In healthcare, dealing with unstructured textual data can lead to critical delays in decision-making, as it is prone to human errors. Healthcare text analytics allows organizing and automating the process of textual data interpretation, improves the efficiency of its use, and enables healthcare professionals to dedicate their time and expertise to essential tasks. Integrating world data for health research into text analytics processes can further enhance the quality of insights derived, leading to more informed and timely medical decisions.
In this article, we will explore healthcare text analytics use cases and suggest solutions for overcoming challenges brought by medical text in healthcare.
Driving Innovation in Research and Development
Research and development forces healthcare advancements, tailoring treatments to individual needs, improving existing therapies, addressing medical needs, and promoting understanding diverse patient populations. At the same time, by gaining insights from medical information collected during the research process, healthcare professionals can understand patients’ issues and needs.
Effortless Medical Report Interpretation
Patient reports have many details, which can be burdensome for healthcare professionals, making their work more stressful and less effective. Analysis of text from notes can help with summarizing the reports, so doctors can quickly make precise decisions with a quick look, which saves time and allows for effective interaction with more patients needing healthcare.
Streamlined Appointment Management
Appointment scheduling and follow-ups consume a significant portion of a doctor’s or administrator’s time, which can hamper the efficiency of work. Healthcare text analytics tools streamline and automate the entire appointment process. By analyzing textual data related to appointments and extracting this information, as appointments are requested, we can understand and respond to them appropriately.
Improved Decision-Making
Whether assessing brain MRIs, interpreting CT scans, or coordinating various medical treatments and procedures, errors in interpreting medical texts can lead to delayed actions with severe consequences. Text analytics helps to gain insights from medical documents (records and notes) to determine the priority of tests and procedures based on urgency, resource availability, and scheduling. This ensures essential tests are conducted promptly, thereby enhancing the efficiency and quality of medical services.
Detecting Fraudulent Activity
Text analytics allows for spotting irregular or suspicious patterns in claims made by medical practitioners, laboratory technicians, and others. Analysis of textual data in healthcare helps identify inappropriate prescriptions, questionable referrals, fraudulent insurance claims, and other forms of deceit within the healthcare system.
The healthcare sector faces the relentless challenge of dealing with over 10,000 diseases worldwide while having cures for only a fraction of them. This underscores the need to save time on repetitive tasks and optimize resource utilization. Text analytics emerges as the healthcare sector’s steadfast ally, simplifying data management, automating tasks, and facilitating well-informed decisions. Its potential is boundless, offering a brighter and more efficient future for healthcare data analysis and management.
Challenges of Healthcare Text Analytics
The study The Role of Text Analytics in Healthcare: A Review of Recent Developments and Applications evaluates recent developments and applications in the field. This research is a valuable resource for understanding the current landscape and the challenges of text analytics in healthcare. Let’s examine the main challenges revealed during the research.
Challenges of Text Mining Applications in Healthcare
- Diverse Data and Modalities: Integrating various data sources presents a challenge due to different formats and structures. Handling diverse modalities, from question retrieval to app categorization, requires adaptable algorithms.
- Algorithm Precision: Text mining applications require addressing the complexities inherent in natural language, healthcare terminology, and the dynamic nature of linguistic expression. Researchers and developers must fine-tune algorithms to ensure precision in keyword extraction and data categorization for reliable insights.
- Adaptation to Unstructured Forums: Extracting valuable information from unstructured forums can be challenging since user-generated content often lacks standardized formats, making it difficult to extract meaningful information. Ensuring significant extraction from diverse user-generated content requires robust Natural Language Processing (NLP) methods.
Challenges of Text Analytics for Clinical Decision Support
- Handling Narrative Clinical Information: In applications where narrative clinical information is used for predictive modeling, handling the complexity of unstructured data becomes a challenge since the nuances in language, diverse expressions, and varying levels of detail in clinical narratives make it difficult to apply conventional analytical approaches. Ensuring accuracy in predictions requires efficient processing of narrative details.
- Real-time Analytics: Efforts towards real-time analytics face challenges in handling large datasets and implementing deep learning for analysis. Ensuring real-time processing efficiency while dealing with diverse healthcare data sources remains a hurdle.
- Integration of Structured and Unstructured Data: Studies exploring the integration of structured and unstructured data for predictive modeling highlight the challenge of aligning and harmonizing different data types due to the differences in their formats. Structured data follows a predefined format, while unstructured data, like narrative clinical notes, lacks uniformity. Achieving seamless integration is crucial for enhancing model accuracy.
Enhanced precision, real-time insights, and seamless integration of diverse data sources will streamline decision-making processes and pave the way for more effective and personalized healthcare services. The HL7 FHIR standard for interoperability in healthcare allows for building a harmonized healthcare ecosystem, ensuring that crucial information flows seamlessly, leading to optimized patient outcomes.
Let’s look at the example of using Microsoft’s Text Analytics tool by the Foundation 29 project, driven by the idea that patients should control their health data. The Dx29 diagnosis support platform reflects this philosophy, seeking to enhance the analysis of genotypes and phenotypes in diagnosing rare diseases through clinical observation.
FHIR for Healthcare Text Analytics
Text Analytics for Health by Microsoft is a bright example of FHIR tools for text analytics in healthcare. This tool specializes in extracting information from biomedical and clinical free-text documents while pioneering the formalization of Natural Language Processing (NLP) output as FHIR resources, aligning with US Core standards.
Foundation 29: Rare Disease Solutions through Text Analytics for Health Structuring to FHIR
The Challenge
Rare diseases, often elusive with subtle symptoms, pose a unique challenge in diagnosis. The need for standardized information for rare cases contributes to a significant information gap, impeding accurate diagnosis and treatment.
Why FHIR
Converting unstructured clinical documents into FHIR resource bundles allows for extracting structured insights from the vast sea of clinical notes. Microsoft’s tool has helped the project ingest and normalize unstructured data and extract meaningful insights, organizing them into FHIR resources. These resources are bundled to represent the clinical document, with each FHIR resource intricately connected to the patient resource. The resulting hierarchy of structured data allows for a comprehensive understanding of patient information, which is crucial for accurate genotype and phenotype analysis.
Results
Physicians and researchers can now extract information from clinical notes with unprecedented accuracy. The structured FHIR output ensures every piece of data is connected to the patient resource, maintaining the context of the clinical narrative.
Leveraging medical text analytics enhances diagnostic capabilities and empowers patients with detailed insights into their symptoms. This shift toward patient-centric data ownership aligns seamlessly with the broader movement toward precision medicine.
Final Thoughts
As you can tell from the use case, leveraging FHIR for text analytics in healthcare addresses the unique challenges of diagnosing rare diseases. The FHIR-first approach ensures unprecedented accuracy in extracting insights from clinical notes, revolutionizing the diagnostic process.
Interoperability is a base for successful analytics in healthcare. For example, our FHIR experts successfully created an FHIR semantic layer analysis platform for Zoadigm using the Kodjin FHIR Server. In this case, using FHIR brings data together and organizes processes that clinical, payer, and research organizations face in healthcare.
The platform also follows HIPAA procedures to safeguard patient data and comply with regulations. The FHIR-first solution deals with healthcare data effectively while ensuring the safety of sensitive information and avoiding penalties for compliance failure.
Leveraging FHIR navigates the complex healthcare data landscape by ensuring seamless integration of diverse data sources, real-time insights from healthcare data, and enhancing decision-making processes.
Edenlab’s team is dedicated to driving advancements in healthcare technology. Contact us today to create a harmonized healthcare ecosystem for your project by leveraging the FHIR standard.
