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The for-profits tended to admit more Medicare patients (80% versus 57%) and their Medicare patients tended to have a higher case-mix (HHRG of 1.34 compared with 1.24).
We have controlled for this by weighting the HHRG variable by the percent of Medicare patients in the agency, but this may not have sufficed, particularly if the case-mix of other patients, Medicaid and private pay, is not highly correlated with that of Medicare patients.
Current CMS regulations do not require OASIS data collection that would allow HHRG assignment for non-Medicare patients.
By using software tools to assign the HHRG upon assessment and to compare the expected payment to the ordered services and supplies, agencies can develop prospective win/loss reports and implement utilization management strategies for high cost patients early in the episode.
The accuracy of the ICD-9-CM code also affects HHRG reimbursement.
By looking back at the changes acute care hospitals made to contend with DRGs, it is possible to identify key success factors and IT tools that may be applicable to home health agencies now being reimbursed based on Home Health Resource Groups (HHRGs) since the October 2000 launch of home health prospective payment.
When changes in a patient's condition dictate, a single episode may be paid under multiple HHRGs.
Using estimates of revenue based on case-mix frequency and national basic reimbursement rates from the Health Care Financing Administration, a probability sample of HHRGs was selected to replace our previous sample of Medicare items.
(We also used log-transformed visit variables, with similar results.) Because the HHRGs were developed to take into account differences in patient case mix that affect the utilization of services, the case-mix variables in our visit regressions were dichotomies to represent the levels of each HHRG dimension.
Some OASIS items are used both to determine the HHRG for payment and as risk factors in the CMS outcome models.
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