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The healthcare industry is increasingly relying on advanced technologies to process large amounts of data. One of the more exciting developments is the use of natural language processing (NLP), where machines process human-generated data, like text or speech, as quickly as they process machine-generated data. It is reported that NLP in the healthcare and life sciences sectors is expected to grow to $3.7 billion by 2025, increasing 20.5% annually.
Translating this critical information into usable data has been an elusive goal for many years. Now, advances in NLP and its integration into healthcare is providing a way to translate human language into actionable data.
By using the following primary techniques to analyze written and spoken text, natural language processing allows healthcare providers to use extracted data to diagnose illnesses, develop treatment plans, monitor patient progress, and more.
Optical Character Recognition (OCR): computers’ ability to read handwritten or printed notes
Named Entity Recognition (NER): NER uses real-world names of people, places or products to organize data.
Sentiment Analysis (SA): the utilization of applying text analysis and biometrics to measure sentiments, feelings, or opinions.
Text Classification (TC): text classification assigns different tags or labels to specific categories.
Topic Modeling (TM): a form of statistical modeling that groups similar words and phrases together.
NLP is becoming an integral and transformative element in many EHR systems and it is forecasted to grow exponentially in the near future. Its primary function is to turn big data into smart data, and it does this by ingesting speech text and other forms of unstructured data and making sense of it. This is vital as it unlocks invaluable information otherwise inaccessible, providing healthcare providers with actionable insights.
The COVID-19 pandemic has highlighted the role of social determinants of health (SODH). Poverty, unstable housing, drug addiction and other social factors can affect individual health. It can also affect access to healthcare.
Clinicians and researchers need this information, but it is often buried in their records. SDOH data in EHRs usually take the form of clinical notes, patient-reported data, notes of patient phone calls, and telehealth transcripts.
With the right NLP algorithms, this data becomes transparent and available. Combined with machine learning and predictive analysis, this data analysis can point the way to the right medical and social interventions.
According to Healthcare IT News, “unstructured data is information that does not have any predefined data models or schemata, so it can be difficult for an enterprise to locate and digest.” This type of data holds critical information and invaluable insights and can be text files, images, transcripts, chat conversations, recordings, etc. 80% of health data is comprised of unstructured data, the impact it would have on health outcomes and the delivery of care would be astronomical if it were accessed and organized.
Chatbots, voice-activated smart assistants, and other speech-driven technology is used to facilitate the delivery of care. According to Health Tech Magazine, “NLP is creating better experiences by expanding patient access to information, cutting transcription costs and delays, and improving the quality of health records. Providers also report the tools can lower stress and allow more face time during appointments.”
NLP also makes real time records possible, which increases efficiencies all around. There are cloud based, AI-powered platforms delivering real-time transcriptions to EHRs.
Improved documentation is the goal of all EHR systems. With NLP, healthcare providers find it easy to document health history, physician notes and other information.
One study tested whether NLP techniques could replace standard documentation for physicians, while it was concluded that it hadn’t that point yet, it could be a useful adjunct to clinical notes. A combination of NLP and dictation was the most accurate, user-friendly way to update patient records.
Providers and insurance companies use numeric codes to identify conditions and make diagnoses. Codes also apply to specific treatments and surgeries. In the past, it was done manually by specially trained coders.
Today, computer-assisted coding (CAC) can generate accurate medical codes directly from clinical documentation and utilizing NLP can increase speed and accuracy. CAC medical coding that incorporates NLP technology “can analyze and interpret unstructured healthcare data using specialized algorithms, extracting the facts that support the codes assigned,” according to Foresee Medical.
Also known as auto-indexing, automated registry reporting is the ability to recognize patterns and extract clinical information from EHRs, registries, practice management systems, external lab reports, discharge reports and other mixed documentation. NLP technology is used to map text to structured fields, satisfying the mapping requirement.
Patients input essential health information into clinical decision support systems (CDSS). NLP technology then groups text classifications and extracts information from free text. This process aids doctors and physicians’ decision making processes regarding patient health outcomes.
Practitioners have discovered that NLP produces highly valuable predictive data. A recent study in the Journal of the American Medical Informatics Association found that evidence from unstructured data in healthcare was more accurate at prediction than structured data. The study noted that software developers have created accurate algorithms to mine that data using NLP.
Natural language processing EHR systems show great promise for computational phenotyping. The use of NLP has improved the ability of providers to document and monitor genome-wide and phenome-wide biomarkers. Using NLP for biomarker discovery improves the accuracy of records and avoids dangerous drug interactions for patients.
Handwritten notes by patients, doctors and nurses often contain important information. If it’s not processes as part of the EHR, however, that data can be lost. With NLP, handwritten notes can become part of the patient’s records. Machine learning combined with NLP can interpret handwriting to produce useful data.
What do these advances mean for healthcare delivery? Providers can expect:
Increased quality of care.
Improved patient and provider relationships.
Enhanced patient health awareness.
Medical natural language processing has created exciting opportunities in healthcare delivery and the patient experience. If you are investing in EHR systems, it would be wise to include NLP-powered algorithms.
At Chetu, our expert developers program software solutions with natural language processing capabilities to analyze structured and unstructured data. These solutions are able to mine web data, business data repositories, audio sources to detect current and emerging trends, they also provide operation insights, and have predictive analytics capabilities. To learn more, contact us.
Chetu, Inc. does not affect the opinion of this article. Any mention of specific names for software, companies or individuals does not constitute an endorsement from either party unless otherwise specified. All case studies and blogs are written with the full cooperation, knowledge and participation of the individuals mentioned. This blog should not be construed as legal advice.
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