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Join Jay K. Joshi, a family physician and the author of Burden of Pain: A Physician’s Journey through the Opioid Epidemic, as we delve into the intricate world of clinical decision-making within evolving health care policies. Our focus extends beyond the surface to delve into the fascinating realm of game theory, revealing how this theoretical framework influences medical choices. Jay brings to light the multifaceted factors shaping legal considerations, decision complexities, and patient welfare in the health care landscape. Tune in for an exploration of the intricate tapestry that weaves the fabric of modern medical decisions.

Jay K. Joshi is a family physician and author of Burden of Pain: A Physician’s Journey through the Opioid Epidemic.

He discusses the KevinMD article, “How legal liabilities and skewed decision-making plague clinical encounters.”

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Transcript

Kevin Pho: Hi, and welcome to the show. Subscribe at KevinMD.com/podcast and get CME for this episode by clicking on the CME link in the show notes. Today we welcome back Jay Joshi. He is a family physician, and he’s the author of Burden of Pain: A Physician’s Journey Through the Opioid Epidemic. Today’s KevinMD article is titled “How legal liabilities and skewed decision-making plague clinical encounters.” Jay, welcome back to the show.

Jay K. Joshi: Thank you for having me.

Kevin Pho: So for those interested in hearing your story, just go back to your prior podcast. Go to KevinMD.com/podcast, click the search bar in the upper right-hand corner and search for Jay’s name to hear his story. But today, let’s go straight into your KevinMD article, “How legal liabilities and skewed decision-making plague clinical encounters.” How did your article come together?

Jay K. Joshi: It was really born out of experience and the insight gleaned from it. As you and I have discussed earlier, one of the things that many physicians struggle with is making individual clinical decisions in light of the clinical data and the patient’s circumstances. We’re very good at crafting clinical studies, aggregating data and identifying best practices, but once we implement individual decisions from that plethora of data, we surprisingly become very non-clinical. I postulate that in many ways, we become influenced by some of the more adversarial aspects of health care, of which I specifically discuss legal liability. I think as physicians, we need to come to terms with how we make decisions and the influencing factors that lead to those decisions.

Kevin Pho: All right, so give us an example or a scenario that would really illustrate your point.

Jay K. Joshi: An example that has probably received the majority of the evaluation and studies to date on implicit biases would be the emergency room setting. Many EDs often require physicians to make clinical decisions on the order of minutes. This requires snap judgments of patients and their clinical conditions, and then making decisions accordingly. Many of those decisions lead to disparities in outcomes, and we’ve seen race-based disparities and gender-based disparities.

Now, to reconcile that, one potential argument, which I completely do not agree with, would be to say, “Well, there must be discriminatory practices among physicians.” I think that’s completely untrue, but it’s hard to argue with the outcomes that have been shown. A better, more meaningful rationale would be to say there are implicit biases that are affecting individual decisions. How do we tease out those individual decisions so we can get to the point where individual decisions meet the clinical rigor that we expect of clinical studies?

So what I do in my paper is I create a framework based on game theory. Now, when I use the words “game theory,” for those not familiar, it has nothing to do with games, and I find nothing humorous in biases and clinical decision-making. It is simply an economic principle that analyzes how decisions are made based upon the rules and frameworks set around those decisions. What I specifically focus on is my own personal experience, and that’s making decisions on prescribing opioids for chronic pain in the outpatient primary care setting.

What I identify are certain factors that would drive clinical decisions: the decision to prescribe and the decision not to prescribe. Now, many of those decisions lead to consequences that are distinctly non-clinical: legal liability, DEA investigations, insurance audits. These factors weigh on the minds of physicians, whether people would like to admit that or not. What I wanted to do is tease out those implicit biases and define them explicitly in the payoff structure that determines how decisions are made. In many ways, it shows how principles of harm reduction and curbing legal liability for physicians can actually lead to more clinically meaningful decisions.

It’s essentially saying that if you limit the downside risk of a bad decision, or a decision that may not be in full concordance with what you believe, then you enable more clinically accurate decisions. I think that’s something we implicitly understand, pun intended, if you will, but I think that we’re just so ingrained in the health care system of malpractice suits and legal liability. I wanted to really raise awareness of the importance of individual decision-making.

Kevin Pho: So you mentioned some of these external factors that influence decisions, and you specifically mentioned legal liability. So how does the threat of a malpractice lawsuit, or the threat of liability, specifically influence physician decision-making?

