Natural Language Processing exploits the potential of unstructured data in the healthcare sector
Natural Language Processing (NLP) exploits the potential of unstructured data. While conventional analysis tools are not able to handle text adequately, Natural Language Processing quickly and effectively identifies important data points from text-based documents. These can then be analyzed and processed in various ways.
NLP often works in the background and makes everyday work easier for doctors, researchers and many others who depend on clinical data within an organization.
Natural Language Processing is therefore often not a stand-alone product, but is often used within existing systems and technology applications. NLP supports data-driven decision making in different key areas of healthcare.
Information about Natural Language Processing at Wikipedia. wikipedia.
Why Natural Language Processing in Healthcare?
Healthcare companies (e. g. hospitals and research organizations) manage and use large amounts of unstructured text documents. It is estimated that 80% of the information about patients is presented in an unstructured form.
However, in order to conduct research effectively, improve clinical treatment standards and evaluate treatment outcomes simply and as automatically as possible, structured access to the content of these documents by Natural Language Processing is required. The knowledge (e.g. in doctor’s letters, pathology reports, clinical reports) is crucial to achieving these goals.
Examples and applications of Healthcare Natural Language Processing
Improve and accelerate clinical documentation and billing:
Natural Language Processing offers the possibility to collect all important data in a few seconds. Instead of having to check countless cases, the NLP technology specifically identifies documentation gaps and automatically generates suggestions for improvement. This is absolutely essential for correct coding and billing based on the ICD codes, for example. Consequently, NLP serves to reduce costs and in parallel with the increase in earnings in clinical organisations.
Support of cohort analysis in patients by NLP
Usual mechanisms for identifying cohorts in clinical research are not able to scale the requirements and the required response time at affordable conditions for many investigators. The use of Natural Language Processing can significantly reduce the costs of cohort identification. NLP can be used to define a number of inclusion/exclusion criteria, enabling researchers to simultaneously retrieve structured and unstructured clinical data. The result can be used for the recruitment of potential study participants as well as for feasibility studies.
Support in decision-making by NLP
For example, NLP supports the extraction of important data from pathology reports and doctor’s letters to create patient profiles. These serve as a basis for recommendations regarding the further treatment of patients. This significantly increases the quality of clinical reports, improves the completeness of patient profiles and therefore enables better medical monitoring and care for patients.
Highly qualified research in the field of healthcare
User-defined and empirically induced search patterns can be used to ensure compliance with guidelines for treating patients with specific medical conditions. Based on text documents, NLP can be used to determine the patient’s condition and compare it with the physician’s observations in order to enable a prediction of the future quality of life.
More appropriate and effective drug treatment
The anamnesis of drugs and their reactions are of utmost importance for future medical treatment. In particular, the detection and avoidance of side effects is absolutely essential for the patient’s safety. The traceability of the patient’s drug intake in the context of possible side effects plays a decisive role in the further development of personalised medicine in the interest of patients. Genetic markers of undesired side effects can be identified at an early stage. This important information is already available in large quantities in unstructured clinical documentation. With the help of Natural Language Processing, this information can be obtained quickly and efficiently and used for the benefit of patients.
Research & development of medicines
NLP accelerates drug research and development. Large amounts of data and information on the drug in question and its effect can be efficiently combined and evaluated by NLP. Natural Language Processing can also support the targeted monitoring of a drug after its market launch or quickly detect undesirable side effects.
Automated compilation of texts by Natural Language Processing
There are different use cases for the combination of texts. On the one hand, clinical information can be extracted and displayed in multiple reports of the same patient so that doctors can quickly capture the more comprehensive medical history of a single patient.
On the other hand, natural language processing can be used to quickly summarize clinical information on pre-defined patient groups at the patient or document level in the form of annotations. This information can be used to identify medical terms to find cohorts of patients based on frequency and relevance of terms.
Our software Health Discovery includes a variety of tools for analyzing unstructured patient data using Natural Language Processing. We analyze medical letters, pathology and radiology reports and many other data sources. From this we extract diagnoses, medicines, laboratory values, localizations, personal characteristics and much more information. Negations (“exclusion of”) and statements on diagnostic certainty (“suspicion of”) are recognized and assigned to the corresponding entities.
Health Discovery enables meaningful predictions based on patient data. In the area of rare diseases, we make predictions for diagnosis and predict the course of therapy for eye diseases. In the context of DRG coding we propose billing-relevant diagnosis and procedure codes, in cardiology we compare guidelines with patient data and identify patients who need a pacemaker. These and many other use cases can be implemented with Machine Learning.
Health Discovery can be easily adapted to individual customer needs. Powerful analysis apps can be developed on the basis of web-based interfaces. The modular architecture enables seamless integration of Natural Language Processing into existing infrastructures.
Natural Language Processing ready to go with just a few clicks!
In this video we will show you how to configure Health Discovery as a text mining and natural language processing platform in just a few minutes and how to analyze large amounts of medical documents and patient data according to diagnoses, symptoms, prescriptions, special features and other criteria.