Healthcare providers manage about 137 terabytes of data daily, mostly unstructured, including medical images, clinical notes, and genetic test results. This data is crucial for patient care but poses significant challenges due to its complexity and varying sizes.
The volume of healthcare data is rapidly increasing, growing at a rate of 47% per year. For instance, a standard chest X-ray might be 15 megabytes, while a digital pathology file can reach 3 gigabytes. With 80% of healthcare data being unstructured, managing and utilizing this data effectively is a major challenge.
In the following article, we will explore these challenges and how Jackalope provides essential solutions.
Healthcare providers manage an enormous volume of data daily, approximately 137 terabytes, most of which is unstructured. This includes a wide array of formats such as medical images, clinical notes, and genetic test results. Unstructured data, crucial for patient care, poses significant challenges due to its complexity and the varied sizes of its components.
Structured VS Unstructured Data Comparison
Knowing the differences between these data types is key to using them effectively to improve patient care and make healthcare operations more efficient.
Structured Data
Structured data is highly organized, machine-readable, and typically stored in relational databases. It can be easily entered, queried, and analyzed using standard tools.
Examples
- Patient demographics: age, gender, address
- Medical data: diagnosis codes (ICD-10), procedure codes (CPT), drug codes (NDC)
- Billing and insurance information: policy numbers, billing details
Advantages
- Ease of Search and Analysis: Quickly searchable and analyzable.
- Standardization: Adheres to predefined schemas, ensuring consistent data entry and exchange.
- Efficiency: Optimized for quick and accurate processing of tasks like patient record updates and billing.
- Data Integrity and Consistency: Maintains reliability through strict formats and validation rules.
- Interoperability: Facilitates easy data sharing and integration across healthcare systems.
Disadvantages
- Inflexibility: Limited to predefined fields, missing complex or nuanced clinical information.
- Data Oversimplification: May miss critical details by oversimplifying patient information.
- Limited Adaptability: Struggles to incorporate new medical knowledge and integrate new data types, such as genomic information or wearable device data.
Unstructured Data
Unstructured data lacks a predefined format, making it hard for traditional systems to interpret without advanced tools. It is stored in formats unsuitable for relational databases.
Examples
- Medical notes and narratives
- Imaging data (X-rays, MRIs, pathology slides)
- Multimedia information (surgery videos, audio recordings, clinical photos)
Advantages
- Richness of Information: Provides comprehensive details like patient histories and diagnostic images, leading to accurate diagnoses and tailored treatments.
- Flexibility: Captures diverse information from various sources, accommodating the complex nature of medical care.
- Enhanced Patient Care: Offers a complete picture of a patient’s health, supporting holistic and effective care.
- Improved Research and Analysis: Detailed data supports advanced analytics and machine learning, driving medical innovations.
- Real-Time Data Utilization: This can be analyzed immediately, aiding urgent clinical decisions.
Disadvantages
- Difficult to Manage and Analyze: Lacks predefined format, requiring advanced tools like NLP and machine learning for organization and insight extraction.
- Complex Processing Requirements: Needs significant computational power and specialized software, increasing costs and processing times.
- Storage and Accessibility Challenges: Requires more storage space and complex systems, making quick querying and analysis difficult.
Why is Unstructured Data So Important?
- Rich Clinical Insights
Unstructured data holds detailed, nuanced information often missing in structured datasets. For instance, a doctor’s notes may capture critical observations about a patient’s response to treatment that structured data cannot.
2. Personalized Patient Care
The depth of unstructured data, like clinical narratives and genomic information, enables personalized treatment plans tailored to individual patient’s unique health profiles.
3. Enhanced Medical Research and Innovation
Unstructured data provides comprehensive information essential for groundbreaking studies and medical discoveries, revealing patterns and connections invisible in structured data.
4. Improved Healthcare Outcomes
Analyzing unstructured data helps improve diagnostic accuracy, track disease progression, and monitor treatment outcomes in real time, enhancing overall healthcare delivery.
Core Challenges of Unstructured Data
- Data Integrity
Unstructured data varies in quality and format, leading to inconsistencies that undermine data integrity. Ensuring accurate and up-to-date information is challenging, especially with rapidly changing health statuses and treatments.
2. Storage Needs
The large volume of unstructured data, including high-resolution images and lengthy patient narratives, requires scalable storage solutions to accommodate growth without compromising access or performance.
3. Data Security
Unstructured data, often containing sensitive personal details, is vulnerable to cyberattacks. Protecting this data requires robust security measures, including advanced encryption and continuous monitoring.
4. Data Sharing
Unstructured data’s diverse formats and standards pose interoperability challenges, complicating seamless and secure data sharing across healthcare systems.
