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Submission Type

Empirical Research

Keywords

Learning Health System; Health Care Operations (HCO); Methods

Abstract

Purpose: Identifying care needs for newly enrolled or newly insured individuals is important under the Affordable Care Act. Systematically collected patient-reported information can potentially identify subgroups with specific care needs prior to service use.

Methods: We conducted a retrospective cohort investigation of 6,047 individuals who completed a 10-question needs assessment upon initial enrollment in Kaiser Permanente Colorado (KPCO), a not-for-profit integrated delivery system, through the Colorado State Individual Exchange. We used responses from the Brief Health Questionnaire (BHQ), to develop a predictive model for receiving care in the top 25% for cost, then applied cluster analytic techniques to identify different high cost subpopulations. Per-member-per-month cost was measured from 6-12 months following BHQ response.

Results: BHQ responses significantly predictive of high cost care included self-reported health status, functional limitations, medication use, presence of 0-4 chronic conditions, self-reported ED use during the prior year, and lack of prior insurance. Age, gender, and deductible-based insurance product were also predictive. The largest possible range of predicted probabilities of being in the top 25% of cost was 3.5% to 96.4%. Within the top cost quartile, examples of potentially actionable clusters of patients included those with high morbidity, prior utilization, depression risk and financial constraints; high morbidity, previously uninsured individuals with few financial constraints; and relatively healthy, previously insured individuals with medication needs.

Conclusions: Applying sequential predictive modeling and cluster analytic techniques to patient-reported information can identify subgroups of individuals within heterogeneous populations who may benefit from specific interventions to optimize initial care delivery.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

DOI

10.13063/2327-9214.1258

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