31 de desembre 2019

30 de desembre 2019

How confirmation bias contributes to polarization

Confirmation bias in the utilization of others’ opinion strength

Humans tend to discount information that undermines past choices and judgments. This confirmation bias has significant impact on domains ranging from politics to science and education. Little is known about the mechanisms underlying this fundamental characteristic of belief formation. Here we report a mechanism underlying the confirmation bias. Specifically, we provide evidence for a failure to use the strength of others’ disconfirming opinions to alter confidence in judgments, but adequate use when opinions are confirmatory. This bias is related to reduced neural sensitivity to the strength of others’ opinions in the posterior medial prefrontal cortex when opinions are disconfirming. Our results demonstrate that existing judgments alter the neural representation of information strength, leaving the individual less likely to alter opinions in the face of disagreement.
The notion that the strength of disconfirming opinion is not necessarily proportionate to its impact on belief change is in accord with anecdotal and ‘real-world’ observations in domains ranging from science to politics. The underlying process is remarkably flexible, with the neural circuitry involved switching on a trial-bytrial basis from high sensitivity to relative neglect, contingent on whether the opinion is confirmatory or disconfirming. This process may leave the individual less likely to alter opinions in the face of disagreement.
Interesting article from Nature. So what? Is there any exercise to train the neural sensitivity of our posterior medial prefrontal cortex? If so, I would suggest these exercise to some guys.

26 de desembre 2019

Ethical algorithms like hammers?

Main messages:
All decision-making—including that carried out by human beings—is ultimately algorithmic. The difference is that human decision-making is based on logic or behaviors that we struggle to precisely enunciate. If we humans had the ability to describe our own decision-making processes precisely enough, then we could in fact represent them as computer algorithms. So the choice is not whether to avoid using algorithms or not, but whether or not we should use precisely specified algorithms.
Machine learning is a powerful tool that has many extant and potential benefits.  Technology companies such as Google and Facebook of course rely on products powered by machine learning for much of their revenue—but as these techniques grow in applicability, their scope and societal benefits grow as well.
The result is that, at least for a while, the critics of the algorithmic approach may often be right. There are many consequential domains where algorithmic tools are still too naive and primitive to be fully trusted with decision-making. This is because to model the forest, we need to start with the trees. This book offers a snapshot of exciting strands of research aimed at developing ethical algorithms, many of which are still in their very earliest days.

23 de desembre 2019

Global pharmaceutical market vs. local regulators

Regulating Medicines in a Globalized World: The Need for Increased Reliance Among Regulators

As defined by the World Health Organization (WHO), recognition occurs when a regulatory authority accepts the regulatory decision of another authority “as its own decision;”— reliance takes place when a regulatory authority takes into account the work products of another authority (e.g., inspection reports, scientific assessment reports, joint assessment reports produced together with another authority) to help inform the receiving authority's own regulatory decision, which, in the end may differ from the made by the initial authority using the same products. 
Recognition and reliance are the first steps towards an improvement of real coordination between regulatory bodies. If the pharmaceutical market is global, the regulators should cooperate for a coherent global regulation.

22 de desembre 2019

Anchoring and adjusting heuristic

Effect of Teaching Bayesian Methods Using Learning by Concept vs Learning by Example on Medical Students’ Ability to Estimate Probability of a Diagnosis

Why do we need more bayesian reasoning?. Can we improve clinical decision making? The answer in this JAMA article.
Although bayesian reasoning has been widely promoted in the literature as a diagnostic strategy, there has been scant evidence that teaching bayesian reasoning actually improves diagnostic accuracy. Providing learners with relatively brief instruction on these abstract concepts appeared to significantly improve their diagnostic performance in comparison with simply providing a number of relevant examples or no relevant instruction.
 The previously reported discrepancy between human probability estimation and optimal bayesian probability estimation has become traditionally accepted in the psychology and medical literature as cognitive biases, including base-rate neglect, anchoring bias, confirmation bias, and representativeness, all of which suggest suboptimal revision and have been purported to be a primary cause of diagnostic error.13,14 However, on closer scrutiny, much of this evidence was derived from situations that were not representative of the typical diagnostic setting. One highly cited study9 used a screening situation in which the base rate was very low and any positive test result, even one with excellent operating characteristics, would have most likely been a false-positive result. Participants in that study also appeared to exhibit semantic confusion by confusing the posttest probability with the conditional probability that was presented in the problem-solving exercise.

