Artificial Intelligence in Healthcare

Artificial Intelligence (AI) in Healthcare: Can it improve healthcare equity?

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There has been a lot of news about what AI is, with OpenAI's ChatGPT having more than 100 million users sign on within two months of launch. Simultaneously, there has been substantial news coverage about potential problems with AI, particularly with Microsoft Bing's AI chatbot's threatening and odd behavior², as well as concerns whether AI will eliminate jobs.

The concerns have even caused several well-known computer and AI experts, including Elon Musk, CEO of SpaceX, Tesla and Twitter, and Steve Wozniak, co-founder of Apple, to pen a letter calling for AI developers to stop development until "we are confident that their effects will be positive, and their risks will be manageable."³

The questions arise: Is AI technology safe for use in healthcare? And in terms of how to advance health equity, can AI bridge the divide?

Assessing health equity

Healthcare disparities, social determinants of health, and data problems.

The CDC defines health disparities as "preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health that are experienced by socially disadvantaged populations."⁴ Causes of health disparities include poverty, environment, inadequate access to healthcare, behaviors, and educational inequalities.

One metric used to assess health equity is a term called Social Determinants of Health (SDOH), which refers to the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect health and quality of life.

AI is particularly adept at searching and analyzing large datasets and in identifying meaningful patterns in data. But a challenge exists in leveraging AI to analyze SDOH: although much of these datasets are available somewhere in Electronic Health Records (EHRs), they tend to be recorded in free-text fields and are not easily aggregatable.⁵

Healthcare datasets often lack comprehensive descriptive data that clearly define the included population segments. Therefore, if the AI systems are developed and validated on datasets that are incomplete or inaccurate, the accuracy of the AI algorithms to predict a certain outcome may be diminished

This is a common way that bias can be introduced into AI algorithms. Using AI technology that has been developed on faulty data can lead to inaccurate analysis and the development of flawed recommendations, which, if implemented, can widen the health disparities gap⁶

As a result, many experts in healthcare AI emphasize the need for full disclosure of the steps taken to ensure the dataset is cleared of errors, omissions, and bias that can impact the performance of an AI tool.

The potential of AI

What AI can do.

As of November 2022, the FDA approved more than 520 AI tools to support healthcare delivery.

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The potential for AI to improve workflows, efficiencies, and accuracy of diagnosis and treatment is easy to imagine. Harvard's School of Public Health estimates AI can save the medical industry up to $150 billion by 2025.

The benefits of AI

What can AI do to boost health equity? Here are some potential benefits:

  • Create more accurate social and health data to support policies that drive improved health equity. An ongoing National Institutes of Health project, All of Us, is attempting to "bridge the gap for historically under-recognized populations by collecting observational and longitudinal health data on over one million Americans."8
  • Help identify the problems. Health systems collect enormous amounts of data about their patient populations. AI can be used to help these organizations analyze the data to understand where providers should focus for the best return on investment (ROI) for positive patient outcomes.7 Writing for HIT Consultant, Dr. John Sargent, founding partner of BroadReach Group, provided an example:
    • A case manager checks into the system and reads an email about a patient, John Doe. An AI-powered algorithm flagged John Doe with two issues that might make managing his diabetes more difficult. First, his current provider is not a native Spanish speaker. And second, John Doe does not currently have a vehicle. These two health inequities could affect John Doe's ability to both acquire the right information and make it to his clinic appointments.  
  • Identify "next-best actions." Dr. Sargent noted that identifying the problem was important but needed to be accompanied by successful actions to neutralize the identified barriers. "By using AI to provide predictive and prescriptive recommendations in a culturally sensitive way, we can bridge the equity gap," he wrote.
    • In the case of John Doe, the first recommendation would be to find him a physician that speaks Spanish. The second would be to implement telehealth services if he cannot resolve his transportation problems. As Dr. Sargent wrote, "If Amazon can predict which book on the history of World War II I should read next based on my buying history, certainly we can use similar technology to predict what issues will arise for our patients and what we need to do to intervene."
  • Better allocate resources. This relates to ROI. Using AI in healthcare offers a more strategic look at assets, both skills and resources, to meet equity challenges. Within the context of John Doe, here are some questions that may arise from a resource analysis:
    • Is there a need for more Spanish-speaking doctors?
    • Or should they be deployed to other clinics or locations?
    • Should we invest in more telehealth applications, or partnerships with transportation providers to mitigate lack of transportation as a barrier to healthcare access?
  • Clinician decision support. AI has the potential to be used by physicians to assist in diagnosing conditions and customizing the optimal plan of care for their patients. It could also potentially decrease clinician administrative burden by providing access to data from multiple sources, such as medical images, EHR data and even consumer devices like smartphones and activity trackers that collect health-related data.

The role of AI

AI in Healthcare: A Powerful Tool.

AI is a tool. It won't solve all health equity issues on its own, nor will the healthcare industry. Resolving health equity issues in the U.S. will require efforts involving healthcare, technology companies, government, and social and community resources. But if leveraged effectively, AI can be a powerful tool deployed to reduce health inequities, reduce healthcare expenditures, and enable physicians and healthcare providers to care for their patients more effectively.

About the author:

Dr. Christine Gall.

Dr. Christine Gall, Head of Healthcare Marketing

Dr. Gall has been a healthcare leader for over 30 years. As a nurse, she has practiced in inpatient, outpatient, and homecare settings, allowing unique insights into the continuum of care. Dr. Gall has designed and implemented multiple clinical programs aimed at addressing gaps in services and care for underserved patients.

Prior to joining T-Mobile, Dr. Gall consulted with local government to support their pandemic emergency response. In her role at T-Mobile, Dr. Gall leads the product marketing strategy for healthcare, collaborating with healthcare leaders and clinicians to create telehealth and mobility solutions that address the greatest challenges of the day. She believes that T-Mobile's powerful 5G network is key to addressing health disparities and barriers to access that impact population health.

Dr. Gall's academic credentials include a Bachelor of Science Degree from the University of Wisconsin-Milwaukee, a Master of Science Degree in Healthcare Management from the Lubar School of Business Administration at the University of Wisconsin-Milwaukee, and a Doctorate Degree in Public Health Leadership from the University of Illinois Chicago. Her Dissertation, funded by the State of Ohio, was on the topic of Mass Casualty Pandemics. Dr. Gall is a Six Sigma Green Belt and has a Certification in Business Analytics from the Wharton School of Business at the University of Pennsylvania.


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