Text analytics is the process that analyses unstructured text, extract relevant information, and transform it into structured information that can be leveraged in numerous ways. Unstructured data is ubiquitous in blogs, tweets, call centers, online news, etc, and companies are rapidly adopting text analytics technology to gain an insight into this information and gain a competitive advantage. Text analytics in the healthcare industry is a boon for this modern era.
According to marketsandmarkets, the text analytics market projected to grow from $3.2 billion in 2016 To $8.8 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 17.2% during the forecast period. The major growth drivers of the market include the growing need for social media analytics and the increasing need for predictive analytics for businesses.
Text analytics provides a more tactical approach through actionable insights from analyzed data. Further, the technology is expected to gain ground over the next few years owing to its ability to predict and forecast consumer behavior. The technology is used across an array of applications including brand-reputation management, market research, competitive intelligence, and customer service & support.
Text analytics has become helpful in the healthcare sector during the coronavirus pandemic to explore the information about the coronavirus. Text analytics is software that helps to create proper text data from unstructured text from trends, uncover insights, and patterns.
Text analytics in the healthcare and life science segment is the fastest-growing segment in the text analytics market due to the growing demand to tackle big data and deliver valuable insights from it
The healthcare industry is overwhelmed with data. Much of this healthcare data is in the form of unstructured text, such as doctor’s notes, medical publications, electronic health records, clinical trial protocols, medical encounter transcripts, and more. Healthcare organizations, providers, researchers, pharmaceutical companies, and others face an incredible challenge in trying to identify and draw insights from all that information. Unlocking insights from this data has massive potential for improving healthcare services and patient outcomes.
With Text Analytics for health, users can detect words and phrases mentioned in the unstructured text as entities that can be associated with semantic types in the healthcare and biomedical domain, such as diagnosis, medication name, symptom/sign, examinations, treatments, dosage, and route of administration.
Here are the 4 ways Text Analytics Impact the Healthcare Industry
- Treatment effectiveness
- Patient profile analytics
- Healthcare management
- Prevent Fraud and abuse
The effectiveness of medical treatment is evaluated by implementing Text analytics. Text Analytics compares the causes, symptoms, and courses of treatments. A report on courses of action is generated. This report displays the effectiveness and associates the various side-effects of treatment. It also helps to determine proactive steps that can reduce the risk of affliction.
2.Patient Profile Analytics
Patient profile analytics is used to identify individuals who would benefit from proactive care or lifestyle changes. Using text analytics, we can extract patient’s data from various sources and store the information in a patient profile graph. The patient profile graph provides a holistic view of the patient’s information. With Text Analytics, patient populations by demographic characteristics can be determined. By doing so, we can identify groups using the most resources, reduce expenditures through better disease management and text-based decision support assistance can be used at clinical call centers for complex patient scenarios.
Text analytics is used to better identify and track chronic disease states and high-risk patients, design appropriate interventions, and reduce the number of hospital admissions and claims. Text Analytics can be used to view readmission data and resource utilization and compares its data with the current scientific literature to develop better diagnosis and treatment protocols.
4.Prevent Fraud and Abuse
Fraud and abuse are detected after an analysis of text data to establish norms and then identify unusual or abnormal patterns of claims by physicians, laboratories, clinics, or others. Among other things, the data analysis highlights inappropriate prescriptions or referrals and fraudulent insurance and medical claims.