Any student of start-ups knows when introducing a product into the market, you first evaluate the market to determine whether the product will be adopted – assuming the percentage of adoption, or niche segment of adoption within the market. Then you introduce a minimum viable product to validate your evaluations. And pivot and adapt the product as necessary – and then finally release the full version of the product into the market.
A stepwise, formulaic approach, proven time and time again to work.
An approach, that when forgotten, has produced catastrophic failures out of presumed surefire successes. We know the stories – Crystal Pepsi, Coke 2.0, Google glasses, and most recently, Quibi, a short-form online streaming service. In all these cases, the anticipation did not match the actual adoption within the market. There was a disconnect.
A similar disconnect that is likely to disrupt vaccine distribution and adoption among the American population. This pandemic has taught us many things about healthcare, but not all the lessons have yet become manifestly apparent, some lessons we will come to learn. Among these is the disconnect between how patients and healthcare administrators view healthcare consumerism.
We know healthcare is trending towards a decentralized, technology-heavy, patient-centric model of care. Many loosely call this trend healthcare consumerism, and assume it to be a uniform trend in which we will uniformly march in stride towards this new form of healthcare. But this is not the case.
There is already a large disconnect between the general patient population and healthcare administrators. Most patients see healthcare consumerism far differently than administrators, at times in diametrically opposite ways. The push towards price transparency is a clear example. The current Center for Medicare and Medicaid Services administrator, Seema Verma, advocated for price transparency by comparing hospital prices to car shopping. In an op-ed she stated that people do not shop for cars without knowing the price – so in effect, patients should not receive medical care without knowing the price of care either.
But hospital administrators complained that full price transparency will create a massive data repository of information that many would not know how to make sense of it. And an overabundance of information can lead to misinformation and eventually clinical decisions made against the best interest of the patient. As a result, selective pricing data – limits on disclosures – protect patients by preventing them from making mistakes arising from misinterpreting information taken out of context.
Interestingly, both those for and those against price transparency are arguing along the principles of healthcare consumerism but arguing across opposite perspectives. A microcosm of the growing disconnect as healthcare consumerism becomes more influential – a trend that will continue in the next phase of COVID-19 when administrators will be grappling with the public as to why they should take the vaccine.
They will tout the incredibly high efficacy rate. They will tout the novel development techniques used to rapidly develop the vaccine. They will tout the race for COVID-19 vaccines as the latest American breakthrough, comparing the COVID-19 vaccine with the Polio vaccine. And while we largely see Dr. Jonas Salk as a folk hero, and the Polio vaccine as a testament to American ingenuity, we forget the many issues that came after a vaccine was discovered.
We forget the batches of vaccines that led to numerous deaths. The cold war competition between Dr. Salk’s vaccine that consisted of multiple injections compared to Dr. Sabin’s oral vaccine. We forget that we initially opted for Dr. Salk’s vaccine because it was developed faster, but eventually adopted Dr. Sabin’s vaccine because it was easier to administer and far safer. A shift finalized in 1961 when the American Medical Association recommended that Salk’s vaccine should be replaced with Sabin’s oral formula.
In other words, the initial vaccine that was adopted in the market was not the vaccine that was eventually adopted for widespread use. The initial vaccine, while effective and no doubt saved lives, proved to have too many risks – limiting overall adoption. Risks that perhaps could have been discovered if development had not been rushed – if instead, we had adopted a more pragmatic approach of selective release, pivoting and adapting, and concluding with a larger scale release.
Like introducing a start-up into the market.
But healthcare has come a long way since the mid-twentieth century. And many would argue that the Food and Drug Administration’s robust oversight of vaccine development through a multi-phrase trial design has already addressed any risks found in a COVID-19 vaccine.
After all, we already have three vaccine manufacturers who tout at least a 90% efficacy in mounting an immune response. And 90% is good, right?
Well, a number is only as good as the context underlying the number itself. And to date, most vaccine trials have focused on a limited population, with subjects that had a robust immune system before the trial began. A far cry from the high-risk patient population that the federal government is calling to receive the vaccine initially. And when adjusted for high risk populations outside of a traditionally controlled clinical trial, that 90% quickly dips to be quite lower.
How much lower – not even the scientists developing the drug know. We are using mRNA vaccine development technology that was developed as recently as 2015 by Stanford University and the Bill and Melinda Gates Foundation to study a virus that we are still learning about.
We are not even sure how to compile efficacy across clinical study designs. AstraZenica/Oxford obtained a 70% efficacy by averaging data across two different trials with two different doses – something that has traditionally never been done.
And when the vaccine is administered across various patient populations, the efficacy will automatically go down, since we have to account for the random array of patient behaviors, exposures, and socioeconomic constraints – variables not accounted for in controlled clinical trials that have proven to decrease vaccine efficacy in epidemiologic studies done on other vaccines in the past.
All of which will most certainly decrease the efficacy well below 90% for the COVID-19 vaccines.
We are spit-balling far more than anybody would like to admit. Which would be perfectly fine if we just admit to it – if we just embrace a start-up approach to vaccine development, acknowledge what we know and do not know, and test the market accordingly.
We would identify regions in which patient confidence in the vaccine is lower, or patient demographics that may prove more variable, and consequently less effective, than others – but most importantly, we may better anticipate the inevitable risks that will appear early in the vaccine distribution and adoption.
Errors that will further erode confidence in the vaccine and will lower the number of Americans willing to take the vaccine.
Errors that will exacerbate the disconnect between healthcare administrators and patients. With the former encouraging patients to accept the risks and continue taking the vaccine, and the latter unwilling to accept the risk and demanding the right to refuse the vaccine due to personal safety concerns.
Arguments built on the foundation of healthcare consumerism, attempting to convince the other that the numbers prove their point – that the vaccine is somehow safe yet dangerous because of what the numbers say. Healthcare administrators will use the data to show the vaccine is effective while patients and patient advocacy groups will then use the same data that shows the vaccine is dangerous.
Highlighting the inherent problem in using healthcare data when arguing along principles of healthcare consumerism. The numbers are interdependent – they can be used to prove just as much as they can be used to disprove. And if news of decreasing vaccine effectiveness becomes apparent, then even fewer people will support the vaccine, further decreasing the efficacy data. Revealing an underlying interdependence between healthcare statistics and aggregate patient behavior.
The numbers are influenced by the default tendencies in our healthcare behavior. And should not be used to dictate healthcare decisions. Rather than use numbers to guide decisions, we should use the numbers as one of many reference points to determine what healthcare behaviors make sense – based upon what we know and do not know. And for the latter, we should implement concepts from the start-up world to address future unforeseen risks that may influence the numbers negatively.
This would be effectively applying the principles of healthcare consumerism.
