Health IT Marketing and Boomer, Gen X, and Millennial Workflows

I gotta brag. I’m not an anthropologist, but I love reading about anthropology, and lots of my friends in medical school were medical anthropologists. I use ideas from anthropology to inform my systems engineering thinking about health IT. With some trepidation, I finally waded into the anthropology of health IT, and wrote a blog post. Eventually I sent it to the former HHS CTO, who happens to have an anthropology degree. And she tweeted this!

That’s my brag! Made my week, it did!

Since that Eat Your Beans post, I’ve been thinking a lot about digital and computational anthropology … wait! Don’t run away! This really is connected to health IT marketing and generational divides. First, just a little background. Anthropology is the study of human societies and cultures and their development. The theory of generations (generations X, Y, Z, did you know that “Boomers” are Gen I?) was proposed in 1923 by Karl Mannheim, a sociologist. Sociologists and anthropologists both study societies and how humans behave. Sociologists focus on the big picture, such as social groups and society. Anthropologists drill down into the nitty-gritty of individual behavior in groups. If you put the word “computational” in front of something, it usually means the nitty-gritty is so nitty-gritty that there is enough detail to actually simulate behaviors. Closely related to computational anthropology is digital anthropology, in which anthropologists study interactions between humans and digital technologies.

Aha! Digital anthropology is clearly relevant to how “generational perspectives are influencing healthcare technology, and additionally, how can we (as health IT leaders) can strive to incorporate and include diverse generational needs into the industry roadmap” (from this week’s #HITsm chat). But computational anthropology is also relevant to “including every generation in our health information technology thinking”.

The rise of computational anthropology is fueled by so-called “big data.” Digital technology is so woven into our professional and persona lives, that its “data exhaust” (love that term!) can be used to track us, understand and empower us, but also raises enormous ethical and privacy issues. The fascinating thing in this latter regard is that the field of anthropology has adopted a sophisticated system of ethics regarding dealing with data about human behavior. In some ways, it surpasses current health IT principles for handing sensitive personally identifiable health data. For example, consider the following from the Wikipedia entry about digital anthropology…

“Online fieldwork offers new ethical challenges. According to the AAA’s ethics guidelines, anthropologists researching a community must make sure that all members of that community know they are being studied and have access to data the anthropologist produces. However, many online communities’ interactions are publicly available for anyone to read, and may be preserved online for years. Digital anthropologists debate the extent to which “lurking” in online communities and sifting through public archives is ethical.”

Lurking during a tweetchat potentially being unethical? Wow!

But let’s assume, for the moment, that anthropological ethics and Internet-Of-Things cybersecurity issues can be adequately managed.

How might ideas from digital and computational anthropology potentially guide a health IT marketer?

The first thing to realize is that digital anthropology is applied anthropology, from which marketing research increasingly incorporates methods. In fact, there is a Journal of Business Anthropology (a sub-discipline within applied anthropology) and the sub-discipline of marketing anthropology. Anthropology is an increasingly popular minor among marketing students. Degrees in digital marketing anthropology are surely just around the corner.

What about workflow? Digital anthropology can be used to collect and interpret consumer and patient life-flows (essentially “workflows”, but more general than mere work settings, including family and other personal activities). Computational anthropology provides representations and models into which these data and interpretations can flow and inform. At the top of this list are agent-based simulations. Agent-based simulations are really cool. So cool, I recently attended the Anylogic user conference in Nashville to learn more about agent-based simulation. Anylogic develops and markets the most sophisticated agent-based simulation software on the market. Anylogic can also simulate more traditional discrete event simulations (popular among industrial engineers for simulating patient flows, where I got my start in healthcare workflow) and dynamic systems. Agent-based simulations simulate “agents”, which are basically simplified representations of humans, though I am sure they could simulate other kinds of agents, such as cattle behavior at the level of individual cows, and so forth.

Here are some of the various workflow notations compatible with AnyLogic.

With so much compute power available today, look at the scale of current agent-based simulation research! Surely human behavior after a nuclear attack is an important public health topic!

Here are a couple animations driven by agent-based simulation.

The following is a simulation of conference attendees interacting with the lunch queue. This not as impressive as either of the previous agent-based simulations, but there is the thing. It was created, from scratch, in just a couple hours in front of an audience. Each of the “attendees” (in the simulation, not the audience in which I sat) is essentially a tiny, virtual workflow system. Each attendee is modelled as a state machine, which is the formal terminology for a model of workflow being executed by a workflow engine while interacting with environment inputs.

