Unveiling the Shadows: Exploring the Dark Side of AI in Healthcare

In my last two articles on AI, I attempted to cover the positive impact of AI on the healthcare and pharmaceutical industry. In this article, I am sharing my thoughts on the flip side of AI. Thanks to Stanley Russel , whose comment on my last article prompted me to explore the dark side of AI.

The application of AI has become pervasive across the industry. While everyone around the corner is estimating positive aspects, healthcare is no exception. The application of AI in healthcare and pharmaceuticals is poised to be a game-changer in the coming days. On the flip side, I see the downside of AI could be categorized into four buckets

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I: Human Aspect: At this moment human beings are standing at the crossroads amid the AI revolution. Adaptation of AI could be equally rewarding & disappointing. Let us evaluate which area is going to be impacted due to the application of AI.

a)????? Job Displacement:

? i.?People employed in performing the routine & mundane work due to application of AI are going to impacted badly. As the machine can perform the routine task more efficiently & swiftly.

?ii.?Jobs which might be vulnerable; Administrative tasks such as scheduling, billing, and record-keeping, data entry and transcription tasks, medical coding etc.

b)???? Adoption challenges:

?i. In country like India, skills of the human capital are diverse. There is a huge gap between competency level between urban & rural manpower.

?ii.?The second challenge could be habit formation. EMR penetration is almost at its initial phase. With the advent of AI people need to be adaptable with new skills. Hence, it’s going to make unrest for people who are unable to change as per the new requirements.

c)????? Possible Overreliance:

?i. Too much technology reliance might impact on the cognitive ability of the human being.

ii.?It's crucial to maintain a balance and ensure that AI is used as a tool to support, rather than replace, human expertise.

d)???? Cultural & Organizational change:

?i. I still remember when we were instructed to implement a paperless hospital culture. There was a huge unrest from clinicians to the paramedical staff.

ii.?Well, the reason for the same is adopting a new culture which is like moving against the tide. It requires a lot of willingness & incentive to adopt a new culture.

II: Financial Aspect: Any technology during the initial phase of its innovation is expensive & dynamic. AI is no exception. Implementation of AI in an organization requires a careful evaluation of the below four categories of financial implication in conjunction with the ROI (Return on Investment).

a)????? Technology procurement & licensing:

?i.?Healthcare often operates in a thin margin. Adoption of AI in a healthcare system needs to be earmarked with a committed budget which will be going to impact on the existing financial decision-making process.

b)???? Upgradation of Infrastructure:

?i.?The integration of AI may necessitate upgrades to existing IT infrastructure. This includes investing in high-performance computing systems, storage solutions, and network capabilities to handle the increased computational demands of AI applications.

c)????? Data management & security:

?i.?Patient information is confidential. It’s the primary responsibility of the healthcare provider to protect the data from any type of theft or attack.

ii. Investments are needed in cybersecurity measures, data encryption, and compliance with regulatory requirements to protect patient information from breaches. Which need a new set of technical knowhow & financial investment.

d)???? Training & Skill development:

?i. How brilliant a technology might be, it needs to be operated by the existing employees.

ii.?Organization not only plan for additional capital expenditure to laid out the technology but also must enable the existing manpower with technical knowhow for the smooth implementation.

iii.?This is going to add cost burden to the existing environment.

e)????? ROI Analysis:

?i.?The implementation of AI is not going to be same across the value chain as there is a diverse difference in healthcare delivery.

ii. Hence evaluating the potential benefits such as improved diagnostic accuracy, streamlined workflows, reduced operational costs, and enhanced patient outcomes against the initial and ongoing costs of AI implementation.

