How to Leverage Unstructured Data in Healthcare With AI

In recent years, the amount of data generated in healthcare has grown exponentially. On average, a person generates more than one million gigabytes of health-related data during their lifetime according to a IBM study. This amount is equivalent to 300 million books worth of patient data. From electronic health records (EHRs) to imaging data, patients are generating vast amounts of information that can be used to improve diagnosis, treatment, and outcomes. However, a significant portion of this data remains unstructured, making it difficult to extract meaningful insights.

In fact, an estimated 80% of patient data is unstructured, consisting of free-form text, images, and other forms of data that are not easily organized or can be directly imported into software for data processing or analysis. It takes a skilled person to understand what they see or read, and extract meaning or assigning some structure to the unstructured data. This presents a significant challenge for healthcare providers and researchers who are seeking to leverage data for patient care.

Despite these challenges, there is a growing interest in leveraging unstructured data to revolutionize patient care. By harnessing the power of natural language processing, machine learning, and the large language model like ChatGPT, healthcare providers can extract insights from unstructured data that were previously impossible to obtain.

In this article, we'll explore the potential of unstructured data in healthcare and how it can be used to enhance patient outcomes. We'll also examine the challenges and ethical considerations associated with handling unstructured data and provide real-world examples of how unstructured data is already being used in healthcare. Finally, we'll look to the future and discuss the opportunities and challenges that lie ahead in leveraging unstructured data for patient care.

Unstructured data refers to any data that is not machine readable since it’s not organized based on pre-defined formats or structures. Unlike structured data, which is organized into fields and tables, unstructured data can include free-form text, images, videos, and other forms of data that do not have a specific format.

In healthcare, unstructured data is generated in a variety of ways, including:

• Electronic health records (EHRs): EHRs can contain both structured and unstructured data.  The unstructured ones include free-form text, such as clinical notes and physician observations, that are not easily organized or analyzed using structured data methods.
• Imaging data: Medical images, such as X-rays and MRIs, can contain vast amounts of unstructured data that can be difficult to analyze without a professional to first interpret it and convert it into important notes.
• Clinical notes and transcripts: Clinicians often record notes and conversations with patients in unstructured formats that can be difficult to be converted into structured formats.
• Social media and patient-generated data: Patients are increasingly sharing health-related information on social media and other online platforms, creating large volumes of unstructured data that can be used to enhance patient care.

Despite its potential, unstructured data presents several challenges for healthcare providers and researchers. They are often noisy, meaning that it contains irrelevant or redundant information that can make it difficult to extract meaningful insight. Moreover, most hospitals lack standard procedures to structure or organize countless unstructured data.

However, with the advent of AI technologies, it is now possible to extract meaningful insights from unstructured data with higher accuracy and less time. Intelligent Document Processing solution integrates various AI technologies and is capable of analyzing large volumes of unstructured data and identifying patterns and relationships that were previously impossible to detect in order to automatically convert unstructured data from various sources into structured data.

Despite the benefits of structured data in healthcare, the majority of data generated in the industry remains unstructured. One reason for this is the sheer volume and variety of data sources, including medical records, lab results, insurance claims, and clinical notes. Healthcare organizations struggle to manage and analyze this data because it often lacks the necessary structure and standardization to be easily processed and analyzed.

Another reason for the persistence of unstructured data in healthcare is the continued use of outdated communication technologies. For example, faxing remains a common method of communication in the industry, despite its limitations. Fax machines produce unstructured documents that require manual data entry to extract and categorize information. This can be time-consuming, error-prone, and inefficient, leading to incomplete or inaccurate records.

The healthcare industry has been slow to adopt more modern communication technologies that would facilitate the creation of structured data. For example, electronic health records (EHRs) have been widely adopted, but the data they contain can still be unstructured as we mentioned earlier.

However, the development of IDP solutions like FormX that can leverage AI technologies and large language models like ChatGPT or GPT-4 to extract structured data from unstructured sources represents a significant step forward for the industry. With these technologies, healthcare organizations can process and analyze unstructured data more effectively, improving patient outcomes, reducing costs, and enhancing decision-making. As healthcare continues to evolve, the adoption of modern communication technologies and IDP solutions will be critical to achieving the full potential of structured data in the industry.

The use of unstructured data in healthcare is still in its early stages, but the potential for its use in improving patient outcomes and advancing medical knowledge is immense. As AI technologies, such as Natural Language Processing (NLP), Optical Character Recognition (OCR), Machine Learning (ML), and Large Language Model like GPT-4, become more mature, the healthcare industry can transform these unstructured data into structured formats that can be used for various purposes.

By converting unstructured healthcare data into structured data, healthcare providers can gain deeper insights into patient care. IDP solutions can help extract data from various sources and send them to EHR systems to identify patterns, trends, and correlations that may not be apparent without sufficient amount of data. This information can be used to develop more effective treatment plans, optimize patient care, and improve patient outcomes.

Manual data entry processes are time-consuming, error-prone, and can be costly. With the amount and complexity of unstructured data in the healthcare industry, manually converting unstructured data into structured formats can be a financial burden. IDP solutions can help automate these processes, significantly reducing the time and resources required to manage unstructured data.

By automating data extraction, IDP solutions can help healthcare organizations streamline their operations by automating manual processes. This can free up staff to focus on more complex tasks, improving efficiency and reducing the time and resources required to manage unstructured data.

By converting unstructured healthcare data into structured data, IDP solutions can help healthcare organizations analyze patient data to identify trends, patterns, and correlations. This information can be used to make more informed decisions about patient care, resource allocation, and operational planning.

By transforming unstructured data trapped in physical images or documents into digitized and organized formats, IDP solutions can help healthcare organizations process and retrieve information more quickly and accurately. This can reduce the time required to manage and process unstructured data, improving efficiency and productivity.

Once the unstructured data are organized and digitized by IDP solutions, healthcare organizations can improve collaboration and communication since internal departments or different health organizations . This can improve teamwork and coordination, resulting in better patient outcomes and more effective healthcare services.

We’ve explored the potential of unstructured data in healthcare and discussed some of the challenges associated with leveraging this data. While unstructured data accounts for a significant portion of the data generated in healthcare, it is often underutilized due to difficulties to organize them.

This is where FormX can help. Our platform uses advanced natural language processing, machine learning, and large language models like GPT-4 to transform unstructured data into structured, usable data that can be easily analyzed and integrated with other data sources.

By using FormX, healthcare businesses can accelerate patient onboarding, gain valuable insights into patient health and disease, improve clinical decision-making, and advance medical knowledge. Our platform can help you leverage unstructured data from a variety of sources, including patient-generated health data, clinical notes, and medical images.

Unlock the full potential of your healthcare data by contacting us today to learn more about how we can help you turn unstructured data into actionable insights.