The perfect storm of surveillance capitalism

THE AGE OF SURVEILLANCE CAPITALISM: The Fight for a Human Future at the New Frontier of Power

THE DEFINITION

1. A new economic order that claims human experience as free raw material for hidden commercial practices of extraction, prediction, and sales;

2. A parasitic economic logic in which the production of goods and services is subordinated to a new global architecture of behavioral modification;

3. A rogue mutation of capitalism marked by concentrations of wealth, knowledge, and power unprecedented in human history;

4. The foundational framework of a surveillance economy;

5. As significant a threat to human nature in the twenty-first century as industrial capitalism was to the natural world in the nineteenth and twentieth;

6. The origin of a new instrumentarian power that asserts dominance over society and presents startling challenges to market democracy;

7. A movement that aims to impose a new collective order based on total certainty;

8. An expropriation of critical human rights that is best understood as a coup from above: an overthrow of the people’s sovereignty.

Last month in my post on the book: Modern monopolies I wanted to highlight the current trend towards monopolies using platforms as a business model. Now you may add a complementary perspective with the book: The age of surveillance capitalism. While the former emphasizes the business perspective, the later focus on behavioral prediction surplus and how it is generated. It provides a social perspective of the current "surveillance capitalism". In my opinion there is a lot of current economy that already confirms this view, it is not a future expectation.

Our lives are rendered as behavioral data in the first place; ignorance is a condition of this ubiquitous rendition; decision rights vanish before one even knows that there is a decision to make; there is no exit, no voice, and no loyalty, only helplessness, resignation and psychic numbing; encryption is the only positive action left to discuss.

Surveillance capitalists take command of the essential questions that define knowledge, authority, and power in our time: Who knows? Who decides? Who decides who decides? 

As you may imagine, this is a book that once you started it's impossible to stop reading. Highly recommended if you want to understand current hot topics and social trends.
A perfect storm is an event in which a rare combination of circumstances drastically aggravates the event. This is exactly what we have right now in front of us, and as we are inside the wave we are not able to recognise what's going on.

Medicine as a data science (5)

A guide to deep learning in healthcare

Some months ago, Mckinsey released a guide to AI for executives. It says:

Deep learning is a type of machine learning that can process a wider range of data resources, requires less data preprocessing by humans, and can often produce more accurate results than traditional machine-learning approaches (although it requires a larger amount of data to do so). In deep learning, interconnected layers of software-based calculators known as “neurons” form a neural network. The network can ingest vast amounts of input data and process them through multiple layers that learn increasingly complex features of the data at each layer. The network can then make a determination about the data, learn if its determination is correct, and use what it has learned to make determinations about new data. For example, once it learns what an object looks like, it can recognize the object in a new image.

Now Nature publishes a helpful review article on deep learning in healthcare.

Some of the greatest successes of deep learning have been in the field of computer vision (CV). CV focuses on image and video understanding, and deals with tasks such as object classification, detection, and segmentation—which are useful in determining whether a patient’s radiograph contains malignant tumors

The next step is speech and text. Some advances are already available. Basically, Tensorflow by Google is feeding the beast.

PS. WHO and the classification of digital health interventions 1.0

PS. And the book to read:

When the regulator doesn't care about the danger within us

A must see Netflix documentary: The bleeding edge. It explains how medical devices are introduced in the market without appropriate control.
CBS news explains some details:

Just because it's new doesn't mean it's better, it may be dangerous and damage you for life. Unfortunately, this is the summary.
And the book to read:

Medicine as a data science (4)

The practical implementation of artificial intelligence technologies in medicine

One of the critical issues for AI implementation in clinical practice is about privacy. In this article you'll find a clear statement on the impact of EU regulation:

The GDPR will affect AI implementation in healthcare in several ways. First, it requires explicit and informed consent before any collection of personal data. Informed consent has been a long-standing component of medical practice (unlike in social media or onlinebased marketing), but having to obtain informed consent for an  collection of data still represents a higher bar than obtaining consent for specific items, such as procedures or surgical interventions. Second, the new regulation essentially lends power to the person providing the data to track what data is being collected and to be able to request removal of their data. In the healthcare context, this will shift some of the power balance toward the patient and highlights the importance of ongoing work needed to protect patient privacy and to determine appropriate governance regarding data ownership. 

