May 24, 2019

Cohen-Emanuel podcast

Ezekiel Emanuel on the Practice of Medicine, Policy, and Life

Ezekiel Emanuel is a reflection of his upbringing: a doctor for a father who loved to travel, a mother interested in policy and community activism, and all the competition and friendship that comes with growing up closely with two brothers. Put those together and you wouldn’t be surprised that the result is someone who has worked at both the highest levels of, medicine, policy and academia — though the intense interest in jam might surprise you.

May 17, 2019

Opioid epidemic and the need for urgent measures

Addressing Problematic Opioid Use in OECD Countries

Some months ago I explained my concerns about opioid epidemic. I said that the problem is closer than most people think. In the last five years, there has been a 45% growth in publicly funded opioid prescription  in our country. Now OCDE presents the current situation in a report that highlights where we are and what can be done. The key messages are these ones:
  • Better Prescribing: Doctors can improve their prescribing practices, for instance, through evidence-based clinical guidelines (e.g. for opioid prescription, for adequate medication-assisted therapy for OUD patients), prescribers training, surveillance of opioid prescriptions, and regulation of marketing and financial relationships with opioid manufacturers. In addition, patients and the general public can also benefit from clear educational materials and awareness interventions to enhance their opioid-related literacy and reduce stigma.
  • Better care: Including the expansion of coverage for long-term medication-assisted therapy (e.g. methadone, buprenorphine, naltrexone) coupled with specialised services for infectious diseases management (e.g. HIV, hepatitis) and psychosocial interventions. Some countries have implemented interventions such as the availability of overdose reversal medications for all first responders, needle and syringe programmes, and medically supervised consumption centres.Quality of care must be improved and measured. 
  • Better approach: There can be better coordination across the health, social and criminal justice systems. Governments can consider setting up of coordinated networks among the three sectors aiming to facilitate access to integrated services for people with OUD. In addition to health services, social interventions around housing and employment support, and law enforcement uptake of a public health approach are central.
  • Better knowledge and research: Including the use of big data and impact evaluations to generate new information from different sources along with the application of advanced analytics. In addition, quality of care measurement should be enhanced in areas such as opioid prescription, OUD health care services, and patient reported indicators (e.g. PROMs, PREMs). Research and development is needed in key areas such as new pain management modalities and OUD treatments.

May 9, 2019

Genome editing: the game of biology is about to change

Hacking the Code of Life: How gene editing will rewrite our futures

The foundations of gene editing came about because a scientist in Alacant, Dr. Mojica started to find weird DNA sequences in some bacteria he was studying. After that Profs. Doudna and Charpentier and later Prof. Zhang translated initial findings into practice. Therefore it all started when a microbiologist studied the arms race between bacteria and viruses.
You'll find all these details in a book by Nessa Carey. If you want to understand in plain words what CRISPR is and what may represent for biology, then you have to read it.
The gene editing revolution is creating a technological toolkit that almost any half-decent scientist can lean into and find something useful. On the one hand, that should make us very excited. We can both solve problems and simply indulge our curiosity. But should it also make us worried? Using chisels and a mallet, Michelangelo created some of the most exquisite sculptures we have ever seen. But give the same heavy, sharp tools to someone else, and we can get a very different and much bloodier outcome.
But the same technology can also be used to alleviate human suffering, and if we are smart enough, lessen the impact that our heavy-footed species has on the only planet we know of in the entire universe that supports complex life. We cannot un-invent this technology, we probably can’t even control its spread. So what choice do we really have but to embrace it and use it well, to create a safer, more equal world for all?

May 6, 2019

Decentralisation in health services, is it worth it?

Decentralisation and performance measurement systems in health care

The main trends from the OECD survey results are:

  • Decision-making in health care tends to rest largely with the central government, which has considerable power across many aspects of the delivery of health services. More specifically, central governments are more likely to be responsible for decisions regarding the policy aspects of health care, but have less control over decisions regarding the inputs, outputs and monitoring of health care services. In most countries, sub-national governments have large responsibility for input-related matters, such as determining the outsourcing of services and deciding on the contractual status of staff. On average, local governments have little decision-making power in the health sector, but have the most responsibility with regard to decisions about health care inputs.
  • The role of the central governments in health care does not vary markedly between federal and unitary countries. However, sub-national government decision-making power tends to be higher in federal than in unitary countries. 
  • The majority of OECD countries tends to rely on centralised performance measurement systems, especially to monitor the performance of hospital providers. Systems vary markedly between countries, although some trends across countries exist, including the observation that health performance systems are generally more geared towards improving performance rather than reducing service costs. 
  • Less likely to be monitored under a specific performance framework are providers of ancillary services, retailers and other providers of medical goods, and providers of preventive care. Common reasons for the non-establishment of performance systems in these sectors, and in general, include a lack of capacity at the national level, a lack of available data and challenges to co-ordinate actors.

