Natural Language Process (NLP) is a powerful tool for uncovering hidden secrets within unstructured text to analyze trends and reveal insights.
In healthcare, 60% of the 1.2 billion clinical documents produced in the US each year reside in unstructured narrative documents that would be largely inaccessible for data mining and quality measurement without NLP tools.
With NLP technology, organizations can unlock rich data to analyze patient populations and ultimately improve patient care.
In recent years, the use of NLP in healthcare has primarily been limited to disease-coding and research applications; however, Linguamatics was interested in discovering new opportunities that leverage NLP to enhance patient care and improve hospital efficiency.
Surveying healthcare system CMIOs
To that end, Linguamatics, with the support of the American Medical Informatics Association (AMIA), surveyed healthcare system CMIOs and asked them to share their visions for ways to leverage NLP to enhance patient care and improve hospital efficiency.
The participating CMIOs expressed overwhelming support for using NLP to help preserve the patient narrative and provide the insights required to meet accountable care objectives, including care delivery goals and the pro-active identification of high-risk patients.
They also voiced interest in leveraging NLP for a variety of other applications, including:
- Real-time identification of critical medical data for decision support. For example, the CMIOs supported the use of NLP to improve searches within knowledge bases, such as biomedical literature or clinical trial databases, to maximize the value of these sources.
- Automating the data capture process for disease registries. Currently most organizations largely rely on manual methods to curate the data required for registry reporting, which is widely used to support population health studies. With NLP, information can be automatically extracted, reducing manpower costs and saving time.
- Increasing participation in clinical trials and improving industry collaboration. Academic medical and cancer treatment centers are particularly interested in opportunities to participate in clinical trials – which can be a potential source of revenue - and to collaborate with peers. By using NLP to mine unstructured patient data, organizations can save significant time reviewing charts to identify eligible patients.
- Preserving revenues and minimizing patient leakage. In competitive healthcare markets, patient leakage can be a serious business concern, especially when patients are facing life-threatening health issues such as cancer. However, if providers leverage NLP to identify abnormalities immediately upon receipt of pathology reports, patient follow-up and care coordination can be escalated. Prompt follow-up minimizes the risk that a patient will seek treatment at an alternate facility.
- Mining social media. By using NLP to mine social media, organizations can identify disease trends, such as flu outbreaks, or assess consumer attitudes towards hospital services.
The use of NLP in healthcare will grow significantly in coming years, according to 91% of the surveyed CMIOs.
As healthcare organizations seek to increase automation, maximize revenues, and gain insights to improve the delivery of care, NLP will become an increasingly valuable and essential component for health IT applications.
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