Top 14 Natural Language Processing Use Cases in Healthcare
Introduction
Natural Language Processing or NLP applications in healthcare present some unique and stimulating opportunities. It provides a glide through the vast proportion of new data and leverages it for boosting outcomes, optimizing costs, and providing optimal quality of care.
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Better access to data-driven technology as procured by healthcare organizations can enhance healthcare and expand business endorsements. But, it is not simple for the company enterprise systems to utilize the many gigabytes of health and web data. But, not to worry, the drivers of NLP in healthcare are a feasible part of the remedy.
What is NLP in Healthcare?
The NLP illustrates the manners in which artificial intelligence policies gather and assess unstructured data from the language of humans to extract patterns, get the meaning and thus compose feedback. This is helping the healthcare industry to make the best use of unstructured data. This technology facilitates providers to automate the managerial job, invest more time in taking care of the patients, and enrich the patient’s experience using real-time data.
However, NLP applications in healthcare go beyond understanding human language.
You will be reading more in this article about the most effective uses and role of NLP in healthcare corporations, including benchmarking patient experience, review administration and sentiment analysis, dictation and the implications of EMR, and lastly the predictive analytics.
Top 14 NLP Use Cases in Healthcare
Let us have a look at the 14 use cases associated with Natural Language Processing in Healthcare:
1. Clinical Documentation
NLP healthcare systems help free clinicians from the laborious physical systems of EHRs and permits them to invest more time in the patient; this is how NLP can help doctors. Both speech-to-text dictation and formulated data entry have been a blessing. The Nuance and M*Modal consists of technology that functions in team and speech recognition technologies for getting structured data at the point of care and formalised vocabularies for future use
The NLP technologies bring out relevant data from speech recognition equipment which will considerably modify analytical data used to run VBC and PHM efforts. This has better outcomes for the clinicians. In upcoming times, it will apply NLP tools to various public data sets and social media to determine Social Determinants of Health (SDOH) and the usefulness of wellness-based policies.
2. Speech Recognition
NLP has matured its use case in speech recognition over the years by allowing clinicians to transcribe notes for useful EHR data entry. Front-end speech recognition eliminates the task of physicians to dictate notes instead of having to sit at a point of care, while back-end technology works to detect and correct any errors in the transcription before passing it on for human proofing.
The market is almost saturated with speech recognition technologies, but a few startups are disrupting the space with deep learning algorithms in mining applications, uncovering more extensive possibilities.
3. Computer-Assisted Coding (CAC)
Computer-assisted coding (CAC) is one of the most famous examples of NLP applications in healthcare. CAC captures data of procedures and treatments to grasp each possible code to maximize claims. It is one of the most popular uses of NLP, but unfortunately, its adoption rate is just 30%. It has enriched the speed of coding but fell short at accuracy.
4. Data Mining Research
The integration of data mining, healthcare technology, and big data analytics in healthcare systems allows organizations to reduce the levels of subjectivity in decision-making and provide useful medical know-how. Once started, data mining can become a cyclic technology for knowledge discovery, which can help any HCO create a good business strategy to deliver better care to patients.
5. Automated Registry Reporting
An NLP use case is to extract values as needed by each use case. Many health IT systems are burdened by regulatory reporting when measures such as ejection fraction are not stored as discrete values. For automated reporting, health systems will have to identify when an ejection fraction is documented as part of a note, and also save each value in a form that can be utilized by the organization’s analytics platform for automated registry reporting.
Automated registry reporting can be cumbersome to implement. To achieve the best possible results from the go, we recommend you seek the expertise of Natural Language Processing services.
6. Clinical Decision Support
Advancements in NLP applications in healthcare are poised to elevate clinical decision support. Nonetheless, solutions are formulated to bolster clinical decisions more acutely. There are some areas of processes, which require better strategies of supervision, e.g., medical errors.
According to a report, recent research has indicated the beneficial use of NLP for computerized infection detection. Some leading vendors are M*Modal and IBM Watson Health for NLP-powered CDS. In addition, with the help of Isabel Healthcare, NLP is aiding clinicians in diagnosis and symptom checking.
7. Clinical Trial Matching
NLP applications in healthcare are making significant strides, especially in Clinical Trial Matching.
Using NLP and machines in healthcare for recognising patients for a clinical trial is a significant use case. Some companies are striving to answer the challenges in this area using Natural Language Processing in Healthcare engines for trial matching. With the latest growth, NLP can automate trial matching and make it a seamless procedure.
One of the use cases of clinical trial matching is IBM Watson Health and Inspirata, which have devoted enormous resources to utilize NLP while supporting oncology trials.
8. Prior Authorization
Analysis has demonstrated that payer prior authorisation requirements on medical personnel are just increasing. These demands increase practice overhead and holdup care delivery. The problem of whether payers will approve and enact compensation might not be around after a while, thanks to NLP. IBM Watson and Anthem are already up with an NLP module used by the payer’s network for deducing prior authorisation promptly.
