Thankfully, machine learning and text analytics software can take all that complex data and transform it into clear reports that are easy to assimilate. Text analytics tools have the potential to raise the quality of all healthcare processes for patients and healthcare professionals alike.
What is text analytics in healthcare?
Healthcare analytics is the use of text analytics tools to extract and catalog relevant information from unstructured texts and other data sources. Text analytics software can understand and convey information from doctors’ notes, discharge summaries, clinical documents, and all kinds of health records.
Text analytics tools use a combination of computational and linguistic techniques such as natural language processing. The machine-learning algorithms that empower text analytics software are able to read text, understand its explicit and underlying themes, and produce comprehensive summaries.
What is the purpose of text analytics in healthcare?
Text analytics can be used to improve the speed and efficiency of all operations within the healthcare sector. This allows both patients and healthcare professionals to enjoy simpler and more enjoyable healthcare services.
What is an example of data analytics in healthcare?
In a study conducted in France, it was proven that data analytics can help hospitals organize and allocate resources, thereby avoiding overcrowding and other emergency room challenges. Furthermore, the research team concluded that the software could have been given many more uses than those examined (source).
Text analytics use cases in the healthcare industry
The deployment of Evidence-Based Medicine (EBM)
Evidence-based medicine (EBM) is a novel approach to patient treatment that integrates the best research data available with clinical experience and patient values. During the last years, EBM has raised great interest in the healthcare industry.
Normally, a doctor’s workday is dedicated in large part to reviewing numerous recently published articles and studies. Not only does this take a considerable amount of valuable time away from doctors’ hands, but it also leaves very little time to focus on the value of each study. Thankfully, an EBM approach incentivizes doctors to place their energy and attention to understand the issues relevant to their patients.
With this approach to medicine, doctors can use relevant research, Electronic Health Records (EHRs), and their own empiric knowledge to decide what fits best for every patient. Thanks to the analytic processing of healthcare text, doctors can compare their patients’ symptoms with large patient databases, empowering them to diagnose conditions faster and more precisely.
Analysis of Electronic Health Records (EHRs)
An Electronic Health Record (EHR) is a digital solution for the easy handling of a patient’s medical history. These virtual documents detail all the essential information about a patient in a clear and concise manner. By automating access to information, EHRs effectively streamline the processing of healthcare text.
Important patient data that can be added to an EHR include:
- Demographic information
- Progress notes
- Vital signs
- Past medical history
- Laboratory data
- Radiology reports
By providing access to accurate and clear medical records, EHRs help reduce medical errors. Likewise, delays in treatment due to a lack of information and duplication of tests can be minimized by having a readily available source of a patient’s full history. Patients benefit by having clear access to data about their health, helping them make better decisions.
EHRs can be kept in databases, where they occupy very little space and are safe from dust, humidity, being misplaced, or subject to other forms of human error. Electronic documents also safeguard the privacy of patients, as they are kept in encrypted virtual environments, far away from any prying eyes.
The reduction of hospital readmissions
Hospital readmissions bring a considerable amount of stress to doctors, patients, and hospitals. The implementation of healthcare text software can be used by health professionals to identify high-risk patients. Using their medical history, diagnostic data, and real-time data gathered from medical equipment, doctors can contrast this information with current patient trends and act accordingly.
One of the leading causes of hospital readmissions is a lack of accurate medication history, which leads to medication errors. In many hospitals, it has become customary to direct pharmacists to review all medications for the possibility of them counteracting or creating other health risks. Machine-learning algorithms can perform this same task autonomously and with no errors, potentially reducing hospital visits and saving lives in the process.
Protecting against healthcare fraud
Fraud in the healthcare sector can take many forms. From genuine errors in a bill leading to a financial disbalance to false claims ending up in wrong payments being issued. It is a hospital’s duty to store and monitor thousands of claims, billings, and similar data. The verification and processing of this information can take weeks or even months, leading to hospitals having problems at the time of properly monitoring their finances.