19 de desembre 2019

Medicine as a data science (7)

Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril

The National Academy of Medicine’s Special Publication: Artificial Intelligence in Health Care: The Hope, The Hype, The Promise, The Peril synthesizes current knowledge to offer a reference document for relevant health care stakeholders such as: AI model developers, clinical implementers, clinicians and patients, regulators, and policy makers, to name a few. It outlines the current and near-term AI solutions; highlights the challenges, limitations, and best practices for AI development, adoption, and maintenance; offers an overview of the legal and regulatory landscape for AI tools designed for health care application; prioritizes the need for equity, inclusion, and a human rights lens for this work; and outlines key considerations for moving forward.
A must read

14 de desembre 2019

Pharmaceutical policies

Medicamentos, innovación tecnológica y economía; (2019), nº 160

This issue of Papeles de Economia Española explains the current situation of pharmaceutical policies in Spain. These are the topics:

La industria farmacéutica en la actualidad: un vistazo a sus características

La evolución reciente y perspectivas de la innovación
de medicamentos
Ética, medicamentos e innovación
La economía de la I+D en la industria farmacéutica: un resumen
Innovación y competencia en el sector farmacéutico en la época de la medicina de precisión
La innovación y la industria farmacéutica en España
La I+D en el sector farmacéutico español en el período 2003-2015

La evolución de la organización empresarial en la industria farmacéutica
La política de la competencia en la industria farmacéutica
La contribución del sector farmacéutico al crecimiento, a las exportaciones y a la inversión en España

Gestión de la prestación farmacéutica y compras públicas en la Comunidad de Madrid
Procedimiento de selección de medicamentos en atención primaria en Andalucía
La experiencia de gestión de los medicamentos en Cataluña. Objetivos de salud y económicos

La trayectoria metodológica de la evaluación de la eficiencia y su futuro
El valor de las innovaciones médicas
La evaluación de la eficiencia de intervenciones y tecnologías sanitarias en España

Industria farmacéutica en España: innovación y compromiso social
El sector de los medicamentos genéricos en España
Medicamentos biosimilares: una oportunidad para el Sistema Nacional de Salud
40 años de autocuidado en España.
Un sector consolidado y emergente

12 de desembre 2019

Books of the year

The Economist
Financial Times
Prospect (economics)

Top ten books of 2019 by Eric Topol

11 de desembre 2019

Laboratory medicine as a data science

Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation

Artificial intelligence (AI) and data science are rapidly developing in healthcare, as is their translation into laboratory medicine.
These are the four areas that the authors consider that AI will have impact:

  • Processes and care pathways
  • Laboratory test ordering and interpretation
  • Data mining, early diagnosis, and proactive disease monitoring
  • Personalized treatment and clinical trials
Meanwhile there is a long way ahead.

Jacob Lawrence, This is Harlem, 1943. Gouache and pencil on paper. Hirshhorn Museum and Sculpture Garden, Smithsonian Institution, Gift of Joseph H. Hirshhorn, 1966. Artwork © The Jacob and Gwendolyn Knight Lawrence Foundation, Seattle / Artists Rights Society (ARS), New York; photograph by Cathy Carver

03 de desembre 2019

The fight between commercialism and professionalism in medicine (3)

The Price We Pay
What Broke American Health Care--and How to Fix It

Health care is perhaps today’s most divisive, territorial political issue. But many of the needed solutions are not partisan; they’re American. We are at a pivotal juncture. Spending on health care threatens every aspect of American society. The time for commonsense reform has arrived. All of us can play a part in driving badly needed reforms, both in the marketplace and in the policy world.
As a society, we should embrace a basic set of patient rights, including a right to obtain a timely quote for a shoppable medical service. Lawmakers should look at the price transparency trails blazed by Florida, New Hampshire, and Maine. The prerequisite of any free market is viewable pricing information—not just inflated charges, but the actual amounts of settled bills. New policies should ensure a level playing field to make the free market functional again, to cut the waste and restore competition to the marketplace.
I disagree with the author. Competition it is not the tool for a fairer health care. Wishfull thinking will not drive us to an improvement.

01 de desembre 2019

The fight between commercialism and professionalism in medicine (2)

The Public Creation of the Corporate Health Care System

A second book on the same topic on US healthcare. And the regulatory messages are:
Despite the political uproar surrounding the ACA, many citizens, including the legislation's opponents, acknowledged the need for some type of reform. Indeed, ACA antagonists were most effective raising the specter of how federal programming would worsen health care rather than boasting about prevailing arrangements. Poor service distribution, fragmented care, and uneven service quality had long characterized U.S. medicine. But policymakers and voters were primarily concerned about the uninsured and the exorbitant costs that ranked American health care as the world's most expensive. These flaws helped push the ACA over the finish line. And the program has thus far proven resilient, withstanding presidential and congressional contests as well as significant court challenges.
The ACA built new rooms atop a defective, jerry-built edifice. The public option would have put the nation firmly on the path toward a nationalized, universal system by creating a government-managed plan and using regulations and mandates to enfeeble and eventually drive out private coverage. Readers can decide for themselves the wisdom of creating a centralized system. Nonetheless, because the ACA failed to secure fundamental, structural reform, it will be unable to rein in costs while also maintaining or improving the quality of care. Indeed, this narrative has illustrated how a fusion of public and private power constructed an institutionally tangled health care system that, even under the banner of comprehensive reform, policymakers were ultimately unable to rescue from the insurance company model.
Right now it seems that ACA is not enough. Let's wait and see.