9. AI Chatbots and Virtual Scribe
Although no such solution exists presently, the chances are high that speech recognition apps would help humans modify clinical documentation. The perfect device for this will be something like Amazon’s Alexa or Google’s Assistant. Microsoft and Google have tied up for the pursuit of this particular objective. Well, thus, it is safe to determine that Amazon and IBM will follow suit.
Chatbots or Virtual Private assistants exist in a wide range in the current digital world, and the healthcare industry is not out of this. Presently, these assistants can capture symptoms and triage patients to the most suitable provider. New startups formulating chatbots comprise BRIGHT.MD, which has generated Smart Exam, “a virtual physician assistant” that utilises conversational NLP to gather personal health data and compare the information to evidence-based guidelines along with diagnostic suggestions for the provider.
Another “virtual therapist” started by Woebot connects patients through Facebook messenger. According to a trial, it has gained success in lowering anxiety and depression in 82% of the college students who joined in.
10. Risk Adjustment and Hierarchical Condition Categories
Hierarchical Condition Category coding, a risk adjustment model, was initially designed to predict the future care costs for patients. In value-based payment models, HCC coding will become increasingly prevalent. HCC relies on ICD-10 coding to assign risk scores to each patient. Natural language processing can help assign patients a risk factor and use their score to predict the costs of healthcare.
11. Computational Phenotyping
In many ways, the NLP is altering clinical trial matching; it even had the possible chances to help clinicians with the complicatedness of phenotyping patients for examination. For example, NLP will permit phenotypes to be defined by the patients’ current conditions instead of the knowledge of professionals.
To assess speech patterns, it may use NLP that could validate to have diagnostic potential when it comes to neurocognitive damages, for example, Alzheimer’s, dementia, or other cardiovascular or psychological disorders. Many new companies are ensuing around this case, including BeyondVerbal, which united with Mayo Clinic for recognising vocal biomarkers for coronary artery disorders. In addition, Winterlight Labs is discovering unique linguistic patterns in the language of Alzheimer’s patients.
12. Review Management & Sentiment Analysis
NLP can also help healthcare organisations manage online reviews. It can gather and evaluate thousands of reviews on healthcare each day on 3rd party listings. In addition, NLP finds out PHI or Protected Health Information, profanity or further data related to HIPPA compliance. It can even rapidly examine human sentiments along with the context of their usage.
Some systems can even monitor the voice of the customer in reviews; this helps the physician get a knowledge of how patients speak about their care and can better articulate with the use of shared vocabulary. Similarly, NLP can track customers’ attitudes by understanding positive and negative terms within the review.
13. Dictation and EMR Implications
On average, EMR lists between 50 and 150 MB per million records, whereas the average clinical note record is almost 150 times extensive. For this, many physicians are shifting from handwritten notes to voice notes that NLP systems can quickly analyse and add to EMR systems. By doing this, the physicians can commit more time to the quality of care.
Much of the clinical notes are in amorphous form, but NLP can automatically examine those. In addition, it can extract details from diagnostic reports and physicians’ letters, ensuring that each critical information has been uploaded to the patient’s health profile.
14. Root Cause Analysis
Another exciting benefit of NLP is how predictive analysis can give the solution to prevalent health problems. Applied to NLP, vast caches of digital medical records can assist in recognising subsets of geographic regions, racial groups, or other various population sectors which confront different types of health discrepancies. The current administrative database cannot analyse socio-cultural impacts on health at such a large scale, but NLP has given way to additional exploration.
In the same way, NLP systems are used to assess unstructured response and know the root cause of patients’ difficulties or poor outcomes.
What Immediate Benefits Can Healthcare Organizations Get By Leveraging NLP?
Healthcare organizations can use NLP to transform how they deliver care and manage solutions. Organizations can use machine learning in healthcare to improve provider workflows and patient outcomes.
Here is a wrap-up of the use of Natural Language Processing in healthcare:
1. Improve Patient Interactions With the Provider and the EHR
Natural language processing solutions can help bridge the gap between complex medical terms and patients’ understanding of their health. NLP can be an excellent way to combat EHR distress. Many clinicians utilize NLP as an alternative method of typing and handwriting notes.
2. Increasing Patient Health Awareness
Most need help comprehending the information even when patients can access their health data through an EHR system. Because of this, only a fraction of patients can use their medical information to make health decisions. This can change with the application of machine learning in healthcare.
3. Improve Care Quality
NLP tools can offer better provisions for evaluating and improving care quality. Value-based reimbursement would require healthcare organizations to measure physician performance and identify gaps in delivered care. NLP algorithms can help HCOs do that and also assist in identifying potential errors in care delivery.
4. Identify Patients With Critical Care Needs
NLP algorithms can extract vital information from large datasets and provide physicians with the right tools to treat complex patient issues.
