The use of AI among doctors in the U.S. nearly doubled in 2024, according to a survey by the American Medical Association (AMA). AI technologies are used for different tasks like helping with medical decisions, assisting with diagnostic images, and automating administrative work. While these AI tools can improve patient care and make processes faster, they also bring more risks for handling protected health information (PHI) incorrectly.
PHI is private patient information protected by the Health Insurance Portability and Accountability Act (HIPAA). It includes medical histories, diagnoses, lab results, and other personal details. When AI tools work with this data, there is a higher chance of data being leaked or shared without permission. For example, in February 2024, Change Healthcare, Inc. announced the largest healthcare data breach in history affecting 190 million people. Another breach affected 483,000 patients at six hospitals due to an AI workflow vendor’s platform. These events show how weaknesses in AI and vendor systems can put patient data at risk.
Medical practice administrators and IT managers need to know that breaches not only hurt patients’ privacy but also can cause delays in appointments and treatments. Also, breaches can damage a healthcare group’s reputation and bring heavy legal and money penalties.
Research shows that most data breaches happen because of human mistakes. In 2023, studies said that up to 82% of data breaches in some countries were partly caused by employee errors. These errors include falling for phishing scams, mishandling data, or using unauthorized software that hurts security.
In healthcare, where PHI must be well protected, employees need special training about AI-related risks. Without this, staff may accidentally cause risks by using unsanctioned AI tools without proper oversight. This “shadow IT” bypasses security steps and exposes PHI to breaches.
Besides knowing AI risks, employees must learn how to use approved HIPAA-compliant tools correctly. This means following rules for multi-factor authentication (MFA), keeping passwords secure, and following protocols that protect the privacy, accuracy, and availability of patient data.
Healthcare groups that give good security training can lower the chance of breaches a lot. Training changes employees from weak points into a first line of protection along with firewalls and antivirus software.
Security training for healthcare workers should go beyond just basic rules. It should happen regularly, be interactive, and focus on AI threats and healthcare data privacy laws.
Important parts of this training include:
In healthcare, many AI solutions come from third-party vendors who work with PHI for medical practices. HIPAA requires that these relationships have Business Associate Agreements (BAAs). These agreements legally make vendors follow strict rules about how PHI is used and protected.
A good BAA must clearly say that the vendor cannot use protected data in unauthorized ways, including training AI models without patient permission. It should also describe cybersecurity standards vendors must meet, like following NIST (National Institute of Standards and Technology) guidelines and reporting data breaches right away.
For medical practice administrators and IT managers, it is important to carefully review vendors’ security policies. Only choose vendors who can quickly detect and stop breaches. This helps protect patients and the organization from large data breaches that are costly and disrupt work.
AI-based workflow automation tools are becoming common in healthcare front office work, such as scheduling appointments, patient communication, and phone answering. Companies like Simbo AI focus on phone automation that helps manage calls using AI-powered answering services.
Though these tools can make work faster, they bring security concerns. Automated systems must handle PHI carefully and run on HIPAA-compliant platforms. If AI tools are used wrong, patient data can be leaked through network weaknesses or improper secondary use.
Employee training should include:
Medical practices using AI for front office tasks should check security often and train staff to make sure automation helps care without risking patient privacy.
When healthcare groups invest in employee training about AI risks and HIPAA rules, they reduce chances of data breaches that can hurt operations. Breaches often disrupt patient appointments, delay treatments, and block access to important information. Stopping these problems helps keep care running smoothly.
Also, patients and partners expect healthcare providers to have strong cybersecurity. Studies show nearly two-thirds of consumers avoid groups with recent cyber incidents. Training employees shows that the organization cares about patient privacy. This can build more trust and support.
As AI use grows fast in healthcare, ignoring training on AI risks and correct use of HIPAA tools is a risk too big for any practice to take.
The rise of AI use and past data breaches show the need for employee training on AI security risks and HIPAA rules. Medical practice leaders in the U.S. should focus on:
By following these steps, healthcare groups can better protect patient data, reduce work disruptions, and keep the trust needed for good care in a technology-driven world.
The primary categories include Clinical Decision Support Systems (CDSS), diagnostic imaging tools, and administrative automation. Each category processes protected health information (PHI), creating privacy risks such as improper disclosure and secondary data use.
BAAs legally bind AI vendors to use PHI only for permitted purposes, require safeguarding patient data, and mandate timely breach notifications. This ensures vendors maintain HIPAA compliance when receiving, maintaining, or transmitting health information.
PHI can be shared without patient authorization only for treatment, payment, or healthcare operations (TPO). Any other use, including marketing or AI model training involving PHI, requires explicit patient consent to avoid violations.
Breaches expose sensitive patient data, disrupt IT systems, reduce availability and quality of care by delaying appointments and treatments, and risk patient safety by restricting access to critical PHI.
Careful vendor selection is essential to prevent security breaches and legal liability. It includes requiring BAAs prohibiting unauthorized data use, enforcing strong cybersecurity standards (e.g., NIST protocols), and mandating prompt breach notifications.
Employees must understand AI-specific threats like unauthorized software (‘shadow IT’) and PHI misuse. Training enforces use of approved HIPAA-compliant tools, multi-factor authentication, and security protocols to reduce breaches and unauthorized data exposure.
Covered entities and business associates must ensure PHI confidentiality, integrity, and availability by identifying threats, preventing unlawful disclosure, and ensuring employee compliance with HIPAA law.
Secondary use of PHI for AI model training requires explicit patient authorization; otherwise, such use or disclosure is unauthorized and violates HIPAA, restricting vendors from repurposing data beyond TPO functions.
Providers should enforce rigorous vendor selection with strong BAAs, mandate cybersecurity standards, conduct ongoing employee training, and establish governance frameworks to balance AI benefits with privacy compliance.
Short breach notification timelines enable quick response to incidents, limiting lateral movement of threats within the network, minimizing disruptions to care delivery, and protecting PHI confidentiality, integrity, and availability.
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