Moving Beyond Buzzwords in Healthcare

There are a lot of buzzwords in healthcare. From slick marketing copy to conferences, zoom sessions and everyday conversations, we toss around buzzwords to mean a myriad of different things, sometimes left to the interpretation (or imagination) of the reader. The healthcare landscape is a mystifying maze for purchasers and consumers disoriented by a lack of truth in advertising. What are some of the most common healthcare buzzwords being used in 2022?

Artificial Intelligence (AI)

When a machine shows intelligence properties similar to a human, it is called Artificial Intelligence (AI). AI allows machines to mimic human behavior. The term AI is being used as the “next shiny object” in healthcare, receiving a ton of venture capital dollars. The problem is that many vendors use the term, but not all are true AI vendors, which creates mistrust. Erin Brodwin did an investigative report for Axios this week on Olive, and uncovered that, like many other startups, their claims to fame have been overstated. Olive inflated the capability to drive more investments, rather than focusing on the quality of their product offering. Investors who were looking to bet on a new generation of startups, hopped onboard without researching the validity of Olive’s claims. We need to stop using the catchphrase “AI” to describe a myriad of use cases that are not incorporating the use of true Artificial Intelligence. The current healthcare model is only beginning to use AI in limited situations. So calling everything AI when it’s not, is a little like a magic hat trick, using a lot of conditional statements that don’t measure up to truths.

Machine Learning (ML)

Machine Learning (ML) is a branch of AI that focuses on allowing systems to learn and improve over time. AI and ML may seem like similar terms, but they are not the same. Machine learning is a method by which a machine attempts to achieve artificial intelligence. The system achieves this by observing data gathered from past experiences and finding common patterns. In 1959, Arthur Samuel, one of the pioneers of machine learning, defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed.” Machine learning intends for systems to make precise decisions using collected data without intervention or programming from humans. There is nothing new in “machine learning” or “deep learning” that has not been done for at least 30 years, the term is just being tossed around more frequently today as a subset of AI, especially in healthcare.


Interoperability is the ability for healthcare systems and devices to seamlessly exchange information. It has been used for years to describe the information shared from Electronic Medical Records (EMR). People use the words integration and interoperability interchangeably, but there’s actually a pretty big difference between the two. An interface is like a bridge that lets two programs share information with each other. An interface doesn’t allow you to sync data between systems in real-time. In order for two systems to be considered truly interoperable, they need to be able to exchange data and present it in a useful (and consumable) way. Achieving true healthcare interoperability across the care continuum is a top priority for industry stakeholders, yet the capacity to exchange health data is not enough to deliver on the promise of interoperability. Despite technological advancements in healthcare documentation, health data remains largely disparate today.


Care that is patient-centered, considers patient preferences, traditions, cultures, and lifestyles is patient-centric. It places the patient in the center of every healthcare situation, as an integral member of the care team. The National Academy of Medicine (NAM), defines patient-centered as: “Providing care that is respectful of and responsive to individual patient preferences, needs, values, and cultures, ensuring that patient values guide all clinical decisions.” Patient-centricity has been thrown around to discuss everything from medication adherence to compliance, regulatory, and communication preferences. Patient-centricity is not about buzzwords and marketing statements, it is a fluid concept, as there is no one-size-fits-all in patient-centricity. It is a phrase that will continue to expand as our systems and services, apps and wearables continue to evolve, making it easier to communicate with patients at every step of their healthcare journey.

Build a Culture of Transparency

Buzzwords may be fun, and easy to use interchangeably, but integrity is the best calling card for vendors, providers, and patients. Transparency with internal and external stakeholders is essential for quality, accountability, informed decision-making, and customer retention. At Boston Software Systems, 100% of clients interviewed stated that we “keep our promises.” Our support and loyalty scores are consistently high because the product “works as promoted” and delivers “a money’s worth return on investment.” Read more in the Best in KLAS 2022 industry report. Then, let’s “take this offline (a popular 2020 buzzword).” We look forward to sharing our successes with you.

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