May 9, 2022

Pharma, big pharma (9)

 Inside the FDA: The Business and Politics Behind the Drugs We Take and the Food We Eat

Inside the FDA takes a closer look at the practices, people, and politics of this crucial watchdog in light of the competing pressures and trends of modern society, revealing what the FDA is supposed to do, what it actually does-and fails to do-who it influences, and how it could better fulfill its mandate. The decisions that the FDA makes are literally life and death. Inside the FDA provides a sophisticated account of how this vitally important agency struggles to balance bureaucracy and politics with its overriding mission to promote the country's health.



 

May 8, 2022

Pharma, big pharma (9)

 Devalued and Distrusted: Can the Pharmaceutical Industry Restore its Broken Image?

Starting with "4 Secrets that Drug Companies Don't Want You to Know," Devalued and Distrusted provides a fact-based account of how the pharmaceutical industry works and the challenges it faces. It addresses such critical issues as:

  • Why pharmaceutical R&D productivity has declined
  • Where pharmaceutical companies need to invest their resources
  • What can be done to solve core health challenges, including cancer, diabetes, and neurodegenerative diseases
  • How the pharmaceutical industry can regain public trust and resuscitate its image

Our understanding of human health and disease grows daily; however, converting science into medicine is increasingly challenging. Reading Devalued and Distrusted, you'll not only gain a greater appreciation of those challenges, but also the role that the pharmaceutical industry currently plays and can play in solving those challenges.



 

May 7, 2022

Pharma, big pharma (8)

 Drug Truths: Dispelling the Myths About Pharma R & D

This book answers the questions about the process and costs of pharmaceutical R & D in a compelling narrative focused on the discovery and development of important new medicines. It gives an insider's account of the pharmaceutical industry drug discovery process, the very real costs of misperceptions about the industry, the high stakes--both economic and scientific--of developing drugs, the triumphs that come when new compounds reach the market and save lives, and the despair that follows when new compounds fail. In the book, John LaMattina, former president of Pfizer Global Research and Development, weaves themes critical to a vital drug discovery environment in the context. This is a story that Dr. LaMattina is uniquely qualified to tell.

TABLE OF CONTENTS

PART I A MATTER OF THE HEART 1

CHAPTER 1 CHOLESTEROL DRUGS ARE UNNECESSARY 3

CHAPTER 2 INDUSTRY IS MORE INTERESTED IN "ME-TOO" DRUGS THAN IN INNOVATION 13

CHAPTER 3 IT TAKES INDUSTRY TOO LONG TO DISCOVER NEW DRUGS 23

PART II THE ROLE OF PHARMACEUTICAL R&D IN HEALTH CARE 39

CHAPTER 4 DRUGS ARE DISCOVERED BY ACADEMIA 41

CHAPTER 5 NEW MEDICINES ADD COSTS BUT LITTLE BENEFIT 50

CHAPTER 6 BIG PHARMA HAS FAILED AND SHOULD LEARN FROM BIOTECH SUCCESS 59

PART III THE PROFIT MOTIVE 69

CHAPTER 7 THE INDUSTRY INVENTS DISEASES 71

CHAPTER 8 NEW DRUGS ARE LESS SAFE THAN TRADITIONAL MEDICINES 79

CHAPTER 9 INDUSTRY SPENDS MORE ON ADVERTISING THAN ON R&D 91

CHAPTER 10 INDUSTRY DOES NOT CARE ABOUT DISEASES OF THE DEVELOPING WORLD 100

PART IV THE FUTURE 109

CHAPTER 11 BIG PHARMA'S DAY HAS PASSED 111

CHAPTER 12 FINAL REFLECTIONS 122



May 6, 2022

Two-tier healthcare, or paying twice for the same (2)

 Are we heading for a two tier healthcare system in the UK?

Private healthcare boom adds to fears of two-tier system in UK

Extrapolation from a recent poll suggests that about 16 million adults in the UK found it difficult to access healthcare services during the pandemic, and of these, one in eight opted to access private healthcare.1 This could create the conditions for a two tier system, whereby those with the means to pay have access to healthcare more quickly than those who don’t. This would jeopardise the high levels of support the NHS has enjoyed since its establishment and have serious implications for equity in access to healthcare services.

