Top 4 Challenges Working with Unstructured Data in Healthcare
Oct 30, 2024
Gradient Team
The Shift Towards Unstructured Data in Healthcare
In healthcare, unstructured data is growing at an exponential rate, driven by the digitization of patient records, telemedicine, medical imaging, and wearable devices. Studies indicate that over 80% of healthcare data is unstructured, and this percentage continues to grow as digital health technologies proliferate.
Unstructured data in healthcare includes critical information such as clinical notes, imaging scans (like X-rays and MRIs), pathology reports, genomic data, and even real-time monitoring from patient devices. While these data sources provide valuable insights for enhancing patient care, they also present significant challenges for healthcare organizations aiming to streamline their processes. The difficulty in extracting and utilizing this data, combined with stringent regulatory standards, often results in it being underutilized or kept dormant such as institutional knowledge that has to be passed on manually over time.
It’s safe to say that the primary reason why most data processes aren’t automated today is simply because it involves unstructured data. For most healthcare organizations and companies, leveraging this data would require a dedicated data science and machine learning team - a heavy investment in time, money and resources. That’s why today we’ll dive into the top 4 challenges around what healthcare organizations face when dealing with unstructured data and how to get around it.
Top 4 Challenges Working with Unstructured Data
1) Data Storage and Organization
One of the most significant hurdles for healthcare organizations is efficiently storing and organizing unstructured data. Medical images, lab results, free-text patient records, and genomic data generate massive amounts of information, much of which doesn’t fit neatly into traditional databases. Unlike structured data like patient demographics, unstructured formats such as radiology reports or multimedia medical files are difficult to store in conventional systems.
Healthcare providers must invest in advanced storage solutions like cloud-based systems, NoSQL databases, or distributed file systems capable of scaling to accommodate the variety and volume of unstructured data. These systems need to not only store large datasets but also ensure that this data remains accessible and organized in a way that healthcare professionals can easily retrieve relevant information during critical moments of patient care.
2) Data Processing and Analysis
Extracting valuable insights from unstructured data is especially challenging in healthcare, where the ability to quickly analyze complex datasets can be a matter of life and death. Traditional analytics tools are typically designed for structured data, and they often struggle to process unstructured formats like diagnostic images, clinical trial data, and physician notes.
To overcome this, healthcare organizations increasingly rely on advanced technologies such as natural language processing (NLP) and machine learning (ML) to extract meaningful information from unstructured sources. However, deploying these technologies at scale requires specialized expertise in data science and AI, which many healthcare providers lack. Furthermore, the manual nature of extracting insights from unstructured data creates inefficiencies and slows down care delivery, making it difficult to streamline operations.
3) Data Quality and Consistency
Maintaining data quality and consistency is a persistent issue in healthcare due to the fragmented nature of unstructured data. Patient records, notes, lab results, and imaging scans are generated across various platforms and often lack standardization. For instance, two physicians might document a patient’s symptoms differently, making it challenging to ensure that data is uniformly recorded and interpreted.
Inconsistent data quality leads to incomplete or incorrect patient information, which can negatively impact care outcomes. The lack of uniformity in unstructured data can also hinder interoperability, as healthcare organizations struggle to share information across different systems and departments, further complicating efforts to achieve coordinated care.
4) Security and Compliance
Healthcare organizations are particularly vulnerable to cyberattacks, as unstructured data often contains sensitive patient information, including health records, diagnostic images, and billing information. Securing this data is critical not only for protecting patient privacy but also for complying with regulations like the Health Insurance Portability and Accountability Act (HIPAA).
Unlike structured data, which can be encrypted and protected in uniform ways, unstructured data is more difficult to secure due to its variety of formats and sources. Additionally, healthcare providers must ensure compliance with various regulatory frameworks, which often impose strict guidelines on how data must be stored, accessed, and protected. Ensuring that unstructured data remains secure while maintaining compliance adds another layer of complexity to the already challenging landscape of healthcare data management.
