As the data collection methods have extreme influence over the validity of the research outcomes, it is considered as the crucial aspect of the studies
May 2025 | Source: News-Medical
Health care generates enormous amounts of data every day through electronic health records (EHRs), clinical notes, research articles, and insurance claims. But much of this data is unstructured and unusable for analysis to identify actionable insights. This is where Named Entity Recognition (NER) comes into play.[1]
NER is an important text mining technique to extract useful information from unstructured data within the field of health care. NER in health care provides identification and categorization of valuable entities (e.g., medical terms, patient names, drug names, dates, etc.) to improve decision-making and operational efficiencies.
Named Entity Recognition (NER) is an automated approach to identifying and classifying entities mentioned in the text, including:
Names (Patients, doctors…)
Diseases…
Medications…
Dates…
Locations…
NER is an essential process to convert unstructured healthcare data into a structured, usable form that supports analysis and decisions.[2]
The healthcare sector has no shortage of problems due to the sheer amount and complexity of data generated. Here are some of the major issues that result:
Challenge | Effects on Healthcare |
Unstructured Data | Hard to process and analyse successfully |
Data Overload | Key insights often go unnoticed |
Manual Analysis | Time-consuming and prone to error |
NER automates valuable information extraction from unstructured data allowing healthcare to make more timely and informed decisions.
NER is widely applied within key areas of healthcare, from supporting clinician decision-making to supporting research and fraud detection.
NER is already being successfully used in a few areas of health care:
Sector | How NER is Used |
Pharmaceutical | Extracting drug names and outcomes from clinical trials to accelerate research. |
Hospitals | Analysing clinical notes to identify important patient data such as relevant patient diagnoses and treatment plans.[4] |
Insurance | Detecting fraud in medical claims through analysis of anomalies in billing data with NER. |
There are several important benefits to NER in health care:
Efficiency:
NER allows for the automation of data extraction and will save time and resources on manual sorting and analysis.
Accuracy:
NER improves extraction accuracy by extracting the correct information out of unstructured text which creates more dependable information.
Cost:
Automating data processing reduces costs related to manual times and errors creating greater financial returns for health-care organizations. [5]
Although NER may be a promising tool, there are challenges to consider:
In conclusion, Named Entity Recognition (NER) is a promising technology that is reshaping the way healthcare data is processed and analysed. Gaining knowledge of entities from unstructured text provides solid evidence for healthcare providers to guide in decision-making, enhance patient care, and improve efficiency.[3] As technology continues to evolve, the impact of NER on healthcare would only increase, allowing for even greater opportunities for improved patient outcomes and efficiency. Unlock the full potential of your healthcare data with Statswork. Explore NER solutions today!
