AI and Healthcare: The Ultimate Solution for Health Equity

AI and Healthcare: The Ultimate Solution for Health Equity

In the simplest sense, healthcare benefits from applying machine learning algorithms and similar cognitive technologies to analyze and act on medical data. A notable AI use case in the medical field is the application of AI in radiology and its impact on health equity.

Peer review is the standard method for double-checking imaging results and ensuring an accurate clinical diagnosis. However, only 3–5% of all the diagnostic images receive a second read when done through the peer-review process. In this case study on screening for lung nodules, AI runs in the quality workflow, acting as a safety net in the background, performing a second read on all images, and flagging anything that is undocumented. This is one of the easiest ways to integrate AI, as the quality workflow doesn’t interrupt the daily work of the radiologist.

In the recent article, Is AI the Ultimate QA? published by the Journal of Digital Imaging, experts suggest “…a different approach to utilizing the technology, which may help even radiologists who may be averse to adopting AI. A novel method of leveraging AI combines computer vision and natural language processing to ambiently function in the background, monitoring for critical care gaps.”

The use of AI in radiology is long overdue. The technology’s ability to gather and process data

and provide well-defined and accurate outputs to end-users can help achieve equitable healthcare outcomes. AI helps radiologists rapidly analyze images, better understand the patient’s condition, and increase their clinical role in providing patient care.

Artificial intelligence platforms are recognized as quality assurance tools in the healthcare setting. By analyzing patient data and other relevant information, enterprise AI can help healthcare professionals reduce medical errors. Once deployed, an AI algorithm can review all diagnostic imaging, working to minimize medical errors while ensuring equity in healthcare.

The Best Way to Improve Care Quality

Issues that cause medical errors and subsequently reduce the quality of care include low staffing levels, fatigue and burnout, and the burden of administrative work. AI adoption can play a significant role in eliminating these issues and promoting health equity. To start, AI supports the diagnosis, characterization, and monitoring of patients using real-time data.

Another key benefit of AI in healthcare is its ability to reduce the burden on practitioners while improving accuracy. An AI platform such as that from Ferrum Health comes with tools designed to scan for medical errors, optimize data collection, archive patient records, and provide timely access to results and analytics. With AI tools in place, medical professionals can proactively manage patients throughout the care journey.

AI Implications for Healthcare Professionals

With the uptake of AI in healthcare, there have been fears that the technology might replace medical practitioners. However, these fears are unfounded. On the contrary, AI applications are meant to augment the healthcare professional’s role. For instance, the best radiology AI enables practitioners to make more accurate and timely medical predictions rather than replacing their work.

AI can transform how healthcare is delivered. According to a recent EU report, the benefits of AI in healthcare go beyond improving outcomes. Incorporating an AI platform into your healthcare system enhances the patient experience and enables them to conveniently access healthcare services.

AI has infinite applications in healthcare. Whether you want to use the technology as a quality safety net to prevent provider burnout or manage administrative functions, the possibilities are endless. With the uptake of technology in healthcare, AI adoption should be a key priority for all healthcare stakeholders.

Sutter Health’s Chief of Digital Health, Dr. Albert Chan, says “Artificial intelligence is one way we can enhance our digital capabilities in healthcare. It can complement the work of our clinicians – enhancing their ability to care for patients. Our work with Ferrum helps illustrate this approach.”

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Simplifying Healthcare AI - Ferrum's AI Hubs enable clinicians to focus on their patients and the care they need, not complicated technology deployment.

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