Clinical NLP — Enhancing EHR Processing in Healthcare

Maruti Techlabs
4 min readJul 24, 2023
Clinical NLP — How to Apply NLP for EHR Optimization

Care coordination for health providers has been eased mainly with the help of EHRs (Electronic Health Records). But with 70% of clinically relevant data being stored in practitioner notes in the EHRs, care coordination needs more than just EHRs.

Due to the lack of structure and uniformity in such practitioner notes, drawing insights from the data stored in EHRs is still a major challenge. It can be primarily solved by EHR intelligence and EHR optimization. And this is where NLP in healthcare comes in.

NLP (Natural Language Processing) provides a computational approach to synthesizing such content. Simply put, clinical NLP helps unlock valuable insights in EHR data, improving patient outcomes and overall healthcare quality.

What is an EHR (Electronic Health Record)?

An Electronic Health Record (EHR) is a digital version of a patient’s health information. It contains comprehensive and up-to-date records of a patient’s medical history, diagnoses, medications, allergies, test results, and other important health-related information.

EHRs are designed to provide a more comprehensive view of a patient’s health status by integrating data from various sources, such as hospital records, physician notes, lab test results, and imaging studies.

Why Do We Need NLP for EHRs in Healthcare?

NLP for EHR (Electronic Health Record) or clinical NLP is beneficial as a significant amount of vital medical data is stored in unstructured free text fields within EHRs.

Since these are unstructured free text, there needs to be more standardization of content, format, or quality. Hence, converting these unstructured free text fields into valuable, quantifiable data is challenging.

Clinical NLP can analyze free text fields in electronic health records by training algorithms to identify important information based on patterns and rules learned from many EHR notes.

Additionally, NLP techniques can capture unstructured data, analyze the grammatical structure, and determine the meaning of the information.

To summarize, clinical natural language processing is a promising approach to rapidly analyzing massive amounts of EHR notes, extracting quantitative data, and providing insights.

How Can Clinical NLP Benefit EHR Processing?

How Can Clinical NLP Benefit EHR Processing?

1. Extracting Data from Medical Notes

Most medical writing is free-form, full of jargon and acronyms, and may have typographical or spelling errors. Since there is no universal vocabulary of medical acronyms, their meanings are only sometimes clear or easily understandable.

Algorithms utilizing clinical NLP in EHR automation extract vital information from clinical notes, such as diagnoses, recommendations, timetables, and false symptoms.

2. Categorizing Clinical Data

Once the relevant information is extracted, it is organized according to categories like patient demographics, medical history, and current medical issues for easier access and analysis.

This can facilitate systematic research and help healthcare providers make more informed decisions based on the available data.

3. Summarizing Text

Clinical NLP can also summarize vast amounts of data extracted from clinical notes.

With the ability to analyze the grammatical structure and categorize the text, NLP algorithms can create concise summaries of clinical notes for a group of patients. This can help researchers and physicians quickly identify medical symptoms and treatments.

4. Enhancing Phenotyping Potential

A phenotype is the outward manifestation of a genetic characteristic in an organism, which can include physical appearance, behavior, and bodily functions. Doctors use phenotyping to group patients and compare data easily. Structured data is preferred for phenotyping because it is easy to extract and analyze.

However, despite the preference for structured data, around 80% of all patient data still needs to be structured, making it difficult to use for phenotyping. Clinical NLP can extract and analyze unstructured patient data. This facilitates the creation of phenotypes for patient groups with a large amount of data.

5. Data Visualization for Chart Analysis

Chart reviews often necessitate the knowledge and experience of a Registered Nurse (RN) because they include reading through several reports on a patient to get a comprehensive understanding of the patient’s medical history.

To help doctors quickly understand a patient’s medical history, clinical NLP summarizes and visually represents information for the chart review.

6. Identifying Patient Groups for Testing

Patients for clinical trials have typically been selected by more time-consuming and error-prone techniques, such as manually checking medical records. If the condition is uncommon, there are even fewer patients available.

NLP technology integrated within EHRs is efficient for e-screening and patient cohort identification, as it recognizes keywords and displays relevant information to experts.

7. Supporting Administrative Tasks

Clinical NLP aids in healthcare administrative tasks, like making follow-up calls after a patient visits to a doctor.

By extracting essential information from reports and creating patient profiles, clinical NLP can assist in determining appropriate follow-up recommendations.

8. Improving the Standard of Care

Healthcare practitioners can effectively manage patient care by electronically communicating health information across multiple systems.

EHRs facilitate easy retrieval of medical history and make the invoicing and coding processes more streamlined.

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