More details inside.

Potential roles of AI-based technologies in healthcare.

 Integration of patient health information at multiple interfaces.

Medicine as a data science (3)

High-performance medicine: the convergence of human and artificial intelligence

If you want to know the current state of artificial intelligence in medicine, then Eric Topol review in Nature is the article you have to read. A highlighted statement:

There are differences between the prediction metric for a cohort and an individual prediction metric. If a model’s AUC is 0.95, which most would qualify as very accurate,
this reflects how good the model is for predicting an outcome, such as death, for the overall cohort. But most models are essentially classifiers and are not capable of precise prediction at the individual level, so there is still an important dimension of uncertainty.

And this is good summary:

Despite all the promises of AI technology, there are formidable obstacles and pitfalls. The state of AI hype has far exceeded the state of AI science, especially when it pertains to validation and readiness for implementation in patient care. A recent example is IBM Watson Health’s cancer AI algorithm (known as Watson for Oncology). Used by hundreds of hospitals around the world for recommending treatments for patients with cancer, the algorithm was based on a small number of synthetic, nonreal cases with very limited input (real data) of oncologists. Many of the actual output recommendations for treatment were shown to be erroneous, such as suggesting the use of bevacizumab in a patient with severe bleeding, which represents an explicit contraindication and ‘black box’ warning for the drug. This example also highlights the potential for major harm to patients, and thus for medical malpractice, by a flawed algorithm. Instead of a single doctor’s mistake hurting a patient, the potential for a machine algorithm inducing iatrogenic risk is vast. This is all the more reason that systematic debugging, audit, extensive simulation, and validation, along with prospective scrutiny, are required when an AI algorithm is unleashed in clinical practice. It also underscores the need to require more evidence and robust validation to exceed the recent downgrading of FDA regulatory requirements for medical algorithm approval

Therefore, take care when you look at tables like this one:

Prediction	n	AUC	Publication (Reference number)
In-hospital mortality, unplanned readmission, prolonged LOS, final discharge diagnosis	216,221	0.93* 0.75+0.85#	Rajkomar et al.96
All-cause 3–12 month mortality	221,284	0.93^	Avati et al.91
Readmission	1,068	0.78	Shameer et al.106
Sepsis	230,936	0.67	Horng et al.102
Septic shock	16,234	0.83	Henry et al.103
Severe sepsis	203,000	0.85@	Culliton et al.104
Clostridium difficile infection	256,732	0.82++	Oh et al.93
Developing diseases	704,587	range	Miotto et al.97
Diagnosis	18,590	0.96	Yang et al.90
Dementia	76,367	0.91	Cleret de Langavant et al.92
Alzheimer’s Disease ( + amyloid imaging)	273	0.91	Mathotaarachchi et al.98
Mortality after cancer chemotherapy	26,946	0.94	Elfiky et al.95
Disease onset for 133 conditions	298,000	range	Razavian et al.105
Suicide	5,543	0.84	Walsh et al.86
Delirium	18,223	0.68	Wong et al.100

1. LOS, length of stay; n, number of patients (training+ validation datasets). For AUC values:
2. *, in-hospital mortality; + , unplanned readmission; #, prolonged LOS; ^, all patients; @, structured + unstructured data; + +, for University of Michigan site.

In search for a fair price-setting in cancer drugs

Pricing of cancer medicines and its impacts

We all know that new cancer drugs represent a challenge for the whole society. Expectations from drug firms are high and public and private budgets do not increase according to such expectations. A technical report released by WHO sheds light on the issue.
Just one statement:

Overall, the analysis suggests that the costs of R&D and production may bear little or no relationship to how pharmaceutical companies set prices of cancer medicines. Pharmaceutical companies set prices according to their commercial goals, with a focus on extracting the maximum amount that a buyer is willing to pay for a medicine. This pricing approach often makes cancer medicines unaffordable, preventing the full benefit of the medicines from being realized.

