Introduction
As the old saying goes, “With great power comes great responsibility.” The advancement of technology gave humans a lot of power through the use of artificial intelligence, along with the responsibility of maintaining user’s data privacy as well as security.
As the technology around artificial intelligence expanded, it explored the benefits of generative AI and natural language processing (NLP) that helped machines to accurately understand and interpret human language and generate relevant responses to the users.
Like every other industry, natural language processing in healthcare has proved to be extremely beneficial to provide improved patient outcomes.
Further, with the help of natural language processing in healthcare, professionals are protecting patient information by automating the process of Protected Health Information (PHI) removal from Electronic Health Records (EHRs).
In this blog, we will explore how NLP in healthcare is used to automate the process of PHI removal and secure patient data processing, along with the benefits of NLP in healthcare.
PHI Removal With Natural Language Processing In Healthcare
Protected health information (PHI) is the information that is specific to patients in the healthcare industry and is used to identify specific patients.
This includes information such as names, addresses, various dates (date of birth, death, admission or discharge date), social security numbers, medical records, etc.
Fig. 1
In a nutshell, PHI contains any health information that can be linked to an individual to identify them, whether it is stored and transmitted electronically, orally, or on paper.
Natural language processing in healthcare is used as a healthcare data privacy solution as it uses machine learning for PHI removal which is necessary for HIPAA compliance automation. This is because without safeguarding health information of individuals instances of identity thefts, discrimination and other data security harm to patients can increase exponentially.
Let us now understand what PHI removal is, why it is necessary, and how natural language processing in healthcare helps to automate the PHI removal process.
What is PHI Removal, and Why is it Necessary?
PHI removal or de-identification is the process that involves stripping patient documents of the information that can lead to the identification of patients.
By removing PHI, healthcare institutions ensure that patient data can be used for various secondary purposes, such as data research and analytics, without violating patient privacy.
PHI removal is necessary due to compliance with legal, ethical, and practical considerations that help to build and maintain public trust in the healthcare industry.
As per the legal considerations of HIPAA, patients’ protected health information needs to be protected to ensure that there is no unauthourised access or disclosure of patient information.
According to ethical considerations, it is the duty of healthcare professionals to ensure patient data privacy.
Further, as per the practical considerations, by removing PHI, healthcare departments can use the healthcare data for research and analysis while ensuring data privacy in healthcare.
Role of NLP in PHI Removal
The healthcare industry uses NLP for healthcare data security, which helps in the privacy-preserving of healthcare data.
By using advanced NLP techniques for PHI removal, natural language processing in healthcare is able to automate the identification and removal of PHI from various unstructured texts, such as discharge summaries, clinical notes, or other medical records.
To achieve PHI removal, NLP models are trained to recognise PHI-related patterns, which leads to PHI data masking, hence, removing the PHI data with the help of automated de-identification tools used in the NLP algorithms.
Through the use of natural language processing in healthcare for PHI removal, healthcare institutions can gain various benefits, such as:
- Accuracy and efficiency
- Cost-effectiveness
- Scalability
- Compliance and security
- Improving data utility
Process Of PHI Removal Using NLP In Healthcare
The various steps followed in the process of PHI removal using natural language processing in healthcare include:
Fig. 2
Data Collection and Preparation
The first step in the removal of PHI using natural language processing in healthcare is the collection and preparation of data.
For this, the healthcare providers gather all the relevant documents that may contain protected health information of patients, like various medical records, clinical notes, lab reports, discharge summaries, etc.
Once the documents are gathered and collected, they are stored in a digitised and secured database for further process steps.
For data preparation, the stored data is cleaned to remove any inconsistencies or errors that may impact the performance of the NLP model.
This first step of data collection and preparation is extremely important as it sets the base and foundation for the correct and efficient removal of PHI using natural language processing in healthcare.
NLP Model Training and Development
The second step of PHI removal is to develop and train the natural language processing model for effective PHI removal.
In this step, the healthcare providers select an appropriate machine learning algorithm and train it on a labelled dataset that includes various examples of protected health information.
During the training, the NLP model is trained in order to recognise the various patterns associated with PHI, and hence the model learns to differentiate between PHI and non-PHI texts.
Further, various advanced techniques like deep learning are used in this step to improve the performance of the model.
PHI Identification and Removal
After the NLP model is trained, the natural language processing model is applied to all the collected healthcare documents to identify and remove protected healthcare information (PHI).
