What is Data, Really?

There is pain, and there is pain’s shadow, a demarcation defined by philosopher C.S. Lewis when describing the impact pain has upon a person afflicted.

In much the same vein, we must look at disease and the presentation of disease as its shadow – as we would treatment and the presentation of treatment as its shadow. The two are inextricably linked, and the interaction between the two – disease and disease presentation, treatment and treatment effectiveness – defines the quality of care, as experienced by the patient.

When the FDA recently approved a drug to treat Alzheimer’s disease, the medical community reacted in outrage. The data evaluating the effectiveness of the drug never focused on any of the symptoms of the disease, which is diagnosed through the clinical presentation of the symptoms. Instead, the drug was approved based on its ability to reduce the concentration of a protein found in the brain of those afflicted with the condition. A protein which has never been used to diagnose nor monitor the progression of Alzheimer’s disease.

Even the study never defined a direct relationship between protein concentration and symptomatic presentation. In fact, most Neurologists specializing in neurodegenerative, age-related disorders emphasize the clinical presentation over any test or lab – advocating for the use of testing and lab work only to confirm what is suspected upon clinical presentation.

So then, what are we truly treating with this new drug?

Alzheimer’s is a clinical condition, defined symptomatically, diagnosed based upon the symptoms presented.

We do not diagnose nor treat a complex neurodegenerative disease through the imaging of a protein biomarker. We diagnose and treat through the clinical presentation of symptoms.

A disease is a disease based upon how it presents. And a treatment is a treatment based upon how it treats the presentation of the disease.

The relationship is fundamentally experiential. To discern the relationship through imaging or by any other means to violate the fundamental relationship between disease and treatment, and the respective shadows.

The FDA’s decision to approve the drug based upon imaging data demonstrates a staunch movement towards analytics and data driven medicine. The reaction by the medical community demonstrates that no matter how analytical or data driven healthcare becomes, it will always remain a fundamentally subjective discipline – as much art as science.

Data can glean relationships, reveal what is related, symptom to disease or disease to treatment. But data cannot understand the nuances of these relationships.

How things relate in healthcare is far more complicated than the information provided by data. If we presume data is the answer to the questions in medicine, then we will quickly realize that the answers cannot encapsulate the full scope of the questions.

The data behind the drug’s approval focused primarily on the concentration of the protein associated with Alzheimer’s disease and secondarily on the clinical presentation of the disease itself.

Reflect upon that – in a clinical study evaluating whether a drug should be approved for the treatment of a disease, the defining symptoms were not the primary endpoints of the study.

Instead the data simply assumed that by lowering the concentration of the protein, we automatically treat the condition, a gross simplification that conflates correlation with causation – in other words, an error of logic.

Yet this was the data that the FDA evaluated and based its approval upon.

A disturbing trend towards utilizing data as the primary means of diagnosis and treatment that the pandemic should have curbed, as it was a time when we recognized that the perception of the data matters more than the data itself.

That for patients, the perceptions of fear mattered more than the rationality of clinical care.

The data is only the data. The perceptions of care and the treatment of the patient are so much more.

To distill the effectiveness of treatment for a drug – the first to be approved for Alzheimer’s disease in twenty years – down to imaging data disregards the subtle nuances of clinical presentation that initially defined the disease and its treatment.

Medicine was an art before it was a science. Then it became both an art and science.

We should not let the science overwhelm the art. For without the art, we are left with only the science, the empty data.

What is data, really?

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