And I basically became very interested in the silences, what we were not allowed to talk about or what we, just by custom, didn’t talk about when we came together to talk about making the world better.

So the Aspen consensus was: You can tell the rich and powerful in our age to do more good, but you can never tell them to do less harm. You can tell them to give more, but you can’t tell them to take less. You can tell them to share the spoils of extreme capitalism, but you can’t tell them to renovate capitalism.--Anand Giridharadas

Working at the level of systems means that problem components are in interdependent and constantly changing relationships. Linear approaches that address one component or factor at a time are inadequate in addressing dynamic conditions.

Stakeholders show a variety of motives and behaviors for engaging with systems. Designers must reconcile competing goals and performance expectations through participatory methods that value stakeholder input and ongoing feedback from the system.

And issues of sustainability, technological feasibility, and economic viability are crucial to making design solutions work over time.--Trend I: Complex Problems--Meredith Davis AIGA

Why write words into conversations if you are not willing to dig deeper and illuminate contradictions? ​ I think what the investigation has left me with is thinking about, even as a writer, how do I make sure that I am using my power, however limited it is, to interrogate systems and ask the questions you’re not supposed to be asking, instead of doing the kind of journalism or thinking or speaking that merely props up power.  But it also shows up for me every day in thinking about, am I using my voice to say inconvenient things that might cause me to lose friends or social capital, to put it in those market terms, but that are part of maybe pushing us, in some small way, to where we need to get as a society?--On Being Krista Tippett in conversation with Anand Giridharadas

All around, us the winners in our highly inequitable status quo declare themselves partisan of change. They know the problem, and they want to be part of the solution. Actually, they want to lead the search for solutions. They believe that their solutions deserve to be at the forefront of social change. They may join or support movements initiated by ordinary people looking to fix aspects of their society. More often, though, these elites start initiatives of their own, taking on social change as though it were just another stock in their portfolio or corporation to restructure. Because they are in charge of these attempts at social change, the attempts naturally reflect their biases.

The initiatives mostly aren't democratic, nor do they reflect collective problem-solving or universal solutions. Rather, They favor the use of the private sector, and its charitable spoils, the market way of looking at things, and the bypassing of government. They reflect a highly influential view that the winners of an unjust status quo--and the tools and mentalities and values that helped them win--are the secret to redressing the injustices. Those at greater risk of being resented in an age of inequality are thereby recast as our saviors from an age of inequality.--Winners Take All: the elite charade of changing the world--Anand Giridharadas

Try and find the book. I think it is an important discussion that needs to happen. I agree that most of us are decent people trying to prop up an indecent system. But whom are we serving when economics of billion dollar revenues are valued instead of improved patient outcomes, when investments are made in direct to consumer advertising, lobbyists, and the latest M&A are broadcast as successes? There are harms associated with doing good. Why aren't we discussing them on a broader platform? Why are so many of us in pain? Is the solution a "pill" or upstream investments to reduce social vulnerabilities?  ​What are we actually being sold?

The ACCME's Accreditation Criteria require providers to produce educational activities that are designed to change competence, performance, or patient outcomes. Providers are then required to analyze the changes that were achieved as a result of the activities.
Figure 4 illustrates the percentage of CME provided in 2017 that was designed and/or analyzed for changes in competence, performance, and/or patient outcomes.--2017 ACCME Data Report

1. CME has responded to the changing health environment. Healthcare is practiced in teams--and clinicians need to learn how to work effectively as teams for the benefit of their patients. While CME traditionally has been focused on physicians, it has never excluded other professions. This data report shows that the number of interactions with learners other than physicians, such as nurses, pharmacists, and physician assistants, has increased nearly 90% over the past 10 years. Overall, learner interactions (physicians and other learners) grew 57% during that time.
   
   
2. Why does this growth matter? Degree and training programs are the foundation of clinician education; for the rest of their careers, clinicians rely on accredited continuing education to help them provide high-quality, safe, compassionate, and effective care to the patients they serve. Those activities help clinicians absorb and process rapidly changing medical information and practice an ever-diversifying set of complex skills.--Graham McMahon, MD, MMSc Accreditation Council for Continuing Medical Education (ACCME)

When you know better, you do better (in theory)

Inspiration is cheap, rigor is expensive--Cassie Kozyrkov

A lot of modeling is how much crap you can take--Lauren Hutton

Approached without care, data mining can reproduce existing patterns of discrimination, inherit the prejudice of prior decision makers, or simply reflect the widespread biases that persist in society.

