Information Technology Reference
In-Depth Information
Chapter 3
Statistical Methods
IntroductIon
Ultimately, a patient severity index is used to compare patient outcomes across healthcare providers.
If the outcome is mortality, logistic regression is used. If the outcome is cost, length of stay, or some
other resource utilization, then linear regression is used. A provider is ranked based upon the differen-
tial between predicted outcome and actual outcome. The greater this differential, the higher the quality
ranking. There are two ways to increase this differential. The first is to improve care to decrease actual
mortality or length of stay. The second is to improve coding to increase the predicted mortality or length
of stay. Ultimately, it is cheaper to increase the predicted values than it is to decrease the actual values.
Many providers take this approach.
However, there are some issues with regression itself when modeling healthcare outcomes. In particu-
lar, they require some general assumptions that are rarely satisfied; in many cases, these assumptions are
known to be false. All start with an assumption of a random sample. In using billing data, just what are
the variables and how can billing information have an assumption of randomness? Patient demographic
information is entered into the billing data as are codes related to the patient's diagnoses, and also the
procedures performed. What is considered to be random is the patient. Under general model assump-
Search WWH ::
Custom Search