It is a great report, a must read. Impossible ot summarise in few words.

April 29, 2019

How much would you pay for a gene therapy?

Perspectives on Gene Therapy: Defining and Demonstrating Value to Payers

Kymriah has been included in the NHS and nobody but the regulator knows the price. Therefore, in Spain the answer to the question about the willingness to pay for gene therapy depends absolutely on a few officials in the Ministry, not so many, and they decide in a closed meeting without any transparency and outside of any legal procedure for public budgets. And nothing happens, that's great!. Unbelievably, that's it, the price is confidential and rule of law is useless in a failed state.

PS. Therefore, take note. There is no role for health economics evaluation, forget it forever.

Philip Stanton

April 25, 2019

Do sin taxes work?

The Use of Excise Taxes to Reduce Tobacco, Alcohol, and Sugary Beverage Consumption

The summary:

Of the 188 countries that reported 2016 tobacco tax and price data to the WHO, 173 levied an excise tax on manufactured cigarettes (61). Tobacco taxes have increased in many countries since the 2005 entry into force of theWHO’s Framework Convention on Tobacco Control. The treaty emphasizes the effectiveness of tax and price increases in reducing tobacco use, particularly among young people.On average, cigarette excise taxes account for 32% of the price in LMICs and 48% in HICs. Many, but not all, countries tax some or all other tobacco products, generally at rates well below the rate imposed on manufactured cigarettes. 
Nearly all governments levy excise taxes on at least some alcoholic beverages. Of the
192 countries that provided data to the WHO in 2012, 155 levied an excise tax on beer, 138 on wine, and 151 on distilled spirits; alcohol sales were banned in some of the nontaxing countries (52, 62). Alcoholic beverage excise taxes appear to be relatively low, according to the limited information provided.  As with cigarette taxes, alcohol excise taxes account for a lower share of price in LMICs than in HICs (both lower, in general, than for cigarettes). Among 74 reporting countries, excise taxes as a share of retail prices ranged from a low of 0.3% in Kyrgyzstan to a high of 44.9% in Norway, with an average of 17.3%. Taxes as a percentage of price are, generally, lowest on beer and highest on distilled spirits.
In 2014,Mexico became the first country in the Americas to adopt a significant tax specifically on SSBs, a one-peso-per-liter tax that raised taxed beverage prices by about 10% (13). Since then, other countries have adopted more significant taxes to reduce SSB consumption and promote health, including several US localities, South Africa, the United Kingdom, Ireland, Portugal, Saudi Arabia, the United Arab Emirates (UAE),Dominica, and Barbados.Most aim to raise retail prices by at least 10%, with a few resulting in more significant increases
Well, unfortunately the article explains the current status (and it helps) though the impact is much more difficult to measure.

April 24, 2019

Succesful populists: the age of elected despots

These are selected paragraphs from Martin Wolf excellent op-ed Elected despots feed off our fear and rage:

To be successful, a populist demagogue has to project belief in himself as a man of destiny. Self-obsession and even megalomania help; they may well be essential. In a compelling book, Disordered Minds, the Irish writer Ian Hughes suggests such men are narcissists or psychopaths. To a non-expert eye, they do appear deranged. How else can one sell the idea that “I alone am the people’s salvation” to oneself?

If such a leader wishes to subvert democracy, it is, alas, not that hard to do, as Harvard’s Steven Levitsky and Daniel Ziblatt argue in How Democracies Die. First, capture the referees (the judiciary, tax authorities, intelligence agencies and law enforcement). Second, sideline or eliminate political opponents and, above all, the media. Third, subvert the electoral rules. Supporting these assaults will be a fierce insistence on the illegitimacy of the opposition and the “fakeness” of information that does not align with whatever the leader finds useful to state.