 

 

May 5, 2022

Two-tier healthcare, or paying twice for the same

 Is Two-Tier Health Care the Future?

In this book, leading researchers explore the public and private mix in Canada and within countries such as Australia, Germany, France and Ireland. We explain the history and complexity of interactions between public and private funding of health care. We also explain the many regulations and policies found in different countries used to both inhibit and sometimes to encourage two-tier care (for example, tax breaks). If a Canadian court strikes down laws restrictive of two-tier, Canadian governments can (i) permit and even encourage two-tier care to grow; (ii) pass new regulations that allow a small measure of two-tier care; or (iii) take positive steps to eliminate wait times in Canadian health care, and thereby reduce demand for two-tier care. We argue for option three as the best means to ensure Canadian principles of equity in access, ensure timely care, and fend off constitutional challenges.




May 4, 2022

Against black box medicine (2)

 Time to reality check the promises of machine learningowered precision medicine

Both machine learning and precision medicine are genuine innovations and will undoubtedly lead to some great scientific successes. However, these benefits currently fall short of the hype and expectation that has grown around them. Such a disconnect is not benign and risks overlooking rigour for rhetoric and inflating a bubble of hope that could irretrievably damage public trust when it bursts. Such mistakes and harm are inevitable if machine learning is mistakenly thought to bypass the need for genuine scientific expertise and scrutiny. There is no question that the appearance of big data and machine learning offer an exciting chance for revolution, but revolutions demand greater scrutiny, not less. This scrutiny should involve a reality check on the promises of machine learning-powered precision medicine and an enhanced focus on the core principles of good data science—trained experts in study design, data system design, and causal inference asking clear and important questions using high-quality data.



April 28, 2022

Provider payment strategies to improve health (2)

  Per un nou marc d’avaluació i contractació de serveis basat en el valor de la salut

Our current payment system needs a complete overhaul. This is the report that we have prepared to contribute with proposals for reform. Hope someone will take it into account.


Presentation of the report, Today at 17h

April 27, 2022

Efficient health insurance as a first best

 Sick Insurance: Adverse Selection and Regulation of Health Insurance Markets

When heterogeneity in consumer tastes and needs, and in cost and quality of products, are publically observable, markets can price, sort, and match these variations, and product choices made by consumers yield demand signals that foster efficient resource allocation. These conditions hold, roughly, for a broad swath of economic activity, allowing lightly regulated private markets to successfully approximate allocative efficiency. However, in health care systems around the globe today, participants do not necessarily see the big picture of lifetime health costs and quality of life, and in many systems the incentives that consumers and providers face do not promote efficient allocation of health care resources. Information asymmetries are the fundamental source of difficulties in health insurance markets and in efficient provision of health services. Additional factors contributing to poor performance of health markets include (1) government regulation that is intended to protect the disadvantaged and promote equity, but creates incentives antagonistic to allocative efficiency, (2) inefficient provider organizations and non-competitive conduct, sometimes sheltered by government policies, and (3) behavioral shortcomings of consumers in promoting their own self-interest, including inconsistent beliefs regarding low-probability future events, myopia, and inconsistent risk assessment.

The seminal contributions to economic analysis of Kenneth Arrow, George Akerlof, Joe Stiglitz, Mike Spence, Mike Rothschild, and John Riley establish that when there are information asymmetries between buyers and sellers, adverse selection, moral hazard, and counter-party risk can result, causing markets to operate inefficiently or unravel. Asymmetric information between buyers and sellers, or market regulations that restrict competitive underwriting and force common prices for disparate products, can induce adverse selection. Moral hazard occurs when effort to avoid risks cannot be observed by sellers and stipulated in insurance contracts, and buyers have less incentive for risk-reducing effort when some of their potential losses are covered. When the productivity and cost of medical interventions is not known to all parties, then buyers and third-party-payers may not make informed decisions on therapies. Counter-party risk occurs when sellers evade payment of benefits for losses, or fail as agents to respect the interests of the consumers who are their principals. Adverse selection of buyers with high latent risk or low risk-reducing effort, or sellers with high counter-party risk, make insurance less attractive to buyers, and may cause insurance markets to unravel. Administrative overhead will induce less than full insurance. By itself, this does not make insurance market outcomes inefficient, but increasing returns to scale in administrative costs may lead to an inefficient concentrated market.

