I needed this yesterday. 

None of us appreciate it when our clinician’s head is buried in a computer, when what we really want is to be heard and taken care of. But, when so much has to be done within a very short timeframe, what if we as a provider miss an important clinical clue? There has got to be a better way…

Rapid and efficient diagnoses are why tools such as the first automatic blood pressure monitor were invented.  Of course, the days of Seymour B. London’s 1965 design - a prototype using a blood pressure cuff, a column of mercury, a microphone and a fish tank pump - are long gone. Now all vital signs can be checked within a few minutes, including blood pressure, electric heart signals, blood oxygen, and temperature - far more quickly and accurately than a rushed human with an armful of heavy equipment in a noisy clinical setting.

At Linguamatics our goal is to provide healthcare professionals with software that helps them do their jobs better. Giving physicians time back to be more personally attentive during the patient visit, is a high priority. Patients want to be heard. Just have a look at how many bad physician reviews follow the theme of a negative bedside manner - even if the physician achieves the right clinical outcome.

In the spirit of decreasing human error, increasing patient-physician face-time (and of course, the alternative use of a fish tank pump), we at Linguamatics are delighted to introduce our I2E Asynchronous Messaging Pipeline (aka AMP).


Enhancing problem list reconciliation with Natural Language Processing (NLP): Improve patient care quality with population health text mining analytics

The shift from volume to value-based compensation is driving provider demand for better insights into the health of patient populations. Providers recognize that access to more complete patient data can enhance their ability to deliver cost-effective care and high quality outcomes. This is especially true for patients with multiple chronic conditions, who typically have more complicated care needs and higher hospital utilization rates.

Figure 1 High risk patients are frequently suffering from complex comorbidities

Typically, physicians refer to problem lists when assessing a patient’s health and evaluating treatment alternatives. Problem lists rely on coded disease states and offer a concise view of a patient’s medical issues. Unfortunately, these lists are often incomplete or out of date. Consider, for example, a patient who is referred to an orthopedic surgeon for a broken wrist. If the problem list only includes details of the wrist injury, the physician may not be immediately aware of underlying chronic conditions, such as diabetes, that could impact the best course of treatment and outcomes.


Until recently, the use of natural language processing (NLP) in healthcare has been primarily limited to research efforts and population health within academic medical centers. However, with the proliferation of unstructured data from electronic medical records, providers are now seeking to harness the potential of their data and considering a variety of use cases for NLP technology.[1] That’s the conclusion of a recent KLAS report entitled “Natural Language Processing: Glimpses into the Future of Unstructured Data Mining.”

The report includes insights from 58 provider organizations and examines the various ways providers are currently leveraging NLP technology, as well as some of use cases poised for wider adoption. Coding and documentation applications represent the broadest use of NLP engines. But it is clear providers have a growing interest in NLP solutions that advance their population health initiatives. An increasingly popular use case, involves applications that use NLP to mine unstructured data within patient populations and include predictive analytics to identify at-risk patient populations.

 A few of the major findings from KLAS’s report are summarized below.

How is NLP being used today?


During his January 2015 State of the Union speech, President Obama announced details of his administration’s Precision Medicine Initiative, which promises to accelerate the development of tools and therapies that are customized to individual patients. Precision medicine focuses on disease treatment and prevention and considers the variability in genes, environment, and lifestyle between individual patients.

Precision medicine takes into account healthcare’s relatively minor role in impacting a patient’s overall health and well-being, compared to the larger roles of genetics, health behaviors, and social and environmental factors. The precision medicine approach thus requires that providers have access to a wealth of patient-specific data. Thanks to advancements in genetic testing and new technologies, such as patient portals and remote monitoring devices, a wide variety of patient data is now readily available. Unfortunately, clinicians may have difficulty extracting data that is clinically relevant because much of the information is stored in an unstructured format.

Consider how a physician would glean information from a paper medical chart prior to EMRs. To understand a patient’s complete health status, the doctor would search through pages and pages of notes - obviously a time-consuming and error-prone task.


Shifting payment models based on quality and value are fueling the demand for insights into the health of populations. This demand requires the analysis of vast amounts of patient data. For example, before healthcare organizations can implement pre-emptive care programs, they must first identify the relative risk of their patient population. This is based on a variety of clinical, financial, and lifestyle factors, including:

  • Problem list of patients, especially chronic conditions
  • Procedures, medications and other hospital data
  • Claims information
  • Risk factors such as tobacco, alcohol and drug use
  • Availability and accessibility of health services and social support.

As illustrated in Figure 1, a healthcare population typically includes a relatively small percentage of the highest-risk patients, though these least healthy patients usually account for the biggest percentage of overall healthcare costs.

 

Figure 1: Level of patient risk associated with population segments and
their cost implications; a relatively small segment of the population
accounts for a disproportionate percentage of healthcare costs