To meet the high demand for value-based health care and comply with quality measures, healthcare organizations are increasingly switching to AI-powered solutions to interpret complex, unstructured data and generate workable intelligence. This data could be in the form of text, visual, or speech. Harnessing AI power in healthcare can open the doors to extraordinary opportunities and reinforce healthcare organizations' collective investment in human resources, capital, and time. Natural Language Processing (NLP) immensely improves healthcare outcomes by transforming various aspects of the industry, including free-text, clinical documentation, clinical trials, decision-making processes, automated reporting, and data mining research, among others.

NLP Use Cases in Healthcare 

1. Improving patient information 

One area in healthcare that NLP has tremendously transformed is electronic health records (EHR). Often, information about patients, such as their medical history, current state, or medical diagnosis reports, is stored in hardcopy, unstructured data that can easily be lost, misplaced, or destroyed, resulting in data loss. Besides, filling medical records manually is quite time-consuming and ultimately leads to tedious paperwork. EHR systems solve these challenges as physicians, and other healthcare workers can access data about patients from an electronic device. NLP has completely transformed the way information is captured with the use of voice assistants and text dubbing. Some of its benefits include time-saving, more categorized data, the ability to perform searches in the system, and vacating a couple of rooms hosting paper-based health records for other medical purposes. Patients can also access their health records electronically and track their condition from their gadgets effortlessly. Results can be sent via email, saving patients time and money. Moreover, governments and insurance companies have benefited from the integration of NLP solutions in healthcare since reports are generated automatically, and necessary information is issued upon request. NLP solutions in healthcare also benefit regulatory agencies and insurance companies by automating report generation and maintaining data integrity.

For instance, Mercy Healthcare Company, an HCO running forty hospitals in the United States with over 900 physicians and 45,000 co-workers, was one of the early adopters of NLP in their EHR systems in the chain of hospitals and clinics to provide unified patient care across the continuum. A study conducted by data pundits from Data Science, Kerry Bommarito and Nick White, revealed that Mercy had gained real-world insights from the accurate data generated and tremendous reduction of document time.

2. Enabling physicians to spend sufficient time with patients

One major challenge in health care delivery is the inability of doctors to commit adequate time to patients and provide unbroken attention when serving them. Indispensable administrative roles, which include ensuring that all critical documentation is completed, are one of the major challenges that interfere with their main role or result in burnout. The introduction of Natural Language Processing in Healthcare is gradually solving this problem. NLP solutions are replacing handwriting and typing techniques with voice notes. Also, NLP tools are being used to interpret speech and accurately update health records. These highly-efficient approaches enable doctors to make notes while examining patients – avoiding replication of efforts, and ultimately committing more time and attention to patient care. Moreover, NLP is turning out as a helpful technique in assessing the accuracy of medical notes. For example, the Saudi Arabia-based Nahdi Medical operates a chain of health facilities in Saudi Arabia and is a leading adopter of NLP in the region. Physicians in this healthcare organization admit to having a patient-centric model of healthcare delivery, thanks to NLP technologies.

3. Extraction and interpretation of clinical notes

Health care delivery is not only based on the health profile of the patients but also includes the prescription of medications based on test outcomes to recommend further procedures. Physicians require accurate information to make correct decisions regarding patient treatment. NLP performs exemplarily in improving healthcare outcomes by accurately extracting information from diagnostic medical reports, doctor's letters, lab reports, etc., and interpreting clinical notes. NLP solutions are also adopted where EMR-based information stored in native text format becomes strenuous and time-consuming to extract with traditional techniques.

For instance, medical practitioners at the Toronto General Hospital, a Canadian-based Health organization use Autoscribe, an NLP-based solution developed by Mutuo Health Solutions to extract and interpret clinical notes. The system helps the institution with subtle nuances to improve patient and business care.

4. Enhancing patient awareness, engagement, and health literacy 

Today, regulatory directives and industry policies recommend patient participation in health care. Healthcare organizations have patient portals where patients can access their medical reports, monitor their health, and concentrate on self-health superintendence. Unfortunately, these portals are characterized by numerous limitations regarding the education and knowledge of patients. Consequently, their participation is discouraged and the significance of the portals declines. The incorporation of NLP resources in healthcare to define complex terms in decipherable language has increased patient awareness and engagement. These solutions facilitate a better understanding of electronic health records and other IT resources/tools/platforms by patients, promoting greater involvement and making more informed decisions regarding their health. For instance, the American Association of Retired Persons (AARP) has benefited immensely from the adoption of NLP in enhancing the health literacy of people above the age of 50 for improved healthcare.

5. Delivering value-based health care

NLP tools are used to help patients switch from traditional treatment plans to value-based care. Physicians use NLP algorithms to examine patient care and identify potentially dangerous gaps in healthcare cycles. Also, NLP solutions are used to set up benchmarks for doctors and gauge free text. In addition to removing errors involved in the extraction of information, NLP tools are used to determine the quality of health care delivery. Doctors can use them to ascertain the quality of treatment offered to patients and compare it with established standards. This dramatically improves the outcome of care delivery in healthcare. for instance, HealthLinks, a Healthcare consulting firm based in Jeddah has strategically collaborated with Saudi's health ministry to deliver value-based care to millions of patients that visit their facilities annually.

6. Improving coordination of health care

Medical scientists are leveraging NLP algorithms and machine learning (ML) to improve health care coordination by examining huge volumes of unstructured medical data and drawing workable insights from them. Results from initial experiments have exhibited promising potential for enhanced coordinated health care, particularly from a behavioural perspective. Additionally, the integration of NLP in healthcare administrative roles has a great impact on the industry. For instance, NLP resources enable the staff to gather and autofill all vital information quickly and accurately. They help health administrators to identify errors in health documentation and make recommendations. This way, these significantly reduce operational costs and, at the same time, increase revenue for HCOs. For instance, Plasticity, a U.S.-based, developed an NLP solution that is designed to understand the deeper context of large volumes of unstructured data like patients' information, medical history, or treatment plant. Also, the system helps physicians spot errors in documentation processes and make suggestions, saving operational costs and time. 

Conclusion 

The evolving set of use cases signifies that incorporating Natural Language Processing in healthcare improves care delivery, patient safety, and experience. It enables medical practitioners to invest their time in delivering care while assisting them in making wise decisions based on factual, accurate data. NLP solutions also minimize the time spent undertaking administrative tasks by automating operations. Arguably, NLP is a transformative technology that is driving a better quality of health care.