You may find here former posts on the same topic.

PS. My comment on genetics in clinical practice in GCS 69, p.96

TATE MODERNEXHIBITION

THE C C LAND EXHIBITION

PIERRE BONNARD

THE COLOUR OF MEMORY

Beyond nudges

Beyond Nudges — When Improving Health Calls for Greater Assertiveness

From NEJM

And a book:

Barbarians at a drug firm

After Enron, Valeant Pharmaceuticals is one of the largest corruption scandals of Wall Street. If you want to know the details, you just have to connect Netflix. In episode 3 of the documentary series Dirty Money, you'll  find The Drug Short episode. You'll understand what happens when barbarians take unethical decisions, although they were legal. Meanwhile, the regulator was on vacation in the US.
Highly recommended.

PS. If you don't have Netflix and you are interested on how to proceed like a Valeant executive in the energy sector, then you have to watch "Enron: the smartest guys in the room".

Incentives and behavior

THE MORAL ECONOMY
WHY GOOD INCENTIVES ARE NO SUBSTITUTE FOR GOOD CITIZENS

For those interested in pay for performance, the book by Samuel Bowles will open their minds to new perspectives. The rationale behind the Homo economicus needs to be adjusted to ethical and altruistic motives, professionalism in other words. This explanation of the prisoner's dilemma is helpful.

In the Prisoner’s Dilemma game, defecting rather than cooperating with one’s partner maximizes a player’s payoff, irrespective of what the other player does. Defecting in this game is what game theorists call a dominant strategy, and the game is extremely simple; it does not take a game theorist to figure this out. So, assuming that people care only about their own payoffs, we would predict that defection would be universal.

But when the game is played with real people, something like half of players typically cooperate rather than defect. Most subjects say that they prefer the mutual cooperation outcome over the higher material payoff they would get by defecting on a cooperator, and they are willing to take a chance that the other player feels the same way (and is willing to take the same chance.

When players defect, it is often not because they are tempted by the higher payoff that they would get, but because they know that the other player might defect, and they hate the idea that their own cooperation would be exploited by the other. We know this from what happens when the Prisoner’s Dilemma is not played simultaneously, as is standard, meaning that each person decides what to do not knowing what the other will do, but instead is played sequentially (one person chosen randomly moves first). In the sequential game, the second mover usually reciprocates the first player’s move, cooperating if the first has done so, and defecting otherwise. Keep in mind the fact that avoiding being a chump appears to be the motive here, not the prospect of a higher payoff. 

Stanton at Galeria Barnadas

The corporatization of medicine

Private Equity Acquisition of Physician Practices

From Annals of Internal Medicine:

The current environment is accelerating the disappearance of independent practices and fueling the corporatization of medicine. Many of the largest practices have already been acquired by a hospital, insurer, or private equity firm. No peer-reviewed evidence examines the effect of private equity acquisitions on the quality and cost of patient care; physician professionalism; or the experience of patients, physicians, or staff; little evidence examines the effect of hospital or insurer acquisitions.

Technology driven disruption in health insurance

Digital is reshaping US health insurance—winners are moving fast

From McKinsey

Digital has begun to reshape health insurance markets. Payers in the United States have been slow to digitize and are still behind other industries in their use of artificial intelligence and automation, as well as in customer satisfaction. They’re now starting to catch up. Both incumbents and disruptors are making substantial and growing investments in digital programs.

The bill of new drugs

The Contribution Of New Product Entry Versus Existing Product Inflation In The Rising Costs Of Drugs

In the US there is a huge concern over drug prices. The question is what's driving expenditure growth, new product entry or inflation? In new product entry we have two categories, generic drugs and innovations (in specialty drugs). The answer appears in the latest issue of Health Affairs.

