Introduction
One of the most important branches of artificial intelligence, NLP, or natural language processing, helps AI systems to interpret, understand, and imitate human language.
As technology advances and spreads across various sectors and industries, NLP in healthcare has proven to be a transformative force in the healthcare industry.
As data breaches and cyber threats have become so common, it is extremely important to ensure the confidentiality and security of patient data to build patient trust. With natural language processing in healthcare, the detection and protection of sensitive patient data has improved exponentially, building the trust of individuals in technology.Â
Healthcare institutions are able to secure patient information with NLP as it has the ability to analyse huge volumes of unstructured data and identify sensitive information which can be protected by implementing data security measures.
In this blog, we will understand the concept of NLP and the various use cases and benefits it has in the healthcare sector.
Understanding Natural Language Processing (NLP)
Let us first understand the concept of natural language processing and the process used to make it efficient and effective.
What is Natural Language Processing (NLP)?
Natural language processing is a branch of artificial intelligence that is responsible for the accurate and effective communication between computers and human beings with the use of natural language.Â
Natural language processing helps computers to understand, interpret, and generate human language in a meaningful and useful manner.
NLP carries out a wide range of tasks, such as text analysis, machine translation, sentiment analysis, speech recognition, etc.Â
Further, NLP in healthcare is extremely important as it helps in the processing of unstructured data (clinical notes and patient records) in order to extract relevant information and derive valuable insights.
How Does NLP Work?
There are various steps and techniques that NLP uses to effectively process and analyse natural language data. Such steps include the following:
- Tokenisation: Tokenisation involves breaking down the words into individual steps or tokens. This helps the NLP system in further analysis by focusing on the individual elements of the text.
- Part-of-Speech Tagging: This step ensures that each token is assigned a grammatical category, such as noun, verb, etc. This helps the NLP system understand the role of each word in a text, which is important for accurately interpreting the entire text.
- Named Entity Recognition (NER): This step involves identifying and classifying entities, such as names, dates, medical terms, etc., within the text. As a result, this step helps in sensitive data detection in healthcare.
- Parsing: Parsing helps the NLP system understand the relationships between words by analysing the grammatical structure of the sentence. As a result, the NLP system is able to understand the structure and meaning of the sentence, leading to effective and accurate information extraction.
- Sentiment Analysis: This step determines the sentiments or emotional tone of the text. In the healthcare industry, sentiment analysis helps to effectively understand patient feedback and experiences.
- Text Generation: This final step helps in the generation of relevant and precise text based on the given input. In the healthcare sector, this step is typically done to generate clinical notes and patient summaries.
The above given steps are the common steps used in the working process of NLP. In some cases, it may require additional steps as well such as machine translation (translating text from one language to another), text summarisation (shortening long documents into relevant and precise summaries), and speech recognition (converting the spoken language into text).
Use Cases Of NLP In Healthcare
Some of the use cases of NLP in healthcare which helps in protecting healthcare data with AI include:
Clinical Documentation
One of the most significant use cases of NLP in healthcare is the automation of clinical documentation. As healthcare providers often spend considerable amounts of time in documenting patient treatments, outcomes, and interactions, natural language processing in healthcare helps to streamline this process.
This is done automatically by extracting relevant information from clinical notes and generating precise and relevant structured documentation.
By automation of clinical documentation, the healthcare providers are able to save their time which can be focused to improve patient care and also reduce the probability of errors, ensuring the accurate recording of critical information.
Patient Data Extraction
Natural language processing helps healthcare institutions extract valuable insights from unstructured patient data.Â
For example, NLP in healthcare can be used to analyse the detailed information of a patient’s medical history, symptoms, and treatment plans given in clinical notes to identify various patterns and trends, such as the effectiveness of certain treatments or the prevalence of certain conditions or symptoms.
As a result, healthcare providers can use this information to make data-driven and valuable insights-based decisions, leading to improved patient care.
Identifying Sensitive Information
Healthcare institutions protect patient data using NLP in healthcare as natural language processing has the ability to automatically identify sensitive information (PHI) within patient records. The sensitive information may include details such as personal identification numbers, treatment plans, medical history, etc.
