The urgent answer to the coming black box medicine

Black box medicine and transparency

 The series of reports Black Box Medicine and Transparency examines the human interpretability of machine learning in healthcare and research:

1. Machine learning landscape considers the broad question of where machine learning is being (and will be) used in healthcare and research for health 

2. Interpretable machine learning outlines how machine learning can be or may be rendered human interpretable

3. Ethics of transparency and explanation asks why machine learning should be made transparent or be explained, drawing upon the many lessons that the philosophical literature provides

4. Regulating transparency considers if (and to what extent) does the General Data Protection Regulation (GDPR) require machine learning in the context of healthcare and research to be transparent, human interpretable, or explainable

5. Interpretability by design framework distils the findings of the previous reports, providing a framework to think through human interpretability of machine learning in the context of healthcare and health research

6. Roundtables and interviews summarises the three roundtables and eleven interviews that provided the qualitative underpinning of preceding reports 

Each report interlocks, building on the conclusions of preceding reports.

Meanwhile you can start with the executive summary. Does anybody care about it? 

Payment systems during the pandemic

 Balancing financial incentives during COVID-19: A comparison of provider payment adjustments across 20 countries

Key messages:

•Públic payers assumed most of the COVID-19-related financial risk.

•Income loss was not a problem when providers were paid by salary, capitation or budgets.

•Providers paid based on activity were compensated through budgets or higher fees.

•New FFS payments were introduced to incentivize remote services.

•Payments for COVID-19 related costs included new fees, per-diem and DRG tariffs.

 Paolo Gasparini at KBR

Beyond supply and demand economics

 What Students Learn in Economics 101: Time for a Change

On the need for a new perspective in economics education: 

The question thus arises: how do we want students to use the supply and demand apparatus when there may be excess demand or supply in equilibrium—as in the labor or credit markets when lending and hiring is analyzed using a principal–agent model? A Bowles and Carlin: What Students Learn in Economics 101 related question arises in other markets if the out- of-equilibrium rent- seeking behavior of firms and individuals generates significant excursions away from the intersection of the supply and  demand curves determined by economic fundamentals.

Our response is that in many settings “where the supply and demand curves cross” is not the correct answer. Importantly, this does not amount to an abrogation of the “laws of supply and demand” or a reduction in their force. It requires instead that we break away from the benchmark of the intersection of the two curves, either because that intersection may not exist, or may not be where the market is heading as occurs, for example, during a bubble.

I absolutely agree. 

 Currently at KBR

The Dahlgren and Whitehead model 30 years after

 The Dahlgren-Whitehead model of health determinants: 30 years on and still chasing rainbows

From the authors review:

Reflection on the past 30 years has helped us identify where to go from here, to develop the model so that it is adapted to the burning issues of the day. First, we need to find ways to better illustrate the vertical links between the social, economic and cultural determinants of health and those of lifestyle. This is needed to reinforce the point that many lifestyles are structurally determined. There is a  common, flawed assumption that the lifestyles of different socio-economic groups are freely chosen, ignoring the reality that lifestyles are shaped in important ways by the social and economic  environments in which people live.

Second, there is a current debate about the importance of the commercial determinants of health and whether they have been neglected by the public health community, including a critique of these not being given sufficient prominence in the Dahlgren and Whitehead model (Maani et al., 202015). By ‘commercial determinants’, Maani and colleagues refer to factors that adversely influence health, which stems from the profit motive; the examples they give concentrate on the strategies of tobacco, alcohol and food and beverage producers to promote their products. While we acknowledge that the impact of commercial interests should always be analysed, we deliberately do not define ‘commercial interests’ as a determinant in its own right to be included in the  rainbow model. In a rebuttal to Maani and colleagues, we explain how we consider profit-driven commercial interests as ‘driving forces’ that are related to almost all determinants of health except genetic factors.

 

This did not have to be a global pandemic

 spike. THE VIRUS vs THE PEOPLE THE INSIDE STORY

The title of chapter 9 of this book is: "This did not have to be a global pandemic". 

