The Intersection of AI, Cybersecurity, and Data Platforms in Healthcare

Editor’s Note: This article is based on a recent webinar entitled, Healthcare Tech Insights: AI, Trends, and Platforms Demystified, presented by Dave Ross, Chief Technology Officer, and Tim Zenger, VP of Market Insights, at Health Catalyst.

Integrating augmented intelligence (AI) and advanced data platforms has revolutionized how health system executives and medical professionals make critical decisions. However, as these technologies become more prevalent, so do the complexities surrounding their implementation and management.

One of the most pressing issues in this realm is cybersecurity—a paramount concern directly impacting decision-making processes.

This article delves into the intricate relationship between augmented intelligence, data platforms, and cybersecurity within the healthcare sector, offering valuable insights gleaned from a recent webinar presented by Dave Ross, Chief Technology Officer, and Tim Zenger, Vice President of Market Insights, at Health Catalyst.

The Risks of Cybersecurity and Privacy Breaches

Recently, a group known as BlackCat orchestrated cybersecurity breaches, employing a contractor named Notchy to initiate targeted attacks on U.S. healthcare organizations, Zenger explained. Notchy successfully breached Change Healthcare, obtaining substantial data, prompting BlackCat to demand $22 million in ransom. However, BlackCat took the money and ran, leaving Notchy with the data but no payment. Afterward, Zenger shared that Notchy had moved to a different organization called RansomHub to issue a second ransom demand for Change Healthcare’s data on the dark web.

This incident shows that ransomware attacks are becoming increasingly sophisticated, with the “ransomware-as-a-service” market expanding, he said. This also emphasizes the critical role employees play in cybersecurity breaches. Ross said that even with security measures and training, one person’s mistake can cause significant damage to a healthcare system.

Ross emphasized the importance of organizations prioritizing privacy and security when utilizing new technologies such as augmented intelligence (AI). Organizations must address these significant concerns and risks before employing them in their workflow or business processes. While cybersecurity problems have existed for some time. Now, they are easier to execute and more critical for organizations to address as the stakes are higher. This is particularly true with the emergence of new data and analytics solutions in healthcare.

One key measure healthcare organizations can take to prevent cyber-attacks is implementing robust encryption protocols to secure patient and other organizational data. This includes using encryption technologies to protect sensitive information from unauthorized access. Additionally, regularly updating security systems and software patches can help mitigate vulnerabilities that hackers often exploit to access medical records, Ross suggested.

Furthermore, Ross noted that conducting regular cybersecurity training for staff members—educating employees about best practices for recognizing phishing scams, preventing downloads that introduce malware to computer systems, and adhering to password policies, for instance—is crucial in building a culture of awareness and vigilance against potential cyber threats. 

The presenters said that by exploring all facets of technology investments to maximize return on investment (ROI), healthcare organizations can streamline operations and ensure they are equipped to address emerging cybersecurity threats that directly affect decision-making processes at every level.

The Promise and Pitfalls of Augmented Intelligence

Meanwhile, how are health system leaders addressing the buzz surrounding AI? The development of generative AI has significantly improved the usefulness and realism of AI applications. Ross explained that innovations like transformer architecture, such as ChatGPT, have made it easier to use AI in various business use cases. 

Previously, progress in AI adoption had been relatively steady, with more traditional methods, such as predictive analytics, showing incremental growth. That is, until generative AI, which creates natural language responses, was introduced. Zenger said integrating this technology into business processes further accelerated its acceptance in the healthcare sector to improve operations and administration.

Ross stressed the need to carefully consider “democratizing” AI in healthcare settings, however, recommending close monitoring and careful deployment rather than widespread implementation. He also highlighted the value of aligning skeptics and enthusiasts within an organization to promote responsible and incremental adoption of AI technology. Organizations need both viewpoints, he said.

Zenger stated that organizations with clear goals and effective change management processes can use tools more effectively and are better equipped to utilize AI. He believes that the main challenges in using AI usually stem from people, processes, culture, and organizational norms, as these obstacles are more common than technological limitations.

Indeed, barriers often arise due to resistance or the need for more understanding from individuals within the company, Zenger added. Fragmented processes and norms within the organization can also impede the adoption of AI.  Yet, both agreed that AI can revolutionize healthcare operations through predictive analytics and augmenting routine administrative tasks, improving efficiency, reducing costs, and enhancing patient care.

A Data Platform Strategy Prioritizing Modern Healthcare Needs

The presenters also agreed that health systems should implement a data strategy that employs a flexible and modular data platform that can adapt to emerging data and analytics needs, including cybersecurity and AI adoption. 

Ross said a vital characteristic of a modern data platform involves seamless integration with different systems, facilitating data sharing across multiple platforms. Cloud-based data systems make sharing and collecting data easier, improving how various systems work together.

Ross also shared how modern data platforms are designed to simplify the implementation of solutions, allowing health systems to quickly scale and expand without requiring a significant initial investment or outdated manual file transfers. Ross added that new data platforms ensure approved users can easily retrieve the necessary information and access capabilities, including self-service features like data science and machine learning tools.

However, he suggested that a robust data governance framework is essential for maintaining data quality and security within a flexible platform architecture. Ross briefly touched on the importance of health systems establishing clear policies and protocols for accessing, sharing, and managing data to guarantee compliance with regulatory requirements and maintain privacy and trust. By prioritizing ongoing monitoring and evaluation of their data platform strategy, health systems can continuously adapt to safety protocols while fostering innovation and efficiency in healthcare delivery.

Economic Issues Impacting Trends in Healthcare

Finally, while some organizations may continue with traditional practices, others actively embrace change and lead in innovation in healthcare delivery despite facing labor market challenges, rising labor costs, inflationary pressures, and other macroeconomic forces, the pair said.

As AI’s importance increases, healthcare organizations must balance current challenges with future opportunities for innovation and growth. This means finding a way to address the healthcare industry’s present issues while planning for future advancements. By doing so, organizations can stay competitive and continue providing patients with high-quality care that protects their most valued assets: health and personal data.

Additional Reading

Would you like to learn more about this topic? Here are three articles we suggest:

5 Ways HITRUST Common Security Framework Protects Data

A Five-Step Readiness Plan to Harness Augmented Intelligence in Healthcare

How One Hospital System Advances Clinical Documentation Improvements with Augmented Intelligence