Watch the above animation and just think of the possibilities for modeling different generations and their interactions with digital technologies! Increasingly we have the data. We have means to model workflow behaviors and execute workflow models. We can study personal and professional workflows executing within interactive environment. And we can do so within and between demographic generations within families, among friends, and between patients and healthcare systems.

Sound like science fiction? Workflow research really is finally moving out of the healthcare organizational setting and into patient’s lives. Check out this diagram of workflow interactions and information flows between a patient outside of a healthcare organization and the healthcare organization itself (from the recent Healthcare Systems Process Improvement Conference in Orlando).

Also see out my previous post, Actuarial Science, Accountable Care Organizations, and Workflow.

“Workflow⁰ is a series¹ of steps², consuming resources³, achieving goals⁴.”

⁰ process
¹ thru graph connecting process states (not necessarily deterministic)
² steps/tasks/activities/experiences/events/etc
³ costs
⁴ benefits

If one modifies my definition of workflow, though within my subscripted limits, to …

“Process is a series of events, consuming expected resources, achieving expected benefits.”

… you’ll arrive at a stochastic process closely resembling actuarial science’s generalized individual model (page 35 in Fundamental Concepts of Actuarial Science, a great review or introduction by the way!).

During my student days, we spent a lot of time estimating parameters and distributions, and then predicting behaviors of these stochastic processes. Sometimes we did so analytically with complicated equations (Markov Models). Sometimes we fell back on computer simulation (Monte Carlo).

A quick review of actuarial science literature indicates many of these same techniques are used today.

Back to the subject at hand…

Patient journeys are workflows. If they are workflows, then we can model them, inform and test those models with data from digital medical anthropology research, and then simulate those patients interacting with digital healthcare technology using ideas from computational anthropology.

If you Google generational differences, you’ll find hundreds of tables that look like this.

These generational difference tables compare and contrast live experiences, goals and values, resources and constraints, and typical behaviors of Baby Boomers, Gen Xers, Millennials and other generations. Adapt these insights to adopting and consuming digital health technology and information. Collect increasingly available data (subject to ethical constraints). Use data to inform and drive simulations of personal and professional life-flows and workflows. Compare simulations to what we observe in the real world. And then systematically improve these simulations.

In doing so we will gain greater insight into the differences and similarities between different generations regarding adopting and consuming digital health technology and information.

Consider this scenario, one I believe will be possible within five short years.

Consider a population health system covering five million members. Imagine 20,000 medical and administrative staff (by the way, I just pulled that number out of a hat). Further, imagine various pieces of the IT systems being proactive, that is, agent-like. Roughing in the models would start with a combination of generational differences and risk stratification. Patient states include well, acutely ill, chronically ill (and if so, which chronic conditions). Staff states include off-duty, on-duty, ideal, and busy (and if so, which patient-directed activities are they qualified for). Now imagine you are a health IT marketer. Instead of working for a health IT vendor or health IT oriented marketing and PR agency, you’ve made the transition to working for a health system. You’re job is to understand and facilitate the diffusion health IT technologies into the homes and hands of covered population health system members. Here are some additional states: unadopted, adopted-but-not-optimized, optimized. Now, based on a variety of data, from qualitative and quantitative applied digital anthropology research, estimate the probabilities of transitions between states. (Possible role for machine learning here!) Workflows are series of these state transitions, which can be simulated, to fit various other data sets and generate predictions. For example, which kinds of health IT technologies (apps, calls, chatbots…) introduced to who (Boomers, Gen X, Millennials…) influence transition probabilities between which states (well, acutely ill, chronically ill…), and probabilistic models of impact on population health system resources (number of personnel required, kinds of personnel), under different assumptions about which technology initiatives are undertaken (which kinds of patients are supplied with which kinds of health IT technologies). If you think this kind of simulation requires astounding amounts of data, it does. But we now live in the Big Data era. The data is there or potentially there. The real problems with this simulation are managing its complexity and data ethics issues. However, if researchers can undertake an agent-based simulation involving between 10 million and 20 million individuals in the aftermath of multiple Manhattan nuclear blasts, then agent-based simulations of health IT diffusion and effects on clinical outcomes and costs are surely at least almost already possible!

If I have stimulated your imagination and interest, check out my Health Standards article, Marketing Workflow Is An Incredible Opportunity To Differentiate Health IT Products, And You!, which ends this way:

“Workflow: It’s not just for industrial engineers anymore!”

I’ll see you at the Including Every Generation in our Health Information Technology Thinking #HITsm tweetchat! Noon EST today.

Further reading:


On Periscope!

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