By Mike Popovich, CEO of STChealth

Three generations of health data have brought us to this moment:  We now have data so robust that we can use it to enable dramatic improvements in vaccination rates while improving the entire health ecosystem. We are there. So what now?

Let’s start with a quick recap of the journey:

GENERATION ONE. A measles outbreak? Measles was over, defeated by modern science and public health and yet, there it was again, this time in the early 1990s, startling the public and causing a stir. In 1991, with help from NGOs and state public health agencies, the CDC launched an initiative to track childhood diseases and childhood immunizations. We can look back now on that data reporting initiative as Generation One of using data technology to improve health outcomes by improving vaccination rates. If you’ve been around health care since those days, you’ll remember that the early reporting of immunizations involved collecting data on paper forms, and then submitting it via PCs, LANs, and dial-in reporting.

GENERATION TWO. A decade later, in 2001, after 9/11, there was the danger of anthrax and the worry about smallpox. Those concerns led to a focus on preparedness and with it came dollars to upgrade the technology of reporting: web-based data, and the resources to grow the system.

GENERATION THREE. These days, starting after the spread of Covid in 2021, there have been fresh resources for health infrastructure and the ability to move to cloud platforms and plug-and-play systems, allowing for open ecosystem connectivity.

So, in those three leaps forward, each roughly a decade apart, we see how three public health emergencies allowed the pillars of health information to be put in place. Along the way, we went from monitoring childhood vaccines at clinics and pediatrician offices to gathering data from most of the healthcare system, including capturing the work of the vast army of healthcare workers employed in pharmacies.

This means that we are NOW IN A POSITION TO TRULY REALIZE THE DREAM OF ’91. Said another way, after more than three decades,

IT’S ABOUT TIME!

We now have so much good data that the new challenge is how to put the information to work. Here are some of the ways:

IT’S TIME TO BRING DATA TO MORE USERS

First, it’s about time that we move the data outward, getting it into more hands. For instance, we at STChealth will be providing vax data to 2500 dialysis centers. While it’s terrific that those centers will be getting solid information to put to use, won’t it also be a chance to do more? Couldn’t it also provide an opportunity for those centers to experiment with new partnerships with vaccine providers? Couldn’t it offer the chance to test vax programs such as different vax incentives?

IT’S TIME TO BE SHOCKED

It’s also about time that we open up the data to organizations that can study it and find new ways to let that data tell its stories. Let’s get the data to researchers at the FDA, the NIH, in the pharmaceutical community, and at universities. It’s time for a state health department to team up with a state university to dig deep into the numbers and see what new insights are waiting there. Here’s the point: Turn the data over to the people who’ll love it. Give it to them and you might be shocked at what could happen. It’s time to be shocked.

 IT’S TIME TO GET SMALL

We have the data to track the effectiveness of immunization programs at the local and neighborhood levels – to really know where programs work and with whom. This means having the capacity to identify problem areas and test targeted programs in response.

IT’S TIME TO GET BIGGER

As we have better local data, we start to see patterns in communities and how those link together. Regional patterns will appear, and we will begin to understand how regional cultures – lifestyles, travel patterns, shopping and health challenges — cut across state lines. The data has regional stories to tell and regional partnerships are needed.

IT’S TIME TO CLOSE THE LOOP, TO KNOW WHAT’S WORTH THE COST

Don’t let’s guess and hope about what works. Let’s know the outcome of new programs. Let’s know which healthcare dollars are smart investments. Let’s use the data to tell us what a new campaign changed and didn’t change. And then let’s calculate the health care dollars saved and justify the investments.

IT’S TIME TO SEE THE FUTURE  

The data we now have is so strong that it can not only answer the question “What happened?”, but can answer “What if?” questions. We can create models to answer questions like, “What if we ran this campaign in schools?” The model can tell us “do this” and “do that” to maximize outcomes. 

Summing up: 

The data is here. The tech is here. The possibilities offer such potential that we believe it’s time to set a big goal. Here’s what we’re considering:

PROJECT PROTECT 2030

Goal: Increase adult immunizations by 20% by 2030

&

Increase community pharmacy revenue for their immunization programs by 20%

Interested in joining in? Please let me know.

But whether you are intrigued by Project Protect or not, as you read about the possibilities for the new levels of data, I hope you will grab an idea or two and experiment with it.

THE PROBLEM AHEAD

If you’re intrigued by the idea of doing more with data, you are probably thinking ahead to the problems to be encountered, chief among them those people in positions of authority who don’t want the data to tell its stories because they might not like what they hear. So, in anticipation of needing to persuade policy people to give permission and/or dollars to innovate, I asked our editor, Dale Dauten, to visit with our favorite marketing expert, Alan Quarry in pursuit of some insights and suggestions.

