Health Care's Guide to Machine Learning

What is machine learning?

There’s a good chance you’ve heard the term “machine learning,” but, if you’re like most people, you might not know exactly what it means. Machine learning (ML) is one form of artificial intelligence (AI), and it refers to software that has self-learning or self-improving capabilities — in other words, computers that can learn without humans intervening or assisting.

Part of our everyday lives

If you’ve ever received a fraud inquiry from your credit card company asking about a suspicious charge, you’ve experienced the results of ML. Likewise if you’ve received movie recommendations based on your viewing history from a streaming service.

How machine learning works

ML uses historical and real-time data to take action and can either minimize the need for or augment human judgment. How it works depends on your goal. For example, you may be trying to:

• Classify patients or members into one group or another, helping identify the number of patients who are at risk for a specific condition, such as COPD
• Identify outliers in a large group of data, like the handful of suspicious reimbursement claims mixed in with millions of accurate ones

When you know the desired output of the model ahead of time, it is called "supervised" learning.

ML can also be used when you don't know what you're looking for ahead of time, which is called "unsupervised" learning. This approach is good for answering open-ended questions, like “which service lines should we invest in to increase our market share?” or “which members are most likely to engage when offered a treatment plan?” The output from the model will use existing patterns within the data to help leaders uncover potentially new areas for investment or new understanding.

Advantages in health care

The health care industry is known for being late to adopt new technologies — partly due to the high-risk nature of health information data. Yet other industries with similarly onerous regulation, such as the financial industry, have figured out how to benefit from ML in a secure way.

Why should health care embrace ML?

The following trends are quickly forcing an evolution in how AI is used.

How can health care benefit?

As ML moves from retrospective to real-time analysis, these technologies have the power to help influence healthy decisions, reduce costs and enable efficiencies. Here are just a few examples:

• Monitor patient data to predict hospitalizations, readmissions and disease progression — and take preventive action
• Automate prior authorizations to remove friction from the system
• Streamline more mundane legal, HR, benefit plan and call center tasks such as processing paperwork to better allocate resources
• Predict the likely outcome of events and recommend specific actions to take to avoid waste
• Anticipate documentation needs to accelerate the revenue cycle and achieve a denial-free future
• Connect consumers with the right resource at the right time through behavioral pairing — delighting customers and resolving issues faster

Health care use cases

Key considerations

When you're considering a health care use case, keep these key steps in mind²:

• Identify an opportunity for improvement: today's AI is best suited for very narrow use cases, like predicting readmissions following a specific surgical procedure or improving call center responsiveness. Consider two different applications: an "unsupervised" model to identify these areas for improvement, and a "supervised" model to enable your people to take action.
• Determine your organization’s place in the maturity curve. Use descriptive analytics to understand your current performance and the populations you serve, and then graduate on to more advanced predictive or prescriptive analytics once that foundation is in place. Cross-functional alignment and leadership support is critical.
• Conduct a thorough evaluation of technology vendors to ensure they provide transparency and deliver results.
• Determine which metrics you’ll use to measure performance.
• After deploying your solution, iterate and optimize it.

A continuous process

To keep ML projects running effectively, remember the following:

• Data collection and maintenance — Data must constantly be refreshed to keep predictions accurate
• Measuring ROI — Pick use cases where you can set metrics and measure progress over time

Appendix: The growth of ML and AI

The theory behind ML — a technique called backpropagation — was established in the late 1980s, but it has just begun to make a big impact. Why? Because its three prerequisites didn’t exist until recently:

Data:

The quantity of recorded data has exploded in recent years thanks to trends like digitization and the evolution of the Internet of Things. In health care, the adoption of electronic medical records (EMRs) in the early 2000s created vast stores of previously inaccessible information. Even so, while a seemingly endless amount of data exists, most companies aren’t using it to its fullest potential due to cost and limited expertise.

• Organizations must first be able to generate and collect data at scale.
• Then they must be able to curate and clean the data to make it useful.
• To use data in a way that fully enables sophisticated decision-making, organizations need:
  • Strategic data acquisition: The infrastructure to generate/capture proprietary data and supplement it with relevant third party data.
  • Central data warehouse and access: If data is trapped in silos, its utility is limited.
  • Pervasive automation: Digitizing processes and reducing manual interventions makes it easier to track metrics more accurately and offers further opportunities for improvement.

Technology:

• Computing power has finally reached the tipping point that allows it to crunch such big number sets. Graphic processing units (GPUs) have been especially impactful. Their processing power allows data scientists to create complex ML and deep learning models that are capable of ingesting larger and more diverse data sets.

Talent:

Getting the right data — and sufficient quantity — is just the beginning. Creating systems that maximize data’s potential requires an interdisciplinary team.

• In order to make algorithms that are useful, organizations need more than mathematicians and coders. They need full-fledged data scientists and experts in the use cases they are pursuing — especially in health care.

ML and the AI ecosystem

AI broadly refers to machines that can act in a smart way. It's easy to get many of the terms mixed up, so let's take a moment to define other forms of AI, how they are applied in health care and how they relate to or differ from ML:

1. Richard K. Miller & Associates. Healthcare Business Market Research Handbook 2017–2018, p. 61.

2. Health IT Analytics. 5 steps for planning a healthcare artificial intelligence project. Available at https://healthitanalytics.com/features/5-steps-for-planning-a-healthcare-artificial-intelligence-project. Accessed March 12, 2019.