Countless potential benefits of AI have been touted, from helping improve outcomes based on clinical imaging, to helping find sense in large datasets.
Artificial intelligence has already found valuable applications in healthcare, and quality data will be vital in its expansion.
What is AI beyond a buzzword? Artificial intelligence, which includes many types of algorithmic and statistical software, has become the next big thing. Yet its promise of revolutionary advances isn’t just hype. The FDA already has authorized close to 1,000 AI-enabled medical devices, and hundreds of drugs submitted for the agency’s approval used AI in their discovery and development.
Yet, the question remains: Is AI truly intelligent, or is it merely a sophisticated tool? More critically, will it replace healthcare workers, augment their expertise, or become just another item in the clinician’s ever-expanding toolkit?
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Practical Applications of AI in Healthcare
Countless potential benefits of AI have been touted, including improved clinical imaging analysis, enhanced polyp detection, and the ability to link medical history, symptoms, and treatment to patient outcomes, providing valuable insights for better care and improved results. Although the jury is still out, it appears that, for many use cases, AI tends to perform best when it is used as an adjunct to clinical expertise, not as a replacement.
AI can be particularly helpful in terms of administrative tasks. Take, for example, electronic health records (EHRs). Initially touted as a tool to streamline workflows and enable data-driven insights, EHRs have instead become synonymous with inefficiency and fragmentation, and have led to physicians spending more than a third of their clinical time on chart review, taking time from patient interviews and physical examinations.
To prevent AI systems from exacerbating issues with EHRs, they must easily integrate into existing clinical workflows, and naturally enhance physician decision-making, not challenge it. For example, studies have demonstrated that an AI designed to respond to patient messages can draft responses that can be edited by physicians to be more empathic, ultimately leading to improved patient outcomes and lower physician workload. Conversely, poorly designed and integrated AI can add to a physician’s workload and increase the risk of burnout.
Ethical Challenges of AI
Despite its promise, AI presents many ethical challenges, particularly stemming from a lack of appropriate regulation and oversight. AI development has even been referred to as the Wild West. The use of unchecked algorithms may amplify engineers’ inherent biases and prejudices.
Even outside of medicine, the algorithms that form the basis of AI systems have had issues with inherent bias. In one judicial case, algorithms used ZIP codes to make unappealable probation decisions, with no single authority taking ownership of the algorithm or decisions made by it.
It’s the same in the medical world. For instance, early AI-based models for the detection of skin cancer have not been fully trained on people of color, leading to underdiagnosis of cancerous lesions.
How can inherent biases be mitigated? Diversity and transparency must be integrated into all steps of the AI process, from collection of data to monitoring results and ensuring accountability. AI systems should supplement, not supplant clinicians.
The Role of Data in Using AI for Clinical Decision Support
Given the issues surrounding artificial intelligence, the path to its use in the future lies in the breadth, depth, and quality of its data. Comprehensive data is necessary to enable unbiased machine learning, identify opportunities for meaningful improvement, and establish a foundation for pragmatic solutions that are both cost-effective and clinically efficacious.
d2i’s suite of analytics solutions is at the forefront of this effort, prioritizing data integrity and quality, and delivering actionable insights that inform better decision-making. Physicians, as both scientists and caregivers, are uniquely positioned to scrutinize analytical outputs, ensuring they align with clinical realities and improve outcomes.
By addressing variation, highlighting performance gaps, and promoting transparent feedback loops based on analytics and AI, physicians can empower healthcare organizations to achieve continuous improvement in cost, quality, and outcomes.
Ultimately, the integration of AI into healthcare must be deliberate, collaborative, and data-driven. A successful and ethical AI implementation hinges on the close collaboration of clinicians and data scientists. d2i is leading the way by delivering solutions that bridge the gap between data and decision-making. Contact d2i to learn how our suite of analytics solutions can help you harness the power of AI and achieve continuous improvement.