Thoughts that align and come together form an idea. Observations that align and come together form a hypothesis. Ideas and hypotheses tested over time form a theorem. A theorem repeatedly cited and tested becomes a fact. And facts go on to influence additional thoughts and observations in the future.
Such is the method of thinking we have defined and standardized in science and in healthcare. The convergence of thoughts and observations are generally taken to support a broader set of ideas and hypotheses that we then codify as theorems and facts. The divergence of thoughts and observations, on the hand, means something quite different – that we have either missed a critical point, or that we are headed down an erroneous pattern of thinking. Divergence is often synonymous with error or mistakes, particularly in the world of clinical practice, where the rapid assimilation of facts and observations leads to reflexive diagnosis and treatments.
Any given set of presenting symptoms and clinical signs, or lab data or imaging results, points to a definitive differential diagnosis. Healthcare is subtly reflexive in that regard; we assign linear correlations to our thought patterns. Any given set of thoughts or observations is nearly always associated with a particular disease – like cause and effect.
Clinical textbooks and bed side clinical instructors teach medicine this way. For a patient with two different, elevated blood pressure readings, first check basic lab work, and then order a medication. If the lab work is normal, then adjust the medication dose until the blood pressure has been normalized to the parameters agreed upon for appropriate blood pressure control. Observation, plan of action, treatment – linear correlations of cause and effect.
We practice medicine this way because it is the most effective way to standardize patient care across different clinical scenarios and different patient-provider interactions. In the process, we have created a healthcare system that behaves according to default tendencies. If we have a set of symptoms, we default to a particular diagnosis. The established training, the convergence in thinking, overwhelms the practice of medicine.
But there are limits to this approach, the convergence of thinking and doing, particularly when we come across divergence thoughts and observations – something far more common in healthcare than you would think. Take for example one of the most common chronic diseases, diabetes. We know diabetes is the likely diagnosis when there are multiple elevated blood sugars with no separate, acute cause for the sugar spike. We know diabetes is monitored by an aggregate measure of blood sugar, called hemoglobin A1c (HbA1c). And we assume that the HbA1c levels correlate linearly with the average blood sugar.
The formula that calculates HbA1c is a linear regression of blood sugars, a fancy method of averaging of the blood sugar readings in a person. So it would make sense to assume that it is a pretty good proxy for how a person’s sugar is being maintained.
Unfortunately, it is not uncommon for providers caring for people with diabetes to encounter situations in which the HbA1c and blood sugar simply do not match. Sometimes, there is an underlying condition, such as hemolytic anemia. But when this apparent discrepancy in lab work appears in people with no underlying conditions, no covert noncompliance with medication, or any other reasons, we are left with divergence information that we are not able to really make sense of.
This is when the reflexive, convergence thinking breaks down. Do we trust the real time blood sugar readings or do we trust the aggregated average? HbA1c levels can change over time even when blood sugar remains constant. Studies that evaluate HbA1c levels relative to its ability to accurately measure blood sugar found the correlation varies with the duration of diabetes and whether patient is attempting to lower their blood sugar.
The linear correlations between HbA1c and actual blood sugar vary not only with the underlying sugar levels, but with the change in the sugar levels, and how extreme the sugar levels are to begin with – as excessively high and low sugar levels are less accurately measured by HbA1c than more traditional ranges of sugar.
Which means the tool we use to measure blood sugar is itself a variable, not a constant frame of reference we can use to measure blood sugar with absolute certainty. Rather, there will always be some inherent probability, such uncertainty.
In the world of physics, uncertainty is synonymous with quantum mechanics, mathematical principles underlying an interpretation of the world based upon probability states and energy levels. And of late, these principles have been applied to other sciences to help make sense of previously misunderstood or conflicting concepts, including biology. Quantum biology studies the applications of quantum mechanics and theoretical chemistry to biological objects and problems. And it has developed to the point where it is no longer an application of physics, but its own field outright.
Biology has embraced uncertainty. And it is about time healthcare embraces uncertainty as well. Which may be the lasting impact of artificial intelligence and machine learning in healthcare. But for now, whenever we discuss applications of these two concepts in healthcare, it is always from the perspective of simplifying or automating healthcare.
