Using Data to Inform Value Based Health Care
One of the important issues we think a lot about are the metrics and analytic tools that we think our customers will need to practice “Value-Based Health Care.” That is, health care based on the quality of the outputs as opposed to simply the volume of care.
A recent Harvard Business Review blog post discusses these issues and lays out some recommendations. These make a lot of sense as does the basic idea of paying for quality and outcomes and trying to conserve costs.
But the data story is complicated. First there is the issue of leveling the playing field in terms of outcomes. High quality could be the result of low complexity of cases, has opposed to purely the best care. High quality should also be significant, not random variation. Positive change should have at a minimum some degree of statistical significance. In addition, the system that measures quality should accurately reflect what it is measuring. It needs to be properly calibrated and should not contain systematic error that overstates quality just by how a measurement is taken.
We think these are critical factors that both payers and providers need to take into account in their analytic strategies. First, are they adjusting for complexity when judging relative outcomes? Second, when they are measuring outcomes, are they seeing change that is, as best as can be determined, real and not the result of a random move? Finally are the measurement tools in control. That is, when the tools detect a change from ill to better, or from score 1 to score 10, is that change an accurate reflection of what really happened.
In future posts we will talk about working with these factors in software and operations.