Leading up to #HIMSS20, the Health IT world is abuzz with the terms Artificial Intelligence (AI) and Machine Learning (ML). In a recent HealthITNews article, Optum stressed, “AI is of paramount concern today.” We see the use of the term AI over 151 times in HIMSS20 sessions, bios, vendors, and solutions. It’s a catch phrase for everything from neural networks to image recognition, speech recognition, natural language processing, process automation, machine learning, and human-assisted behaviors. It’s important to understand what is being promoted, because there’s a lot of hype in the use of this terminology and technology at HIMSS20.
Artificial Intelligence refers to something made or produced by humans, with the ability to understand or think. AI is not a system, but rather implemented in a system and is meant to simulate intelligent thought. Because we do not have the capability to simulate human intelligence at this point, many of the companies using the term “AI” are, in reality, leveraging machine learning techniques. Machine Learning is more about scanning volumes of data for more accurate predictions. Machine Learning allows a system to learn new things, Artificial Intelligence enables a system to make decisions based on this learning. AI is the broader concept, with ML as the engine that drives success.
In #HealthIT, we are looking for easier ways to connect, organize, and sort through data, to meet regulatory compliance, support data-driven decisions, reduce burnout, and improve patient outcomes. By combining Machine Learning capabilities with techniques like process automation, users can analyze data before acting on it, and make smarter decisions based on inputs and algorithms. Repetitive tasks like checking revenue cycle errors, claims submissions, prior authorizations, and billing disputes are easy “wins” for this type of Machine Learning. It can be applied via the same route as manual data entry, which makes it an ideal tool to apply at the front-end of the revenue cycle, for example. By “learning” the claims process, it can check claim status, fill in missing information, and offload administrative tasks so that the staff can handle what’s most important for human interaction.
The effort required to identify the best use cases and control risks for both Artificial Intelligence and Machine Learning dramatically exceeds prevailing norms in most organizations. You’ll likely hear a lot of “yes, we can do that” at HIMSS20 vendor booths and sessions. However, implementing a Machine Learning solution in healthcare requires a multi-disciplinary approach, a thorough understanding of workflows, and a depth of knowledge about accuracy, security, and compliance. It’s important to partner with a company you can trust, one who understands the interdependencies of data, and the critical alignment to clinical workflow that is a necessary component of future success.
In the sea of HIMSS20, you will find Artificial Intelligence and Machine Learning solutions described at one out of every four vendor booths. According to Health Evolution, “89 percent of providers and 88 percent of payers plan on investing in Artificial Intelligence and Machine Learning solutions in the next three years.” This makes it a popular term to tag onto if possible. Sadly, the advertised use cases and terminology are as varied as the applications in which they are being used. Are you thoroughly confused yet? When sifting through the sea of “AI” enabled technology booths at HIMSS20, keep in mind that a little bit of knowledge can be a dangerous tool. There are plenty of vendors standing under that inflated AI “umbrella.”
Take your time. Define the problem. Then look for a solution that solves the problem, integrates easily, and aligns to existing technologies and workflows, improving the end-user experience. Whether AI, automation, ML, or RPA, the best predictor of future success is rooted in the right alignment to application and business need. This calls for a vendor with experience in healthcare. Healthcare-specific vendors are familiar with the nuances found in hospitals and health systems. In the fast-moving waters of #HIMSSanity, with 1,300 leading health information and technology vendors, it may be difficult to separate the hope from the hype.
If you’re seeking to buy an Artificial Intelligence or Machine Learning solution, it’s important to be aware of what exactly can be automated and what can’t, with the present technology. AI and ML are both currently evolving technologies. The industry is gaining some perspective on how to best leverage and apply these solutions to healthcare settings to achieve quantifiable results. What we’re seeing is an increase in accuracy, a reduction in manual data entry, and a better way to manage data-driven tasks like migration, revenue cycle management, and logistics.
We won’t be presenting at HIMSS20 this year but we are available for a quick 30-minute call (866-653-5105) to provide a bit of pre-conference guidance. This is an exciting time to be involved in healthcare. There are tremendous innovations coming to the #futureofwork, regarding both Artificial Intelligence and Machine Learning. And they will empower people to do more than they ever could before.
Why Boston Software Systems?
A reputable healthcare automation partner can help you identify processes that will benefit most from increased automation and allow you to scale with ease. Finding a partner with an exclusive healthcare focus ensures the solution will be aligned with workflows, reduce pain-points associated with process implementation, and speed alignment to future health goals.
Download a whitepaper or schedule time to chat with us before, during, or after the conference. With 30 years of experience in healthcare, the team at Boston Software Systems understands the challenges and looks forward to providing you with a thorough understanding of the best long-term and short-term investments to improve organizational efficiency and maximize ROI.