But rather than simplifying and automating healthcare, we need to embrace the complexity of healthcare by developing tools and frameworks that help us understand how to practically incorporate these overbearing principles into everyday clinical practice. Artificial intelligence and machine learning can help us do that. The potential applications are everywhere.
We know there are upper limits to patient engagement. No matter how many times we engage with patients or attempt to nudge them to do something, there is a diminishing marginal utility – eventually the benefits fail to produce any incremental, positive results. There is no linear correlation between patient intervention and patient compliance – it is very much multifactorial.
An everyday concept most physicians know intuitively, but simply accept without question – failing to rationalize this premise in quantum terms. Instead of seeing this as a limitation in patients, we should assess the relative utility of each engagement to identify the relative value of each form of engagement in overall patient behavior. Assess the probability of an engagement to produce the desired effect of that engagement. Do not think of patient behavior in absolute terms, but as a combination of related behaviors all vying for relative influence on overall patient behavior.
Which is quantum mechanics being applied to healthcare.
So much of healthcare is presumed to be cause and effect, when it is really a set of correlations. We believe something to be linearly related because it is easy for us to understand, even though we know the relationship is far more complex.
All the medications promoted early in the COVID-19 pandemic, whether it was hydroxychloroquine, remdesivir, or azithromycin, were never intended to be direct cures. We simplified the logic underlying the pathophysiology to think of these medications as curative, but they were never intended to be that. Rather, the medications addressed one aspect of the overall inflammatory process that formed among patients affected with COVID-19. And by addressing that one aspect, we presumed we can reduce the overall impact of the virus.
A lot of times it worked, but a lot of times it did not. And each medication had its relative benefits to side effects, depending on the patient and the extent of the inflammation. We did not really know how effective the medications would be – we just assumed it might work and treated the patient empirically based on how the symptoms were progressing. In some instances, the patient might have gotten better with or without the medication, but because we gave the medication, we figure it was due to the medication. That is a false attribution.
That is also falsely assuming a direct correlation of cause and effective, when the relationship is more complex, more multifactorial.
The most immediate impact of quantum healthcare will not be any direct treatment or intervention. Rather it will be a shift in our thinking. Our ability to elucidate relationships that we once believed to be directly cause and effective to be more correlative and multifactorial in nature. But that new mode of thinking will be the proving ground to new treatment modalities, new types of interventions, and new forms of patient engagements.
But it starts by embracing the surprisingly common divergence that exists in healthcare.
Max Planck said science advances one funeral at a time, implying the figurative death of the old ways of thinking will usher in a new way of thinking. Healthcare can only advance once we end this reflexive form of thinking, simplifying what we observe into convenient linear relationships, and integrate principles of uncertainty into our everyday practice of medicine.
Advancing clinical medicine into quantum healthcare.
Vaccination rates vary by county, determined by local factors
COVID-19 has disproportionately affected certain underserved and high-risk populations, including people of color, those with underlying health conditions, and those who are socioeconomically disadvantaged. Ensuring access to COVID-19 vaccines for these communities can help address the disparate health effects of the virus and achieve herd immunity.
The Biden administration has identified vaccine equity as a priority, but states and local jurisdictions vary in how and the extent to which they prioritize equity. Given that vaccine roll-out in the U.S. is inherently local, understanding how vaccination rates vary at the local level is important for informing outreach efforts and addressing equity.
Earlier CDC analysis found that, as of early March, counties with high social vulnerability had lower vaccination rates than counties with low social vulnerability.
Source: Kaisesr Permanente Foundation
Dr. Anandi Gopal Joshi, the first Indian physician trained in the United States
Anandibai travelled to New York from Kolkata (Calcutta) by ship, chaperoned by two female English missionary acquaintances of the Thorborns. In New York, Theodicia Carpenter received her in June 1883. Anandibai wrote to the Woman’s Medical College of Pennsylvania in Philadelphia, asking to be admitted to their medical program, which was the second women’s medical […]