Enhancing Data Security and Privacy: Addressing Concerns in AI-Enabled Healthcare Systems

All healthcare systems must prioritize data security and privacy. Apart from the ethical concerns of inadvertently sharing highly personal details, privacy legislation in healthcare —specifically HIPAA (the Health Insurance Portability and Accountability Act) — dictates that personally or individually identifiable information must be protected. 

As artificial intelligence (AI) becomes integrated into various healthcare systems, there are concerns about additional privacy compliance challenges this presents. Yet AI could revolutionize the healthcare sector, improving diagnosis accuracy and speed, resource allocation, and remote health monitoring. 

Finding ways to mitigate security risks and data privacy concerns is essential to ensure the healthcare industry of tomorrow enjoys the very real benefits AI has to offer.

AI-Enabled Healthcare Systems

An accurate diagnosis or effective treatment plan requires large volumes of data: genetics, lifestyle, and previous treatments. AI can help pull all this information together seemingly effortlessly. For example, AI could help electronic health record (EHR) systems show a list of medications that could present adverse effects for a patient based on previous interactions. 

In addition to healthcare organization revenue cycle management, other assistance includes: 

AI in Medical Imaging

Doctors miss up to 40% of cancerous tumors when manually evaluating mammograms, the screening images doctors use to diagnose breast cancer. AI could report on cancers missed by the human eye using pattern recognition to note areas that match previous patients with similar diagnoses. Studies suggest that this support could help doctors make more accurate diagnoses.

AI in Patient Care

Automated suggestions for personalized treatment plans or notifications for open appointment slots are just a couple of key applications. 

While all these advancements are positive for both patients and providers, there are several concerns. Some issues arise because third-party vendors running AI applications may lack the necessary experience to meet HIPAA compliance standards. Another problem is that developers must train healthcare AI models on patient data. Although they aim to keep this data anonymous, some have already failed to do so.

Google’s DeepMind AI project accessed the personal information of 1.6 million patients in the UK. Although this data helps improve advanced detection of kidney disease, the project should have kept patient information anonymous at all times. Given that, it's reasonable to worry that similar scandals could happen in the U.S.

Addressing Data Security Concerns

Data mismanagement is a serious business concern. The average cost of a data breach is elevated in the healthcare sector — around $10.93 million compared to $4.45 million across other industries. The financial risk isn’t the only worry. The loss of reputation from mishandling patient data could cause lasting damage to an organization.

The DeepMind debacle highlights that privacy concerns in AI-enabled systems are well-founded. Another common vulnerability in AI-enabled systems includes disparate devices. For example, a wearable device that connects to a smartphone to send medical data has several network entry points. Malicious actors can use these multiple connections as opportunities to access a healthcare organization’s network.

When you include the AI service’s provider and their networks, the potential for cyberattacks is exponential. There are, however, several strategies organizations can follow for enhancing data security in healthcare AI.

Ensuring Data Privacy

Healthcare data protection strategies must be robust and consistent, but also include the potential to protect data in the face of rapidly shifting technologies. 

Integrating AI through third-party providers means that healthcare providers must:

• Confirm that third parties understand HIPAA compliance and can demonstrate robust cybersecurity principles.

• Ask pertinent questions about how AI models are trained on patient data and what patient data protections are in place during this process.

• Always inform patients how their data is used and obtain consent where necessary.

• Note all concerns immediately and contact authorities where and when appropriate.

Patient data plays a vital role in developing healthcare AI, but developers must obtain consent before using it. AI and healthcare firms must work together to secure informed consent and prioritize transparency for everyone involved in handling and managing patient data.

Creating Meaningful Partnerships to Protect Patient Data

AI-powered healthcare is essential for improving patient outcomes, yet privacy and data security concerns remain. Healthcare organizations must prioritize ensuring that all patients understand how their data is used and can provide informed consent. Healthcare organizations must demand that all organizational partners, providers, and consultants understand these concerns.

AI companies can transparently demonstrate how they prioritize the anonymization of patient data. The more open these firms are about their training journeys, the more trust they'll gain from partners within the healthcare sector.

While data security in AI healthcare is a concern, the proper support can help you use electronic systems that protect the data of patients, employees, and organizational leaders. Talk to TempDev about optimizing your NextGen EHR and EPM systems to ensure compliance and data security in your organization — even as AI becomes more widely integrated across the healthcare sector.