Streamlining Data Processing: Medical Entity Abstraction Solutions for Healthcare and Life Sciences

The market size of big data in healthcare is expected to reach $78.03 billion by 2027. However, the ever-increasing volume of processed information, including unstructured healthcare data, makes it challenging to organize and systematize. Over 80% of healthcare data is unstructured and it’s increasing at the rate of 47% every year. Structured information is crucial for efficient data processing and analysis, enabling seamless integration. NCBI study suggests that structured EMR data helps reduce the risk of errors in decision-making by 57%. Medical entity abstraction leverages technologies like Natural Language Processing and machine learning to extract and structure information from unstructured data. This process is vital in healthcare and life sciences for improving data accuracy, enhancing clinical decision support, and streamlining administrative tasks.

Challenges of unstructured medical data

Unstructured medical data includes diverse sources such as clinical notes, lab reports, imaging data, and discharge summaries. Such unstructured data may lead to multiple complexities in the healthcare world.

• Time-consuming and error-prone manual data entry: The Journal of the American Medical Association reported that clinicians spend nearly two hours on administrative tasks for every hour of patient care. This contributes to clinician burnout and increases the likelihood of data entry mistakes, which can have serious implications for patient safety.
• Data retrieval and interoperability: According to the Office of the National Coordinator for Health Information Technology (ONC), only 50% of healthcare providers can easily integrate patient data from external sources. This fragmentation hampers comprehensive patient care and coordinated treatment efforts.
• Impact on patient care and operational efficiency: The inability to efficiently process and retrieve unstructured data directly impacts patient care. Delays in accessing critical patient information can lead to suboptimal treatment decisions.
• Lack of standardization: Health Affairs highlighted that 72% of healthcare organizations struggle with data standardization issues, which limits their ability to leverage data for clinical insights.
• Compliance and regulatory concerns: Handling unstructured data also raises compliance and regulatory challenges. Ensuring adherence to regulations like HIPAA is crucial for data privacy and security. As per Ponemon Institute, healthcare data breaches cost an average of $7.13 million per incident in 2020, underlining the importance of robust data management practices. 

Medical entity abstraction: Technology and techniques

Medical entity abstraction refers to the process of extracting structured information from unstructured medical data using advanced technologies and techniques. It utilizes cutting-edge technologies such as Natural Language Processing (NLP), Deep Learning (DL), Machine Learning (ML), and Artificial Intelligence (AI) to analyze unstructured medical data. It helps to identify and extract relevant entities, relationships, and concepts from clinical notes, lab reports, imaging data, and other sources. For example, Reveal HealthTech’s natural language virtual assistant facilitated increase the adoption of device and improve patient care for a leading medical device manufacturer. 

Key techniques:

• Entity linking: Entity linking involves identifying mentions of entities in text and linking them to standardized concepts in knowledge bases or ontologies. For instance, MetaMap, developed by the National Library of Medicine, links clinical terms to concepts in the Unified Medical Language System (UMLS).
• Named entity recognition (NER): NER algorithms identify and classify named entities (e.g., diseases, medications, procedures) mentioned in text. Epic Systems uses NER algorithms to extract structured data from unstructured clinical narratives, facilitating billing and coding processes. For example, take a look at how Reveal HealthTech provided insights into the process and significance of medical entity abstraction for real-world data providers within the healthcare industry.  

Benefits of medical entity abstraction

Through the integration of these technologies and techniques, medical entity abstraction empowers healthcare and life sciences organizations to harness the full potential of their unstructured data, driving innovation and improving patient outcomes.

• Improved patient record management and data retrieval streamline clinical workflows, enabling healthcare providers to access comprehensive patient histories promptly.
• Enhanced clinical decision support systems (CDSS) empower clinicians with real-time insights and recommendations derived from structured patient data.
• Streamlined billing and coding processes automate administrative tasks, minimizing errors and accelerating reimbursement cycles.
• Operational efficiency and cost reduction are achieved through the automation of routine tasks and resource allocation optimization.
• Improved patient outcomes result from more informed decision-making enabled by structured data and advanced analytics.
• Facilitation of research and analytics accelerates scientific discovery and innovation. Using AI-powered data analytics platforms, research institutions can analyze large datasets to identify trends, patterns, and potential treatments.
• Statistical benefits, such as reductions in data processing times, error rates, and operational costs, underscore the transformative impact of medical entity abstraction. 

Conclusion

The convergence of medical entity abstraction with EHRs and the progress in AI and NLP herald exciting prospects in healthcare. These trends promise personalized medicine and predictive analytics, empowering clinicians with tailored insights and enhancing patient care in the evolving landscape of healthcare technology.

About us:

At Reveal HealthTech, we’re dedicated to revolutionizing healthcare through innovative technology solutions. With expertise in medical entity abstraction and cutting-edge AI, we empower healthcare organizations to unlock the full potential of their data, driving better patient outcomes and operational efficiency.