In principle, the problems of asymmetric information can be overcome by government operation or regulation of health services; in practice, there remains a major mechanism design problem of designing incentives that handle the asymmetries; e.g., “single payer” systems permit additional levers of control, but information asymmetries cause principal-agent problems even in command organizations. Legal mandates and regulations can make adverse selection worse. Government policy on private health insurance markets often reflects a social ethic that individuals should not be denied health care because of inability to pay, expressed for example in requirements that hospitals admit uninsured patients with life-threatening conditions, and a social ethic that insurance contract underwriting should not be based on risk factors such as gender, race, and pre-existing conditions. When these requirements are not publically financed, they are implicit taxes on insurers and providers that are at least in part passed through to consumers as higher premiums that increase the effective load for low-risk consumers. Both the higher loads and the prospect of public assistance as a last resort reduce the incentive for consumers to buy insurance and to pay (or copay) for preventative care.

The United States has, more than any other developed country, relied on private markets for health insurance and health care delivery. These markets have performed poorly. Denials and cancellations, exclusion of pre-existing conditions, and actuarially unattractive premiums have left many Americans with no insurance or financially risky gaps in coverage. Administrative costs for health insurance in the United States are seven times the OECD average. These are symptoms of adverse selection. Delayed and inconsistent preventative and chronic care, arguably induced by incomplete coverage, have had substantial health consequences: the United States ranks 25th among nations in the survival rate from age 15 to age 60. This impacts the population of workers and young parents whose loss is a substantial cost to families and to the economy. If the U.S. could raise its survival rate for this group to that of Switzerland, a country that has mandatory standardized coverage offered by private insurers, this would prevent more than 190,000 deaths per year.

Given the damage that information asymmetries can inflict on private market allocation mechanisms, the obvious next question is what regulatory mechanisms can be used to blunt or eliminate these problems. This involves examining closely the action of adverse selection and moral hazard, and the tools from principal-agent theory and from regulatory theory that can blunt these actions. There is an extensive literature relevant to this analysis that can be focused on the regulatory design question. Less well investigated are the impacts of consumer behavior, particularly mistaken beliefs. This paper examines these issues, and studies the impacts of regulations intended to promote equity and efficiency. More practically, this paper investigates these issues with reference to the private market in the United States for prescription drug coverage for seniors, introduced in 2006 and subsidized and regulated as part of Medicare.

The efficient regulatory design is mandatory universal insurance, this is the answer. But it has to be eficient, otherwise appears duplicate insurance, paying twice for the same. This is the worst second best, a combined failure of mandatory and private coverage.



April 26, 2022

Against black box medicine

Explainable machine learning practices: opening another black box for reliable medical AI 

In regulating medical AI, we should address not only algorithmic opacity, but also on other black boxes plaguing these tools. In particular, there are many opaque choices that are made in the training process and in the way algorithmic systems are built, which can potentially impact SaMD-MLs performances, and hence their reliability. Second, we have said that opening this alternative black box means explaining the training process. This type of explanation is in part documenting the technical choices made from problem selection to model deployment, but it is also motivating those choices by being transparent about the values shaping the choices themselves—in particular, performance-centered values and ethical/social/political values. Overall, our framework can be considered as a starting point to investigate which aspects of the design of AI tools should be made explicit in medicine, in order to inform discussions on the characteristics of reliable AI tools, and how we should regulate them. We have also highlighted some limitations, and we have claimed that in the future it will be necessary to empirically investigate the practice of machine learning in light of our framework, and to identify more nuances in the values shaping ML training.

We want to end this article by repeating that the problem of explaining opaque technical choices is not an alternative to explain the opacity lying at the algorithmic level. Unlike London, we think that the worries about algorithmic opacity in medicine are more than justified. However, we leave any consideration on how the two opacities are connected to each other for future works.

Huge business interests are at  stake, who cares about citizens?



Didier Lourenço at Galeria Barnadas