People will want to trust such a leader whenever they desperately wish to believe that someone powerful is on their side in an unjust world. That is what happens when trust in the institutions and norms of a complex democracy falters. When faith in sober policymaking disappears, the charismatic figure emerges as the oldest kind of leader of all: the tribal chieftain. When things become this elementary, the difference between developing and so-called advanced democracies can well melt away. True, the latter have stronger institutions and norms and a more educated electorate. In normal circumstances, that may be enough to resist. Some argue it will remain enough. Yet, we are human. Humans adore charismatic despots; they always have.

April 19, 2019

On the effectiveness of digital health technologies


Every other day we here about a new health app, and new digital advances in healthcare. Too often, any innovation is considered effective without any deep analysis. Now, NICE provides a guide for this specific issue.

The economic impact of a DHT can be assessed using an appropriate analysis of the economic information collected. The type of economic analysis done should be determined by the financial consequences of adopting and implementing the DHT from a payer or commissioner perspective. The appropriate level of economic analysis depends on the type of decision needed and likely financial commitment. To reflect the range of commissioning decisions associated with DHTs, we have proposed 3 levels of economic analysis (see table 8).
Many DHTs will start at a basic economic analysis level but, with additional information and data about the technology and its comparators, a more robust economic analysis can be undertaken. The higher levels of economic analysis needed depends on the financial commitment required including, for example, the level of upfront investment, the likelihood of opportunity costs and the certainty of the realisation of the benefits.

April 17, 2019

AI in healthcare, a podcast

How A.I. Is Humanizing Healthcare with Dr. Eric Topol

Can A.I. and machine learning make healthcare more humane, loving, and passionate?

Dr. Eric Topol thinks so.

Dr. Topol (Website | Twitter) is a geneticist, medical researcher, and author of Deep Medicine. He has written over 1100 peer-reviewed articles and is one of the top most-cited medical researchers in the world.

In this episode, Chad sits down with Dr. Topol to talk about how A.I. and machine learning are putting the patient experience back at the forefront of healthcare. Dr. Topol also explains why you don’t actually own your own medical data and what steps we need to take to get it back.

April 9, 2019

A lifetime fair drug pricing system

When Is The Price Of A Drug Unjust? The Average Lifetime Earnings Standard

Is there any measure for unfair pricing in drugs?. According to Ezequiel Emanuel prices should not
"exceed 11 percent of the average American’s disposable income. This suggests that current prices for many drugs are excessive and unjust."
Currently, average lifetime costs for health care are estimated at 31 percent of disposable income. Drugs account for 17 percent of health care expenses. A threshold for medical care as a share of disposable income that is set 10 percentage points higher than the current average amount spent on medical care (at 41 percent, or $261,907) is generous, as is a threshold for drug costs as a share of medical costs set 10 percentage points higher (at 27 percent, or $70,715) than the current share. Using these standards, the costs for all of the drugs a person takes in a lifetime should not consume more than 27 percent of medical costs, or $70,715. This constitutes 11 percent of lifetime disposable income.
He achieves this conclusion after applying these principles:
1. Complete life. The unit of analysis should not be a year or other limited time frame, but rather the impact over a whole lifetime
2. Limited resources. The just price of a drug should reserve enough resources for people to pursue valuable life activities
3. Value. There should exist a close relationship between the actual benefits of an intervention and its price
4.  Comprehensiveness. Life activities other than health matter; in considering the benefits of a treatment, we should also consider how it affects education, employment, and other valuable life activities
This article represents a deep change of perspective on drug pricing. Cost-effectiveness of individual drugs are not enough, a lifetime and societal perspective is necessary. I agree in this part, however methodological implications are huge and uncertain.

Bonnard at Tate modern right now

April 5, 2019

Personal Health Data Cooperatives

Personal Data Cooperatives – A New Data Governance Framework for Data Donation and Precision Health
The Ethics of Medical Data Donation
Given that personal data can be copied, individuals are entitled to copies of their data and individuals are the ultimate aggregators of all their personal data, citizens are elevated to new roles at the center of health research and a novel personal data economy. There, citizens, not some multinational company, control the use of and benefit from the intellectual and economic value of these data.
In a chapter of the book you'll find a description of MIDATA cooperative, a swiss case. My impression is that this is the appropriate approach. A closer initiative is SalusCoop. However, as happens in any public good, its governance is always the foremost issue.

