By Guillermo Ortiz de Zárate
Context
As federations of regulatory state boards, we have a great responsibility in protecting the public health, safety and welfare. It becomes very tangible than when we are protecting our elders through the careful vetting of the competence of those who will direct the institutions where they live.
As regulatory organizations we have the privilege to set the rules of engagement for generations of professionals that desire to enter the industry. This is accomplished by methodical research to determine the knowledge, skills, and abilities (KSAs) required to become minimally competent to execute these professional roles in a matter that is safe to the public they serve. The protection then comes in the form of a license to practice, only valid in the jurisdiction where it is issued, and it is usually subject to ethical and professional standards and continued education.
The license is issued on the evidence that the person has in fact those KSAs, and this is in fact why our organizations trade in information. We are Information Management companies. Our ability to protect the public relies entirely on the fidelity of the proxies we create to measure competence and ultimately, how capable we are in protecting this information, assuring its reliability and integrity.
This is why it is of extreme importance that we not only develop fair, valid, and defensible methods to measure competence, but that we also have data practices that protect the information. A false positive could put our elders in danger, but a false negative could prevent a capable individual to participate in the profession.
Even as I write how important it is that our organizations develop these practices and go through much needed digital transformations, in many cases years behind current trends, I am convinced the most impending urgency our organizations face is uncertainty.
It is hard to comprehend for the human brain how fast the world is changing. The pace of change induced by technological advances that are growing exponentially presents a complexity that we might not be prepared to confront.
As the legend of the creator of chess reminded us, geometric growth is hard to imagine for the human brain. In that story, we were presented with the idea that doubling the number of grains of rice for each square on a chess board would produce a reasonable amount of rice, only to discover that the amount of rice would be 264 grains, a volume equal to 18 quintillion. Or 271,248 mount Everests of rice. Something our brains cannot compute or imagine.
In his book Thank You For Being Late, Thomas Friedman makes the point that humans adaptability is linear, but the pace of change of the world is geometric, and this presents a critical situation for us. According to Friedman, we are entering the “second half of the chess board”, in terms of the intensity of the pace of change that we will experience. The second half of the chess board is where numbers get incomprehensibly big, and when thinking about the changes that we might experience in the coming years it is hard to imagine what that might feel like.
Friedman says that we will never again experience temporary change, but that change will be constant. I imagine change will not constant, as it will be accelerating as years go by.
AI
Artificial Intelligence is one of the drives or change, and it is accelerating at a clear geometric pace. The field of Artificial Intelligence is quite broad, and what we need to understand is that the models we are creating have advanced from being tools that would help us predict an outcome to generating and creating. Neural networks that mimic how our brains work through connected nodes that can learn, large-language models that understand context and the ambiguity of colloquial languages, are some of the tools we are seeing growing faster than we can adjust to. Methods and architectures to improve AI’s capabilities are growing exponentially, and so is the computing power and its affordability.
Nvidia’s latest chip has the power to process 1.4 exaFLOPS of operations, which is the 1.4 followed by 18 zeroes. In volume, that would be 1.4 quintillion. Sounds familiar?
If AI is growing exponentially, doubling its capabilities and affordability from now on is hard to imagine. Along with AI’s capabilities growth, the capital invested in these technologies is also growing at a similar pace:
Considerations
What can we do with the growth of AI in our field? I argue that what we need to consider is twofold. On one hand, we need to adopt some of these technologies ourselves to speed up our delivery of value, either in the determination of required competencies, or on our ways to gather the evidence of competency and issue our credentials.
There are tools that can help us develop educational products, improve our examinations and develop and deliver at a greater pace. We can improve our capacity to run our operations from marketing to customer service by adopting revolutionary tools that can out-perform humans. We can create efficiencies, increase our automations, do more with less, and yet we must understand the impact on the fairness and ethics of our programs.
Most importantly, we need to understand and be at the forefront of the adaptability curve to how these technologies will change the KSAs required to perform our professional work in a way that is safe for the public we serve. This is, in my mind, the biggest hurdle we will face.
