Academic Medical Center
Payer
Provider
Jane Reed

Better diagnosis needs more than diagnosis codes

It’s well known that cardiovascular diseases are one of the major causes of death both in the US and globally. This level of disease puts great pressures on health systems to manage the patient load, both at the population level and at the individual level. As with all diseases, treatment is more effective and less costly if patients can be diagnosed earlier on their care journey. One barrier here is that diagnosis codes for conditions such as valvular heart disease can be inaccurate and vary across health systems. More information resides in the unstructured text of medical records but this is slow and tedious to extract manually.

Fast accurate diagnosis of aortic stenosis with Natural Language Processing

A recent short paper by Solomon et al from Kaiser Permanente Northern California (KPNC) used Natural Language Processing (NLP) algorithms to extract detailed clinical information from echocardiography (ECG) reports. NLP is an Artificial Intelligence (AI) technology used to transform free, unstructured text in documents and databases into normalized, structured data suitable for analysis. Their results were more accurate than using diagnosis codes to identify aortic stenosis, for a patient cohort of over 500,000 individuals.

Read more

Payer
Pharma
Provider
Jane Reed

Recently, I was fortunate to chair a panel discussion where the potentials of Artificial Intelligence (AI) and Machine Learning (ML) were fiercely discussed. AI/ML seems to be talked about everywhere at the moment, heralded as a solution for many challenges in pharma and healthcare (and indeed many other industries). But, does AI/ML really warrant this level of excitement and hype?

AI/ML leaders in advanced statistics, NLP, deep learning, neural networks come together

One thing that is often overlooked in these discussions is that AI/ML is a broad group of diverse disciplines, not a singular item. AI/ML includes advanced statistics, Natural Language Processing (NLP), deep learning, neural networks, and many other mathematical and computer science specialities. The event I attended was a Linguamatics-hosted dinner. The panel of five experts covered a broad span of expertise across different AI/ML disciplines, and ranged from academia to small and large pharma:

Read more

Payer
Simon Beaulah

How to Choose the Right Natural Language Processing Solution

These days there is a lot of talk about AI in respect to Artificial intelligence, but AI has another abbreviation, Augmented Intelligence. Artificial Intelligence implies a level of machine automated processing with no human intervention whereas Augmented Intelligence is the use of technology to enhance human performance. One AI technology that has been proven to support both interpretations is Natural Language Processing, or NLP for short. This is not a new technology, in fact, Linguamatics has been successfully completing projects using NLP since 2001.

For Payers and Health plans investigating new technologies there are many areas where NLP can support improved efficiency and business insights: HEDIS medical record review for hybrid measures, Medicare risk adjustment, clinical review/medical necessity and risk stratification to name just a few. The first four areas are about using NLP to improve efficiency of often manual processes by extracting key insights from medical records and summarizing the findings; the last example is a more automated analysis of large-scale populations to identify high risk members based on Social Determinants of Health and disease severity information.

The growth of interest in NLP and AI has led to more and more businesses claiming to have AI solutions that can help healthcare organizations to make the most of their unstructured data. The question is: how do you decide which NLP offering actually works, and which NLP solution is right for you?

Read more

Academic Medical Center
Cancer Center
Payer
Provider
Elizabeth Marshall (MD)

Until recently I kept hearing claims such as, “Vaping is so much better for you than smoking…” My response is “It’s just a matter of time before the data will let us know”. Turns out the data is starting to speak, and it doesn’t have a positive outlook on the matter. Vaping is really in its infancy, and research even more so. We are still discovering which additives are in which types of vaping cartridges. Let’s compare vaping to tobacco: the Cancer Council in Australia reports that tobacco has been grown in the Americas for nearly 8,000 years, and the first significant medical reports weren’t out until the 1950s and 1960s. Relatively speaking we are early to this arena. Fast forward to the present where we know cigarettes have over 4,000 chemicals, 70 of which are known carcinogens.

Read more

Biotech
Pharma
Jane Reed

Natural Language Processing: Standing on the shoulders of giants

Many of us know the joys and sorrows of research. Weeks, months and years can pass, developing hypotheses, working in the lab or clinic, analyzing results, sometimes going back to square one, but then writing the paper, and finally, seeing the final version published and in print. The intent is that your research is shared, discussed, re-used, so that others can build on it, “standing on the shoulders of giants,” as Isaac Newton famously said.

Traditionally, getting information out of written papers for re-use has been manual; individuals reading, reviewing and extracting the key facts from tens or hundreds of papers by hand, in order to summarize the most up to date research in a field, or understand the landscape of information around a particular research topic. Over the past few decades Artificial Intelligence (AI) tools, such as Natural Language Processing (NLP), have evolved that can hugely speed up and improve this data extraction. NLP solutions can enable researchers to access information from huge volumes of scientific abstracts and literature; developing strategies and rules that drill deep into literature for hidden nuggets, or more broadly, ploughing the landscape for the nuggets of desired information.

To give you a couple of examples, I’ll share two use cases, both published recently, that use Linguamatics NLP platform across published literature, enabling researches to benefit from years of previous research.

Read more