It can even have the perverse result of exacerbating existing inequalities by suggesting that historically disadvantaged groups actually deserve less favorable treatment.--Big Data's Disparate Impact Barocas & Selbst

Right now, about four million American citizens have almost no congressional voting power, not even the diluted power of Californians or Texans. Of these four million people — these citizens denied representative democracy — more than 90 percent are black or Hispanic.
​
They are, of course, the residents of Washington, D.C., and Puerto Rico. Almost half of Washington’s residents are black, and nearly all of Puerto Rico’s are Hispanic.

“Big Data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” – Dan Ariely

The results are pretty outrageous. The Senate gives the average black American only 75 percent as much representation as the average white American. The average Asian-American has 72 percent as much representation as a white person. And the average Hispanic American? Only 55 percent as much. That’s right — the structure of the United States Senate treats a Hispanic citizen as only about half as important as a white citizen.

Advocates of algorithmic techniques like data mining argue that these techniques eliminate human biases from the decision-making process. But an algorithm is only as good as the data it works with.
Data is frequently imperfect in ways that allow these algorithms to inherit the prejudices of prior decision makers. In other cases, data may simply reflect the widespread biases that persist in society at large. In still others, data mining can discover surprisingly useful regularities that are really just preexisting patterns of exclusion and inequality. Unthinking reliance on data mining can deny historically disadvantaged and vulnerable groups full participation in society.
Worse still, because the resulting discrimination is almost always an unintentional emergent property of the algorithm’s use rather than a conscious choice by its programmers, it can be unusually hard to identify the source of the problem or to explain it to a court.--Big Data's Disparate Impact Barocas & Selbst

Who are you talking to right now? Who is it you think you see? Do you know how much I make a year? I mean, even if I told you, you wouldn't believe it. Do you know what would happen if I suddenly decided to stop going into work?

​A business big enough that it could be listed on the NASDAQ goes belly up. Disappears! It ceases to exist without me. No, you clearly don't know who you're talking to, so let me clue you in. I am not in danger. I am the danger. A guy opens his door and gets shot and you think that of me? No. I am the one who knocks!--Walter White, Breaking Bad

Where I am headed next...and hope to see you!

The Government are very keen on amassing statistics - they collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams.--Josiah Stamp

Fast food will always exist because of the demand—how many of us have repeatedly had those quick bites that our doctors have repeatedly told us stay away from?

​But this is the very reason that we need more people to work on understanding and warning about the ingredients that make fast food (methods) harmful; to study how to reduce the harm without unduly affecting their appeal; and to supply healthier and tastier meals (more principled and efficient methods) that are affordable (applicable) by the general public (users).

Because each of us is unique, any attempt to “personalize” must be approx- imative in nature. But this is a very different kind of notion of approximation, compared to the traditional large-sample asymptotics, where the setup is to use a sample of individuals to learn about the population they belong to.

​In contrast, individualized prediction is about finding a sequence of proxy populations with in- creased resolutions to learn about an individual. This leads to an ultimate challenge for Statistics (and statisticians): how to build a meaningful theoretical foundation for inference and prediction without any direct data?--Xiao-Li Meng, Whipple V.N. Jones Professor of Statistics, Harvard Faculty of Arts & Sciences Editor in Chief of the Harvard Data Science Review

In a traditional academic model, we group students together, usually by age, and around middle school, by age and perceived ability, and we shepherd them all together at the same pace. And what typically happens, let's say we're in a middle school pre-algebra class, and the current unit is on exponents, the teacher will give a lecture on exponents, then we'll go home, do some homework. 

​The next morning, we'll review the homework, then another lecture, homework, lecture, homework. That will continue for about two or three weeks, and then we get a test. On that test, maybe I get a 75 percent, maybe you get a 90 percent, maybe you get a 95 percent. And even though the test identified gaps in our knowledge, I didn't know 25 percent of the material.Even the A student, what was the five percent they didn't know?

Out, out, brief candle! Life's but a walking shadow, a poor player That struts and frets his hour upon the stage And then is heard no more; it is a tale Told by an idiot, full of sound and fury, Signifying nothing.--Macbeth by Shakespeare

As similar drugs hit the market, the pundits race to see where the profits are likely to be highest. Case in point look no further then immuno-oncology drugs or other categories within personalized medicine. ​ "For now, sales are a useful metric fo starting and staying on a checkpoint (Inhibitor)" I would argue that patient outcomes like "overall survival" will be the metric that delivers. We struggle to reconcile patient centered care with the aggregated data that leads to FDA approval. But meanwhile, premature media hype of early phase prognostic factors and clever pre-launch hype have become the harbors of safety investors have in the crosshairs.

 My shareholders expect me to make the most profit. That's the ugly, dirty truth.--Martin Shkreli

time to get our hands dirty...