Convergence
he problem is not AI, or any technology in isolation. The problem is that there are a multitude of technologies that are advancing at a similar exponential pace that when combined could critically threaten or change what we know and understand today about senior care. Some examples:
Natural Language Processing
The ability of artificial intelligence to understand contextual colloquial language is expected to achieve PhD levels in the next year. You are already using multiple tools that can do this, like Alexa, Siri, Cortana, and many other.
ChatBots
Similarly, chatbots, made famous by ChatGPT, are exponentially growing greatly disrupting our dependency on human interactions to solve almost any problems.
Large Language Models
Our ability to communicate across cultures and languages and understand context, ambiguity, sarcasm, and irony is now a reality and will only get better.
Augmented Reality
The combination of wearable devices, with next generation computer vision, will continue to grow, enabling humans to process environmental information in ways never imagined.
Internet of Things
With smart devices at home, work, in our cars, and our bodies, the huge amount of data generated is growing as fast as machine’s ability to process it.
5G, 6G and Beyond
The hyper connectivity and hyper speeds will continue to improve as new materials and methods keep doubling availability and affordability of connectivity.
Computer Vision
Self-driving cars can now process millions of data points to create immediate context for instant decision-making. This ability is rapidly improving.
Health Diagnosis and Sensors
The world of health care is being disrupted by home-diagnosis tools, from wearable sensors to automated continuous medication and is enhanced by machine learning.
Generative AI
The ability to create high fidelity content, in multiple dimensions, from pictures, to video, to voice and sound, create a threat for the world with deep-fakes, but also a potential to automate care for elders in ways never imagined before.
If we were to combine all of these technologies, and the ones that we are not aware of yet, what could be the future of elder care look like in 5 to 10 years? Probably very different and automated, with less human interactions, minimizing health, safety, and welfare concerns and democratizing quality access to the masses.
Are we ready to change the requirements for licensure, to understand the new and different KSAs that are indispensable to protect our public, to change our programs to remove unnecessary impediments so we only require what is essential, in a fair, valid, and defensible way?
What is at risk is our relevance. If we don’t keep up with the pace of change, we will potentially create barriers of entry that are more costly than the benefits of the profession itself, creating a void of talent and essentially forcing the regulatory environment to open up to new methods to advance this industry.
What Can We Do?
We must change, and we must change fast. Regulatory agencies take years to change the rules of engagement, define new model laws and regulations, and adapt the competency standards. Having legislatures and boards adopting them afterwards is even harder. This pace of change is too slow for our times, and as we enter the “second half of the board” times, our ability to stay relevant will become more difficult.
We must train ourselves to innovate, evolve and increase our turn-around time to make changes to our regulatory frameworks. The anti-regulatory sentiment will put more pressure on agencies that cannot keep up with the pace of change.
Perfect is the enemy of good, and when we go for perfection and full adoption of a new model, we take our time. The alternative is shorter cycles with smaller changes that gradually adjust the rules of engagement to current practices and future technologies. You should be anticipating now what would the new KSAs needed to be licensed when technology is aiding the future professional. This might mean that you might have to try new standards before there is a legal framework for it. You might need to run experiments with progressive jurisdictions. These changes might affect your revenue models, and you might need to cannibalize some of them to create the environment for change. You might have to rethink the boundaries of what your organization can do, since what your organization needs to do could be vastly different.
Practice foresight, analyze the environment and use that information to inform your strategic plan, which might not last 5 years anymore.
Invest in your digital transformation. You cannot keep up with the pace of change if you still have manual processes and poor information management. The risks are too big to ignore.
Adopt new technologies. But only when there is a business need and advantage.
Manage risk, don’t avoid it. Activity is not a measure of outcome. You need to generate actionable insights and work on what’s most impactful first towards the desire outcome, with speed and decisiveness.
Most importantly, you have to change.