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

April 21, 2022

Drug scarcity

Shortages of medicines in OECD countries
Even in wealthy economies, access to medicines is increasingly affected by medicine shortages – an issue exacerbated with the onset of the COVID-19 pandemic. The aim of this paper was to examine the extent and nature of medicine shortages in OECD countries (pre-COVID-19) and explore the reasons for this growing global problem. Although differences in monitoring mechanisms make multi-country analyses challenging, a sample of 14 OECD countries reported a 60% increase in the number of shortage notifications over the period 2017-2019. While the complexity of pharmaceutical manufacturing and supply chains hampers root cause analyses, available literature suggests that shortages, as reported by marketing authorisation holders, are predominantly due to manufacturing and quality issues. Nevertheless, commercial factors - and the policy settings that influence them - may play an important role. Although several OECD countries have implemented policy measures to mitigate, monitor and prevent shortages, more robust data and further analyses of root causes and effective policy responses are needed. The way forward should involve a global approach that engages all relevant actors and looks beyond the health care sector alone.


April 18, 2022

Attacking health care

PERILOUS MEDICINE.  The Struggle to Protect Health Care from the Violence of War

As a member of the Scientific Committee of Healing across the divides, please let me suggest this book by Leonard Rubinstein

Pervasive violence against hospitals, patients, doctors, and other health workers has become a horrifically common feature of modern war. These relentless attacks destroy lives and the capacity of health systems to tend to those in need. Inaction to stop this violence undermines long-standing values and laws designed to ensure that sick and wounded people receive care.
Leonard Rubenstein—a human rights lawyer who has investigated atrocities against health workers around the world—offers a gripping and powerful account of the dangers health workers face during conflict and the legal, political, and moral struggle to protect them. In a dozen case studies, he shares the stories of people who have been attacked while seeking to serve patients under dire circumstances including health workers hiding from soldiers in the forests of eastern Myanmar as they seek to serve oppressed ethnic communities, surgeons in Syria operating as their hospitals are bombed, and Afghan hospital staff attacked by the Taliban as well as government and foreign forces. Rubenstein reveals how political and military leaders evade their legal obligations to protect health care in war, punish doctors and nurses for adhering to their responsibilities to provide care to all in need, and fail to hold perpetrators to account.
Bringing together extensive research, firsthand experience, and compelling personal stories, Perilous Medicine also offers a path forward, detailing the lessons the international community needs to learn to protect people already suffering in war and those on the front lines of health care in conflict-ridden places around the world.


April 13, 2022

Alternative payment models for pharmaceuticals

Outcomes-based reimbursement for gene therapies in practice: the experience of recently launched CAR-T cell therapies in major European countries

Kymriah® and Yescarta® have relatively uniform list prices across the EU5, and are reimbursed according to their marketing authorisations. In France and the UK, reimbursement is on the condition of collecting additional data (at the cohort level) and subject to future reassessments; elsewhere, rebates (Germany) or staged payments (Italy and Spain) are linked to individual patient outcomes.

 Kymriah® and Yescarta® were two of the subject therapies used to pilot and validate the Valtermed system for roll-out in November. Kymriah® is reimbursed in the Spanish NHS through two outcomesbased, staged payments based on data collected through the Valtermed system: one at the time of treatment (reported to be 52% of the total €320,000 ), and a second payment at 18 months (reportedly the remaining 48%), provided that the patient has achieved and sustained a complete response to the treatment. Yescarta® was approved for reimbursement in July 2019, and follows a similar approach to Kymriah®, however, with payments linked to survival as collected through Valtermed: reportedly a first payment of €118,000, and a second payment of €209,000

 Adolf Mas (1907) exhibition at KBR

April 7, 2022

Data & Society

 A Primer on Powerful Numbers: Selected Readings in the Social Study of Public Data and Official Numbers

This publication is intended to be a non-exhaustive syllabus organized around a series of teachable or debatable claims concerning  the influence institutions of authority have on how data and numbers are created, as well as how that information is used by the datafied state to make fundamental decisions about democratic policy and process. 

Official numbers are the foundation upon which modern societies trust data. An official number is different from any other number because it’s given with authority and always there for the taking. Official data sets come out of bureaucratic and corporate offices and are imbued with the authority of those in power.

Six key arguments that center on the authority of data:

  1. Modern societies are built to trust in official numbers (they even let official numbers make key decisions);
  2. Official numbers are made, not found;
  3. We forget that official numbers have to be made even when things are going well;
  4. Institutions make public data and they make data public;
  5. Official numbers are political; and
  6. Consensus on official numbers requires work.



April 1, 2022