MCAC Says Cost Data, Randomized Studies Best for Medicare Coverage
Panel speaks informally, but clearly, as it reflects upon evidence for technologies seeking coverage.
Members of the Medicare Coverage Advisory Committee (MCAC) made it clear in a December 8 meeting of the group's executive committee that they believe randomized controlled clinical trials-the longest, most difficult, and costliest form of data-are the most reliable type of evidence for making Medicare coverage decisions on new technologies. Panel members also spoke of their preference for considering cost and cost-effectiveness of medical products in making coverage judgments, and some argued that new technologies should demonstrate added value above currently available alternatives before they are covered.
MCAC is the public advisory committee established last year by the Health Care Financing Administration (HCFA) that is now beginning to advise the agency on which technologies should be covered by Medicare-much as advisory panels advise FDA on which products should receive regulatory approval. In testimony at the meeting, HIMA warned MCAC that decisions on coverage criteria should be made through an open, public rulemaking process, not by MCAC itself. Though the executive committee issued no formal decisions on criteria following HIMA's comments, committee members nevertheless expressed their strong preferences both about the type of criteria they think is most appropriate and the relative hierarchy among types of evidence.
The executive committee also chose not to act upon coverage recommendations on specific technologies from two of its sub-panels-one offered recommendations on lab tests that gauge how a cancer patient will react to certain types of chemotherapy; the other on the combination of stem cell transplantation and high dose chemotherapy for the treatment of multiple myeloma. Instead of acting on the recommendations, the MCAC executive committee returned them to the sub-panels for reconsideration when the executive committee completes its work on a formal, structured method for analyzing evidence and reviewing data on technologies.
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