Unlocking Your Unstructured Data
As the volume of unstructured data in healthcare continues to grow, so does the opportunity to automate processes and drive meaningful improvements in patient care and operational efficiency. Today, most health tech companies and providers simply rely on their teams to manually process the data in order to get around data formatting and structure. However, this process is both labor-intensive and susceptible to errors due to the sheer amount of volume and variability in the data. To solve this, Gradient developed a new way for businesses to interact with data - providing the first and only AI-powered Data Reasoning Platform that enables businesses to forge both structured and unstructured data to create data workflows that were unimaginable with traditional tools.
Gradient’s Data Reasoning Platform
Gradient’s Data Reasoning Platform is the first AI-powered and HIPAA compliant platform that’s designed to automate and transform how providers and health tech companies handle their most complex data workflows. Powered by a suite of proprietary large language models (LLMs) and AI tools, Gradient eliminates the need for manual data preparation, intermediate processing steps, or a dedicated ML team to maximize the ROI from your data. Unlike traditional data processing tools, Gradient’s Data Reasoning Platform doesn’t require teams to create complex workflows from scratch and manually tune every aspect of the pipeline.
Schemaless Experience: The Gradient Platform provides a flexible approach to data by removing traditional constraints and the need for structured input data. Enterprise organizations can now leverage data in different shapes, formats, and variations without the need to prepare and standardize the data beforehand.
Deeper Insights, Less Overhead: Automating complex data workflows with higher order operations has never been easier. Gradient’s Data Reasoning Platform removes the need for dedicated ML teams, by leveraging AI to take in raw or unstructured data to intelligently infer relationships, derive new data, and handle knowledge-based operations with ease.
Continuous Learning and Accuracy: Gradient’s Platform implements a continuous learning process to improve accuracy that involves real-time human feedback through the Gradient Control System (GCS). Using GCS, enterprise businesses have the ability to provide direct feedback to help tune and align the AI system to expected outputs.
Reliability You Can Trust: Precision and reliability are fundamental for automation, especially when you’re dealing with complex data workflows. The Gradient Monitoring System (GMS) identifies anomalies that may occur to ensure workflows are consistent or corrected if needed.
Designed to Scale: Typically the more disparate data you have, the bigger the team you’ll need to process, interpret, and identify key insights that are needed to execute high level tasks. Gradient enables you to process 10x the data at 10x the speed without the need for a dedicated team or additional resourcing.
Even with limited, unstructured or incomplete datasets, the Gradient Data Reasoning Platform can intelligently infer relationships, generate derived data, and handle knowledge-based operations - making this a completely unique experience. This means that teams can automate even the most intricate workflows at the highest level of accuracy and speed - freeing up valuable time and overhead.
Under the Hood: What Makes it Possible
The magic of the Gradient Data Reasoning Platform is its high accuracy, quick time to value, and easy integration into existing enterprise systems.
Data Extraction Agent: Our Extraction Agent intelligently ingests and parses any type of data into Gradient without hassle, including raw and unstructured data. Whether you’re working with PDFs or PNGs we’ve got you covered.
Data Forge: This is the heart of the Gradient Platform. AI automatically reasons about your data - re-shaping, modifying, combining, and reconciling your structured and unstructured data via higher order operations to achieve your objective. Our Data Forge leverages advanced agentic AI techniques to guide the models through multi-hop reasoning reliably and accurately.
Integration Agent: When your data is ready, Gradient will ensure that your data can be easily integrated back into your downstream applications via a simple API.
With Gradient, businesses can focus on the outcomes—whether it’s driving customer insights, ensuring regulatory compliance, or optimizing production lines—without getting bogged down in the operational intricacies of data workflows. By automating complex data workflows, organizations can achieve faster, more accurate results at scale - reducing costs and enhancing operational efficiency. In a world where data complexity continues to grow, the ability to harness that data through automation is not just a competitive advantage—it’s a necessity. Take a look at some healthcare use cases in detail that healthcare providers and health tech companies are using Gradient for today.