This is no new information or statistic that over 80% of the healthcare data available for stakeholders is unstructured. The rise of EHRs has exponentially made it easier for healthcare professionals to access, store, and modify interoperable data for their purposes. To give you a brief example of the different types of unstructured data available on EHRs, here’s a quick list:
Clinical notes from patients, prescriptions, diagnoses, descriptions of symptoms, treatments, and more
Discharge summaries involving insights on a patient’s hospitalization, medications, diagnosis, prognosis, follow-up care recommendations, and more
Pathology and radiology reports
Medical images such as X-Rays, MRIs, CT Scans, Ultrasounds and more
However, conventional methods of extracting critical information from EHRs have been predominantly manual, involving human hours in identifying individual parameters, information, and attributes for insights. But with the increased use of Artificial Intelligence (AI) in healthcare, specifically AI-powered clinical NLP models, it has become easier for healthcare professionals to locate and extract unstructured data within EHRs.
In this article, we will shed light on why it’s beneficial, how this can be done seamlessly (in AI mode), and the challenges in the process as well.
Advantages Of Using NLP To Extract Clinical Information From EHRs
Increased Efficiency
Humans are prone to errors and often encounter issues with time management, resulting in delayed deliveries of healthcare data or timely delivery with compromised quality. By automating the task with AI-mode NLP models, such instances can be mitigated. The automation reduces manual labor, speeds up extraction of entities such as medications, labs, allergies, etc., enabling clinicians & data scientists to focus more on decision-making rather than data wrangling.
Enhanced Data Completeness
Critical insights from unstructured data that might get overlooked by humans can be detected and compiled by AI models when trained on large, diverse datasets. This results in comprehensive databases of inferences and insights that aid in airtight research, innovation, diagnosis, and medical care — especially when models are fine-tuned for healthcare NLP tasks.
Timely Identification of Risks
AI-powered clinical NLP can quickly identify potential risks such as medication interactions or adverse events, allowing for timely interventions. Models powered by predictive analytics techniques and AI in mode of risk detection can even predict the onset of certain hereditary diseases or lifestyle-prone diseases based on available EHR data.
Improved Patient Care
Information extracted through AI-mode NLP supports targeted interventions, personalized treatment plans, and better communication between healthcare professionals. For example, flagging high risk allergies or adverse drug reactions earlier, enabling preventive care.
Enhanced Research Potential
By leveraging AI-driven NLP to extract structured data from vast, unstructured EHRs, researchers gain access to large-scale clinical datasets for epidemiological studies, population health, and discovery of medical insights that would otherwise stay hidden.
Extracting Details From Unstructured EHR Data 101: A Sample Workflow
The process of extracting insights from unstructured EHR data is systematic and must be done on a case-by-case basis. The domain requirements, healthcare organization-native concerns and challenges, purpose-driven applications, and their surrounding implications are subjective and that is exactly why the process should consider such factors influencing your organization and its vision as well.
However, like every approach has a specific workflow or a rule of thumb approach, we have listed a primer for you to refer to as well.
Data Acquisition & Preprocessing: The first step is to compile EHR data containing clinical notes, medication lists, allergy lists, and procedure reports. AI-mode preprocessing includes de-identification, cleaning, normalization, and tokenization to prepare data in consistent formats (text formats, structured vs unstructured).
NLP Processing / AI Model Training: The compiled data is then fed into your NLP algorithms or AI models to analyze the text data, identify key clinical entities such as diagnoses, medications, allergies, and procedures. Training in “AI mode” involves supervised learning, sometimes unsupervised or semi-supervised learning, using labeled datasets.
Information Extraction: Based on whether your model follows supervised or unsupervised learning strategies (or hybrid AI mode), it extracts relevant information about each entity, including its type, date, associated details, severity, dosage, etc.
Validation & Clinical Oversight: Once the AI-powered model extracts information, it must be validated by healthcare professionals for clinical accuracy. Human-in-the-loop systems and expert feedback loops ensure extraction is reliable.
Data Integration & Interoperability: The structured data is then integrated into the EHR system or other relevant databases. Ensuring compliance with HL7 FHIR, other healthcare standards, and supporting interoperability.
Clinical Utilization & Feedback Cycle: The integration enables healthcare professionals to use extracted information for clinical decision-making, research, and public health initiatives. AI mode feedback loops help improve model accuracy over time, adapting to new types of data or linguistic patterns.
Challenges In Leveraging NLP To Extract EHR Data
The task of extracting unstructured data from EHRs is ambitious and can make the lives of healthcare stakeholders simpler. However, there are bottlenecks that could hinder the seamless implementation process. Let’s look at the most common concerns so you can proactively have strategies to tackle or mitigate them.
Data Quality, Variety & Bias: The accuracy of NLP extraction depends on the quality, consistency, and representativeness of EHR data. Different formats, terminologies, incomplete records, or biased samples can degrade AI model performance.
Privacy, Security & Compliance in AI Mode: Measures need to be implemented to ensure patient privacy and data security during NLP/AI-powered processing and storage. Regulatory guidelines like GDPR, HIPAA, etc. must be adhered to. This includes de-identification, secure storage, and access controls.
Clinical Validation & Interpretability: Extracted information requires validation by healthcare professionals to ensure its accuracy and clinical relevance. Complex terminologies, ambiguous phrasing, or rare conditions may confuse models. Also, AI-mode systems must be explainable so clinicians trust them.
Integration, Interoperability & Standards: Extracted data needs to be seamlessly integrated with existing EHR systems and other healthcare IT systems. AI models should support HL7, FHIR, SNOMED, RadLex, etc., to ensure interoperability.
Scalability & Maintenance: In AI mode, systems require continuous retraining, monitoring, and versioning to account for new clinical practices, evolving medical terminology, or changes in documentation style.
Cost & Resource Requirements: Developing, training, validating, and deploying AI-powered NLP systems demands investment in data annotation, expert oversight, computational resources, and qualified personnel.
Final Thoughts
In short, the potential is limitless when you deploy AI-powered NLP to extract healthcare data from EHRs. For fool-proof implementations, we recommend addressing the challenges, enforcing clinical oversight, and ensuring responsible deployment in “AI mode.”
If you’re looking to pave the way for airtight compliance to healthcare data mandates and get the best AI training data for your models, you can get in touch with us. Having been an industry pioneer, we understand the domain, your enterprise visions, and the intricacies involved in training a healthcare-native, AI-optimized clinical NLP model. Reach out to us today.