According to the National Health Care Anti-Fraud Association (NHCAA), the financial losses caused by healthcare fraud amount to billions of dollars every year. In the U.S. alone, some estimates say that more than $300 billion are lost to fraud annually (source).
Detecting the evidence of fraud and collecting it as legal evidence can hastily and efficiently be achieved using text analytics tools. When dealing with legal disputes, time is always of the essence. Healthcare data analytics can help detect incongruences much faster, giving hospitals the resources to both protect against fraud and legally defend themselves.
Optimizing the supply chain
Supply chain management accounts for a large portion of a hospital’s operating expenses. A major advantage of data analytics is the possibility of having real-time visibility of supply chain operations. Thanks to this perk, hospitals can avoid wasting or misusing valuable and expensive medicines, while also circumventing supply round-tripping.
State-of-the-art machine-learning algorithms improve healthcare forecasting capabilities, leading to products that arrive at the right place at the right time. The competencies of this software can create a fast and streamlined supply chain, while also freeing up staff to focus on more strategic duties.
6 benefits of text analytics in healthcare
1. Enhancing the relationship between patients and healthcare professionals
A hospital that integrates efficient text analytics for healthcare applications is guaranteeing its patients they are receiving the best health services available. Text analytics in healthcare can use sentiment analysis and other computational-linguistic tools to devise the feelings of customers towards their treatments and experiences.
2. Improving healthcare products and services
The vast volumes of unused data gathered by hospitals can be given many purposes, including the improvement of drug research and development. Furthermore, the implementation of healthcare text analytics helps patients understand their conditions better, leading to better patient compliance with treatments.
3. The optimization of medical reports
Interpreting medical records takes away valuable time and can become a headache when leading with multiple pages of reports daily. Due to text analytics tools’ ability to summarize vast volumes of data instantaneously, doctors can understand the contents of a report at first glance. This helps health professionals take less time to tend to patients and make more accurate decisions.
4. Streamlining the appointment booking process
Following up and booking patient appointments takes up about one-third of a health administrator’s time. Hospitals can save time, money, and resources by automating the entire appointment process. Text analytics software can organize and schedule appointments, improving the patient booking experience for doctors, patients, and administrators.
5. Protecting your organization against fraudulent activities
Thanks to text analytics, unusual patterns in healthcare data are much easier to recognize. This allows for the cross-checking of the statements made by medical practitioners, phlebotomists, polyclinics, and other healthcare professionals with complete databases of patient trends. Moreover, unfitting prescriptions and referrals can also be recognized by text analytics tools.
6. Text analytics tools help with decision-making
Doctors are required to make life-critical decisions on a daily basis. Because people are always prone to error, having a digital assistant letting one know all the facts before they take action can make a huge difference. Text analytics tools can supply doctors with precise data from tests, scans, and procedures with critical details such as the time these were documented. Armed with this knowledge, doctors can make more informed decisions.
Best text analytics tool for the healthcare industry
The ideal text analytic tool is able to seamlessly collect, analyze, and report on data of any kind. The state-of-the-art analytics systems powered by Semeon can be adapted to work with any data set, including healthcare data volumes of any size and complexity.
Thanks to Semeon’s text analytics platform, tasks that are complex, tedious, and time-consuming can be completed at a moment’s notice. The implementation of healthcare text software frees healthcare professionals from long hours of mundane paperwork, allowing them to save their time and energy to focus on improving the lives of their patients.
Here are some of the feautres of our text analytics platform:
- Multichannel sourcing
- Multilingual processing
- Sentiment analysis
- Extraction of multi-word expressions
- Context-based categorization
- Category hierachy
- Category drill-down
- Sentiment override
- Sentiment tracking overtime
- Intent evaluation
- Re-query existing data
- Intuitive dashboards
- Powerful multi-layered filters
- Rich search feature