And says,

WHAT IF THE TAPE had run differently? What if those six miners who developed pneumonia after clearing droppings from bat caves in southern China† had been properly investigated? Imagine what could have happened if doctors had traced their infections back to a novel SARS-related corona-virus most commonly seen in bats. What if there had been a global system not only to pick that up, but also to raise an alert that humanity had no immunity against the virus, to immediately share virus samples globally with scientists and to trigger research into putative vaccines and treatments?

None of this is easy: it is a struggle to identify clusters and unmask a completely new pathogen at work. But, equally, none of it is beyond the capability of science.

This is what we desperately need: surveillance in the shadowlands where humans and animals overlap, and specifically when those exchanges are leading to illness. We need to be asking: which viruses in wild and domestic animals are crossing back and forth between humans and animals? How can we spot these perilous cross-species breaches and how should we respond?

The pathogens that keep me awake at night are those against which humanity is defenceless. So, in the next five years, we need to document these gaps in the global human immune landscape. For a decade now, I have been wondering whether seasonal influenza, or winter flu, may not be the viral enemy we think it is, because the world has a degree of protection against many of the strains in the flu family.

A crucial book to understand the origins of the pandemic and what should be done for the next one.

 

Against body commercialism (3)

 Self-Ownership, Property Rights, and the Human Body: A Legal and Philosophical Analysis

Against body commercialism (2)

 Banking on the Body: The Market in Blood, Milk, and Sperm in Modern America

Kara Swanson traces the history of body banks from the nineteenth-century experiments that discovered therapeutic uses for body products to twenty-first-century websites that facilitate a thriving global exchange.

Against body commercialism

 De la solidaridad al mercado. El cuerpo humano y el comercio biotecnológico

AI everywhere (7)

 Machine Learning and AI for Healthcare. Big Data for Improved Health Outcomes

At a glance (2)

 Health at a Glance2021

Nudging and public policy

 Psychology and Behavioral Economics. Applications for Public Policy

This is a textbook on applied behavioral economics for public policy issues. In chapter 5 you'll find the health section and this is the summary:

Where people live, what they eat, how careful they are about taking their medications, and even what they do in their spare time are very much related to the quality of their lives and their health-related outcomes. While our genetic makeup accounts for a signifcant portion of our health outcomes, we know that health is also heavily infuenced by what are known as social determinants: education, wealth, neighborhood safety, housing, and health literacy, among many others. Throughout the day, we face many decisions that have a direct or indirect impact on our health and quality of life. Many of these choices can be infuenced toward healthier options by behavioral interventions.

This chapter presents behavioral insights and interventions that have a high potential to impact the health of community members, reduce disparities, and improve their overall quality of life. These insights and interventions range from increased medical adherence to improved nutritional choices using nudges, regulations, provision of information, or rewards for positive behaviors

 

Choosing how to choose

 Making Better Choices: Design, Decisions, and Democracy

Making Better Choices is about how we make decisions together and the tools we use to get to those decisions. We make joint decisions out of necessity because the choices we make affect each other. Each decision we take has a consequence. This book reinforces why we need better systems design and analyses given the consequences of our decisions. It is also about carefully thinking about the values of the choices we make, whether they occur in a small meeting of individuals in a local association or community or in a national election. It will illuminate the differences between sincere behavior and strategic behavior to defeat an opponent in voting, the latter being quite common. The book will also review different voting systems, what their original intents were, and what their deficits are. In trying to bring all these topics together and more, the authors realized that the book is in essence an outcome of the arranged marriage between social choice and systems engineering that they conducted. The more one begins to explore the aspects of social choice and systems engineering, the more one realizes how much they have in common, and how much more they can offer if they are unified.

Charles E. Phelps is one of the authors of this book. He departs from health economics and enters into social choice. Great book. In the epilogue:

We live in a world with the consequences of pretending that complex systems have binary answers. Having open discussions on these complexities is sometimes difficult, perhaps even painful. But awkwardness and pain on such issues merely represent hidden problems that inhibit our collective progress. Ignoring them or stifling discussion about them will not make them go away.

