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Natural language processing in healthcare

Farah Amod July 02, 2024

Definitions

Natural language processing (NLP) is a rapidly changing field of artificial intelligence that is transforming the healthcare industry. Using advanced algorithms to extract meaningful insights from unstructured data, NLP empowers healthcare professional experiences and drives innovation. A study titled, A Systematic Review of natural language processing in Healthcare states,” NLP facilitates the easy access and retrieval of valuable and meaningful healthcare information. In addition, NLP has the potential of reducing medical costs and errors.”

Understanding natural language processing in healthcare

Natural language processing is a branch of AI that enables computers to understand, interpret, and generate human language, whether spoken or written. In healthcare, NLP is particularly valuable as it can analyze unstructured data generated within the industry, including electronic health records, clinical notes, patient feedback, and medical literature.

By applying sophisticated NLP techniques, healthcare organizations can unlock the hidden insights buried within these data sources, leading to more informed decision-making, streamlined workflows, and improved patient outcomes. From optical character recognition to named entity recognition and relationship extraction, NLP offers a versatile toolkit to tackle various healthcare challenges.

Enhancing clinical documentation and decision-making

One of the primary applications of NLP in healthcare is streamlining clinical documentation and supporting physicians in their decision-making processes. Doctors often find themselves burdened by manually entering patient notes into electronic health records (EHRs), which can contribute to burnout and detract from direct patient care.

NLP-powered speech recognition tools such as Nuance's Dragon Medical One, can dramatically reduce the time spent on clinical documentation. These solutions enable physicians to dictate their notes directly into the EHR, with the NLP engine automatically transcribing and structuring the information.

Beyond clinical documentation, NLP can also assist healthcare providers in making real-time, data-driven decisions. By analyzing the wealth of information in EHRs and other clinical documents, NLP algorithms can identify patterns, predict patient outcomes, and provide personalized treatment recommendations.

Revolutionizing medical coding and billing

Another application of NLP in healthcare is medical coding and billing. Traditionally, this process has been labor-intensive and error-prone, as human coders manually review clinical notes and assign the appropriate medical codes for insurance claims.

NLP-powered computer-assisted coding tools can automate this process, deriving the necessary information from doctor notes and patient records and providing the corresponding insurance codes. Solutions like the 3M 360 Encompass System use NLP to aggregate and analyze patient documentation, offering auto-suggested codes and detailed review and approval tools.

By streamlining the medical coding process, NLP can help healthcare organizations maximize their revenue reimbursement and reduce the risk of coding errors. This benefits the provider and enhances the healthcare system's overall efficiency, ensuring that patients receive the appropriate coverage and care.

Accelerating clinical trial recruitment and management

Natural language processing is also making waves in clinical trial management, where it is helping to address the persistent challenge of patient recruitment. Studies have shown that up to 80% of clinical trials are delayed or terminated due to the inability to enroll sufficient participants.

NLP-powered solutions, such as the one developed by Inspirata, can mine clinical documents and unstructured data to identify eligible trial candidates. By transforming complex eligibility criteria into simple questions, these tools empower potential participants to quickly filter through numerous clinical trials and find the ones that best suit their needs.

Moreover, NLP can also assist in the design and site selection of clinical trials, helping researchers to parse through a wealth of information, including news feeds, patents, medical literature, and similar trial descriptions.

Harnessing patient sentiment analysis

Natural language processing in healthcare extends beyond clinical applications and can also be used to gain valuable insights into patient sentiment and experiences. NLP-powered tools can identify aspects of healthcare delivery that are frustrating patients and pinpoint areas for improvement by analyzing customer reviews, social media posts, and survey responses.

For instance, comments like "It took me 30 minutes to get through to the receptionist on the phone” can provide valuable feedback on the administrative staff's performance. Similarly, sentiment analysis of survey results can help healthcare facilities understand how patients feel about their care, enabling them to implement targeted improvements.

In a recent study, researchers developed an NLP solution that could classify patient survey responses into positive and negative sentiments with a 90% accuracy rate, matching the manual classification performed by human experts.

Advancing drug discovery and pharmacovigilance

The applications of natural language processing in healthcare extend even further, with significant implications for drug discovery and pharmacovigilance. By sifting through medical literature, patient data, and other unstructured information, NLP can help scientists identify the biological origins of diseases and discover potential drug candidates.

Solutions like the one offered by Lymba use NLP to analyze data from various sources, including gene cards, PubMed publications, and clinical trial documentation. The tool then builds an ontology of existing drugs, indicating promising research areas for discovering new, more effective treatments.

Furthermore, NLP can also be employed to analyze social media feeds and patient records to identify adverse drug effects, correlating side effects with dosage and frequency of use. This can inform pharmacovigilance efforts, ensuring the safety and efficacy of medications throughout their lifecycle.

Overcoming the challenges of NLP implementation in healthcare

While the benefits of natural language processing in healthcare are undeniable, the successful implementation of this technology is not without its challenges. Healthcare organizations must work through various obstacles, from the industry's specific language requirements to issues related to human language complexity and concerns around bias and explainability.

The healthcare domain is known for its extensive use of jargon, acronyms, and abbreviations, which can pose a hurdle for general-purpose NLP algorithms. To address this, healthcare organizations must invest in retraining and fine-tuning these models to align with their medical professionals' unique vocabulary and writing styles.

Additionally, NLP systems must be capable of handling the nuances of human language, such as inferences, semantics beyond keywords, and negation. Failure to address these complexities can lead to inaccurate interpretations and suboptimal decision-making.

Bias and explainability are also concerns when deploying NLP in healthcare. Healthcare algorithms have been known to exhibit biases, such as underestimating the severity of illness in certain patient populations. To mitigate these risks, healthcare organizations should prioritize the use of explainable AI, which provides transparency into the decision-making process, and curate datasets that are designed to detect and address specific types of bias.

Implementing natural language processing in healthcare

Integrating natural language processing into healthcare operations requires a well-structured, strategic approach. Healthcare organizations can follow these steps to ensure a successful NLP implementation:

Identify potential use cases: Thoroughly analyze the pain points and challenges within your healthcare organization, and determine the specific areas where NLP can provide the most value, whether it's clinical documentation, medical coding, patient sentiment analysis, or drug discovery.
Build or acquire an NLP solution: Decide whether to build a custom NLP solution or opt for an off-the-shelf tool. Custom-built solutions offer greater flexibility and integration with legacy systems but may require more resources and technical expertise. Off-the-shelf solutions can be a more cost-effective and quicker-to-deploy option but may require more adaptation to fit your organization's unique needs.
Prepare a training dataset: Even if you choose an existing NLP solution, you will likely need to retrain the model to optimize its performance for the healthcare domain. Curate a detailed, bias-free dataset that accurately represents your target population and clinical practices.
Train and validate the model: Thoroughly test the NLP model for accuracy, bias, and integration with your existing workflows. Ensure the algorithms comply with relevant healthcare regulations, such as the FDA and GDPR.
Integrate with existing systems: If you opt for a custom-built solution, work closely with your technology partner to integrate the NLP tool seamlessly with your legacy systems, ensuring a smooth transition and minimal disruption to your operations.
Monitor and continuously improve: To maintain their effectiveness, the NLP algorithms must be monitored and adjusted on an ongoing basis. Regularly audit the models, address any emerging biases, and update the solutions to reflect changes in your healthcare sector.