| 摘要 |
The expected charges for treating a patient are estimated by the use of linear regression techniques wherein variables and coefficients of estimate models are built from historic patient data of all episode types (in-patient, office visits, etc.) and which include secondary and collateral illnesses that greatly affect the cost of treating a patient for the primary illness. In addition to evaluating the primary illness, the evaluation of collateral illness is also made possible since the processing techniques are not simply averaging data for the primary illness as has been done in the past. This allows physicians to understand the magnitude and types of resource usage that are appropriate for particular illnesses. The present invention allows better management of many aspects of the healthcare system by significantly reducing a component of the difference between the estimate and actual charges for treating a particular illness of a patient that is due to the general state of health of the patient. This then allows the remaining difference to be attributed to differences in the way that health service providers treat each illness of a patient. Estimates are made for the expected cost of providing specific episodes of care, to treat each illness in its entirety by use of both in-patient and out-patient services, and to provide the specific procedures used for the appropriate management of each illness of each patient. |
