# Prediction-based medicine (PBM)

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We need a new paradigm for doing medicine. I make the case by first speaking about the problems of our current paradigm of evidence-based medicine.

The status quo of evidence-based medicine
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While biology moves forward and the cost of genetic-sequencing dropped a lot faster than Moore's law the opposite is true for the development of new drugs. In the current status quo the development of new drugs rises exponentially with [Eroom's law](http://blogs.sciencemag.org/pipeline/archives/2012/03/08/erooms_law). While average lifespan increased greatly about the last century in Canada the average life span at age 90 increased only 1.9 years over the last century. In 2008 the Centers for Disease Control and Prevention reported that [life expectancy in the US declined from 77.9 to 77.8 years](http://articles.latimes.com/2010/dec/09/news/la-heb-stroke-20101209). After [Worldbank data](http://data.worldbank.org/indicator/SP.DYN.LE00.IN?locations=DE)) Germany increased average lifespan by two years over the last decade which is not enough for the dream of radical lifespan increases in our lifetime.

When it costs 80 million to test whether an intervention works and most attempts show that the intervention doesn't work we have a problem. We end up paying billions for every new intervention.

Eric Ries wrote "The Lean Startup". In it he argues that it's the job of a startup to produce validated learning. He proposes that companies that work with small batch sizes can produce more innovation because they can learn faster how to build good products. The existing process in medicine doesn't allow for small batch innovation because the measuring stick for whether an intervention works is too expensive.

In addition the evidence-based approach rests on the assumption that we don't build bespoke interventions for every client. As long as a treatment doesn’t generalize about multiple different patients, it’s not possible to test it with a trial. In principle a double-blind trial can't give you evidence that a bespoke intervention that targets the specific DNA profile of a patient and his co-morbidity works.

The ideal of prediction-based medicine
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The evidence-based approach also assumes that practitioners are exchangeable. It doesn't model the fact that different physical therapist or psychologists have different skill levels. It doesn't provide a mechanism to reward highly skilled practitioners but it treats every practitioner that uses the same treatment intervention the same way.

Its strong focus on asking whether a treatment beats a placebo in double-blind studies makes it hard to compare different treatments against each other. In the absence of an ability to predict the effect sizes of different drugs with the literature the treatment that wins on the market is often the treatment that's best promoted by a pharmaceutical company.

How could a different system work? What's the alternative to making treatment decisions based on big and expensive studies that provide evidence?

I propose that a treatment provider should provide a patient with the credence that the treatment provider estimates for treatment outcomes that are of interest to the client.

If Bob wants to stop smoking and asks doctor Alice whether the treatment Alice provides will result in Bob not smoking in a year, Alice should provide him with her credence estimation. In addition Alice’s credence estimations can be entered in a central database. This allows Bob to see Alice’s Brier score that reflects the ability of Alice to predict the effects of her treatment recommendations.

In this framework Alice’s expertise isn't backed up by having gotten an academic degree and recommending interventions that are studied with expensive gold-standard studies. Her expertise is backed by her track record.

This means that Alice can charge money based on the quality of her skills. If Alice is extremely good she can make a lot of money with her intervention without having to pay billions for running trials.

Why don't we pay doctors in the present system based on their skills? We can't measure their skills in the present paradigm, because we can't easily compare the outcomes of different doctors. Hard patients get send to doctors with good reputations and as a result every doctor has an excuse for getting bad outcomes. In the status quo he can just assert that his patients were hard.

In prediction-based medicine a doctor can write down a higher credence for a positive treatment outcome for an easy patient than a hard patient. Patients can ask multiple doctors and are given good data to choose the treatment that provides the best outcome for which they are willing to pay.

In addition to giving the patient a more informed choice about the advantages of different treatment options this process helps the treatment provider to increase his skills. They learn about where they make errors in the estimation of treatment outcomes.

The provider can also innovate new treatments in small batches. Whenever he understands a treatment well enough to make predictions about its outcomes he's in business. He can easily iterate on his treatment and improve it.

The way to bring prediction-based medicine into reality
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I don't propose to get rid of evidence-based medicine. It has its place and I don't have any problem with it for the cases where it works well.

It works quite poorly for body work interventions and psychological interventions that are highly skill based. I have seen hypnosis achieve great effects but at the same time there are also many hypnotists who don't achieve great effects. In the status quo a patient who seeks hypnosis treatment has no effective way to judge the quality of the treatment before he's buying.

A minimal viable product might be a website that's Uber for body workers and hypnotists. The website lists the treatment providers. The patient can enter his issue and every treatment provider can offer his credence of solving the issue of the patient and the price of his treatment.

Before getting shown the treatment providers, a prospective patient would take a standardized test to diagnose the illness. The information from the standardized test will allow the treatment providers make better predictions about the likelihood that they can cure the patient. Other standardized tests that aren’t disease specific like the OCEAN personality index can also be provided to the patient.

Following the ideas of David Burn’s [TEAM framework](https://feelinggood.com/team-vs-cbt), the treatment provider can also tell the patient to take tests between treatments sessions to keep better track of the progression of the patient.

When making the purchasing decision the patient agrees to a contract that includes him paying a fine, if he doesn’t report the treatment outcome after 3 months, 6 months and 1 year. This produces a comprehensive database of claims that allows us to measure how well the treatment providers are calibrated.   
Various Quantified Self gadgets can be used to gather data. Many countries have centralized electronic health records that could be linked to a user account.

The startup has a clear business model. It can take a cut of every transaction. It has strong network effects and it's harder for a treatment provider to switch because all his prediction track record is hosted on the website.

*Thanks to various people from the Berlin Lesswrong crowd who gave valuable feedback for the draft of this article.*