Your Guide to Augmented Intelligence in Healthcare: Three Hows for AI Success

Technology experts estimate that AI in healthcare will increase at a40 percentcompound annual growth rate from 2021 to 2028. However, Jason Jones, our Chief Analytics and Data Science Officer, defines AI in healthcare differently than other experts. In hisfirst podcast installmentof Owning the Future of Healthcare, Dr. Jones explains the role ofaugmented intelligence(AI) in healthcare—instead of the more familiar term, “artificial intelligence.”

In the second installment, Dr. Jones expands on the role of AI in healthcare’s digital transformation and answers the three “Hows” critical to AI success:

1 .增强智能如何与其他数字转型相交叉?

Healthcare today is more digital than ever. Clinicians, analysts, and financial leaders alike turn to technology to ease paperwork burdens and improve operational efficiency. In this digital healthcare landscape, AI plays a critical role because it fuels other digital healthcare domains, such as interoperability, personal data collection, andtelemedicineor on-demand care

For example, telehealth existed long before the pandemic, however COVID-19 accelerated virtual healthcare by over40 percent. Telemedicine allows clinicians to connect with patients via technology without an in-person meeting. While this arrangement already has many benefits—including adhering to social distancing during a pandemic and not requiring patients to miss work for appointments, AI can make telemedicine even more efficient. For example, an organization could create basic questions in the triage process to identify patients with a urinary tract infection (UTI). Based on the answers to these questions, AI can quickly identify the patients with a UTI, allowing them to get treatment sooner and avoid long wait times at the urgent care or emergency department.

#2: How Can Health Systems Effectively Integrate Augmented Intelligence?

Dr. Jones says there are two keys to effective AI integration: Defining the goal that AI can help the system achieve and establishing trust in AI. Without a goal, futile attempts leave organizations frustrated and sometimes lacking trust in AI. A clear goal keeps AI efforts focused and helps team members understand how to measure their success.

TrustingAI in healthcarecan be tricky. Predictive models will get the answer wrong, but that doesn’t mean the team gives up on the model. Instead, when the model gets the answer wrong, a team member should review the model carefully and ask, “Could I have done better?” and “Is this helpful to me?”.

Organizations must make it clear to team members that the objective of AI isn’t to be perfect. In fact, sometimes an end user’s greatest opportunities to improve trust occurs when there’s an error and the team identifies how to improve the model.

#3: How Can Organizations Create Augmented Intelligence for All End Users?

When pursuing AI in healthcare, many organizations skip a very critical step: identifying end users in the organization and what they are trying to do. End users are typically a variety of different users, including engineers, clinicians, and financial analysts. For example, an IT individual will have a different set of needs from a data scientist, who has different needs from a clinician.

了解每个终端用户，他们在寻找什么，以及AI如何帮助他们在特定角色中发挥作用，是AI成功的关键。详细的最终用户信息还可以帮助从所有数据的含义中分离噪声，因为它提供了关于哪个洞察力可以帮助哪个角色的具体指导。

Another benefit of internal persona research is a deeper understanding of team members’ AI knowledge. This can keep the data science team from creating complex models that require AI expertise and keep them focused on simple, easy-to-use models. Practical models allow users to quickly recognize if they make good or bad decisions without understanding the complexities of an algorithm. As such, they can focus more on implementing change based on accurate insight from the model.

The Augmented Intelligence You Never Wanted but Now You Need

随着人工智能在医疗保健领域越来越普遍，现在是时候让组织接受并拥抱将其应用于医疗系统的能力了。With expertise and simplepredictive models, health systems can successfully implement and scale AI, delivering accurate insight to decision makers in minutes instead of weeks.

Viewthe full podcast.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

• Three Reasons Augmented Intelligence Is the Future of AI in Healthcare
• Delivering the Right Insight to the Right Person: How Workflow Automation Optimizes EHR Decision Support
• Your AI Journey Starts Here: A Four-Step Framework for Predictive Analytic Success
• Expanding AI in Healthcare: Introducing the New Healthcare.AI™ by Health Catalyst
• Safeguarding the Ethics of AI in Healthcare: Three Best Practices