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What Hospitals Need To Know About Machine Learning

Artificial intelligence (AI) is a hot topic in almost every industry, but especially pertinent in today’s healthcare landscape. Organizations are in the midst of the exciting process of integrating AI into their human-centric workflows. Advances in technology are making better outcomes possible—for patients, providers, and hospitals at large.

More specifically, machine learning is poised to unlock a host of new possibilities for hospitals that can implement this AI tech in a meaningful way. Software applications that use machine learning “become more accurate in predicting outcomes without being explicitly programmed.”

What do hospitals need to know about machine learning? Let’s take a closer look. 

The Human Role in Machine Learning

It’s true that machine learning entails algorithms performing tasks without humans needing to tell them exactly what to do. But that doesn’t necessarily mean hospitals should expect to implement machine-learning technology then stand back and watch it go. Human users still play a crucial role in refining results. Machine learning can be supervised, unsupervised or semi-supervised depending on its specific function.

One-way human users can “teach” machine-learning algorithms to produce better results is by offering simple feedback. Approval or disapproval from human users teaches this technology about relevancy over time—freeing up people to take a supervisory role rather than having to outright program and guide the technology.

Machine Learning in a Hospital Setting

But what does machine learning actually look like in a hospital setting? There are numerous real-world applications showcasing a wide range of uses. TechEmergence outlines three primary use cases:

  • Analytics: Machine learning and AI-driven healthcare analytics provide essential solutions for hospitals by helping to determine key indicators, monitor current performance and even predict future outcomes.
  • Chatbots: Machine learning can help chatbots automate certain communication processes, saving practitioners time and reducing instances of miscommunication between parties.
  • Health tracking: Machine learning aids in monitoring patient health and predicting future outcomes based on data collected in real time.

One example of analytics in action is the Capacity Command Center Johns Hopkins Hospital implemented in an effort to improve operational flow. The hospital has experienced a 60 percent improvement in complex patient admissions since implementing this AI-driven solution, among other benefits. While staff members do play a large role in interpreting insight, identifying problem spots and acting accordingly, the machine-learning algorithms make it possible to aggregate data on a large scale.

According to the Chief Administrative Officer for Emergency Medicine and Capacity Management, Jim Scheulen, “The Capacity Command Center is giving front-line managers real-time information about their business. Instead of relying on old data and information, we use information that’s very active, interactive and transparent so we can make the most informed decisions for our patients and their care.”

Integrating Machine Learning into Your Business Plan

Hospitals have to walk before they can run. Just like any other major technological development, it’s important to update your business plan, particularly when it comes to your IT strategy. First of all, you need to know what you want to get out of machine-learning algorithms before utilizing them. Otherwise, you’ll have no realistic way to determine the return on investment.

It’s also crucial to understand which human users will play a role in your AI- and machine-learning processes. Hospital employees, administrators, and data specialists typically have very little free time; it’s a naturally fast-paced environment as is. Understanding the human role in your AI strategy before implementation will help you budget time and resources appropriately—a key step in avoiding putting additional strain on any one department or team.

This is just the beginning of what hospitals need to know about machine learning, but it’s a step in the right direction.

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