Databases for Healthcare Uncertainty

Databases for Healthcare Uncertainty

All we know is all we know – or so the saying goes.

But often what we think we know, we do not really know – we lack awareness of the underlying uncertainty. So we assume things to be true that are false, and presume to be acting on facts and data when we are responding to uncertainty.

All because we do not know, what we do not know.

Healthcare is rife with such uncertainty, to the point that the uncertainty determines the course of clinical decision-making. We order tests or perform procedures, to diagnose an unknown condition we can only understand through the initial, presenting symptoms.

When we look at healthcare this way, we see it more through the doubts and unknown’s, than the facts and what we assume to be the known’s.

But modern healthcare does not look at itself this way. We have an overwhelming tendency to look at the data, assuming what we see to be true, conflating what we see with all there is to know.

We are biased towards the data.

Uncertainty in healthcare is far more pervasive than we like to admit, an important problem that has only grown in importance throughout the pandemic. We saw how emphasizing evidence-based medicine simply led people to question the evidence itself. The doubts in the data defined the reaction to the data – whether it was mask-wearing or receiving the vaccine.

Uncertainty, with little exaggeration, defines the behavioral response to healthcare for most of the public.

Yet our understanding of uncertainty is limited, due in part to the absence of a unified, coherent concept of healthcare uncertainty. There are multiple meanings and varieties of uncertainty, often not distinguished or acknowledged.

Even the literature on uncertainty in healthcare is fragmented, and whatever insights have been published or studied have been inconsistently adopted into clinical practice.

The closest thing we have is in the United Kingdom (UK), an entire database that focuses on uncertainty, called the Database of Uncertainties about the Effects of Treatments (DUETs). They focus on the gaps in data, the missing pieces in the clinical evidence – determining future course of research and developing proposed studies to address the gaps and missing pieces.

The evidence gaps registered in the database are derived from different types of scientific publications originating from a host of sources, including Cochrane Collaboration, NICE Guidance, and James Lind Alliance Research Priorities.

All of which sounds promising, and if properly implemented, can effectively guide the direction of research towards the most pressing uncertainties.

But this database, like most uncertainties in healthcare, is unknown and poorly integrated with other research institutions across the world. Relegated to the ivory towers of Oxford University, this database is hardly ever discussed in the United States.

And no such program even exists in the United States – nothing under the NIH or even the broader umbrella of the Department of Health and Human Services.

If we want to advance healthcare in the post-pandemic world, which we find ourselves inching towards, we need to reframe our thinking of healthcare.

Data is good, but studying the response to data is better. Increasing our knowledge in healthcare is good, but increasing our understanding of uncertainty is better.

And if the pandemic taught us anything about healthcare, it is that we must do better.

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