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Addressing Bias in Artificial Intelligence in Health Care Keyphase: Artificial Intelligence in Health Care

Digital technology is here to stay, but what do clinicians truly think about it? We have learned that digitally fluent clinicians—the emerging Healthcare Digirati—are essential to unlocking the full potential of digital health investments.  As a clinician myself, I understand that technology is the key to unlocking greater access, elevating experiences and improving outcomes. But what do others have to say about the future of digital health? We aimed to find out by surveying more than 300 clinicians across the United States, as part of the 2021 HIMSS State of Healthcare report, to understand how they perceive the role of technology in their practice and what the future could hold.

Our respondents had good regional representation: about 75% were MDs and more than half were either influencers in technology decisions or were the decisionmakers themselves. Some of what we learned may surprise you. The four truths outlined below highlight the key findings.

The pre-pandemic consumer experience is set for extinction

As we see more and more healthcare systems embracing digital transformation, clinicians are adopting the tools that are part of that investment. Our survey found that 71% will continue to use digital health tools to the same or greater extent.

People value tools that help streamline and enhance their care experience and make it more convenient. But to work, we must embed these tools in workflows and coordinate across digital, physical and virtual environments.

Using AI to augment clinical work is not as far off as we thought

It may seem that clinicians are wary of the influence of artificial intelligence (AI), but in fact, 76% of clinicians said that AI is not a threat to their job security. A surprising majority (80%) show interest in AI for clinical uses, relying on AI to do more than administrative use cases, such as for transcribing notes.

Clinicians are leaning toward more clinical use cases where they can augment what they currently do. When AI can provide additional insights in a clinical setting, clinicians can increase productivity by getting to informed answers faster. It can also alleviate the burden on clinicians by automating certain tasks. We’ve seen this in play during the pandemic—and even before. The rise of symptom checkers popping up across the market is a great example.

Mass General Brigham (formerly Partners HealthCare) created the AI-based COVID-19 Screener to help assess whether patients should be evaluated for COVID-19. A simple chat interface asks a series of questions to help with pre-hospital triage. The system can screen high numbers of patients rapidly to alleviate the burden on the organization’s hotline and reduce the number of patients visiting facilities in person for assessment.1 Tools like this save clinicians triage time and more quickly gets patients the care they need.

This enthusiasm for AI is a great asset as we face a tough task ahead in addressing higher expectations for personalization. We can bring more data into clinical decision making to address these rising expectations all while reducing the cost to serve. However, this requires investment in the technology, which leads us to the next truth.

An appetite to invest in AI and digital health does not equate to an appetite to scale

Clinicians (68%) believe that the long-term impact for digital health is positive, even though they view the required investment as a barrier. More than two-thirds of those we surveyed (76%) believe that investments in digital health will increase over the next five years.

Although seeing the value in AI and digital health is a plus, this outlook is not enough. Health systems need defined metrics that prove the value of investments. Most healthcare technology investments start with a pilot—and they stay there. Being stuck in pilot mode hinders the ability to scale the technology, which is the only way to unlock its full value and potential.  

To get out of pilot mode, you need to quantify that these tools make a difference in terms of improving access, outcomes and experiences or reducing costs. Try to measure value as early as possible to give the organization reasons to invest beyond the pilot.

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