence of reliable success [ 2 , 6 ]. Most of these high-cost health care users are older adults [ 5 ].…”