In this retrospective study of pharmaceutical pricing data for 2005–16, we found that increases in the costs of specialty and generic drugs were driven by the entry of new drug products, but rising costs of brand-name drugs were largely due to inflation in existing medication prices.

The costs of oral and injectable brand-name drugs increased annually by 9.2 percent and 15.1 percent, respectively, largely driven by existing drugs. For oral and injectable specialty drugs, costs increased 20.6 percent and 12.5 percent, respectively, with 71.1 percent and 52.4 percent of these increases attributable to new drugs. Costs of oral and injectable generics increased by 4.4 percent and 7.3 percent, respectively, driven by new drug entry. The rising costs of generic and specialty drugs were mostly driven by new
product entry.

We would need similar data for our country. Nobody knows anything, prices are confidential.

PS. On drug pricing scams.

Count Basie and the beginning of swing

Multisided platforms as monopolists

MODERN MONOPOLIES: What It Takes to Dominate the 21st-Century Economy

Platform business is the hottest topic in organizational economics. Linear business  considered as a traditional value chain is exactly the opposite of the economics of platforms. Two years ago I explained in several posts the emergence of this model. In 2004 Tirole and Rochet defined the network externalities that emerged from multisided platforms. Nowadays it is the hottest topic and a new book explains the consequences.

The secret to tech companies’ success lies not in the tech, but in it’s business model. A platform, by definition, creates value by facilitating an exchange between two or more interdependent groups. So, rather than making things, they simply connect people. The book helps you understand what made these companies so successful, how to tell a good platform from the bad, and how you could build one too

And by definition, platforms are the new modern monopolies different from the ones we have known.

Although monopolies get a bad rap, they’re not always a bad thing. In the short term, modern monopolies are often a boon to consumers. They bring valuable new inventions to market, and, in the case of platforms, they build new communities and markets that would not exist otherwise. The downside comes much later, as the monopolist ages and starts to crowd out potential new competitors without delivering new value.

Today Amazon is the most valuable company in the world. That's it. Let's wait for the downside. It may be too late to react. Regulators should read today Jean Tirole and apply his recommendations.

PS. Go to the conclusion and you'll find this statement:

So where should you be looking to next? Well, there are a few industries where the three factors are starting to converge
The first industry is health care, which we’ve touched on at several points in this book. Here you have platforms connecting doctors and patients in new ways, like Doctor on Demand, Teladoc, and ZocDoc. However, these platforms are just going after the low-hanging fruit. There’s still a tremendous amount of waste and inefficiency in the healthcare sector, especially in the United States. And wherever there’s waste and inefficiency, there’s a platform opportunity. For instance, although they’re relatively popular with casual consumers, wearable health devices are just starting to make their way into formal health care. These devices offer tremendous potential for improving patient wellness. But in order for them to be useful, a platform will need to build a unified network of doctors and patients. Despite the recent entrance of Silicon Valley heavyweights Apple and Google, this market is still wide open.

PS. Four additional useful books on the same topic:

Evans and Schmalensee are the best authors on this subject and this is the recommended book for economists.

A historical perspective on platforms.

A book that connects platforms with other topics of interest.

A management perspective on platforms.

Allocating reseach funds by lottery

Contest models highlight inherent inefficiencies of scientific funding competitions

Research funding needs reform. In PLOS Biology, you'll find a controversial proposal: lotterys.

As fewer grants are funded, the value of the science that researchers forgo while preparing proposals can approach or exceed the value of the science that the funding program supports. As a result, much of the scientific impact of the funding program is squandered.

Unfortunately, increased waste and reduced efficiency is inevitable in a grant proposal competition when the number of awards is small. How can scarce funds be allocated efficiently, then?

As one alternative, we show that a partial lottery that selects proposals for funding randomly from among those that pass a qualifying standard can restore lost efficiency by reducing investigators' incentives to invest heavily in preparing proposals.

My impression is that we are not prepared to accept such a mechanism for allocating resources. In a world that claims for transparency, research funding allocated by the chance of winning a lottery seems like a joke. (Fortunately the authors of the article didn't received any fund!)