April 1, 2019

March 30, 2019

Medicine as a data science (6)

Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists

While some physicians are lobbying for creating more specialties, Jah and Topol argue exactly the opposite. Radiology, pathology and in vitro diagnostics should be under the same umbrella: "the information specialists":
Because pathology and radiology have a similar past and a common destiny,
perhaps these specialties should be mergedinto a single entity, the “information specialist,” whose responsibility will not be so much to extract information from images and histology but to manage the information extracted by artificial intelligence in the clinical context of the patient.
 There may be resistance to merging 2 distinct medical specialties, each of which has unique pedagogy, tradition, accreditation,and reimbursement.However, artificial intelligence will change these diagnostic fields. The merger is a natural fusion of human talent and artificial intelligence. United, radiologists and pathologists can thrive with the rise of artificial intelligence. 
The history of automation in the broader economy has a reassuring message. Jobs are not lost; rather, roles are redefined; humans are displaced to tasks needing a human element. Radiologists and pathologists need not fear artificial intelligence but rather must adapt incrementally to artificial intelligence, retaining their own services for cognitively challenging tasks.A unified discipline, information specialists would best be able to captain artificial intelligence and guide medical information to improve patient care.
You may agree or not. Technology is breaking barriers and creating bridges. Food for thought.

Josep Segú - Brooklyn Bridge

March 27, 2019

The deep side of medicine and the gift of time

Deep Medicine

Nowadays the impact of Artificial Intelligence in Medicine is unknown. Every other day you may hear about robots and how they will replace humans. Nobody knows about it, distrust charlatans. The only thing that is real is what is already happening. Eric Topol has tried to do this in his new book Deep Medicine. But at the same time he considers that AI will let physicians humanise medicine, "the gift of time", and says:
"As machines get smarter, humans will need to evolve along a different path from machines and become more humane"
This may be Eric Topol's desire, nothing to add. My view is quite different. I'm not sure about the contribution of AI to a humanised medicine . This has to do with professionalism, not with AI. And the incentives for professionalism are plunging, while commercialism is on the rise. This is the key issue.
The remaining elements of the book are of interest to explain the current state of advances in apps and tools for clinical decision making. You'll find helpful information and a great summary of AI in medicine. However, my suggestion is that you can forget the subtitle of the book: "How artificial intelligence can make healthcare human again". It's naïve.

March 22, 2019

The Theranos contretemps as a serious scandal (2)

The DropoutPodcast by ABC Radio & ABC News Nightline

The inventor

Now you can hear the ABC radio podcast in 6 chapters on Theranos scandal. Report at The Verge. Highly recommended.

And the HBO new documentary explains all the details in 2 hours. The trailer:

March 20, 2019

#CRISPRWHO: notes on a new scandal

Open AccessOpen Access license
#CRISPRbabies: Notes on a Scandal

This week:
An advisory panel to the World Health Organization has called for the creation of a global registry to monitor gene-editing research in humans, the organization announced yesterday (March 19). The recommendations of the 18-person committee, which was established following news late last year that Chinese scientist He Jiankui had carried out human gene editing in secret, are aimed at improving transparency and responsibility in the field, the announcement says.
The panel’s advice did not go so far as to call for a moratorium on all human germline editing, unlike some other groups. Last week, a group of scientists and bioethicists from seven countries penned a commentary in Nature that argued for “a fixed period during which no clinical uses of germline editing whatsoever are allowed.” Such a moratorium would allow time for ethical and moral debate and for the agreement of an international regulatory framework, they wrote.
After the initial #CRISPRbabies scandal , we are facing a new one. The WHO pannel is asking for a registry instead of a moratorium. The battle has finished. Game over. From now on, the human being  will be affected from such decision. One of the worst decisions in the human history.

March 17, 2019

Improving the pharmaceutical regulation production function

Using Routinely Collected Data to Inform Pharmaceutical Policies

With the broadening of data available for officials to regulate markets, things could change. The issue is specially relevant for pharmaceuticals. Up to now if you want information about the market you have to use IMS data. Now governments that pay the drugs bill can use their own data to improve regulation. Better knowledge could represent better regulation if it is performed appropriately and on a timely basis. The OECD report tries to put all these elements together and highlight the opportunities ahead.
This report provides an overview of patient-level data on medicines routinely collected in health systems from administrative sources, e.g. pharmacy records, electronic health records and insurance claims. In total 26 OECD and EU member countries responded to a survey addressing the availability and accessibility of routinely collected data on medicines and their applicability to developing evidence. The report further explores the utility of evidence from clinical practice, looking at experiences and initiatives across the OECD and EU.
Governments will have to improve big data capabilities and add new talent.