It is extremely important to use natural language processing for HIPAA compliance, which focuses on the protection of sensitive patient data by complying with data protection regulations.Â
Through tagging and classifying sensitive information, NLP systems are able to flag data that is required to be secured while ensuring that it is not exposed to unauthorized individuals.
Redacting Sensitive Information
After identifying sensitive patient information, NLP in healthcare is used to redact or anonymise sensitive data within patient records. This involves replacing all the sensitive patient information with placeholders or removing it altogether, ensuring patient privacy with NLP.
As a result, it serves as a patient data privacy solution while the data remains useful for analysis and further research purposes.
Redaction is specifically important in cases where patient data is to be shared with third parties, such as researchers or other healthcare providers, as it ensures that patient privacy is maintained.
Benefits Of NLP In Healthcare
Natural language processing is considered to be one of the healthcare data protection techniques that provides numerous benefits, especially in the field of protecting patient data. Some of these benefits are:
Improved Data Security
NLP in healthcare helps considerably improve data security as it automates the detection and protection of sensitive patient information.
As healthcare institutions identify sensitive data within patient records, they can implement NLP-based patient data encryption and access controls as forms of robust security measures in order to protect the patient data.
As a result, there is a reduction in the risk of data breaches and unauthorized access, ensuring that the patient information is kept secure and confidential.
Enhanced Data Integrity
NLP in healthcare is able to enhance data integrity as it has the ability to identify inconsistencies and anomalies within the patient records.
Additionally, natural language processing also helps healthcare institutions to maintain accurate and complete patient records as it ensures that the patient data with the healthcare providers is accurate and reliable.
It is important to maintain data integrity, specifically in the healthcare industry, as it helps to provide high-quality care to the patient while ensuring that the sensitive patient information is handled as per the required regulatory requirements.
Reduced Risk Of Data Breaches
By automating the process of sensitive data detection and protection, NLP in healthcare reduces the probability and risk of data breaches.Â
NLP helps healthcare institutions to identify sensitive information within patient records and apply robust security measures which further helps the institutions to prevent unauthorized access and data breaches.
As a result, there is an improvement in patient information protection and a reduction in the cost of data breaches and regulatory penalties.
Better Decision-Making
As NLP in healthcare provides healthcare providers with timely and accurate information, they are able to make better and insightful decisions. This is because healthcare providers use NLP to identify patterns and trends by extracting relevant information from patient records and clinical notes, leading to informed clinical decision-making.
As a result, there is an improvement in patient care as care is delivered to them in a more effective and efficient manner.
Conclusion
NLP applications in medical data protection have proven to be extremely useful in ensuring patient privacy in the healthcare industry.
By integrating NLP into healthcare, the healthcare industry enjoys several benefits in addition to the detection and protection of sensitive patient information, which it is required to maintain as per several regulations.
By using advanced algorithms and machine learning techniques, natural language processing in healthcare is able to automate the detection and protection of sensitive information which helps to ensure that the patient data remains confidential and secure.
We at CrossML help the healthcare industries implement and integrate NLP in their existing workflows to help them maintain compliance with regulatory and data protection standards, leading to cost savings and improved patient outcomes along with increased patient trust.
FAQs
NLP can be used to detect sensitive patient data by analyzing clinical notes, EHRs, and other unstructured data to identify sensitive patient information. Various techniques like NER are employed through NLP to automatically tag and classify sensitive information while ensuring that it is properly handled to maintain confidentiality.
The best ways to protect patient information using NLP include implementing automatic redaction and anonymisation of sensitive patient data, using advanced encryption techniques as security measures, and using access controls to restrict the access of data to unauthorized personnel. Further, NLP also has the ability to monitor and flag unusual access patterns, which helps the healthcare institution to provide quick responses in case of potential data breaches or unauthorized access.
The various techniques to safeguard patient data with NLP include Named Entity Recognition (NER), data redaction and anonymisation, access control implementation, anomaly detection, and automatic compliance checks.
The key strategies for detecting and protecting patient data with NLP include using NER, applying encryption and access controls, implementing data redaction and anonymisation, monitoring access logs, and conducting regular compliance audits.
