By Jessica Petro
Artificial intelligence is everywhere. Any time you use your smart devices, social media platforms, or interact with a customer service chatline, you’re exposed to the powers of AI. But what about when you go to your doctor’s office?
Health care organizations across the country have embraced the opportunity to integrate and leverage AI during this increasingly difficult operating environment. AI promises to enhance efficiency, improve patient outcomes, and create innovative solutions to complex health challenges. But while embracing new ways to improve care for patients and providers is key to moving forward, there are several important implications to consider.
Privacy and Security
The integration of AI into health care makes protecting patient data more critical than ever before. AI systems process significant amounts of information, including sensitive health data, making it critical to protect these systems from breaches and bad actors. As with any emerging technology, there may be undiscovered weaknesses in AI tools that need to be addressed in real time. Along with this, complex regulatory requirements, such as HIPAA, dictate how patient data can be used and secured, meaning each potential AI model must be carefully vetted.
Health systems also need patient buy-in to use AI-based technology to its fullest potential. Patients may hesitate to interact with AI tools or share their private health information if they don’t understand the safeguards in place to protect their data. By clearly articulating the value that AI brings to care and how the organization is protecting their information, health systems can ease patient concerns and increase engagement with these tools.
Infrastructure
Physical space and infrastructure limitations are also key considerations for health care organizations looking to integrate AI technology.
First, AI tools use significant memory storage and require fast data transmission, which may mean that a health system needs to make upgrades to its cloud storage systems to optimize functionality.
Another implication to consider is how AI tools interact with Electronic Health Records (EHR) systems. Many health care organizations have made significant investments in their EHR capabilities to improve clinician workflow, streamline patient communications, and provide more comprehensive, integrated care. However, integrating AI tools into care delivery also involves ensuring that data can be seamlessly transferred to and from EHR platforms.
Some AI-based solutions might also require redesigned patient spaces, including dedicated rooms for telehealth services or changes to waiting areas to accommodate for virtual check-in kiosks. Additionally, there may be a need for more administrative space to accommodate the staff required to manage these AI tools.
Equity and Bias
Lastly, AI is only as good as the data it is built on. If an AI algorithm or tool uses data that reflects biases, whether intentional or unintentional, there is a real risk for inequitable care. For example, if an AI tool meant to detect a type of cancer is only trained using data from patients with a certain background, it may miss warning signs or symptoms exhibited in other populations.
Both developers of AI technology and the health care organizations looking to implement them must prioritize fairness and equity, ensuring the tools are trained using diverse datasets. Beyond that, health care providers must be trained to use these tools in a way that promotes health equity and reduces bias.
AI is taking the world by storm, but health care organizations should carefully assess what role it can and should play in shaping patient care. Before committing to complete AI transformation, entities must develop the necessary foundation for transparency with patients and staff, while establishing parameters for continued improvement. By proactively addressing potential concerns and closely vetting the tools at their disposal, health care organizations can use AI to create a more effective and equitable system.