HIMSS 17

Information Technology AND Healthcare? Why on Earth would you combine such incompatible career fields?

I can’t tell you how many times I was questioned about this in my past. Early on in my career, no one ever told me that my early pursuits of combining my Computer Operations training in the Air Force with my decision to pursue medicine was actually a good idea. In fact, it was quite the opposite. And yet - this year I can give about 45,000 more reasons (the number of attendees at HIMSS 2017 [1]) on why the path led to a promising merging career field after all.

The “missing link” career - people divided by a common career field.


Risk stratification has, so far, been biased toward structured data due to accessibility issues. As interest in long-term member wellness increases in importance it is the insights trapped in unstructured data that will become the differentiator in a changing and competitive market. The payers who are able to characterize member groups at a fundamentally more detailed level will have the advantage of population insight over those who struggle to do so.

Data sources that are increasing in scale and availability include electronic healthcare records (EHRs) data in Continuity of Care Document (CCD) format from providers, OCR notes about members, and nurses’ notes.

How can payers make effective use of unstructured data to stratify populations more effectively when much of their infrastructure is tied to structured data? Sources of unstructured data contain significantly more detail about members but are much more varied.

Here at Linguamatics Health, our Clinical NLP specialists understand the urgency and complexity of bringing together data sources, both structured and unstructured, in a workflow that gets you to insights you need quickly.


Ever find an acute problem such as a fracture, which shows in a Problem List, but healed months ago? Or perhaps the problem list states a case of bronchitis that may have been transient or may actually be Chronic Obstructive Pulmonary Disease (COPD)? After all, a diagnosis of COPD is a collaboration of symptoms and test results. How many clinicians find the spare time to go retrospectively back in the EHR and calculate a patient’s, “coughing with excessive sputum nearly everyday for at least 3 months of the year, for 2 years in a row” [1]?

But fixing the problem list can be time-consuming and complicated. Isn’t there an alternative (better) way?

Many organizations believe that in order to derive an accurate picture of their population’s health, medication lists can be just as good as their problem list. What if you find a patient taking an atypical antipsychotic medication and they don’t have a diagnosis that coincides on their Problem List? Can we just assume a mental health diagnosis? After all, this conclusion seems logical. Or is it? Is it an oversight on their Problem List or are they prescribed it for an off-label reason? According to the Agency for Healthcare Research and Quality (AHRQ), a 2011 report stated off-label atypical antipsychotic medications uses. This included areas such as; anxiety, ADHD, behavioral disturbances of dementia and severe geriatric agitation, MDD, eating disorders, insomnia, OCD, PTSD, personality disorders, substance abuse, and Tourette's syndrome. [2].

Therefore, can we really make assumptions?


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.