Care Affordability Index or How to Go Beyond the “# of Lives Covered” Metric


Care Affordability Index or How to Go Beyond the “# of Lives Covered” Metric

A Care Affordability Index could constitute a way more informative measure of patient-access performance than the binary “number of lives covered”. And it is not that complicated to implement. A post for dashboard lovers.

We all see our mailboxes bombarded by emails trying to sell us syndicated reports claiming that they will solve the key issues the life science industry is facing for us. Rather than seeing these emails as the annoying spams they probably are, I actually read them, and over time, I have even developed a genuine interest in them. Indeed, trying to charge me USD5,000 for something I know was true in 1996 is not email marketing, it is art. Here is a gem from a recent one –

“Physicians want efficacy, payers want cost-efficiency, and patients want access.” How 1996 is that?

But what if the folks who wrote this email were purposely trying to create an emotional connection to 1996 in the mind of the readers?  After all, our industry is one of the most conservative, risk-averse, ‘old habits die hard’ industries out there. Appealing to that to sell a 5,000 Dollar report is an art. And, the Nokia 3310 is back (yay!). And, Take That just released a new single (uh?).

To be fair, there is nothing outrageously untrue in that statement, but there is nothing that will help you succeed in launching an innovative product either. At least in 2017. It is just another reinforcement of what we think we know about value creation in life sciences, by siloing our approach to customers and applying our own beliefs as a suboptimal proxy to what those customers actually want. I don’t really mind the “physicians want efficacy” bit which is more incomplete than false and the “payers want cost-efficiency” which is more semantically incorrect than dangerously misleading. But the one on “patients want access”, well, irritates me. Probably because I have heard it way too often in my couple of decades in this industry (yes, I was there in 1996, with my Nokia 8910 in one hand and my Palm Pilot in the other. I’m bragging). And because this syllogistical approach to what patients want has probably destroyed more value than anything else in our industry. Yes, the key to understanding the embarrassing disconnect between pharmaceutical forecasts and pharmaceutical sales lies right there.

As Market Access was growing into an established function within our industry, the belief that ‘access’ is the ultimate goal was getting deeper in the industry’s collective unconscious. This has led to an explosion of metrics, key performance indicators, and dashboards of rather limited value. Worst of all is the ‘number of lives covered per geography’. Here is why I believe so:

Maria is a 67-year old cancer patient in the US. She pays a USD105/month premium to benefit from a Medicare drug plan that covers the targeted therapy she is medically eligible to receive. Her 25% coinsurance means she would have to spend USD800/month to actually get it. That plus her monthly premium equals her full pension. She can’t access the treatment. Oh, on the drug manufacturer’s colorful dashboard, Maria is counted as a ‘life covered’.

I could go on and on with multiple examples like Maria’s across different settings and geographies. But I won’t. Because a friend told me my posts should be shorter and more ‘solution-oriented’. And I listen to my friends. Here is a tangible proposal to all dashboard lovers on how to start tracking a patient-access KPI that actually matters.

What matters to Maria is – can she afford the treatment she is medically eligible to receive? A simple way to answer this question is to divide her monthly out-of-pocket treatment cost by her monthly disposable income. That will produce a Care Affordability Index. Now, calculating a Care Affordability Index for each individual patient would be herculean. However, there is a wealth of available data that will allow getting a good estimate of the product affordability at the individual patient level. For instance, in the US, disposable income data are available per state, age group, ethnicity, employment status, and many more stratification variables. These strata could be matched with the average patient cost given the product reimbursement status vis-a-vis the key payers in a stratum, like State Medicaid, Commercial, etc. In total, about 100 strata would already provide an acceptable estimate of the Care Affordability Index of a product. In any case, this estimate would be far more informative than the binary “number of lives covered” indicator.

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