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


METHODS
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.
Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA. Nancy.Perrin@kpchr.org
Nancy A
NA
Perrin
Matt
M
Stiefel
David M
DM
Mosen
Alan
A
Bauck
Elizabeth
E
Shuster
Erin M
EM
Dirks
D000203:Activities of Daily Living; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D016001:Confidence Intervals; D005260:Female; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006304:Health Status; D006801:Humans; D007297:Inpatients / Q000706:statistics & numerical data*; D007345:Insurance Claim Review; D016015:Logistic Models; D008297:Male; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D012306:Risk; D057566:Self Report; D011795:Surveys and Questionnaires; D014481:United States