Artificial intelligence is a tool designed to power innovation, but it’s important to understand its primary fuel: data. Data is required not only for the outputs of AI algorithms but also for their training and operation. Because of this, in sectors where innovation has become driven by technologies like artificial intelligence, data has essentially become fuel for innovation, and it’s important to ensure the safety and quality of this data to stimulate it.
Understandably, many critics have expressed concern over the use of artificial intelligence in healthcare settings, considering the private, sensitive nature of the data used in the field. Patient personal information is not only highly sensitive but also protected by law, meaning there are strict regulations and guidelines dictating how entities in healthcare can use artificial intelligence with regard to patient data.
Why strong data governance is essential for AI in healthcare
However, that doesn’t mean artificial intelligence shouldn’t be used in healthcare whatsoever. Instead, it means there is a need for strong data governance, as this is an essential step in enabling safe and ethical AI use in any industry, particularly ones such as healthcare where the stakes are high. In addition to ensuring compliance with any applicable regulations, strong data governance helps create greater transparency and trust that inspires patient confidence.
It’s important to remember the reason why the healthcare sector wants to deploy artificial intelligence technology in the first place: AI can accelerate innovation and lead to improved patient outcomes. For example, innovators in the healthcare industry have used AI to accelerate drug discovery, conduct more accurate diagnostics, and streamline operations in a way that significantly improves efficiency. But to achieve these outcomes, systems must have access to accurate, well-managed data.
The key to this is creating compliance frameworks that reduce and mitigate the risks of artificial intelligence while still supporting scalable healthcare solutions. Of course, the core of any compliance framework in healthcare is data security and privacy, but these guidelines can also help control other risks, such as algorithmic bias and “black box” risks, ensuring that all decisions and recommendations made by an artificial intelligence are fair and explainable.
Enabling the responsible deployment of AI in healthcare
Ultimately, data governance isn’t about gatekeeping but about collaboration and enabling the responsible and ethical deployment of artificial intelligence. The mindset with which we approach AI shouldn’t be about limiting how we can use the technology, but instead how we can facilitate its use in a way that does not compromise data integrity or patient privacy.
Right now, the key goal of healthcare practitioners who hope to implement artificial intelligence should be to build trust and reliability in these systems. The steps required to achieve this include ensuring data quality and diversity, maintaining transparent communication, and continuous monitoring and validation.
The best way to look at AI systems in healthcare is as an analog to human employees. In healthcare, not even human employees have unfettered access to patient data. There are access controls based on the level of access an individual needs, with checks and balances and supervisory control.
The same philosophy should apply to autonomous systems. Just as approvals and access controls are required of human employees, so too should AI systems require approvals from human overseers.
Indeed, there is a world in which artificial intelligence can revolutionize the healthcare industry for the better, alleviating some of the burden on healthcare workers and contributing to improved patient outcomes. However, for this to happen, the adoption of AI must be done in a way that is responsible and ethical. With this mindset, prioritizing strong data governance, AI can become a reliable partner in patient care.
# # #
About the Author
Chris Hutchins serves as the founder and CEO of Hutchins Data Strategy Consulting. The healthcare institutions benefit from his expertise in developing scalable moral data and artificial intelligence methods to maximize their data potential. His areas of expertise include enterprise data governance, responsible AI adoption, and self-service analytics. His expertise helps organizations achieve substantial results through technology implementation. Through team empowerment, Chris assists healthcare leaders in enhancing care delivery while reducing administrative work and transforming data into meaningful outcomes.
