It’s been several years since I’ve formally written about the Clinical Communication & Collaboration (CC&C) market. During that time, healthcare has experienced an enormous change. New technologies have emerged, digital transformation initiatives have accelerated, and artificial intelligence has become one of the most discussed topics in our industry. Yet despite all this activity, I find myself wondering whether we’re overlooking a much bigger question. Not a question about AI itself, but a question about what history can teach us about the next phase of healthcare technology.
Why Looking Back Can Help Us See What’s Next
Recently, while thinking about future products and industry trends, I found myself reflecting on the early years of my career. The more I thought about it, the more I realized that today’s healthcare technology landscape shares some surprising similarities with an era that transformed the computing industry.
The IBM Era and the Limits of Closed Ecosystems
The year was 1983. At the time, IBM was the unquestioned leader in enterprise computing. From mainframes to personal computers, IBM was everywhere. If you were responsible for buying technology, the decision was often straightforward: choose IBM. It was the safe choice. Organizations trusted the brand. They trusted the support model. They trusted the stability and for many years, that strategy made perfect sense.
There was only one problem. The very thing that made IBM attractive also created limitations. Data, applications, and workflows largely lived within a proprietary ecosystem. If organizations wanted to combine information from different systems, analyze data in new ways, or take advantage of emerging innovations, they often had to do so within the confines of that ecosystem.
How Open Systems Changed the Rules
Then something changed. A movement toward open systems began to take shape. Technologies like UNIX, TCP/IP, and standardized networking protocols created new possibilities. Organizations gained greater flexibility. Innovation accelerated. Most importantly, businesses gained easier access to their own information and greater freedom to decide how that information could be used. The computing industry never looked back.
Healthcare May Be Facing a Similar Turning Point
Today, healthcare may be facing a similar moment. For decades, healthcare technology investments have focused on creating reliable, standardized workflows. Clinical communication platforms have played an important role in that journey by helping organizations reach the right clinician, coordinate care teams, escalate critical information, and reduce delays in care. These capabilities remain essential, but the environment around them is changing.
Communication Is Expanding Beyond Human Interaction
Historically, communication meant connecting people such as a physician communicating with a nurse or a nurse communicating with other care team members. Increasingly, however, communication is expanding beyond human interactions.
New Sources of Clinical Intelligence
- AI systems can identify patient deterioration risks.
- Scheduling platforms can recommend staffing adjustments.
- Patient engagement applications can identify barriers to care.
- Clinical decision support tools can surface relevant insights.
Each of these technologies can generate intelligence, but intelligence only creates value when it can move to the people, systems, and workflows capable of acting on it.
The Real Challenge Isn’t AI, It’s Moving Intelligence
This is where the conversation becomes interesting. Much of today’s discussion around AI focuses on models, algorithms, and automation. Those are important topics. But perhaps the more important question is where intelligence will live in the future healthcare ecosystem. Will innovation be concentrated within individual platforms? Or will it emerge across a broader ecosystem of specialized solutions working together?
History suggests that innovation often accelerates when organizations create environments where information can move freely, new capabilities can be adopted quickly, and technologies can interact without unnecessary barriers. That doesn’t mean organizations should abandon standardization. Quite the opposite. Healthcare requires trusted systems of record that provide the reliability, governance, and consistency essential to delivering safe, effective care.
But healthcare also needs flexibility. Because the pace of innovation, particularly with AI, is accelerating far faster than any one organization, platform, or vendor can keep up with. Perhaps the real challenge for healthcare leaders is not choosing between standardization and innovation. It’s finding a way to achieve both.
A Question Every Healthcare Leader Should Be Asking
As we look ahead, organizations should begin asking an important question: Are our technology decisions creating the flexibility needed to adopt tomorrow’s innovations, or are they unintentionally limiting them? The answer may determine which organizations are best positioned to benefit from the next generation of healthcare technology.
In my next article, I’ll explore why the future of AI may depend less on the sophistication of algorithms and more on something far less glamorous, but far more important: data liquidity and interoperability. Because before intelligence can transform healthcare, it has to be able to move.
What Do You Think?
Are today’s healthcare technology decisions creating the flexibility needed to take advantage of tomorrow’s innovations?
I’d welcome your thoughts in the comments. And if you’re interested in where clinical communication, interoperability, and AI are headed next, subscribe to be notified when Part 2 is published.
