Today’s Managing Health Care Costs Indicator is $340,000
The early July Annals of Internal Medicine has a fascinating simulation which suggests that there isn’t a single “rule” about who should get mammograms. This isn’t surprising; we’ve been thinking about individualized medicine for some time. But this throws into doubt much of the quality reporting we’ve been putting in place over the last 20 years – we’re going to have to go way beyond the percentage of women between 50 and 65 who got a mammogram each year to determine if a provider group is delivering the best possible care!
For starters, this simulation shows that annual mammography is so costly that it costs more than $340,000 per quality adjusted life year to perform annual mammography instead of biennial for all women at any level of risk. Annual mammograms would not be recommended for any women by value-based purchasing guidelines.
Click to enlarge. Source
But this is not all about costs –the rate of false positives is strikingly high. And false positives don’t just cost money – they take an enormous emotional toll on women and their families. This simulation considered the potential negative quality of life for the brief period of time between a false positive screening mammogram and a negative biopsy – and even if that is very small, it still has a big impact on the “value” of mammographic screening.
Click to enlarge. Density by BI-RAD methodology (see article for details)
The value of mammography is highly dependent upon two well-accepted factors: family history and previous biopsy. Breast density is such an important risk factor for breast cancer that this alone could be a reason to recommend different screening intervals.
This is a simulation study – performed with robust sensitivity analysis – and the accompanying editorial warns that we should not change our current mammography practice based on this paper alone. The simulation assumed we would do a screening mammogram to determine breast density at the beginning of each 10 year period, and required a host of assumptions any of which could be disputed.
This paper elegantly demonstrates that we need to start thinking about evidence based therapy based on highly individualized considerations. These considerations will involve genetics, previous medical history, and individual patient preference. It’s going to be harder to effectively develop evidence-based insurance plans – since what is medically necessary for me is different than what is medically necessary for you based on criteria not likely to be found in claims It will also be harder to effectively practice medicine without decision algorithms built into electronic medical records. Neither a simple rule nor a physician’s intuition will be adequate to give us the best medical care.
