Natural Language Processing in the Healthcare Industry
Artificial intelligence (AI) is progressively being adopted across the healthcare industry, and NLP is one of the crucial parts of AI concentrated on the translation and manipulation of human-produced spoken or written data.
In this blog, we describe a couple of promising NLP use cases for the healthcare domain. NLP poses some exhilarating opportunities in the healthcare area to swim through the big amount of records currently untouched and leverage it to improve results, deliver a higher quality of care, and optimize prices.
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.
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Let’s look at some of the benefits of NLP in the healthcare industry.
Medical Coding and Billing
NLP streamlines the way medical coders extract diagnostic, procedural and other clinical information. Rather than a coder reading documents and converting them to alphanumeric codes, NLP reads them and submits the codes to the coder for verification.
This allows the human coder to work on documents that NLP cannot process accurately, and reduces the overall expense of coding medical information. In the end, more accurate and thorough coding results in more accurate and timely billing.
Virtual Nursing Assistants
The rise of virtual nursing assistants capable of communicating with patients using NLP is underway. Regular communication between patients and the nursing bot extends care beyond the walls of the clinic room without burdening existing resources.
Adherence to the patient’s care plan can be monitored, and triggers can notify providers of issues that need human attention. Patients can receive round-the-clock access to support and answers, including help with medication.
Robot-Assisted Surgery
Some surgical robots use AI to apply information obtained from prior surgeries to the current case, leading to progressively better outcomes. Beyond the many well-known advantages, robotic surgery delivers, adding an NLP component allows surgeons to query the system and to direct its actions verbally.
Use Cases — NLP (Natural Language Processing) Technology
1. Comforting patients who become confused and anxious because they do not understand the data being presented to them through a portal website. For instance, NLP can explain the meaning of abbreviations and medical terminology. Rather than leaving the patient to worry or call the physician to explain the report, NLP can educate and possibly also calm the patient.
2. Offering summarized updates of key ideas, concepts, and conclusions contained in large volumes of clinical notes, journal articles and other narrative texts gives practitioners quick access to volumes of information that would otherwise require a lot of time to read through.
3. Easy extraction of data from free-form text and insertion into fixed-field data files, such as the structured fields in an EHR.
4. Handling a physician’s free-form spoken or text query, which is especially useful for queries that require gathering and organizing data from multiple sources.
5. NLP and other AI components can also accelerate the movement away from fee-for-service models and toward value-based healthcare by organizing unstructured health data derived from EHRs and other sources.
Much of that “hidden” big data can shed light on health outcomes for entire populations of patients, which has been impractical until recently.
