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The AI Revolution Series – Addressing Concerns of AI Implementation in Healthcare

Data privacy, accountability, and loss of human interaction are concerns that need to be addressed 


April 24, 2024 – As Artificial Intelligence (AI) increasingly becomes a part of healthcare, it can make service delivery more efficient and effective. However, its rapid integration brings up several important concerns that need careful consideration. 

In this blog, the second of our AI series, we’ll explain the key challenges of implementing AI in healthcare, including issues like protecting consumer privacy, avoiding biases in AI algorithms, maintaining the essential human element in care, and managing the high costs associated with new technology. By exploring these topics, we aim to help you better understand the ethical, practical, and financial implications of using AI in healthcare and highlight the importance of a thoughtful approach to ensure it benefits the ones that matter most—consumers. 

As advocates, we are excited about the benefits that AI can provide to consumers. However, concerns do need to be addressed, namely, consumer privacy and data security. We are also concerned that AI will be used in place of human interaction, but that it does not have the ability to replace the compassion, empathy, and intuition that human providers and caregivers have. To learn more about our advocate’s perspective, check out our full write-up at the end of the blog to hear more of our thoughts!

Privacy and Data Security are a Big Concern of AI Implementation in Healthcare

While the integration of Artificial Intelligence (AI) into healthcare can tremendously benefit consumers and improve health outcomes, it also raises significant concerns. One of the primary concerns is the issue of privacy and data security. AI relies heavily on vast amounts of personal health information for analysis, which raises questions about how this data is gathered, stored, used, and protected against breaches. 

As healthcare systems increasingly rely on vast datasets to train AI algorithms, the risk of data breaches and unauthorized access escalates. For instance, in 2023, over 540 organizations reported healthcare data breaches that impacted over 112 million individuals. Incidents like there where consumer data is breached highlights the vulnerabilities associated with handling the large datasets that are required for AI applications. These breaches not only compromise consumer trust but also pose significant legal and financial risks to healthcare providers.

The primary risk stems from the fact that AI systems require access to detailed patient data to function effectively. This data often includes not just medical histories and treatment records, but also potentially sensitive information like genetic data and social determinants of health. If not properly protected, the data can be a target for cyberattacks and can lead to identity theft and other forms of misuse.

The good news is that there is a way to mitigate these risks. The most significant step healthcare organizations can take is to implement robust cybersecurity measures that include employing advanced encryption methods to secure data both at rest and in transit. Healthcare organizations should also invest in cybersecurity training for all employees to ensure they are aware of the risks and understand how to handle data securely and conduct regular security audits and vulnerability assessments to identify and address potential security gaps. In addition to that, they also need to put access controls in place. Limiting who can access sensitive data and under what circumstances is essential for minimizing the risk of internal breaches. 

Implementing these security measures not only increases the security of consumer data but also aligns with regulatory requirements outlined in the HIPAA Breach Notification Rule. By implementing stringent cybersecurity measures and fostering a culture of security and compliance, healthcare organizations can significantly mitigate these risks. However, it is important to note that due to the sophistication of hackers and prevalence of human error, the risk of a data breach is never zero. 

AI Algorithm Bias and Lack of Infrastructure Can Widen Health Disparities

While consumer privacy and data security is a huge concern with AI implementation, there are other concerns with the implementation of AI in healthcare. One of these is the potential for bias in AI algorithms. If the data used to train AI systems is not diverse, there is a risk that these systems may develop biased algorithms that lead to disparities in care recommendations and outcomes for different populations. A recent article from the Yale School of Medicine highlights this, by exploring how health care algorithms that power AI may include bias against underrepresented communities and thus amplify existing racial inequality in medicine. CMS and the Biden Administration has made it a goal to advance health equity, and without proper principles for eliminating algorithmic bias, we can be further widening that gap.

The good news is that this is already being addressed. The Agency for Healthcare Research and Quality (AHRQ) and the National Institute on Minority Health and Health Disparities (NIMHD) recently convened a diverse panel of experts to explore this topic. The panel released Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care to help eliminate racial bias in health care AI.  

While it is good that these principles are out, it brings up another concern—the infrastructure needed to properly implement AI in healthcare. While AI has the potential to save billions of dollars annually if adopted more widely in healthcare, this implementation requires significant financial investment in technology infrastructure and training. This might not be feasible for all healthcare providers and can potentially widen the gap between well-resourced and under-resourced healthcare facilities like those in rural areas. The good news is that value-based payment models may be able to assist with this, by providing funding for providers to build out their technology infrastructure. 

Addressing Accountability, Transparency, and Human Interaction

The question of accountability and transparency is another concern with AI in healthcare. As AI systems become more involved in care, it becomes challenging to attribute responsibility for decisions made, especially when it comes to errors or adverse outcomes. 

While there are steps being taken to regulate AI in healthcare, like those by the United States and WHO,  there isn’t much in place yet and that can leave consumers hanging in the balance. Ensuring that AI systems are transparent and that there is clear accountability for their recommendations is vital for consumer trust and safety.

Finally, there is the challenge of ensuring that AI complements rather than replaces the human touch in healthcare. While AI can enhance efficiency and decision-making, it cannot replicate the empathy, compassion, and personal connection that human providers and caregivers offer. While we delve into this further in a future blog, maintaining this balance is crucial to ensure that the patient-care provider relationship is not eroded.

Advocates Perspective

As we hear about AI in healthcare, it’s natural to feel a mix of excitement and concern. On one hand, AI promises more personalized and efficient care, which benefits consumers. On the other hand, it raises some big questions about the security of personal information and the widening of already existing health equity gaps. While it’s clear that AI can significantly advance healthcare and improve outcomes and satisfaction for consumers, addressing these concerns is essential. Ignoring AI and its benefits can lead to poorer health outcomes and less access to care, so that isn’t a solution. That is why health organizations and providers need to take active steps to address these concerns so consumers can benefit from all that AI has to offer. 

Stay tuned for our blog next week, as we dive into how AI can improve the quality of care in the home health and home care industry, as well as how it can enhance healthy aging!


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About the Author

Fady Sahhar brings over 30 years of senior management experience working with major multinational companies including Sara Lee, Mobil Oil, Tenneco Packaging, Pactiv, Progressive Insurance, Transitions Optical, PPG Industries and Essilor (France).

His corporate responsibilities included new product development, strategic planning, marketing management, and global sales. He has developed a number of global communications networks, launched products in over 45 countries, and managed a number of branded patented products.

mandy sahhar

About the Co-Author

Mandy Sahhar provides experience in digital marketing, event management, and business development. Her background has allowed her to get in on the ground floor of marketing efforts including website design, content marketing, and trade show planning. Through her modern approach, she focuses on bringing businesses into the new digital age of marketing through unique approaches and focused content creation. With a passion for communications, she can bring a fresh perspective to an ever-changing industry. Mandy has an MBA with a marketing concentration from Canisius College.