This is done in the following steps –
- The NLP model scans the texts of the collected data.
- After scanning, the model detects PHI elements based on the patterns that it learned during the training process.
- The identified PHI elements are then removed/masked to de-identify the text.
The process mentioned above is an automated process that leads to rapid and consistent PHI removal across huge volumes of datasets.
Further, in order to maintain high standards of data privacy and protection, the accuracy of the identification and removal of protected health information is continuously monitored and refined.
Post-Processing and Validation
The final step in the process of PHI removal using natural language processing in healthcare includes post-processing and validation in order to make sure that protected health information (PHI) has been effectively and efficiently removed from healthcare documents.
The process of post-processing involves reviewing the de-identified documents in order to verify the absence of any kind of residual PHI in the text.
The process of validation includes running various kinds of quality checks and audits in order to confirm the accuracy of the PHI removal process.
Additionally, the de-identified text is further tested for usability for secondary purposes and applications, such as research and analytics by the healthcare providers.
Further, in the case where any discrepancy is found, the model is further refined to improve its performance.
Benefits Of NLP In Healthcare
The various benefits of using natural language processing in healthcare include the following:
Fig.3
Improved Accuracy and Efficiency
Natural language processing in healthcare improves the accuracy of data analysis and interpretation. This is done with the automation of various operations that are prone to human errors.
Further, due to the automation of many time-consuming and repetitive tasks, NLP has helped increase the efficiency of various healthcare operations.
As a result, the organisation is able to improve its clinical decision-making and predictive analytics while focusing on more high-value-added tasks that help improve the overall productivity of the healthcare institution.
Cost Savings
Natural language processing in healthcare leads to significant cost savings for healthcare providers. This is done because of the reduced need for manual labour, improved resource allocation, and minimised risk of costly errors and non-compliance penalties.
Further, these cost savings can be reinvested by healthcare institutions to improve patient care and other priorities.
Better Patient Outcomes
Natural language processing in healthcare plays a huge contribution to improving patient outcomes by supporting personalised care, early intervention, and effective and efficient treatment planning.
NLP provides healthcare institutions with timely and accurate insights, which leads to the delivery of high-quality care that is tailored to individual patient needs.
Compliance and Security
Natural language processing in healthcare supports healthcare institutions by providing their compliance with relevant regulatory compliance by automating various processes, such as PHI identification and removal and medical coding.
Due to the automation of processes using NLP, healthcare institutions can ensure consistent regulatory adherence, reducing the risk of non-compliance and penalties while improving data security and integrity.
Conclusion
By integrating natural language processing in healthcare, the industry has seen a transformative change in its operations and results.
By automating PHI removal using NLP, healthcare institutions are able to adhere to HIPAA compliance with respect to the data privacy of the patients while maximising the utility of healthcare data in secondary applications (research and analytics) as well.
With NLP and various other AI technologies in healthcare, the industry is able to improve its patient care, patient outcomes, and operational efficiency.
With the continuous use of natural language processing in the healthcare industry, a revolution in the form of a more effective, efficient, and patient-centered healthcare system is bound to occur.
FAQs
Natural language processing can improve healthcare outcomes by helping in the accurate extraction and analysis of unstructured data, improving clinical decision-making, automating administrative tasks, and facilitating predictive analysis. This helps to improve patient care and operational efficiency as it leads to personalised care, early intervention, and more efficient workflows.
The challenges of implementing natural language processing in healthcare include data privacy and security concerns, understanding the complexity of medical language, integration with existing systems, and ensuring the accuracy and reliability of NLP models. Further challenges in NLP algorithms include obtaining high-quality data for training and addressing potential biases.
Natural language processing impacts patient care in healthcare by providing real-time insights from clinical data, supporting accurate diagnoses, helping in making personalised treatment plans and improving patient engagement with the help of chatbots and virtual assistants. Further, by automating administrative tasks through NLP, healthcare providers are able to focus directly on patient care, hence improving it exponentially.
The key features of natural language processing for healthcare professionals include text mining and information extraction, clinical decision support, predictive analytics, automated medical coding, sentiment analysis, and patient engagement tools.
The ethical considerations of using natural language processing in healthcare include ensuring patient data privacy and security, addressing the biases present in NLP algorithms, obtaining informed consent for data use by the patient, and maintaining transparency in the working and usage of NLP tools.
