It is almost that time of the year. I'm not talking about wassailing or family gathering but the time to re-evaluate financial portfolios or at the very least think of a few things to do better on a fiduciary scale--in the new year.
In our ever expanding algorithm-focused universe calculations of risk are becoming common place. Financial institutions often come to mind as determinations to approve mortgages, car loans, or even credit cards are algorithmic and risk based assessments. Investment risk measures your ability to tolerate potential losses in the face of higher anticipated returns at the expense of higher volatility or risk. Can we apply this to healthcare decisions as well? What would a diversified portfolio in healthcare even look like?
I don't know about you but I don't often hear discussions about Nash equilibria in Risk/Benefit calculations in healthcare. Nash equilibria are described in Game Theory. Think of Game Theory as any strategic interaction--applicable across all disciplines and basically industry agnostic.
These are laws enacted that are self-sustaining. For example, you don't drive through red lights into oncoming traffic because you fear getting a traffic ticket--the real reason is you don't want to die.
Decisions that are good for individuals can sometimes be terrible for groups--In a Nash equilibrium, every person in a group makes the best decision for herself, based on what she thinks the others will do. And no-one can do better by changing strategy: every member of the group is doing as well as they possibly can. In the case of the prisoners' dilemma, keeping quiet is never a good idea, whatever the other mobster chooses.
I would argue that in healthcare and the multitude of stakeholders with opposing strategic incentives, the Nash equilibria breaks down. Think of the suboptimal trade-offs being made all along the care pathway. Payers not wanting to pay claims or opting to underpay may influence choices at the point of care when influenced by say, formulary decisions. The complexity of the available codes to deny a claim are out of the strategic consideration of physicians. Now factor in third-party payers incentivized to generate additional savings.
The solution may be advances in blockchain and factual time stamped compliance and performance. Increased transparency has the potential to create an immutable ledger of cooperation and improved outcomes. Read more at Blockchain and Nash equilibrium in Healthcare.
--Computational complexity theory sheds new light on the “bounded rationality” of decision-makers. Approximation guarantees, originally developed to analyse fast heuristic algorithms, can be usefully applied to Nash equilibria. Game Theory Through The Computational Lens
Time Trade-Off Valuation of Health Outcomes--link to article
You should be aware of any influencers of risk determination. Integrating valuations to characterize utility values for economic evaluation are an important part of capturing the patient voice in a meaningful and authentic way. The most common methods are standard gamble, time trade-off, visual analogue, ranking questions, or discrete-choice experiments. These are important survey questions to include any time you are hoping to measure decision making both on the provider end and patient perspective.
What are the best ways to be informed by patient level data but to also weigh benefits to communities or general populations? There is more to consider than simple poorly constructed evaluations. Stay-tuned...
Although based on mathematical theories, this recent podcast is accessible to even those with limited math interests. With all of the discussion of AI and the intersection of healthcare and technology, we should have a better than basic understanding of how algorithms are executed. Often the number of elements required to be manipulated by a given algorithm are exponential and not polynomial or what is defined as NP-complete?
The solution is to select domains of interest instead of attempting to solve large-scale problems.
I find academic lectures to be helpful in understanding algorithms and game theory. If you want a deeper dive into autonomous and strategic interests in a system described by algorithms Tim is a brilliant lecturer.
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In a world of "evidence-based" medicine I am a bigger fan of practice-based evidence.
Remember the quote by Upton Sinclair...
“It is difficult to get a man to understand something, when his salary depends upon his not understanding it!”