Predicting Medicare Underpayments Using an LMS algorithm

1. Predicting Medicare Underpayments Using an LMS algorithm Ted Shultz December, 2001 University of Wisconsin

2. Vanderbilt Bills Medicare for one amount Bill: Band-Aid $0.12 Aspirin $1.04 New Hip $1,000.00 Gauss $12.00 Gloves $3.75 ------------------- TOTAL $1016.91 MEDICARE Payment: (no explanation) ------------- Total $412.63 Medicare pays Vanderbilt a different amount Problem Explanation ?Why? Problem description

3. Comparison of Methods: Never been done before with Medicare! LARGE data file (443,964 purchases) Simultaneous equations methods: Comparison Inverse matrix method Much to large a matrix to inverted on a convention computer Orthogonal-triangular decomposition(Matlab backslash operator ) Unable to sort though possible answer to determine optimal solution based on input parameters Modified LMS method Slow, but able to bracket answer

4. Techniques used to handle large data file • Requires two days to load and format Matrix! (400Mhz) • Two weeks of calculations (by project definition) • Do all file manipulations in a data base program • Significant time savings • Bracket weights after each weight recalculation • Know Medicare will pay between 0-100% • Automatically resize  • Start larger, but shrink for accuracy • Auto save and resume capabilities are required • CAE tethered server crashes every few days Techniques

5. Only about 1 week of way into calculations Full reimbursement Fixed percent Still moving or negotiated rate Results-Conclusions Guess pay amount Billed amount No payment Potential to have HUGE impact About $32M in charges, $5.5 M in reimbursements