III: Regulatory Aspect: Patient data is sensitive & it should be dealt with utmost care. Though there are few guidelines like HIPAA, GDPR, FDA regulations across it’s implementation & adoption are big question marks.

a)????? Data Privacy & Security:

?i.?Most of the regulations around AI are available in western countries. The Indian Govt. is still evaluating to create a framework around it, however it’s too early.

ii.?This is a grey area which needs to be addressed in a short time to provide data privacy & security.

b)???? Ethical concern:

?i.?Patients are the most important beneficiary of their own data. However, with the advent of EMR & various AI tools it’s critical to figure out how this data is going to be used with or without the consent of the patients.

ii. It might open a completely new chapter in & around ethical & moral standard.

iii.?Patients need to be informed about how their data will be used, and healthcare providers must ensure that AI applications adhere to ethical principles, avoiding biases and promoting transparency in decision-making.

c)????? Clinical Validation and Evidence Requirements:

?i.?Clinical validation holds the highest importance while implementing artificial intelligence as the beneficiaries are invaluable. ?

ii.?However, we are yet to pass through a lot of regulatory driven validation before adopting the novel technology.

iii.?Machine learning & Generative AI are as good as the input we give to them. This involves conducting rigorous studies to demonstrate that AI technology delivers reliable and accurate results in real-world healthcare settings.

?iv.?AI algorithms are trained on historical data, and if this data contains biases, the algorithms may perpetuate and even exacerbate those biases.

d)???? Liability & Accountability:

?i.?The worst part of AI is the weather is still hazy. Especially when the industry is working with data which are very sensitive in nature, lack of liability & accountability may lead to a lot of ambiguity.

?ii. Determining responsibility for errors or adverse outcomes is crucial to ensure accountability and patient safety.

IV: Technology Aspect: The adoption of Artificial Intelligence (AI) in healthcare involves several key technological aspects. These include the development, integration, and management of AI applications to enhance various aspects of healthcare delivery. Here are key technological aspects associated with the adoption of AI in healthcare.

a)????? Data quality & accuracy:

?i.?AI in healthcare relies heavily on high-quality, diverse datasets. Ensuring access to relevant and representative healthcare data is crucial.

ii. This involves data acquisition, storage, preprocessing, and management to feed into AI models effectively.

iii. Integrating AI applications into existing healthcare infrastructure is a significant challenge for accurate data management.

iv. Compatibility with Electronic Health Record (EHR) systems, picture archiving and communication systems (PACS), and other healthcare IT systems are necessary for effective deployment.

b)???? Cyber Security:

?i. Implementing robust security measures to protect sensitive patient data is critical. Encryption, access controls, and secure transmission protocols are essential to safeguard patient information and maintain compliance with data protection regulations.

?ii.?Incorporating technology that ensures compliance with regulatory standards for healthcare AI is essential. This includes adherence to medical device regulations, data protection laws, and other relevant guidelines to guarantee the safety and reliability of AI applications.

c)????? Data Interoperability:

i.?Healthcare system in India is diverse. The diversion varies across the facilities. Most of this system again works in silos. For example, within a hospital chain there is no standardization in machine & process.

ii.?This makes the role of interoperability quite critical. Regulatory bodies may establish standards to ensure interoperability among different healthcare systems and AI applications.

iii.?This helps in seamless data exchange and communication between various components of the healthcare ecosystem, improving coordination and patient care.

In conclusion, the integration of Artificial Intelligence (AI) into healthcare holds immense promise yet is accompanied by challenges explored in the realms of Human, Financial, Regulatory, and Technological aspects. The Human Aspect highlights the pivotal juncture at which healthcare stands, addressing concerns of job displacement, skills adaptation, and the need for a delicate balance between AI efficiency and human expertise. Financial implications, spanning technology procurement to infrastructure upgrades, necessitate meticulous evaluation to maintain financial sustainability. Regulatory complexities, particularly in patient data privacy and ethics, call for comprehensive frameworks. The Technology Aspect underscores the intricate integration of AI, demanding attention to high-quality data, cybersecurity, and interoperability. As healthcare evolves, collaborative efforts to balance innovation with ethical considerations are imperative, ensuring AI becomes a valuable ally in delivering efficient, secure, and ethical healthcare services.

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