Ever since Generative AI (GenAI) began making waves globally, many visionary players have launched efforts to alter their digital landscape. In the healthcare domain, one key area of focus includes conversational bots to address patient queries to enhance the patient experience. Another includes diagnostic support in medical imaging, all while a range of other applications like AI-driven research waits in the wings. The market for GenAI in healthcare could be over USD 22.1 billion by 2032. What are the factors healthcare leaders need to factor in to ensure sustainable success?

Handle with Care ─ Health Data Concerns

GenAI works extracts insights from large volumes of data. Large language models train on this data, most of it unstructured. Modern GenAI solutions tailored for healthcare organizations consume data at a scale never considered before, from medical journals to patient health records. While big data analytics existed in the healthcare tech ecosystem earlier, the insights it generated were often peripheral. Also, direct consumption of the results by all stakeholders was not common. With GenAI, this is changing. Even use cases where patients could directly query systems to gain clinical knowledge about their health are common now.

This is where data security and privacy concerns must take center stage. GenAI systems are trained with sensitive medical information for accurate interpretation. The training practice can involve LLMs and GenAI solutions from several vendors. The risk of unvetted external entities gaining access to the confidential health data of patients is non-trivial in this scenario.

Healthcare is among the most regulated sectors in the world. Strict data protection mandates exist in all markets. Not complying with these mandates can hurt healthcare organizations. GenAI is not exempt from these conditions.

Established regulatory policies like GDPR and HIPAA mandate the development of robust standards that must apply to the information or data management frameworks a healthcare provider develops digitally. These must be designed to enhance patient trust and safeguard patient privacy.

The expansive digital health ecosystem is now a breeding ground for new cyber threats. With GenAI systems requiring deep integration with a growing array of data banks, they are undoubtedly a major target for attackers. Bad actors can exploit any unattended vulnerabilities to access sensitive information.

How can healthcare organizations ensure data security and privacy in the era of GenAI?

The challenge for healthcare leaders while embracing GenAI capabilities is to ensure that sensitive data doesn’t fall into the wrong hands. Here are some of the most effective practices they can implement to feel more confident while driving AI initiatives:

Effective data governance

Establishing a clear, comprehensive, and transparent framework for handling data within the enterprise is critical for healthcare organizations looking to adopt GenAI. A governance framework like this will guarantee the alignment of new digital capabilities like GenAI with all the organizational needs. This will guarantee that only essential processing information is utilized while safeguarding it from unauthorized external access. Every decision-making process, including the ones driven by GenAI systems, will follow a defined data framework that has clarity on the roles and responsibilities of the people involved. This will prevent the misuse of private and sensitive information.

Breaking legacy pushback influence

In several cases, healthcare organizations have multiple legacy applications in their tech stack. As a general rule, these will be poorly adapted to modern software development paradigms and will be hard to integrate with innovations like AI due to the lack of smart connectivity options or interfaces. In such cases, the organization will need to direct their efforts to build middleware or related channels that eliminate delays or missed connections with existing business systems. Proper integration with legacy systems is of critical importance from a security perspective also as poorly guarded integrations will be the target of attackers leading to future chaos and challenges.

Enable better data interoperability

Siloed healthcare data is often a major challenge for enterprises. From EHR to LIS in laboratories, the diverse formats in use within the healthcare landscape can make it hard for GenAI tools to work their magic seamlessly. Organizations need to implement clear data standardization practices and ensure that different departments eventually work towards a singular data standard. This will make it easy for new and emerging technology platforms to accommodate without risks.

Strengthen authentication and anonymization

Eliminating vulnerabilities and attack surfaces on GenAI systems is another critical ask for ensuring data security. Using of measures like multi-factor authentication will help minimize these risks. That will make it difficult for hackers and fraudsters to mimic the operations of GenAI systems to obtain sensitive information from their dedicated connections to the company. The use of powerful encryption measures will also add additional layers of security for sensitive data in transit between GenAI systems and the organization’s data stores. Anonymization of data will guard against the misuse of information in the event of a data breach. Such measures will make it nearly impossible for cybercriminals to determine data ownership, thereby rendering the captured data useless for any further damaging moves.

Implement frequent audits

Staying secure is a journey, not a destination. That’s why it’s important to constantly review how GenAI systems consume information. Healthcare organizations will need to continuously monitor their behavior to identify any vulnerable scenarios that could potentially lead to a breach of sensitive data. From integration points to databases, there should be frequent and strict security audits to ensure that data integrity remains intact all through the cycle.

Select the right tools

A diverse range of software tools, applications, and technologies can be adopted to enable GenAI initiatives. Organizations must thoroughly evaluate these options before selecting the best tools for live deployments. One key criterion should be how compatible they are with complex data security policies enforced by the organization and their ability to adhere to the strictest compliance norms. These organizations must enforce automation initiatives to reduce threat surfaces and ensure that only encrypted networks facilitate inter-system communication.

Here are a couple of considerations that enterprises can evaluate before investing in tools to use.

• Compliance: Ensure the tool complies with healthcare regulations like HIPAA, GDPR, and other relevant standards.
• Data Security: Prioritize tools that offer robust encryption, access controls, and anonymization techniques.
• Interoperability: Choose tools that can easily integrate with existing healthcare systems and standards (e.g., FHIR, DICOM).
• Scalability: Consider the tool’s ability to handle large-scale data processing and adapt to the organization’s growth.
• Vendor Support: Evaluate the level of support and customization offered by the tool’s vendor, especially in the healthcare context.

The bottom line

Only a carefully designed strategic roadmap can enable the secure adoption of GenAI in the healthcare sector. Medical data and use cases can be complex. Gaining a competitive advantage in such a complex environment would require extensive digital competence. Healthcare companies need an experienced technology partner like Xoriant to drive their digital ambitions forward and help embrace capabilities like GenAI seamlessly and without risks. Get in touch with us to know more.