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Privacy Concerns of Artificial Intelligence in Healthcare

Written and medically reviewed by Dorcas Morak, Pharm.D

Updated on June 10th, 2023

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Written and medically reviewed by Dorcas Morak, PharmD

The use of artificial intelligence (AI) in healthcare is growing rapidly, and it heavily depends on accessing patient medical data. As the exchange of medical information increases with the help of AI, it becomes incredibly important to protect individuals' privacy and information. The wider adoption of AI in healthcare has led to the increased focus on privacy and security risks associated with the data involved, resulting in stricter scrutiny and enforcement.

In this article, we will discuss the significant concerns regarding data privacy and security that require careful attention when developing AI-powered products or deciding on their implementation in healthcare delivery.

Data Use Through De-identification

When it comes to incorporating patient health information into AI products, there are important considerations regarding compliance with the Health Insurance Portability and Accountability Act (HIPAA) and state privacy and security laws. It's crucial for both AI healthcare companies and institutions using AI healthcare products to determine if HIPAA or other state laws apply to the data. They should also explore options like de-identification to potentially work around these regulations. De-identifying protected health information (PHI) under HIPAA involves removing specific identifiers.

However, using de-identified data in AI-based products raises additional privacy concerns. As AI systems integrate more data, there's an increased risk of re-identifying the data, even if it was initially de-identified. The growing sophistication of AI systems makes it easier to establish connections between different data elements, which can compromise patients' privacy. It's crucial to continually evaluate the privacy risks associated with AI systems as the volume and diversity of data elements expand.

Vendor Due Diligence: Data Access, Data Storage, and Ransomware

Before entrusting third parties with patient data, it is extremely important to conduct thorough vendor due diligence. This includes understanding how the data is collected, whether it is directly from patient records, and where it is stored. Failing to perform adequate due diligence in these areas can lead to serious legal and financial consequences.

When it comes to data collection, entities that grant system access must comply with legal requirements and can be held liable if the data is not adequately protected. AI technology, like any other technology, is vulnerable to manipulation. Therefore, networks that connect patient data to patient care must be securely safeguarded. Given the increasing number of ransomware attacks targeting the healthcare sector, it is crucial to thoroughly vet and monitor external access points to mitigate potential threats.

Additionally, it is essential to examine how an entity manages data access, implements strong data governance and management practices, and conducts thorough risk assessments. This comprehensive evaluation will help determine whether the benefits of accessing a particular product outweigh the potential risks involved.

Security Safeguards to Protect Healthcare Data

To maintain privacy and foster trust in technology, it is important for AI companies to adopt effective security measures. Here are some key safeguards that should be considered:

  1. Enhanced compliance monitoring: Regular audits and monitoring of information systems and data are necessary to detect potential data breaches. To facilitate this, AI companies should consider utilizing affordable third-party products specifically designed for monitoring. Incorporating such tools into an information security program can greatly enhance security measures.

  2. Access controls: It is crucial to have a clear understanding of who can access the data and algorithms. Strict controls should be implemented based on the level of access granted, ensuring that appropriate security measures are in place. This will help prevent unauthorized access and protect sensitive information.

  3. Training: Providing education and training to personnel and vendors is essential. They should be made aware of their access limitations, restrictions on data usage, and their security responsibilities regarding the data. This includes understanding any limitations outlined in patient consent or authorizations. By emphasizing these aspects, AI companies can promote a culture of security awareness and ensure that everyone involved understands their role in maintaining privacy and trust.

By implementing these security safeguards, AI companies can bolster their efforts to protect data, maintain privacy, and build trust in technology.

AI technologies in healthcare have sparked significant excitement due to their potential advantages. However, maintaining data privacy is crucial to ensure the continued adoption and effectiveness of these AI products. Building trust between patients and physicians is essential for managing progress and reaping the benefits of these advancements, as it assures that AI-based products prioritize and uphold the privacy of patient data.

As always, your privacy is our priority. We are just as committed to safeguarding your confidential information as we are to protecting your health.

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