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.


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:


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.


It Takes a Village to Raise Modern Medicine

Learning from the past

“It takes a village to raise a child” is a popular old African proverb, that in my opinion has a lot of merit. Now that single parents are part of the mainstream, as well as divorced families, and other non-traditional parenting units and methods are adopted; it’s still very important for the nurturing and development to come from many different influences- especially those that are closest. I also believe this old proverb can be applied to not just childrearing, but in other areas, such as how we work together and adopt new methods to make healthcare better.


It is well known that the drug discovery and development process is lengthy, expensive and prone to failure. Starting from the selection of a novel target in discovery, through the multiple steps to regulatory approval, the overall probability of success is less than 1%.

One factor is that the majority of diseases are multifaceted, hence the challenge is identifying the most appropriate patient populations who will respond to specific interventions. A stratified approach has proven beneficial in a number of cancers and genetic diseases, and pharmaceutical companies have a strong interest in understanding how to find the sub-populations of patients to ensure the most appropriate therapies are tested in clinical trials, and applied in broader clinical use.

The ultimate aim of a stratified approach to medicine is to enable healthcare professionals to provide the “right treatment, for the right person, at the right dose, at the right time”; and there are many research initiatives (governmental, private, public) on-going to develop the appropriate knowledge and models.