5. Data Ownership and Privacy
Digital health records and patient-generated data raise complex issues of data ownership and privacy. Balancing patients’ rights to privacy with necessary data usage for care is critical.
6. Addressing Bias
Unstructured data can contain biases from subjective observations or uneven documentation. Correcting these biases is essential for fair and effective healthcare practices.
AI Solutions to Unstructured Data Challenges
Automated Data Standardization and Validation
AI technologies like natural language processing (NLP) and machine learning (ML) transform unstructured healthcare data into standardized formats. They parse clinical narratives and lab results, extracting key health information (symptoms, diagnoses, medication dosages) and mapping it to standardized schemas like HL7 FHIR or OMOP CDM.
- OCR Technology
Tools like Google Cloud Vision and Tesseract convert scanned documents and handwritten notes into editable formats for easier search and processing.
- NLP Tools & LLMs
Platforms like SpaCy and NLTK analyze and standardize medical information from unstructured text into diagnostic and treatment codes.
- Machine Learning Models
Frameworks like TensorFlow and PyTorch enhance data categorization and accuracy by continuously learning from new inputs.
Efficient Data Storage and Advanced Compression
AI-driven technologies optimize storage and compression for large files like medical images. Algorithms dynamically allocate resources and compress data-heavy images (MRIs, CT scans) while preserving diagnostic information.
AI in Health Information Exchanges (HIE) and Standardization
AI automates the preprocessing and standardization of diverse healthcare data into compatible formats (HL7, FHIR). This ensures data consistency and interoperability across healthcare IT systems.
- Data Parsing and Normalization
Extracts and normalizes information from unstructured data for further processing.
- Semantic Mapping
Uses ML algorithms to map data to standardized medical vocabularies like SNOMED CT and LOINC.
- Automated Validation and Formatting
Ensures data integrity and compliance with standards, supporting seamless integration into HIE platforms.
Introducing Jackalope
Jackalope is an advanced AI-powered platform designed to address the challenges of unstructured healthcare data. It automates data standardization, enhances semantic integrity, efficiently handles large datasets, and ensures system compatibility and security.
Data Processing
Automating Data Standardization
Jackalope automates the standardization of unstructured data using AI and ML algorithms to process clinical notes, lab results, and EHRs. It identifies key medical terms and contextual information, mapping them to standardized codes in OMOP CDM and SNOMED CT. This transformation results in uniformly formatted data, enhancing its utility for clinical research, health monitoring, and predictive analytics, and ensuring consistency across healthcare systems.
Efficient Handling of Large Datasets
Jackalope excels in managing large datasets through:
- Process Automation
Reduces the need for manual data entry and review, minimizing errors and inconsistencies.
- Scalability and Speed
Dynamically scales and uses parallel processing to handle large data volumes quickly.
- Reliability Enhancement
Continuously improves accuracy through machine learning, adapting to new data patterns and irregularities automatically.
Semantic Integrity
- Automated Generation of Expressions
Uses AI to create detailed SNOMED post-coordinated expressions, representing complex medical conditions.
- Comprehensive Semantic Capture
Accurately standardizes semantic meanings, especially for rare conditions and new findings.
- Advanced Algorithms for Term Mapping
Maps medical terminology to SNOMED CT and OMOP CDM categories, ensuring data consistency and accuracy.
- Creation of Custom Descriptions
Generates new descriptions when exact matches don’t exist, preserving data granularity and specificity.
- Handling Temporal and Granular Data
Manages time-related information and ensures detailed data management for complex diagnoses, supporting comprehensive patient care and precise clinical decisions.
System Compatibility & Security
Enhancing Interoperability and Global Collaboration
- Standardized Data Frameworks
Jackalope standardizes medical data using OMOP CDM and SNOMED CT, facilitating easy data exchange and understanding across global healthcare systems.
- Global Data Sharing
These standards support worldwide collaboration among researchers, clinicians, and healthcare organizations, enhancing the scope and quality of research and clinical practices.
Improving Data Integrity and Security
- Rigorous Update Schedules
Jackalope maintains stringent update protocols to ensure terminologies and mappings are current with the latest medical standards and discoveries.
- Robust Data Protection Measures
Advanced security features, including encrypted storage, secure authentication, and regular audits, protect sensitive medical information and ensure compliance with international regulations like GDPR and HIPAA.
Conclusion
Handling unstructured healthcare data is tough due to the large amount of clinical notes, medical images, and genetic test results created every day. Jackalope uses advanced AI and machine learning to organize this data, making it easier to access and use for personalized treatments and medical research. This leads to better patient care and more efficient healthcare services.
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