How Does Medical Data De-identification Ensure Patient Privacy?

Several healthcare organizations are shifting their operations to digital platforms these days. With this shift, the efficiency of all the medical processes has increased. One must note that healthcare-related data carries sensitive information. It includes personally identifiable information(PII) and protected health information (PHI). Using such data on digital platforms raises concerns about the security of this sensitive data. Medical data de-identification comes to the rescue here. It ensures the safeguarding of the data of patients without inhibiting the data analysis and research process. 

In this blog, let’s dive deeper into medical data de-identification. Keep reading & keep learning!

What is Medical Data De-Identification?

This technique is used to change or remove patients’ personal information from a medical record that was used to provide a diagnosis or treatment to an individual. Moreover, the main aim of data de-identification is to maintain the patient’s privacy. After de-identification, datasets can be used for research purposes as well. 

Hospitals generally follow the practice of medical data de-identification before using or providing a particular dataset for research purposes. Medical data de-identification ensures patient privacy and provides crucial insights for future use at the same time. If you are looking to source quality datasets to train your AI model then Macgence is your go-to option. For more information, log on to www.macgence.com. 

Why Data De-Identification is Used in the Medical Field?

Medical records include a lot of sensitive information about the patients. This information includes details like their name, address, previous medical records, financial information related to healthcare, insurance status, and more. Such information is quite sensitive and must not be shared. 

However, for research purposes, data is required. So, medical data de-identification removes the PHI from the datasets and makes it apt for research purposes. Such collection of healthcare data can help boost the clinical research process and will also add immense value to the medical community. 

Methods of Data De-Identification

In a medical dataset, there are two types of identifiers: direct and indirect. Before getting the process started, one must be clear about which type of identifier needs to be hidden or removed. 

1. Direct Identifiers: These include names, phone numbers, emails, and more which can directly point out to an individual. 

2. Indirect Identifiers: These include demographic and economic data. Such information does not directly identify a person. Indirect identifiers are quite valuable for medical research and analysis. 

Below mentioned are some of the most common data de-identification methods:

1. Differential Privacy: In this method, data patterns are analyzed without exposing any personal information of the patients.
2. Pseudonymization: This method involves the replacement of unique identifiers with some generalized temporary codes/IDs.
3. Omission: As the name suggests, this method simply removes the direct identifiers like name, phone number, and more from a dataset.
4. Redaction: It is used to mask or erase multiple kinds of identifiers from records including text, images, and audio using pixelation. 
5. Generalization: In this medical de-identification method, precise data is replaced with broader categories. For example, exact cities and pin codes are changed to just the state or country name. 
6. Swapping: In this process, data points are swapped between individuals, such as salaries, to maintain the integrity of the overall data.
7. Micro-aggregation: In this medical de-identification process, similar numerical values are grouped and replaced with the average of the group. 

There are many other medical de-identification methods out there but these are the most used ones. These methods help in maintaining the anonymity of people’s personal information while providing data suitable for research purposes at the same time. 

Benefits of Medical Data De-Identification

1. Data Privacy: As all patients’ personal information is removed from the datasets, their privacy is protected. After medical data de-identification, datasets can even be used for research purposes.
2. Promotes Data Sharing: De-identified data can be shared among organizations. This allows different healthcare bodies to collaborate which in turn is crucial for the development of better healthcare solutions.
3. Enables to Raise Public Health Alerts: Using de-identified data, researchers can spot patterns and issue public health alerts based on them. 
4. Helps in Improvising Healthcare: De-identified data enables the researchers to get deeper medical insights. This leads to better and research-backed medical treatment.

Why Macgence?

So, that was all about the medical data de-identification and how it is playing a crucial role in the evolution of medical research. If you want to anonymize, structure, or unstructure your medical data then check out Macgence. We provide the best AI training datasets in the entire market. 

With Macgence, you get outstanding quality, scalability, expertise, and support. Whether you are an individual medical researcher or you own a medical facility, Macgence has always got your back.

We are committed to adhering to all the ethics so that we can deliver quality results to our clients. Macgence is even conformed to ISO-27001, SOC II, GDPR, and HIPAA regulations. Reach out to us today at www.macgence.com! 

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