March 8, 2019

Never ending health reforms

Reformas pendientes en la organización de la actividad sanitaria

A new issue of Cuadernos ICE shows the current state of the health system. You'll find an article that explains the main constraints to be overcome with all the details. Above all, in my opinion is the quality of institutions. The remaining articles are highly recommended as well.
We are living on a slippery slope and nobody cares about it, it seems that key decision makers have forgotten to read and accept facts as they are. I strongly suggest a reading of these articles. Something should be done to avoid having to rewrite the same a decade later, as it has happened. Maybe the reforms never end because they still have to start.
Take a chance, play your part. Don't wait too long.

PS. Facts (1) and (2)

You can cry a million tears 

You can wait a million years 

If you think that time will change your ways 

Don't wait too long
When your morning turns to night 

Who'll be loving you by candlelight

If you think that time will change your ways 

Don't wait too long
Maybe I got a lot to learn 

Time can slip away 

Sometimes you got to lose it all 

Before you find your way
Take a chance, play your part 

Make romance, it might brake your heart 

But if you think that time will change your ways 

Don't wait too long
It may rain, it may shine 

Love will age like fine red wine 

But if you think that time will change your ways 

Don't wait too long
Maybe you and I got a lot to learn 

Don't waste another day 

Maybe you got to lose it all 

Before you find your way
Take a chance, play your part 

Make romance, it might break your heart 

But if you think that time will change your ways 

Don't wait too long 

Don't wait 

Hmm... Don't wait

Compositors: Jesse Harris / Larry Klein / Madeline Peyroux

Definitely, this is the message

March 7, 2019

Revisiting the economic foundations of health insurance

Choose to Lose: Health Plan Choices from a Menu with Dominated Option

We know that more choice is not always better. Former posts have emphasized this issue. Loewenstein et al. provide remarkable evidence of what happens with health insurance:
Our findings offer perhaps the strongest evidence to date that insurance reveal as much or more about consumer understanding than about actual health-related risk preferences. In this sense, our setting provides a rare opportunity to conduct a specification check on
the standard insurance demand model absent search frictions.
Our findings challenge the standard practice of inferring risk preferences from insurance choices and raise doubts about the welfare benefits of health reforms that expand consumer choice.
If this is so, many books should be rewritten asap.

PS. G. Loeweinstein will be in Barcelona next week at Barcelona Jocs.

March 4, 2019

Pharma landscape

The Global Use of Medicine in 2019 and Outlook to 2023

The summary of IQVIA report:

  • Global spending on medicines reached $1.2 trillion in 2018 and is set to exceed $1.5 trillion by 2023.
  • Invoice spending in the United States is expected to grow at 4– 7% to $625–655 billion across all channels, but net manufacturer revenue is expected to be 35% below invoice and have growth of 3-6% as price growth slows on both an invoice and net basis.
  • Net drug prices in the United States increased at an estimated 1.5% in 2018 and are expected to rise at 0–3% over the next five years.
  • China reached $137 billion in medicine spending in 2018, but will see growth slow to 3-6% in the next five years as central government reforms to expand insurance access to both rural and urban residents, as well as expansions and modernizations of the hospital system and primary care services have been largely achieved and efforts shift to cost optimization and addressing corruption.
  • Medicine spending in Japan totaled $86 billion in 2018, however spending on medicines is expected to decline from -3 to 0% through 2023, due to the effect of exchange rates and continued uptake of generics and offset by the uptake of new products.
  • The number of new products launched is expected to increase from an average of 46 in the past five years to 54 through 2023, and the average spending in developed markets on new brands is expected to rise slightly to $45.8 billion in the next five years, but represent a smaller share of brand spending

March 1, 2019

Rescuing citizens from the "rule of rescue"