Jay K. Joshi: Generally speaking, it creates a certain tendency toward standardization, so it homogenizes decisions right off the bat. For many, that may seem like a good thing, right? We want to have evidence-based medicine following a standard of care, so many would think, “Yeah, we need something like that,” because we want to create certain parameters around clinical decision-making. The problem lies, though, in when you create artificial homogeneity around decisions that are inherently uncertain, and that’s when you start to get into the unintended consequences. Now you’re making decisions that impose a certain conformity when, in reality, individual patient considerations should be given. I think that’s the balance that we’re struggling to find.

So when you look at patients with chronic pain who require prescription opioids, we’ve stigmatized opioids so much that we’ve actually eliminated them from their clinical context. So instead of looking at the patient with a clinical condition requiring a certain medication, it’s a person asking for certain medications. We’ve made that entire context devoid of its clinical principles, and by doing so, we enable other non-clinical factors to influence our decision-making. That’s when the legal liability component starts to become an issue, because it takes away some of those individual clinical considerations.

So it’s one of those things where just enough legal liability is good, and too much produces the opposite effect. By using concepts like game theory and other behavioral academic principles, you can start to tease out the relative effects of legal considerations and clinical considerations, to then see how we are guiding, or, more bluntly, how we are influencing, clinical decisions, and whether the way we’re influencing decisions is to the patient’s benefit.

Kevin Pho: Now, for those who do not have a background in game theory or behavioral economics, give us an example of how this would play out in a real-life scenario.

Jay K. Joshi: Sure. I’ll start with the most common example that’s used in the introduction, and that would be the prisoner’s dilemma. Just real quickly, it’s a scenario where two individuals are apprehended by the police and put in separate jail cells. They are apprehended for the alleged crime of kidnapping a car, and they’re put into separate jail cells. Each of them is told that if they rat on the other individual, they will get a very mild sentence and be released, but if they say nothing, they’ll get a very severe sentence. Now, neither of the individuals may have committed any crime or whatnot, but it would be in both individuals’ best interest not to say anything if they work together. But if there’s no trust and they’re turning against one another, it would be in their best interest to effectively rat on each other.

So it’s a clear example in which you see how the individual incentives differ from the group dynamics, in that both of those individuals clearly should just stay quiet and stick together. But they’re in separate jail cells, and they’re not sure what the other person is thinking, so they act against their best interest because they don’t know how the other individual is acting. The long and short of it is that this can be a basis for understanding game theory, in that you’re looking at individual incentives alongside group incentives.

Kevin Pho: All right, so you gave the examples of prescribing opioid medications and of implicit biases in the emergency department. Are there any other common primary care scenarios where game theory or behavioral economics can help remove some of these externalities from decision-making?

Jay K. Joshi: It can be applied in so many contexts, because it’s a framework for thinking, so you can apply that framework for thinking to many conditions. Let’s talk about hypertension; let’s talk about obesity. If you have a patient who comes in with uncontrolled hypertension, and their systolic remains above 150, 155, you start them on one course of medications. You start them on a diuretic, then you’re going to move to an ACE. You are effectively making judgments on the decisions based upon the blood pressure. You’re assuming the patient is compliant with the medications as prescribed, or at least understands when and how to take them, or at least understands what type of diet to maintain. All of these are assumptions that you make, and another word for assumption is bias.

Now, let’s say you were to look at a Caucasian, affluent male who seems to be well composed, on top of his game and professionally successful. You, as a physician, may be more inclined to believe he is more compliant with taking the medication and with his diet, and you may be more inclined to say, “Oh, well, your blood pressure is still not well controlled. Let me add another medication.” Now let’s say you’re dealing with an impoverished Asian female from Northern California who looks disheveled and doesn’t present herself in the best manner. You may tend to think more along the lines of non-compliance, so you may think, “Well, you know what? Let’s just keep your blood pressure medications as they are. Let’s focus on behavioral changes, let’s focus on diet, let’s focus on compliance with your medications.”

So the method by which you focus on caring for the patient in that encounter is a direct result of the implicit biases that percolate on top, creating a dominant perception, and that dominant perception leads to the clinical care you provide. I use that example to show how all the factors go into treating a patient with hypertension, but what factors you choose to emphasize depend on the biases you choose to acknowledge at the time.

Kevin Pho: Tell us, for those clinicians or medical trainees, how can they start this? What’s the first step in terms of recognizing these biases and removing their influence from decision-making? How can you first start?

Jay K. Joshi: Well, I think it starts by recognizing how you learn about medicine. One of the key things we always focus on is the differential diagnosis. You take together a constellation of symptoms and signs, you piece together information that is known and what is not known, and then you create a list of differential diagnoses that could be the pertinent condition. That is part and parcel of every third-year medical student’s experience, throughout residency and onward. What we learn over time are shortcuts to this. For example, a patient presenting with right lower quadrant pain, vomiting, nausea, an elevated WBC, a fever and elevated temperature: These are diagnostic for appendicitis, and it’s almost reflexive.

