Introduction
Scientific and technological progress is important because it creates essential improvements in various spheres. The healthcare industry is no exception, as it strongly benefits from various innovations. Natural language processing (NLP) is among them, and this technology enables electronic devices to understand and utilize natural language in the same way people do.
In particular, Artificial Intelligence algorithms enable machines to extract and analyze natural language from audio, textual, and video sources. This list of possibilities is not exhaustive, indicating that the specified technology is promising and can offer people multiple advantages. That is why the healthcare industry is actively implementing NLP in its processes, as evident in the spheres of electronic health records (EHRs) and mental health.
Benefits of NLP in EHRs
The nature of NLP makes this technology a valuable tool in the sphere of EHRs. Esteva et al. (2019) mention that a single hospitalization can generate approximately 150,000 pieces of data. Previously, medical professionals had to create and interpret this data, which required a significant amount of time and effort. Now, NLP can come into play because this technology can facilitate the analysis of this information more quickly and effectively. This technology can analyze the available EHRs to discover relevant insights from unstructured data (Esteva et al., 2019). Physicians use these results to improve their decision-making in terms of diagnosis, treatment, and prognosis.
Furthermore, burnout is a significant problem in the medical industry. Esteva et al. (2019) explain that physicians spend approximately six hours of their 11-hour workdays on documentation. As a result, they have little time to interact directly with patients. NLP is an option here because this technology can be utilized as a voice assistant that transcribes patient-physician communication and fills in certain EHR sections (Esteva et al., 2019). Consequently, this tool will help healthcare professionals reduce their burnout and allocate more time to patient care. In turn, this feature will make it more challenging for physicians to miss any critical information disclosed by patients, which will obviously improve the quality of care.
Additionally, NLP can be effective and beneficial in the mental health sphere. According to Le Glaz et al. (2021), providers actively utilize this technology for preprocessing, making it easier for healthcare professionals to analyze patients’ records. Another significant benefit of NLP in the medical sphere is its ability to draw conclusions based on unexplored and unstructured data. For example, NLP can interpret and analyze patients’ daily habits to diagnose mental health conditions, which are often overlooked by providers due to the large volumes of data they manually process (Le Glaz et al., 2021). That is why a growing number of medical facilities introduce NLP into their operations.
Conclusion
In conclusion, sufficient scientific evidence demonstrates that NLP is a requested technology in the healthcare sphere. This innovative solution provides organizations and medical professionals with numerous essential benefits. In particular, NLP saves physicians’ time, reduces their burnout and workload, maximizes time for patient care, and improves mental health care.
The list of these advantages can be continued since NLP performs a particular portion of work that could only be performed by people a few years ago. Thus, this innovative technology demonstrates that modern professionals are equipped with an effective assistant to manage routine tasks and generate valuable insights from unstructured data. This statement ensures that healthcare organizations will continue to integrate NLP into their operations and processes.
References
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Trun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29.
Le Glaz, A., Haralambous, Y., Kim-Dufor, D. H., Lenca, P., Billot, R., Ryan, T. C., Marsh, J., DeVylder, J., Walter, M., Berrouiguet, S., & Lemey, C. (2021). Machine learning and natural language processing in mental health: Systematic review. Journal of Medical Internet Research, 23(5), e15708.