“Stop Selling”

A Conversation with Alan Quarry

By Dale Dauten

When I sent Alan the draft of Mike article that precedes this one, he said we should start with an understanding of data and its transformation into information and knowledge. He started with this drawing…

Spending some time with the delightfully named “Churn, Baby Churn” model, I noticed that surprising word choice for a topic about selling ideas: “better guessing.” That was a good place to begin to understand Alan’s logic:

“If you spend time with people in large organizations,” he began, “you understand that risk-reduction is the key to making decisions. That’s the thing that data — turned into information, turned into knowledge — can give you: reduced risk.”

So, I then wondered, does that mean the key to selling ideas is simply to sell them like they are the equivalent of a weighted blanket and a nice cup of tea? “No,” Alan insisted, “the key is to STOP SELLING and START HELPING PEOPLE BUY. The first thing to do is to make sure you’re not seen as selling – do that and a barricade goes up. Instead, risk-reduction is figuring out how to help.”

Alan next brought up the WIIFM, the old What’s In It For Me, which he called “the world’s most powerful radio station,” suggested that understanding a person’s problems is a good place to begin a conversation. Alan offered an example of the opening words in a helping conversation: “As you have no doubt found [insert problem].” Then, you’d follow up with some version of, “When others have faced similar challenges with [insert problem], they have been successful in addressing that problem with our help. Would you like to know how?”

How could we apply this logic to getting a policymaker to approve a new immunization campaign? Instead of talking about how wonderful the outcome could be, the risk reduction approach might sound more like this…

“We have such detailed data available, down to the neighborhood level, that we could do small, inexpensive tests of different campaign ideas. We could then project the outcomes to the wider population and estimate the health benefits against the cost estimates. Our odds of having a successful campaign go way up.”

Which takes us back to Alan’s “Churn, Baby, Churn” model. Discussing the notion of “better guessing,” Alan compared it to becoming proficient at playing “21” at a casino: “You become proficient by understanding the odds. You’re not always right, but you’re right more often. You become more successful and people notice who’s accurate.”

Alan concluded by saying, “When you stop selling and start helping, the energy of convincing becomes the energy of converting.  The people you help become advocates and ambassadors.”

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STATS OF THE MONTH

IF NOT NOW, WHEN?

By Bill Davenhall, Geospatial Advocate

Humans have a long history of challenging and overturning long-held beliefs and practices, especially ones with a medical or medical implication, so it should not be a surprise to learn about a growing trend in a recently released (2023) CDC study: Trans and Questioning youth in Grades 9-12.

Let me begin by reminding readers about things that the medical field took for granted, including claims that they believed worked, like bloodletting, lobotomies, bed rest for bad backs, and, my personal favorite, stress-caused stomach ulcers. It took decades and, in some cases, generations for these “learned and accepted practices” to disappear and be proven ineffective, if not downright lethal. These are just a few examples of when practitioners got it wrong, but they also inspired a generation of professional researchers to challenge and correct misconceptions. Scientific knowledge continues to evolve, and researchers must also be open to new data, better-designed studies, and contextually and meaningful frameworks for practitioners.

The CDC report estimates that almost 5.8 % of all US high schoolers in grades 9-12 have either questioned their gender identity (2.2%) or have described their gender as Trans (3.3%)  — yet another early signal that this behavioral change is probably not by chance. So, regardless of your opinion(s) about this subject, the data scientist will eventually inform the “practice and the science”. The study’s authors discuss some of their survey results in great detail. (See sections on Limitations, Discussion and Intervention Opportunities – all three are worth a careful read.)

This report should raise a “warning” flag in the immunization ecosystem because it describes, factually, yet another marginalized segment of the US population that will present a challenge for immunization programs and vaccine providers in all states. The report identifies some of the key determinants of the health-seeking behaviors that Trans or Questioning youth will present to the healthcare providers and not just at the pharmacy counter.

The map below contains estimates of what immunization programs might encounter in approaching the gender dysphoria populations. Two hundred eleven (211) counties, located in forty (40) different states that can expect at least 1,000+ high school students in both private and public schools to provide unique opportunities for innovative immunization promotional programs to assure that this particular segment of the youth population will seek and receive adequate immunizations. Perhaps beginning to think about this data sooner rather than later may be the best “innovation”! As I like to remind my readers – “make data do something”!

As always, I appreciate a 2^nd opinion,