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Home Financial Markets

The Actual Cost of COVID-19

Daily Remedy by Daily Remedy
March 27, 2022
in Financial Markets
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The Actual Cost of COVID-19

Risk is something we all claim to know but we actually understand it quite poorly. We all look at it in our own way and we describe it differently. But it is all around us – the same one risk, appearing in a myriad of ways.

In insurance parlance, risk is loosely divided into systemic risk and specific risk. Systemic risk is shared among everyone and specific risk is unique to an individual or group of individuals. When discussing financial markets, we would describe systemic risk as when the market collapses – everyone loses. And we would describe specific risk as when someone invests in the stock of a company that falters into bankruptcy.

But sometimes systemic risk is perceived to be specific. This is because the two are not mutually exclusive, and what is considered to be the overlap between the two is often a matter of interpretation. If the market teeters and someone loses money, at what point is the loss due to systemic risk or based on that person’s investment strategy?

There is no easy answer to that question; it comes down to each person’s perception of risk, which appears differently to us all. Even those who make a living by analyzing risk struggle to fully understand it. Actuaries, professionals who price risk to a future event, use complex models to predict the likelihood of an outcome and the costs associated with it.

These models have become so complex that even those who develop them cannot fully explain them. So they use terms like black box, meaning they are not exactly sure how the inputs generate certain outcomes. Sure they may have some sense of how closely an input relates to an outcome, but that is about it – there will always be an aura of uncertainty.

This is the nature of risk. No matter how granularly you look at it, it will always be difficult to attribute a cause with an effect. But this has not stopped COVID conspiracy theorists from spinning their tales into national narratives.

Recently the CEO of the aptly named life insurance company, One America, said industry-wide life insurance data indicate death rates among working-age people were up 40 percent compared to pre-pandemic rates. Immediately people began to blame vaccines for the rise. And just as quickly this was debunked. But the core issue that ignited the conspiracy was never explicitly addressed.

The reason why this theory gained near spontaneous virality before being discredited, and why many such theories will arise in the coming months, is because we cannot help but attribute some semblance of cause and effect – however superfluous – to systemic risk.

This is how we should understand COVID, as a systemic risk. The virus traveled the world and along the way infected people’s immune systems as much as their imaginations. While we all see COVID in our own way, it is the same virus, only our perceptions differ.

But if it is a systemic risk, many wonder, then why would insurance premiums increase? Well, precisely because the insurance industry is a system. Systemic risks such as COVID are so large in scale that they can cause the breakdown of an entire system.

We all know what the last two years felt like, society as we know it stopped dead in its tracks – and we are still not out of the woods. We do not quite know how this systemic risk will manifest over time, as the risk of COVID begins to dissipate.

Are insurance premiums rising because the systemic risk of COVID overwhelmed the insurance industry, prompting the need to compensate for losses sustained over past years? Or has individual risk increased due to the pandemic – be it from contracting COVID or getting vaccinated or both?

It is likely a combination of changing systemic risk and variable individual risk. An answer few would like to hear and even fewer can grasp. What we are experiencing in real time is how acute systemic risk manifests over time. It all blends into an interpretive blur that no one can quite figure out.

So we find reasons to explain what is inherently inexplicable. After all, it is our nature to make sense out of uncertainty. The only problem is what we consider sensible is unique to each individual.

But ultimately, it is the same risk, just appearing differently – like COVID itself.

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Daily Remedy

Dr. Jay K Joshi serves as the editor-in-chief of Daily Remedy. He is a serial entrepreneur and sought after thought-leader for matters related to healthcare innovation and medical jurisprudence. He has published articles on a variety of healthcare topics in both peer-reviewed journals and trade publications. His legal writings include amicus curiae briefs prepared for prominent federal healthcare cases.

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In this episode, the host discusses the significance of large language models (LLMs) in healthcare, their applications, and the challenges they face. The conversation highlights the importance of simplicity in model design and the necessity of integrating patient feedback to enhance the effectiveness of LLMs in clinical settings.

Takeaways
LLMs are becoming integral in healthcare.
They can help determine costs and service options.
Hallucination in LLMs can lead to misinformation.
LLMs can produce inconsistent answers based on input.
Simplicity in LLMs is often more effective than complexity.
Patient behavior should guide LLM development.
Integrating patient feedback is crucial for accuracy.
Pre-training models with patient input enhances relevance.
Healthcare providers must understand LLM limitations.
The best LLMs will focus on patient-centered care.

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00:00 Introduction to LLMs in Healthcare
05:16 The Importance of Simplicity in LLMs
The Future of LLMs in HealthcareDaily Remedy
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AI Regulation and Deployment Is Now a Core Healthcare Issue

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Ambient Artificial Intelligence Clinical Documentation: Workflow Support with Emerging Governance Risk

Ambient Artificial Intelligence Clinical Documentation: Workflow Support with Emerging Governance Risk

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Health systems are increasingly deploying ambient artificial intelligence tools that listen to clinical encounters and automatically generate draft visit notes. These systems are intended to reduce documentation burden and allow clinicians to focus more directly on patient interaction. At the same time, they raise unresolved questions about patient consent, data handling, factual accuracy, and legal responsibility for machine‑generated records. Recent policy discussions and legal actions suggest that adoption is moving faster than formal oversight frameworks. The practical clinical question is...

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