The amount of healthcare data is growing by leaps and bounds but many healthcare systems are fully leveraging this bountiful data. Because a large amount of this data is unstructured (i.e., vitals, charges, follow-up appointments, encounters, and symptoms, among others), analysts aren’t able to extract and analyze much of this information. Unstructured data usually exists in text form and is more complex and bigger than structured data.
However, natural language processing uses artificial intelligence to tap into a large portion of this unstructured data. Using NLP, analysts can extract and analyze this unstructured data to obtain meaningful insights. It is estimated that 80% of healthcare data exists in the unstructured text form, and NLP, though still evolving, can be used to tap into the potential of this valuable data source.
EHR adoption is proliferating throughout the healthcare sector and the amount of health data being recorded and stored is also increasing rapidly. Though there is an opportunity for analysts to make use of this large amount of data, doing so is difficult because most of it is unstructured, i.e., in the text form. However, NLP promises to unlock the potential of this data by using artificial intelligence to extract useful data from EHRs. Speech recognition capabilities in EHRs are also on the horizon. Although highly useful, most respondents in a survey said they were unhappy with the current performance of their EHRs since they were having to spend inordinate amounts of time on the software rather than on providing healthcare. However, to make the most of NLP, one needs to work with a professional natural language processing company.
How Can Healthcare NLP Improve Outcomes?
Many providers of data science services use NLP to process unstructured data from a variety of sources like the literature, social media, and EHRs and then make this data available to analytics systems for interpretation. Once the text is converted to structured data by a natural language processing company, health care systems can extract insights, classify patients, summarize information, and more. There are 4 main areas in which a provider of data science services can improve healthcare functions. These are predictive analytics, EHR usability, quality improvement, and phenotyping.
Predictive Analytics Through NLP
One of the exciting areas in which NLP can be used is to improve significant population health concerns through predictive analytics. For instance, recent reports suggest that the suicide rate in the US has been increasing in recent times. Therefore, healthcare professionals have been spending much time and resources to understand who is at risk so that they can be dealt with properly. One study aimed at predicting suicide attempts using NLP by monitoring social media. The results clearly showed that there were specific indicators of suicide attempts. These ranged from users posting fewer emojis in text, limited use of emojis, or increased posts of angry or sad content before attempting suicide. The system had a whopping 70% predictive rate, but most tellingly, a 10% false-positive rate.
EHR Data Usability Improves With NLP
Typically, EHRs record patient information by every encounter, which makes finding critical information like social history (which is a robust predictor of readmissions) a lot more difficult. However, a provider of data science services, and more specifically, a natural language processing company, can create an EHR user interface that makes finding critical patient information easier.
The interface can be organized into sections and can include words related to patient concerns described during encounters. The interface can then populate the rest of the page with information related to the particular word or phrase. For instance, all records of fever could be made to show at the top of the page while the bottom could contain notes about the word. This interface makes it easier for clinicians to find hidden data and make a diagnosis based on better and more information.
Clinical Entity Resolver With NLP
A provider of data science services can help you use NLP to extract information about different diagnoses and conditions from patient records. Once done, an ICD-10 Clinical Modification code can be assigned to them.
The ICD-10-CM is an invaluable resource that helps physicians make better decisions. This is possible by cross-referencing diagnoses and symptoms against the relevant ICD-10-CM codes. Some typical health conditions that can be identified with NLP include gestational diabetes mellitus or HTG-induced pancreatitis.
Phenotypic Capability Boost With NLP
A phenotype is an observable biochemical or physical expression of a specific trait or feature in a life form. With phenotyping, clinicians can categorize or group patients to obtain a deeper, more targeted view of the data. For instance, clinicians can group patients that share specific traits. At present, most phenotyping work is being done on structured data because it is easier to extract insights from and analyze.
However, with NLP, analysts can extract elements of interest from unstructured data and analyze them. These could include information on follow-up appointments, encounters, charges, orders, and symptoms.