How can Doctors Benefit by Implementing NLP in Healtcare Projects?
A study highlighted that physicians spend as much as 49% of their time on EHRs and desk work. The same survey also revealed that they could devote only 27% of their day towards clinical patient care.
This excessive paperwork burden is touted to be a significant contributor to physician burnout. This not only takes a toll on the well-being of healthcare professionals but also profoundly impacts patient care.
Application of NLP in healthcare projects is emerging as a potential solution to this problem.
Paperwork Reduction and Increased Efficiency: NLP healthcare systems can interpret and record medical information in real-time, eliminating the need for doctors to sit down and make entries manually. This can significantly reduce the paperwork burden, increasing efficiency and allowing healthcare professionals to focus more on patient care.
Real-Time Clinical Data Analysis: Advanced NLP systems can scan vast clinical text data within seconds and extract valuable insights from piles of data. For example, an NLP medical record summarization model can analyze a patient’s medical history within seconds and generate a comprehensive summary highlighting all the essential clinical findings and previous treatments.
Computer-Assisted Coding (CAC): Another advantage of NLP is the ability of computer-assisted coding to synthesize lengthy chart notes into essential pointers. In the past, the manual review and processing of extensive stacks of chart notes from health records stretched for weeks, months, or even years. NLP-enabled systems can significantly expedite this process, accelerate the identification of crucial information, and streamline the overall workflow.
Implementing Predictive Analytics in Healthcare
Identification of high-risk patients, as well as improvement of the diagnosis process, can be done by deploying Predictive Analytics along with Natural Language Processing in Healthcare along with predictive analytics.
It is vital for emergency departments to have complete data quickly, at hand. For example, the delay in diagnosis of Kawasaki diseases leads to critical complications in case it is omitted or mistreated in any way. As proved by scientific results, an NLP based algorithm identified at-risk patients of Kawasaki disease with a sensitivity of 93.6% and specificity of 77.5% compared to the manual review of clinician’s notes.
A set of researchers from France worked on developing another NLP based algorithm that would monitor, detect and prevent hospital-acquired infections (HAI) among patients. NLP helped in rendering unstructured data which was then used to identify early signs and intimate clinicians accordingly.
Similarly, another experiment was carried out in order to automate the identification as well as risk prediction for heart failure patients that were already hospitalized. Natural Language Processing was implemented in order to analyze free text reports from the last 24 hours, and predict the patient’s risk of hospital readmission and mortality over the time period of 30 days. At the end of the successful experiment, the algorithm performed better than expected and the model’s overall positive predictive value stood at 97.45%.
The benefits of deploying NLP can definitely be applied to other areas of interest and a myriad of algorithms can be deployed in order to pick out and predict specified conditions amongst patients.
Even though the healthcare industry at large still needs to refine its data capabilities prior to deploying NLP tools, it still has a massive potential to significantly improve care delivery as well as streamline workflows. Down the line, Natural Language Processing and other ML tools will be the key to superior clinical decision support & patient health outcomes.
End Note
The advantages of deploying natural language processing solutions can indeed pertain to other areas of interest. A myriad of algorithms can be instilled for picking out and predicting defined situations among patients. Although the healthcare industry still needs to improve its data capacities before deploying NLP tools, it has an enormous ability to enhance care delivery and streamline work considerably. Thus, NLP and other ML tools will be the key to supervise clinical decision support and patient health explanations.
Implementing NLP in healthcare projects is not a holistic solution to all the problems. So, the system in this industry needs to comprehend the sublanguage used by medical experts and patients. NLP experts at Maruti Techlabs have vast experience in working with the healthcare industry and thus can help your company receive the utmost from real-time and past feedback data.
Maruti Techlabs supports leading hospitals and healthcare units with AI-driven NLP services. Our trademark products interpret human behaviour and languages and provide customised search results, chatbots, and virtual assistants to help you benefit from the role of NLP in Healthcare.
FAQs
1. What is an example of NLP in healthcare?
An example of NLP in healthcare is Computer-assisted coding (CAC). It learns data on procedures and treatments to observe each possible code to maximize claims.
2. What are the challenges of NLP in healthcare?
As medical language is often ambiguous, the meaning of written phrases and their meanings can vary in context depending on the writer. Therefore, one of the challenges of implementing NLP in healthcare is understanding the meaning and developing an opinion from clinical text.
3. What are the applications of NLP in medicine?
NLP in medicine aids research by learning scientific literature for trends and insights, extracting relevant data from patient records, and improving clinical documentation.
4. What is natural language processing in healthcare chatbots?
NLP chatbots offer a more human and interactive experience for chatbots. Old-school chatbots without NLP provide a robotic and impersonal experience. Using NLP also offers benefits like automation, zero contact resolution, valuable feedback collection, and lead generation.
