The reason why the adoption of natural language processing (NLP) is soaring is because of its undisputed potential in interpreting complex, unstructured datasets, and in generating actionable intelligence. This data can be in any form such as text, speech, visuals, etc. Harnessing this power can unlock the doors to unprecedented opportunities and maximize the organization’s collective investment in terms of capital, human efforts, and time. NLP works on the same lines. It helps process massive amounts of data present in general linguistic form, and run advanced machine learning algorithms on it to obtain valuable business insights.
NLP is even more useful in the healthcare industry where huge data volumes are churned out incessantly every day. A few aspects of healthcare that the technology is transforming are free-text, clinical documentation improvement, data mining research, automated reporting, clinical trials, and decisions, etc.
The following are a few major applications of NLP technology in healthcare:
NLP helps doctors spend more time with patients.
One of the biggest challenges in care delivery has been the inability of the physicians to dedicate maximum time to their patients and provide undivided attention to them. This is because of the indispensable administrative responsibilities. In addition to consultation, physicians and clinicians must also ensure that all necessary documentation is in place. And to do that, they have to work beyond their working hours quite often. This has resulted in a rise of cases where they reach a burnout stage.
NLP is gradually proving to be a solution to this challenge. Many of them are now replacing handwriting or typing with voice notes. NLP tools can easily interpret the speech and update records accurately.
This is a highly efficient approach as it allows physicians to make notes while talking to patients— thus, avoiding duplication of efforts and enabling them to devote more time to patient care. With clinical dictation, this technology is contributing significantly to an improved quality of care.
Besides saving physicians from an unnecessary burnout, NLP is also turning out to be a great method to determine the accuracy of notes. It’s because when they’re recording the notes in front of a patient, he or she can immediately suggest correction when needed.
NLP extracts and interprets clinical notes accurately.
It’s not only patient health profile which care delivery is based on, but physicians must also refer to prescriptions based on medical evidences to recommend further procedures. Clinicians need accurate information to make critical decisions pertaining to the treatment of their patients. Majorly, they use diagnostic results and notes from doctors to understand patient profiles. Based on this, they take the most appropriate course of treatment. That’s why electronic medical records (EMR) have undeniable importance.
However, there is a challenge. A considerable part of information registered in EMRs, i.e., clinical notes, is in unstructured form. This often makes practitioners grapple while obtaining unstructured information from the system for automated analysis.
NLP has been successful in improving the healthcare process and outcomes by effectively interpreting clinical notes. It extracts details from diagnostic reports and doctors’ letters, and ensures the completeness and accuracy of patient health profile.
The NLP technology is even more required when the information stored in EMRs is in narrative text form which would be difficult and time-consuming to extract using regular approaches.
NLP can increase patient awareness and involvement.
Over the recent few decades, regulatory guidelines and industry practices have given rise to an increased need for patient involvement in care. Unlike the past, healthcare providers today have a patient portal which patients can access any time to access their reports, keep a track of their health, and focus on self-health management. They’re also in control of whom their health information should be shared with.
However, it’s not as easy as it sounds. These portals have certain limitations with respect to the education and awareness of patients. They aren’t always able to interpret their health data. This way, their involvement in the treatment is lessened and the value of these portals goes down. Moreover, physicians may have to spend extra time to educate patients and tackle their insecurities related to their test reports, which might give way to the previous challenge stated in this blog.
In this case, providers can use NLP tools to define complex terms in a language decipherable by patients. This step can facilitate a better understanding of EHR and other IT tools among patients, enabling them to be more involved and make an informed decision regarding their health.
NLP can be used to deliver value-based care.
NLP tools may also offer a more efficient way to evaluate and improve care quality. It can empower patients to switch from the current treatment plan and opt for value-based care.
Healthcare is a complex process. A significant degree of human involvement in the care cycle leaves room for errors, sometimes potentially dangerous ones. In a sensitive situation, these errors can have a jeopardizing effect on care delivery. NLP algorithms can be used to assess patient care and identify these gaps.
NLP tools can also set up a benchmark for physicians and evaluate free-text. Besides eliminating the scope of errors involved in information extraction, they can ascertain a quality measurement of care delivery. Medical practitioners can leverage them to check the standard of care a patient is being provided with and how it compares with established guidelines. For example, they can pull required pieces of information from electronic records to find out what prescriptions and medications were given to the patients and if they were relevant to the case.
NLP can improve care coordination.
Healthcare outcomes are dependent on several psychological factors that are entirely non-clinical in nature. These factors can be financial instability, mental ailments, social insecurity, substance abuse, etc. For example, someone who is facing financial difficulties may not be on top of the course of treatment and fail to get desired results. A major concern that may arise out of these indirect determinants is prolonged treatment leading to higher healthcare costs. Traditional approaches tend to miss taking these factors into consideration while extracting clinical data and determining a treatment plan. This is because the information on these determinants is scattered and hard to access.
Medical researchers are using NLP and machine learning to improve care coordination by analyzing large sets of unstructured health data and drawing actionable insights from them. Initial experiments conducted using NLP algorithms have shown promising potential for improved coordinated care, especially in behavioral aspects. Clinicians can rely on them to identify patients who are susceptible due to the limitations of current diagnostic systems.
In addition to the areas of healthcare operations given above, NLP is also making a difference in administrative tasks. It can help the staff collect and autofill all critical data quickly and correctly. Additionally, it can spot errors in documentation and make suggestions. This way, it’s highly capable of reducing operational costs and increasing revenue for healthcare organizations.
Conclusion
NLP is paving the way to a brighter future for healthcare delivery and patient experience. It will not be long before it enables physicians to invest their maximum time in patient care while helping them make informed decisions based on real-time, accurate data. NLP is also reducing the time spent in administrative activities by automating workflows. However, to realize optimum value from NLP, it will be critical for healthcare providers to refine their data management models.
This digital age is bringing latest technologies like AI, NLP, and Blockchain to fruition for an industry like healthcare. In the coming years, we will see the potential of these transformative technologies unravel to drive a better quality of care for everyone.
