With AI adoption seeping into every conceivable industry, it stands to reason that large industries such as healthcare will have concerns about responsible AI adoption. The world is still learning about everything AI can do, and it can be easy to get caught up in the notion of “we can” without considering whether “we should.”
Responsible AI adoption has to be thoughtful and consider patient safety, quality improvement, and governance strategies. Hospitals and other healthcare systems that find a way to deploy AI responsibly can expect better outcomes, reduced administrative tasks and tedious paperwork, and better support for their clinicians.
A focus on better patient safety and transparency
Most patients are familiar with HIPAA laws and the greater push for privacy protection in healthcare settings. As a result, they may have concerns about their privacy and safety with greater AI utilization.
Patient safety depends on the integration of AI systems that are transparent, auditable, and easy for clinicians to question. We are living in an age where AI recommendations can influence diagnoses, treatment prioritization, and administrative documentation. As AI improves, the stakes get higher. Responsible programs used in healthcare settings require information about how the AI was trained, what data was used, what its limitations are, and how its performance is measured over time. Organizations need to be given a way to trace decisions and investigate possible errors before problems become costly and potentially harmful patterns.
The adoption of AI and the need for transparency must be built around trust. Healthcare employees should be aware of the intended uses of AI and have the ability to assess the programs for quality, equity, reliability, or possible bias.
That degree of oversight allows healthcare organizations to use AI as a support tool instead of surrendering all decision-making to the program. Transparency also helps patients better understand how AI is used in their treatment plan.
Bias reduction in healthcare
Since AI has been utilized in healthcare settings, concerns about bias have emerged. As such, the responsible use of healthcare AI must work to reduce bias.
Clinical AI programs can inherit bias from historical care patterns, incomplete datasets, or algorithms with built-in blind spots. If this bias is not addressed, the disparity can widen, leading to worse outcomes and sowing distrust in the medical system among marginalized populations.
To adopt AI responsibly, program performance must be tested across subgroups. Overall accuracy tests or reporting can be unreliable and biased by nature when generalized. Data curation must be aware of bias so that the output can be considered both clinically valid and fair.
Through continuous monitoring, keeping an eye on diverse datasets, and performing regular audits, healthcare organizations can rest assured that their AI systems are as bias-free as possible.
Regulatory pressure surrounding AI use
Regulatory pressure is increasing for AI use in medical settings, and for good reason. Transparency requirements in AI regulations can promote greater fairness and safety in the medical community. For an organization not defined by a single regulatory rulebook, guidelines on responsible AI use help steer healthcare leaders in the right direction for the benefit of not only patients but clinicians as well.
Patients also need to understand the decisions about care that AI is helping to shape. AI can make mistakes. It can have bias and even respond or act unreasonably at times. Without human intervention and regulation, AI can do more harm than good in a clinical setting.
Lowering the administrative burden
One of the primary functions of AI in large organizational settings has been to lower the burden of tedious administrative work. By automating repetitive tasks, supporting better documentation, routing messages, and bringing the most relevant information to doctors and other professionals, AI has been able to do a lot of administrative heavy lifting. These systems have freed medical professionals to focus more on patient care, which could very well transform medical care as we know it.
However, the key to this function of AI working is to be thoughtful in how it is deployed. Users need to make sure that AI is actually removing friction instead of creating new tasks. Systems that are poorly integrated, lack transparency, or are frequently wrong can create more work in the long run. Responsible systems have a clear purpose, measurable outcomes, and keep humans in the loop for clinical judgment calls.
Practical and responsible AI adoption
Responsible AI adoption requires adherence to core practices to enrich healthcare rather than worsen patient care. Clinical use cases should be clearly defined before AI systems are deployed. The patient population must also be defined, and diverse population datasets must be used to train medical AI to avoid bias. Ongoing monitoring of data, adverse events, and outcomes can help keep AI systems transparent and relevant. As governance and regulations evolve, so must the systems.
The broad lesson is significant yet simple: Healthcare cannot adopt AI merely because it is in vogue to do so. It must adopt AI thoughtfully, and because the systems are safe, fair, and useful. Through responsible use, AI can shift from a risky novelty to an asset that is beneficial for both clinicians and patients.
— Chris Hutchins serves as the founder and CEO of Hutchins Data Strategy Consulting. Healthcare institutions benefit from his expertise in developing scalable moral data and artificial intelligence methods to maximize their data’s 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.
