Major Challenges of Using NLP in Healthcare

Research and Markets project a remarkable growth for NLP in healthcare, with an estimated value of $11.5 billion by 2030, expanding at a CAGR of 18.5% from 2023. While its integration into medical practices holds a great potential, it also presents challenges. Let’s check them! 

SPsoft has vast experience in developing and integrating robust natural language processing (NLP) solutions for healthcare organizations. Fill out the contact form to discuss the details.

The Industry’s Unique Linguistic Characteristics

Medical professionals often employ a specific language in clinical documentation, characterized by dense medical jargon, acronyms, and abbreviations that may lack clear context. Almost ⅓ of clinical abbreviations in the Unified Medical Language System Metathesaurus have multiple meanings, adding to the complexity. 

Moreover, terms like "discharge" can have many interpretations, such as referring to a patient's release from the hospital or bodily fluids, depending on the context. When expanding NLP to analyze patient feedback on social media, these systems must adapt to the casual language and emoticons that people use to express emotions.

The successful integration of NLP in healthcare hinges on a deep understanding of the unique linguistic challenges. This understanding is best achieved through the collaborative efforts of professionals and NLP experts, underscoring the importance of each role in this process.

Concerns Associated with Human Language Complexity

Deploying NLP in healthcare is challenging due to the nuanced nature of human language:

• Inferences. NLP models must infer unstated information from the text. For example, mentioning a "relative at bedside" might imply that a patient has social support, even though it is not explicitly stated.
• Understanding Semantics. Accurately grasping the meaning behind phrases is crucial. For instance, NLP must distinguish between "wife helps patient with medication" and "patient helps wife with medication," which have significantly different implications.
• Handling Negation. Correctly interpreting negations is vital, especially when medical notes use them to indicate the absence of conditions, such as "a brain malignancy was ruled out.” It means no tumor was found.

When it comes to NLP solutions, you should consider their sophistication in handling the complexities of healthcare applications. Choosing sophisticated systems is a key factor in ensuring that the nuanced needs of healthcare are adequately addressed.

Biases and Explainability Issues

Like all AI technologies, NLP systems are prone to biases, which can arise during training on unbalanced datasets or through continuous learning. A notable example is gender bias, where algorithms might stereotype professions based on gender. That is problematic in healthcare, where the accuracy and impartiality of patient care are paramount.

Addressing biases in “black box” AI models, such as deep learning models, which do not explain their outputs, is particularly challenging. One approach to mitigate this issue is to use explainable AI, which requires NLP systems to justify their decisions, making it easier to check for and correct biases.

Integrating AI into healthcare also involves several overarching challenges:

• Integrating with Legacy Systems. Many healthcare institutions rely on outdated systems incompatible with modern AI technologies.
• Lacking Adequate Training Data. Healthcare facilities often need help to provide sufficient, high-quality data that accurately represents their patient populations. Collaborating with other institutions to share data or investing in robust data collection efforts can help address this issue.
• Considering Ethical and Moral Concerns. The use of AI in healthcare raises ethical questions, including who is ultimately responsible for treatment decisions and outcomes. It also raises concerns about patient privacy, the potential for AI to replace human judgment, and the impact on medical professionals' roles and responsibilities.

Are you curious about leveraging NLP techs to improve clinical decision-making processes? Write to me at mlazor@spsoft.com, and we will gladly assist you with your healthcare project.

After all, NLP is crucial in enhancing clinical decision support, streamlining electronic health record management, and improving patient interaction by simplifying medical terminology. Thus, it facilitates better communication between patients and healthcare providers.