People feel a need to rescue identifiable individuals facing avoidable death or harm. This is a well known fact  explained in 1968 by the Nobel laureate Thomas Schelling from an economic perspective  and by Jonsen  in the bioethics context in 1986.
"A single death is a tragedy; a million deaths is a statistic." This quote reflects exactly what we are talking about. However, the issue is: Do you accept the rescue at any price with public money?
These previous posts of this blog: (1) and (2) explain the details. I'll not insist on what I've already said. I suggest you have a look at them.
Today you can asess these three facts:
1. A country spends 38m € in drugs for 249 patients in 2018. A lifetime treatment.
2. A country has a waiting list of 132.025 patients for surgery, 123.249 patients for diagnostic tests, and 424.715 patients waiting for a visit to the specialist. Total people waiting: 679.989 patients in a country with 7.543.825 inhabitants. 9% of the population is in the waiting list for a health service. However, 25% have voluntary duplicate insurance and could jump the list. Therefore the exact figure is 12% of inhabitants waiting.
3. A country knows that spending 10m € in addition every year can increase cardiac surgery by 600 interventions. This means 600 critical patients less in the waiting list. With 38m €, the number of cardiac interventions would be 2.280.
 Ask yourself what to do about it, what would you prefer to do with 38m€ every year ? Just apply them to 249 patients or to 2.280 (you are not on the waiting list, and we'll assume the same adjusted quality of life years for both cases). Anyway, it's too late to have your answer, the government has already decided for you, and maybe you don't agree with it, as I don't agree. The government prefers the rescue of 249 citizens.
Just to finish, check this final fact:
This country spends 1.192 € per capita of public budget on health. Another country under the same mandatory tax system is able to spend 1.635 €, 40% more !!!
More money allows to avoid such dilemmas for this country. Ask yourself if you want to stay in the former tax system that is damaging your health. Once you have the answer, you'll understand why this country wants to leave this unfair tax system as soon as possible.

February 23, 2019

Pharma returns

Measuring the return from pharmaceutical innovation 2018

Key findings for top 12 biopharma companies in the Deloitte study.
  • R&D returns have declined to 1.9 per cent, down from 10.1 per cent in 2010 - the lowest level in nine years
  • Returns have been impacted by the growing cost of bringing a drug to market which now stands at $2,168 million – almost double the $1,188 million recorded in 2010
  • Forecast peak sales have declined from last year to $407 million – less than half the 2010 value of $816 million
The growing cost of new drugs includes buying companies for their research (outsourcing research) instead of "producing" R&D within the company. The report will not tell you this minor observation.
Last February I said :
In drug industry the probability of R&D failure is 90.4%. We all know that in the drug costs we are paying also for failures, but we easily forget the figure.
You'll not find any reference to this minor issue. Is there any profitable industry with such a failure rate?

Caro Emerald

February 22, 2019

The bioethics of machine clinical decision making

Artificial intelligence (AI) in healthcare and research
Regulation of predictive analytics in medicine

This is what a brief note from Nuffield Council of Bioethics says about artificial intelligence in healthcare:
The use of AI raises ethical issues, including:
  • the potential for AI to make erroneous decisions; 
  • the question of who is responsible when AI is used to support decision-making; 
  • difficulties in validating the outputs of AI systems; inherent biases in the data used to train AI systems; 
  • ensuring the protection of potentially sensitive data; 
  • securing public trust in the development and use of AI; 
  • effects on people’s sense of dignity and social isolation in care situations; 
  • effects on the roles and skill-requirements of healthcare professionals; 
  • and the potential for AI to be used for malicious purposes.
A key challenge will be ensuring that AI is developed and used in a way that is transparent and compatible with the public interest, whilst stimulating and driving innovation in the sector.
This statement is naive.(From m-w, naive:  marked by unaffected simplicity : INGENUOUS). Up to now, have you seen any transparent algorithm available for imaging, triage or any medical app? For sure not. Therefore, the real key challenge is to stop introducing such algorithms -to ban apps- unless there is a regulatory body that takes into account the quality assurance or effectiveness side (sensitivity and specificity) and the required transparency for citizens.
Until now Nuffield has released only a brief. Let's wait for the report.
If you want a quick answer, check Science this week:
To unlock the potential of advanced analytics while protecting patient safety, regulatory and professional bodies should ensure that advanced algorithms meet accepted standards of clinical benefit, just as they do for clinical therapeutics and predictive biomarkers. External validation and prospective testing of advanced algorithms are clearly needed
 They explain the five standards and give rules and criteria for regulation. It is really welcome.