One of the things we don’t realize about that type of thinking is that it’s predisposing us to biases, right? Those heuristics, those thought patterns that we create to simplify clinical decision-making, are also the sources of the biases. So it’s one of those things, just like with legal liability: Some of it is good, and a lot of it happens to be bad. It’s the same thing with clinical decision-making and the use of heuristics. Some of it is good, and some of it is downright necessary. No physician is going to realistically say they’re going to create a differential diagnosis for all 20, 30, 40, 50 patients that they see in a given time interval. Some of it requires reflexive thinking. But the moment you start to impose too much of that, and not be aware of when there are limitations in using those heuristics, that’s when you start to make clinical decisions that require a little bit more diligence, that require a little bit more than just the reflexive thought pattern.

So for physicians and trainees, medical students and residents, one of the things I would really emphasize is the confidence in the assumptions made in decisions, the confidence in the assumptions made in the decisions. Health care by nature contains an inherent amount of uncertainty; that’s fundamental. The question then becomes how much of that uncertainty is acceptable to gloss over with the heuristic, and how much of that uncertainty needs to be addressed and given additional consideration. As you go through your medical training, as you start to learn the fundamentals, recognize when and how uncertainty plays its role.

Kevin Pho: So in general, do you feel that the pendulum has swung too far toward heuristics and biases, and are you looking to swing that pendulum back more toward the middle?

Jay K. Joshi: I understand the analogy of a pendulum, in the sense that it’s a binomial construct where using these is good and not using them is bad, so to speak. Let me dance around that question and then kind of address it head-on. I think the moment we started to implement scientific methods into medicine, and then in the ’90s started to incorporate evidence-based medicine, we started going toward heuristics and biases, because ultimately the scientific method creates modes of thinking by taking uncertainty and providing facts and theories around it, and then you build upon that. You can’t advance your knowledge in any scientific endeavor without building upon the assumptions that were made. The use of science has done wonders in medicine: chemotherapy, radiation, surgeries, procedures.

The issue, however, which I think has been made quite apparent in the pandemic, is that there’s a limit to how far this can go. The pandemic made very apparent that patient trust and patient autonomy are rate-limiting factors to the advancement of science in health care, and to the use of evidence-based medicine and, by that logic, the use of heuristics. We had vaccines in record time, and we couldn’t get patients to take them in some parts of this country. The reason for that was a trust factor. There’s nothing scientific about quantifying trust; it’s something implicit and inherent.

So I think the pendulum analogy is good in the sense that we’re trying to create a balance between all of those. But I would argue that futurists, people who make predictions about the future, always caution against looking at the future linearly or purely rationally. I would contend that the future of health care really relies on this amorphous blend of understanding how we think, the strengths in thinking in evidence-based medicine, using heuristics and understanding how uncertainty fills the void around it.

The analogy I use, and I write about it in Burden of Pain, is the atom. You have the nucleus, and for the longest time, we focused on the protons and the neutrons and just assumed the electrons were revolving around it, almost like a planetary model of the sun and planets. Now we know that the electrons themselves are not actual particles but orbitals, and that their characteristics actually define the atom more than the nucleus itself. In that same vein, I would contend that science has a role, and evidence-based medicine and heuristics are essential factors as we increase the use of technology in health care. But we can’t forget that it’s the uncertainty surrounding all of that that really influences health care, because those are the factors that drive patient behavior.

Kevin Pho: We’re talking to Jay Joshi. He’s a family physician, and he’s the author of Burden of Pain: A Physician’s Journey Through the Opioid Epidemic. His KevinMD article is titled “How legal liabilities and skewed decision-making plague clinical encounters.” Jay, tell us some of the take-home messages that you want to leave with the KevinMD audience.

Jay K. Joshi: The first thing I would like to leave with the audience is to just be aware of how you make decisions, because awareness is the biggest factor in improving overall clinical decisions. The second thing I would say to the KevinMD audience is to continue to challenge the paradigm of thinking in clinical medicine by pushing for innovative models. Let’s incorporate behavioral economics. Let’s incorporate non-scientific methods of thinking and learning into medicine, because, as we all learn in medical school and quickly forget thereafter, medicine is both a science and an art.

Kevin Pho: Jay, thanks for coming back on the show and sharing your time and insight.

Jay K. Joshi: Thank you, sir.