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Am J Manag Care. 2011 Dec 1;17(12):e472-8.

Self-reported health and functional status information improves prediction of inpatient admissions and costs.

Author information

  • 1Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA. Nancy.Perrin@kpchr.org

Abstract

OBJECTIVES:

To determine whether adding selfreported health and functional status data to a diagnostic risk-score model explains additional variance in predicting inpatient admissions and costs.

STUDY DESIGN:

Retrospective observational analysis.

METHODS:

We used data from a Health Status Questionnaire (HSQ), completed by 6407 Kaiser Permanente Northwest Medicare patients between December 2006 and October 2008. We used answers from 3 items on the HSQ: (1) General Self-rated Health score, (2) needing help with 1 or more activities of daily living, and (3) having a bothersome health condition. We calculated a DxCG relative risk score from utilization information in the year prior to the survey, using electronic medical records. We compared: (1) DxCG as the sole independent variable and (2) DxCG plus the 3 items as independent variables. We estimated area under the curve (AUC) for each model. Any inpatient admission (yes/no) and being in the top 10% of costs (in the year after survey) were the dependent variables for the first and second logistic regression models, respectively.

RESULTS:

The 3 items explained an additional 2.8% and 4.0% of variance for inpatient admissions and top 10% of costs,respectively, in addition to the variance explained by the DxCG score alone. For DxCG alone, the AUC was 0.686 (95% confidence interval [CI] 0.663-0.710) and 0.741 (95% CI 0.719- 0.764), respectively, for inpatient admissions and top 10% of costs and improved to 0.709 (95% CI 0.687-0.730) and 0.770 (95% CI 0.749-0.790) when the 3 self-reported items were added.

CONCLUSIONS:

Using self-reported health information improved the predictive power of a DxCG model to forecast inpatient admissions and patient cost-tier.

PMID:
22216871
[PubMed - indexed for MEDLINE]
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