|
BACKGROUND/RATIONALE:
The Agency for Healthcare Research and Quality Inpatient Quality Indicators (IQIs) are evidence-based quality of care measures that use administrative data to screen for potential quality problems in the inpatient setting. A subset of these measure mortality rates for medical conditions for which mortality has been shown to vary across providers and where evidence exists that high mortality may be associated with poorer quality of care. They represent readily available, low cost measures that we are currently applying to VA data (SDP 06-005-02). However, concerns exist about inadequate risk adjustment associated with these measures since they lack clinical data. The VA Inpatient Evaluation Center (IPEC) currently collects clinical information on all acute hospitalizations and uses this information to monitor and provide feedback to facilities with respect to ICU care. The information available in this database may be used to supplement the existing IQI models to improve their risk adjustment and predictive ability.
OBJECTIVE(S):
This study will explore the addition of clinical data to administrative data-based risk-adjustment models for specific outcomes of acute medical care:
Specific study objectives are:
To compare the ability of an administrative data-based model to one supplemented by clinical data elements to predict:
1) Outcomes from selected acute medical conditions
2) Patients at high and low risk of the specified outcomes.
And to 3) Examine facility-level prediction error associated with models using only administrative data compared to that obtained from models supplemented by clinical data.
METHODS:
This is a retrospective observational pilot study. We will include all VHA acute medical admissions with a principal diagnosis of acute myocardial infarction, congestive heart failure, pneumonia, stroke, gastrointestinal hemorrhage, and hip fracture during FY03 through 07. Clinical and administrative data will be obtained from IPEC database. Outcomes include: in-hospital death (y/n), LOS (days), and ICU admission (y/n). Predictor variables include age, gender, race, comorbidities, laboratory data and medications.
We will construct and compare a series of sequential stepwise logistic regression models for each of the 6 medical conditions and the noted outcomes. These models will involve testing of at three separate risk-adjustment models: 1) an administrative model that uses standard discharge data; 2) an administrative model + additional laboratory data; and 3) an administrative model + additional laboratory and pharmacy data. We will compare models using c-statistics, deciles of risk, as well as examining the average prediction error of each model and the bias attributable to risk adjustment using only administrative data.
FINDINGS/RESULTS:
None at this time.
IMPACT:
In this study, we have a unique opportunity to examine the effect of augmenting administrative with readily available clinical data in predicting adverse outcomes of in-hospital acute medical care. This project represents an important step toward developing a methodology for valid and accurate comparisons of risk-adjusted hospital performance with respect to outcomes of acute medical conditions that can be used to monitor and improve inpatient care in the VA. Further, these methods will be useful to prospectively identify veterans at higher risk of adverse outcomes such that interventions may be implemented to try to decrease this risk.
PUBLICATIONS:
None at this time.
DRA:
Health Services and Systems
DRE:
none
Keywords:
none
MeSH Terms:
none
|