February 21, 2019

Pharm niche busters

The Information Pharms Race and Competitive Dynamics of Precision Medicine: Insights from Game Theory
Economic Dimensions of Personalized and Precision Medicine
Precision medicines inherently fragment treatment populations, generating small-population markets, creating high-priced “niche busters” rather than broadly prescribed “blockbusters”. It is plausible to expect that small markets will attract limited entry in which a small number of interdependent differentiated product oligopolists will compete, each possessing market power.
A chapter in a new book on  Precision Medicine explains the new approaches to a oligopolistic market structure where the size of the market may be determined by biomarkers with a cut-off value suggested by pharmaceutical firms themselves. The dynamics of this market is described according to game theory. Sounds fishy at least.
I already have pending chapters to read of this book. A must read for physicians and economists.

February 16, 2019

Defining roles and skills for digital health

The Topol Review
Preparing the healthcare workforce to deliver the digital future.

The NHS asked Dr. Eric Topol about the new health workforce and how digital health will change the current landscape. A must read:
This is an exciting time for the NHS to benefit and apitalise on technological advances. However, we must learn from previous change projects. Successful mplementation will require investment in people as well s technology. To engage and support the healthcare workforce in a rapidly changing and highly technological orkplace, NHS organisations will need to develop a learning environment in which the workforce is given very encouragement to learn continuously. We must better understand the enablers of change and create culture of innovation, prioritising people, developing an agile and empowered workforce, as well as digitally capable leadership, and effective governance processes
to facilitate the introduction of the new technologies, supported by long-term investment.

February 15, 2019

Who is worse off?

Health, priority to the worse off, and time

The prioritisation of resource allocation towards the worse off is a well known rule. What does this mean exactly?
 There are many dimensions in which someone can be worse off (e.g., in terms of wellbeing, health, opportunities, resources), and there are many ways to give priority to someone (e.g., by giving extra weight to their claims, lexical priority to their claims, or by earmarking a fixed amount of resources for their claims). Furthermore, there are many different reasons why one might want to give priority to benefits to the worse off: is it because it is good to promote equality for its own sake, good to promote equality for other reasons, because benefits to the worse off matter more, because the worse off typically fall under some sufficiency threshold, or for many of these (and maybe other) reasons
The precise argument is described in a recent article that combines the complete lives approach with the forward looking approach, and says:
 I believe that the focus on complete lives has been beneficial in that it is a step away from a complete focus on current distributions of health. However, I think that the arguments presented in this paper give us reason to adopt a more nuanced approach to how to rank individuals in terms of who is worse off with the purpose of giving priority to certain benefits in light of unequal distributions of health over time. Such an approach accepts that both the complete lives view and the forward looking view that only takes into account current and future health states, matter. This leads to the complicated question of how to combine these views. Some work that addresses how to combine  concerns for simultaneous segment inequality and complete lives inequality has appeared recently, but the question needs further attention.
Therefore, it is still a work in progress.

February 8, 2019

The perfect storm of surveillance capitalism

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.

February 7, 2019

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:

February 4, 2019

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:

February 2, 2019

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.

February 1, 2019

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:

PredictionnAUCPublication (Reference number)
In-hospital mortality, unplanned readmission, prolonged LOS, final discharge diagnosis216,2210.93* 0.75+0.85#Rajkomar et al.96
All-cause 3–12 month mortality221,2840.93^Avati et al.91
Readmission1,0680.78Shameer et al.106
Sepsis230,9360.67Horng et al.102
Septic shock16,2340.83Henry et al.103
Severe sepsis203,0000.85@Culliton et al.104
Clostridium difficile infection256,7320.82++Oh et al.93
Developing diseases704,587rangeMiotto et al.97
Diagnosis18,5900.96Yang et al.90
Dementia76,3670.91Cleret de Langavant et al.92
Alzheimer’s Disease ( + amyloid imaging)2730.91Mathotaarachchi et al.98
Mortality after cancer chemotherapy26,9460.94Elfiky et al.95
Disease onset for 133 conditions298,000rangeRazavian et al.105
Suicide5,5430.84Walsh et al.86
Delirium18,2230.68Wong et al.100