How AI Is Solving Medical Errors

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.

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.

Medical errors, including errors of omission, are one of the leading causes of death in the U.S.

The rate of missed diagnoses is estimated to be between 10% and 15%, but less than 10% are reported, according to PubMed. The impact of missed diagnoses on patient outcomes is significant and suggests a solution is needed. This is where oncology AI solutions can support physicians in their daily practice and directly impact patient care.

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 – Omissions, Misdiagnosis, 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 medical 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 misdiagnosis. 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.

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Ferrum Health

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