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Reading: Applying Sequential Analytic Methods to Self-Reported Information to Anticipate Care Needs

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Empirical research

Applying Sequential Analytic Methods to Self-Reported Information to Anticipate Care Needs

Authors:

Elizabeth A. Bayliss ,

Kaiser Permanente Colorado Institute for Health Research; Department of Family Medicine, University of Colorado School of Medicine
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J. David Powers,

Kaiser Permanente Colorado Institute for Health Research
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Jennifer L. Ellis,

Kaiser Permanente Colorado Institute for Health Research
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Jennifer Barrow,

Kaiser Permanente Colorado Institute for Health Research
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Mary Jo Strobel,

Kaiser Permanente Colorado, Department of Complete Health Solutions
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Arne Beck

Kaiser Permanente Colorado Institute for Health Research
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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.

How to Cite: Bayliss EA, Powers JD, Ellis JL, Barrow J, Strobel MJ, Beck A. Applying Sequential Analytic Methods to Self-Reported Information to Anticipate Care Needs. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2016;4(1):14. DOI: http://doi.org/10.13063/2327-9214.1258
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Published on 12 Jul 2016.
Peer Reviewed

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