In Making Better Choices we have seen how the ways we choose affect how organizations make decisions. Two major branches of human thought have approached this issue, but from wholly distinct directions. Systems engineering has created a repertoire of ways for product and service design with performance improvement and new insights as defining features. However, systems engineering seldom deals with the question of how to do this in the context of a group of individuals acting as the decision-maker. A traditional systems engineering approach develops requirements of a desired, even acceptable, solution to the problem but doesn’t necessarily consider where those come from. This is the realm of social choice.

 

AI everywhere (7)

 The Triumph of Artificial Intelligence. How Artificial Intelligence is Changing the Way We Live Together

Topics of the book: 

How Much and What Kind of Artificial Intelligence Can Humans Bear?

“Do You Know How It Was?”: The History of AI

How Does AI Function?: AI Techniques

How Is AI Being Realized?: AI Determines Our Lives

Are You Still Buying or Are You Already “Influencing”?: Trade 4.0

Where to Go with the “Social Fallow”?: Industry 4.0

How Is Our Togetherness Changing?: Social Implications of AI

Paradise Times or the End of the World?: The Future with AI

Ethics of algorithms: the autonomy perspective

 Algorithms and Autonomy. The Ethics of Automated Decision Systems

This is a reference book on the key topics of ethics of artificial intelligence. Selected statements from the last chapter of this open access book:

We have argued that three broad facets of autonomy are affected by algorithmic systems. First, algorithmic systems are relevant to what we owe each other as autonomous agents. That is the focus of Chapters 3 and 4. In Chapter 3 we addressed the material conditions that we owe others and argued that respecting people as autonomous demands that any algorithmic system they are subjected to must be one that they can reasonably endorse. It does not require that they value particular outcomes or that they not bemade worse off by such systems. Rather, systemsmust either comport with agents’ own ends or be consistent with fair terms of social cooperation.We argued that persons being able to reasonably endorse a system turns on the system’s reliability, responsibility, stakes, and relative burden. Chapter 4 turned to the issues of what information we owe others. There we argued that people are owed information as a function of their practical agency (i.e., their ability to act and carry out plans in accord with their values) and as a function of their cognitive agency (i.e., their ability to exercise evaluative control over mental states, including beliefs, desires, and reactive responses). We offered several principles for information access grounded in agency. The second connection between algorithmic systems and autonomy is ensuring the conditions under which people are autonomous. 

In Chapter 5 we considered the relationship between algorithmic systems and freedom.We explained that algorithms bear upon negative, positive, and republican freedomand offered a general account of freedom as ecological non-domination. Key to understanding that ecology is recognizing three key challenges to freedom: affective challenges, deliberative challenges, and social challenges. In Chapter 6 we offered some suggestions for addressing some facets of those challenges. Specifically, we argue that a kind of epistemic paternalismis both permissible and (under some conditions) obligatory. Chapters 7 and 8 shift focus to the responsibilities of agents in light of the fact that they are autonomous. In Chapter 7 we argue that algorithmic systems allow agents deploying such systems to undermine a key component of responsibility, viz., providing an account for actions for which they are responsible. Specifically, we argue that complex systems create an opportunity for “agency laundering,” which involves a failure to meet one’s moral responsibility for an outcome by attributing causal responsibility to another person, group, process, or technology. Chapter 8 addresses a different facet of responsibility. Citizens within democratic states have a responsibility to exercise their autonomy in order to legitimate political authority. That is, they have a responsibility to help ensure that governments, laws, policies, and practices are justifiable. However, some kinds of algorithmic systems hinder citizens’ abilities to do that. They can do so by undermining the epistemic conditions necessary to underwrite the “normative authority” path to legitimacy or by undermining the exercise of autonomy necessary to underwrite the “democratic will” path to legitimacy.