How AI Is Solving Medical Errors
Medical errors are the third leading cause of death and account for nearly 250,000 fatalities annually.
They can occur in any healthcare setting and at any point in the care process.
Artificial intelligence (AI) provides solutions to the medical error dilemma by equipping healthcare professionals
with tools that both improve accuracy and function as a safety net.
Our healthcare system is currently stretched thin, and our healthcare professionals are stressed and facing burnout. Providing patients with the best possible care is always the goal but can be challenging, given the current environment. Technologies such as AI can help practitioners provide healthcare more effectively and prevent medical errors. In addition, AI can manage and analyze patient data in much more efficient ways, ensuring critical information does not go unnoticed that may impact a patient care decision.
The Reality of Medical Errors Today
Preventable medical errors affect up to 7 million patients and cost over $20 billion each year, but the cost is more than just financial. Medical errors compromise patient care and lead to poor patient outcomes and even death.
While the definition of what constitutes a medical error has not been universally defined, there are two major types of errors that should be noted:
- Errors of omission: occur as a result of actions not taken. Example: Failing to prescribe a proven medication with significant benefits for an eligible patient.
- Errors of the commission: occur as a result of incorrect action taken. Example: A medication to which a patient has a known allergy is prescribed.
While errors of omission are more challenging to recognize than errors of commission, they likely represent a more significant problem. In other words, there are likely many more instances in which the provision of additional diagnostic, therapeutic, or preventive modalities would have improved care than there are instances in which the care provided quite literally should not have been provided.
The Dilemma of Radiographic Errors : Omissions, Missed Diagnoses, and Bias
Errors of omission are found across healthcare, with the most common being obtaining insufficient information from histories and physicals, inadequacies in diagnostic testing, and patients not receiving needed medications. In radiology, this could include a completely missed diagnosis or a delay in diagnosis and treatment due to discrepancies and undocumented findings on radiology images.
Bias in healthcare also plays a role in radiographic errors. This includes both unintended human bias and the potential for bias in AI if not properly applied. Health equity implies that everyone should receive the same standard of healthcare, regardless of personal characteristics, identities, or traits such as race or gender. Unfortunately, certain implicit biases have been proven to exist and can have detrimental effects on the quality of healthcare a person receives. Bias can lead to lower quality healthcare among different groups of people.
AI helps to enhance health equity by addressing and combating known biases; however, AI providers should also be mindful of the potential of bias in AI.
Real-world data provides a way to understand how AI bias emerges, how to address it, and what is at stake. For example, a recent study focused on a clinical scenario where AI systems are built on observational data taken during routine medical care. Often such data reflects underlying societal inequalities in ways that are not always obvious. However, if said biases are engineered into AI models and algorithms, AI bias is created and could have devastating results on patients’ quality of care and overall well-being.
A notable benefit of AI in healthcare is its battle against missed diagnoses. Enhancing the accuracy of medical diagnoses requires collaboration among healthcare stakeholders – this is where AI plays a role. Partnering with an enterprise AI company can transform how healthcare professionals provide care. Moreover, AI-augmented healthcare services have the potential to minimize the current burden seen across healthcare.
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.
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, performs a second read on all images, and flags 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 a quality assurance tool 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.
Using AI to Improve Patient Care
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 Ferrum’s AI Hub 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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