What if physicians could offer patients access to a potentially life-preserving test, but could not easily identify which of their patients were eligible?

That is the exact situation many providers have found themselves in since Medicare announced it would begin covering lung cancer screening for patients meeting a certain set of criteria.

In a decision memo published February, 2015, CMS agreed to make Medicare coverage available for a low dose computed tomography (LDCT) lung cancer screening for eligible patients. Patients who are between ages 55 and 77, asymptomatic, are either a current smoker or have quit within the last 15 years, and, have a tobacco smoking history of at least 30 pack-years can now qualify for an annual preventative screening.

CMS added the coverage after determining there was sufficient evidence that LDCT procedures were cost-effective for high risk populations. A study by the National Lung Cancer Screening Trial, for example, found that 12,000 deaths a year could be avoided if high-risk patients underwent a LDCT scan. Lung cancer is currently the leading cause of cancer-related death among both men and women in the US.


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:


February 4, 2016 was World Cancer Day, and February is National Cancer Prevention Month. Throughout this month, individuals and groups worldwide are writing and sharing about the importance of taking steps to reduce your risk of cancer on an individual level and also the importance of cancer research on a clinical level.

Linguamatics are one of the pioneers in investing in Natural Language Processing (NLP) text mining technology to improve patient outcomes and cancer care, and one of the few companies using NLP at all. We have been working in healthcare for over 10 years, and recently announced a collaboration with Cancer Research UK to improve the characterization of cancer patient data for precision medicine.

NLP is growing rapidly in healthcare not only for research, but also now in widespread use for computer aided coding and computer aided document improvement. Simon Beaulah, our Director of Healthcare Strategy,  has published a white paper on 9 ways Natural Language Processing is being used by scientists to improve our (actionable) understanding of cancer. This highlights how, by applying NLP, significant impact can be achieved in improving cancer care by targeting the following areas:


CMIOs-Importance-of-Clinical-NLP

 

The transition to new value-based payment models is spurring provider demand for technologies that enhance patient care and minimize safety risks, and in turn reduce costs. Of particular interest are tools to help providers predict the likelihood of potentially avoidable outcomes, such as a hospital readmission, pulmonary nodules turning cancerous or the contraction of sepsis.

According to a recent Linguamatics survey, most hospital CMIOs support the use of predictive models to improve the quality of care. In addition, CMIOs believe that these models can be enhanced with the use of Natural Language Processing (NLP) to access insightful data from unstructured chart notes.


Clinical NLP Important Applications

The advent of accountable care, meaningful use, and the triple aim is creating an unprecedented demand for insightful patient data. Though structured data reveals valuable information, some 80% of EHR data resides in an unstructured narrative format. Furthermore, of the 1.2 billion clinical documents produced in the US each year, 60% of the valuable information exists in unstructured narrative documents that are largely inaccessible for data mining and quality measurement.

To gain better insight into patient data, providers might be inclined to expand their use of templates to capture discrete observations. Unfortunately, when purely coded templates take the place of free-text narratives, the resulting documentation often fails to capture subtle circumstances of a patient’s story. Frequently the patient narrative is the most effective means of communicating detailed information between healthcare professions.

What alternatives do providers have for preserving the patient narrative, while at the same time gain additional insights from a patient’s complete medical record? One option is to tap into the power of Natural Language Processing (NLP) technology.