Electronic Health Records (EHRs) have become a key part of healthcare management. In the United States, the shift to EHRs has been mainly driven by the need for better patient data management, improved access, and better decision-making. However, much of the data in EHRs is unstructured and challenging for healthcare professionals to analyze. Natural Language Processing (NLP) techniques are increasingly used to address these issues through information extraction and classification. This article presents case studies demonstrating the application of supervised and unsupervised learning methods in EHR data analytics.
EHRs are digital records containing patient history, diagnostic tests, medications, treatment plans, and other relevant health information. Estimates suggest that about 80% of healthcare documentation consists of unstructured data, including clinical notes, observational data, and lab results. This type of data, while informative, poses challenges for analysis and processing without advanced computational methods.
In response, healthcare organizations are increasingly using NLP and machine learning (ML) algorithms. These algorithms can convert EHR data into structured information usable for predictive analytics, clinical support, and targeted care.
Natural Language Processing enables machines to understand and use human language. In healthcare, NLP automates the extraction and classification of important information from EHRs. This process reduces the time specialists spend reviewing large documents, which helps improve productivity and patient care quality.
In a significant case study at a large hospital in the United States, supervised learning algorithms were used to streamline the extraction of medication information from clinical notes. Before this system was implemented, healthcare professionals struggled to access safe medication lists due to extensive EHR documentation. To address this challenge, a dataset of clinical notes was created, including annotated examples of medication prescriptions, dosages, and patient allergies.
The supervised learning model was trained on this dataset, allowing it to learn to identify and classify relevant information based on its inputs. The final model achieved high accuracy in identifying medication orders, leading to a notable reduction in medication administration errors.
Healthcare professionals at this facility reported that automating medication extraction improved record accuracy and reduced their workload. This enabled staff to dedicate more time to patient care, enhancing their interactions with patients and improving health outcomes.
In another case study, a healthcare provider ran a health-monitoring program to identify patients at high risk for chronic conditions. Using unsupervised learning techniques, the organization analyzed a broad dataset of EHRs from thousands of patients. The lack of pre-labeled data was initially a challenge, but advanced clustering algorithms revealed hidden patterns within the dataset.
Through clustering, the organization successfully identified distinct patient groups based on lifestyle factors, medical history, and demographics. This segmentation enabled healthcare professionals to craft tailored intervention strategies that catered to each group’s specific needs.
For instance, one analysis identified a group of patients who were mostly sedentary and had multiple risk factors for heart disease. Healthcare professionals could implement targeted health education programs and preventive measures specifically for this group, leading to improved patient outcomes.
Although the case studies show positive results, integrating NLP into daily clinical practice has challenges. Fewer than 5% of NLP applications in healthcare have successfully integrated into routine workflows. This shows the need for organizations to address barriers that limit the effective use of advanced data analytics.
Training and fine-tuning NLP models can enhance their accuracy and applicability. Some studies suggest that NLP systems perform optimally with extensive datasets, continually learning and refining their processes. Engaging in efforts to ensure diverse training data is crucial for effective model development.
Implementing NLP benefits data use and helps manage EHR burnout among healthcare providers. EHR burnout comes from repetitive administrative tasks that can overwhelm professionals.
As healthcare systems face resource and productivity pressures, physicians and nurses often express frustration with traditional EHR systems’ inefficiencies. By leveraging NLP to automate clinical note summarization, extract necessary information, and identify trends, organizations can relieve staff from tedious tasks, allowing them to focus on patient care.
With advancements in machine learning and artificial intelligence, the future of NLP in healthcare looks promising. Developments may include integrating large language models capable of processing vast amounts of historical text and offering predictive analytics tools that enhance clinical decision-making.
As the healthcare field shifts toward value-based care, the role of automated data extraction and classification is expected to expand. Organizations that utilize NLP to optimize EHR systems may see improvements in patient care, reduced administrative burdens, and better workflows.
Incorporating AI into healthcare processes can lead to more efficient operations. As shown in earlier case studies, automating information extraction with NLP allows for quicker access to critical data and improves the accuracy of medical data analysis.
Healthcare administrators and IT managers can benefit from AI-driven tools that simplify EHR data management. Machine learning can identify predictable clinical data patterns, providing clinicians with timely insights for decision-making.
By applying NLP techniques, organizations can support population health initiatives, automate patient trend monitoring, improve personalized care plans, and ensure proactive health management. AI-powered automation adds value by optimizing resource allocation and enhancing patient engagement.
AI tools can serve as intelligent assistants, aiming to reduce healthcare providers’ administrative burden. These tools can recognize and streamline complex interactions within clinical documentation, including the processing of negative findings and conditions. Understanding negation is key for accurate data interpretation and helps clinicians stay informed about patient conditions and potential diagnoses.
Additionally, intelligent assistants within EHR systems can help manage large volumes of clinical data, automate workflows like clinical note summarization, identify critical alerts, and enhance communication among healthcare teams. This allows quicker access to relevant patient data, supporting informed care delivery and efficient workflows.
The combination of information extraction and classification using supervised and unsupervised learning offers opportunities for healthcare organizations. By integrating NLP and AI technologies, administrators and IT managers can improve EHR data processing, paving the way for better patient outcomes and optimized workflows. Future advancements will further illustrate NLP’s potential in making healthcare more efficient and focused on patients.
As healthcare systems handle the complexities of unstructured data in EHRs, adopting innovative solutions that use advanced data analytics will be crucial for organizations striving to remain competitive.
The article focuses on natural language processing (NLP) techniques applied to electronic health records (EHR) in clinical research and practice.
NLP has the potential to revolutionize clinical research by automating the analysis of unstructured free text in EHR.
Despite its potential, relatively few NLP applications have transitioned into real-world clinical practice.
The article aims to introduce NLP methodologies for EHR analysis, bridging the gap between NLP experts and clinical researchers.
The two major classes mentioned are statistical methods and artificial neural networks (ANNs).
Case studies illustrate tasks involving information extraction and classification/prediction using supervised and unsupervised approaches.
The article discusses state-of-the-art large language models and future directions for research in NLP for EHR analysis.
Benjamin Clay and Henry I. Bergman are the joint first authors of the article.
The article provides clinicians with an understanding of NLP techniques relevant to EHR analysis, facilitating engagement in this evolving research area.
The article is published in ‘Computers in Biology and Medicine’ in April 2025.
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