AI is propelling healthcare beyond human limitations

Introduction

What comes to mind when you hear the phrase, “AI in healthcare?” 

For the typical U.S. consumer, the answer is “optimism” or “excitement”. According to a recent survey, 81 percent of consumers anticipate that AI in healthcare will dramatically improve patient care, enabling faster, more accurate diagnoses, less paperwork, and shorter waiting lines. 

According to the same survey, 70 percent of consumers want healthcare providers to prioritize using AI for treatment. This growing majority recognizes medical professionals’ human limitations  – and the opportunity to make their work easier and more efficient with technology.

Of course, not all consumers are comfortable with AI in healthcare. Twenty-five percent express concern that AI, despite its advantages, will jeopardize patient privacy – and they may be correct. While AI certainly offers huge benefits (for example, automation and increased productivity), it requires data to do so, including patient data. And, inevitably, the sensitive nature of this healthcare data makes it a prime target for cybercriminals and other cybersecurity risks.

Therefore, it is imperative that healthcare providers approach AI with diligence, accountability, and – most importantly – a robust, patient-first data governance strategy. Data governance: “the exercise of authority and control over the management of data assets” is key to ensuring that patients not only receive the best care but also keep their private information just that: private.

As consumer demand increases for automated services and products powered by AI, healthcare providers must learn to manage the risks inherent to this relatively new technology. This article will explore why the pros of AI outweigh the cons, what AI could look like in the healthcare industry of the future, and how healthcare providers can manage AI privacy concerns.

We Need AI in Healthcare

Today, two primary obstacles prevent healthcare providers from offering the highest quality care and the greatest possible patient experience. They are:

1. Underutilized human resources. The World Health Organization predicts a shortage of more than 1.5 million healthcare workers worldwide by 2030. One contributor to this projected shortfall is the underutilization of human resources. When specialists spend time on administrative tasks (e.g., paperwork), their opportunity cost is high.  

Consequently, it is in healthcare providers’ best interest to alleviate “busy work” whenever possible; for example, by using AI tools to automatically scan, identify, and process documents: a process called Intelligent Document Processing

2. Disorganized data. According to Stanford Medicine, the global volume of healthcare data increased by 2,314 exabytes in 2020 alone (one exabyte = one billion gigabytes).  

Unfortunately, much of this data sits unused in servers when it could be saving lives. It’s impossible to harness such a huge volume of data manually; humans are limited to what we can read and remember. Healthcare providers are no exception, so crucial insights go unnoticed, and potential discoveries, diagnoses, and therapies go unrealized. 

Luckily, AI presents a solution. A computer can process data 10 million times faster than the human brain. By capitalizing on this power, AI can give healthcare professionals “superpowers” – serving as an “assistant” by sifting through data on humans’ behalf.

AI will carry healthcare beyond human capability. Just think, What breakthroughs will occur when healthcare workers can leverage the last 100 years of medical data in seconds?

The Time is Now

Still not convinced? Consider the COVID-19 pandemic: perhaps the most topical evidence that the healthcare industry needs AI to plan for the future. In 2020, the world watched as governments, citizens, and healthcare systems around the globe raced to adjust to a new reality. 

Early on in the pandemic, we witnessed a widespread failure to analyze healthcare data. We also saw the consequences: quarantines, death, and economic shutdown. However, as the pandemic continued, we began to see teams worldwide leverage AI to counter the virus.

According to the National Institutes of Health, AI was applied during the pandemic to identify COVID-19 clusters, predict future outbreaks, and make early diagnoses. Researchers even developed a deep learning model called the “COVID-19 detection neural network” (COVNet) to differentiate between COVID-19 and pneumonia based on patients’ chest CT scans.

Planning for the Future

There will be another pandemic. And we will likely be alive to see it. But what we learned from COVID-19, coupled with AI and other modern technologies, can help us develop mitigation strategies, vaccines, therapies, and early-warning systems to prevent a future public health crisis.

However, the success of tomorrow’s AI-led pandemic response relies on today’s data governance. Legal and ethical frameworks are needed to resolve patient privacy concerns, encourage data-sharing between organizations, and allow AI to operate at its fullest potential.

Overcoming the Black Box Mentality

A phenomenon called the “Black Box” mentality occurs when consumers unfamiliar with AI perceive it as enigmatic and frightening. It results from a lack of understanding and transparency. 

To overcome the Black Box, we suggest that healthcare providers engage in the following:

  • Ensure that data-collection tools are user-friendly
  • Address patient privacy concerns with clear communication
  • Be transparent about who has access to patient data — and why
  • Consider and compensate for algorithmic bias

These data governance strategies facilitate the collection of high-quality, representative data. They build trust between patients and healthcare providers, as well as ensure that the collected data remains confidential.

Conclusion

Imagine a healthcare system in which your doctor could focus their full attention on you, the patient, and have their medical expertise backed up by the last century of research and data. 

Imagine if, at the touch of their fingertips, your doctor could access the records of a patient who lived 50 years ago on the other side of the world – a patient who had the same symptoms as you. 

Imagine a healthcare system without paperwork  – without time-and-energy-draining administrative tasks. Imagine a hospital without waiting lines, where you wouldn’t have to wait more than a few moments to learn about your insurance eligibility, the results of your medical imaging, or your diagnosis. This future is not science fiction. It’s coming, and soon.

AI privacy concerns are valid and pressing, but they should not eclipse the net benefits of AI in healthcare. Instead of avoiding AI, healthcare providers must learn to recognize and address its risks so that they, their patients, and the rest of the world can benefit from its advantages.

About Synaptiq

Founded in 2015, Synaptiq is a data science and AI consultancy with over 50 clients in more than 20 sectors worldwide. We develop and deploy actionable solutions using machine learning, machine vision, natural language processing, and other data-driven techniques. Our mission is to help clients discover, organize, and leverage their data to streamline processes, increase productivity, and drive further innovation.

For more information about Synaptiq, please visit www.synaptiq.ai

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