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Monday, June 13, 2016

Next up, #HCSummit16, the 2016 Lean Summit


I'm flying to Miami shortly to cover this year's event (at the Doral this year). Last year's Summit in Dallas was off-the-hook fine. The 2016 agenda:

  • John Toussaint, ThedaCare Center for Healthcare Value
  • Patrick Conway, MD
  • Kathryn Correia
Learning Sessions
  • Leader Standard Work
  • How to Lead by Asking Effective Questions
  • How Government, Healthcare, and Lean Come Together
  • Lean Transformation Across Cultures: The lean journey of a disability hospital & newborn healthcare programme in East Africa
  • Population Health: A journey to deploying real time decision support
  • Lean Dentist
  • Business Intellligence is no longer an Option!
  • Experiments Around the Network AM
  • Experiments Around the Network PM

  • Elizabeth Mitchell
  • John Shook, Lean Enterprise Institute
Learning Sessions
  • Engaging Physicians: Lean as Preventive Medicine for Burnout
  • Results Focused, Process Driven Ambulatory Clinic Redesign
  • Payment Reform: The Employers' Perspective
  • Improving Patient Experience, Patient Safety and Patient Progression through a Lean Management System
  • Doing the Splits in the ED: Emerging Models in Academic Medicine
  • Preparing Senior Leadership and The Lean Office for Organization Transformation
  • Applying Lean to Federal Healthcare Policy, the Story of a Strategic Design Event
  • Experiments Around the Network AM
  • Experiments Around the Network PM
The Lean Summits are comprised of people and organizations who are doing it. No mere theorizing and other abstract talk or dwelling on the myriad vexing problems in the health care system, which we all know exist.

I will be all eyes and ears. In addition to "Leadership" presentations, I'll be particularly interested in seeing evidence of the effective integration of Health IT into lean workflows, given the intractable hand-wringing over the putative "impediments" of digital health InfoTech.


Notice about this new title arrived on my inbox via my ASQ member feed.

Chapter 1 traces the origins of probability as an academic subject and high- lights the pervasiveness of statistics and probability in today’s popular culture. Chapter 2 introduces the reader to counting techniques to determine how many ways particular outcomes can occur. Counting possible outcomes is a fundamental piece of probability calculations. In Chapter 3, we begin the hard and rewarding work of learning probability concepts and rules, including the concepts of mutual exclusivity, sampling with and without replacement, odds, conditional probability, and Bayes’ theorem. Seven detailed examples are included at the end of the chapter to help solidify your understanding. After studying the first three chapters and completing the practice problems included in the companion workbook, readers will be prepared to answer any number of probability questions, from picking socks out of a drawer, to selecting lottery numbers, to choosing colleagues for committees, to deciding whether a manufacturing lot should be shipped to the customer.

Chapter 4 introduces commonly used “named” discrete probability distributions: the discrete uniform, binomial, hypergeometric, geometric, negative binomial (also known as the Pascal), and Poisson. The formulas, parameters, and uses for each distribution are introduced, and worked examples are shown for each distribution type. Useful approximations among the distributions are also presented, and a summary of the distributions is tabulated at the end of the chapter.

Chapter 5 covers continuous probability distributions, among them the well-known normal (also known as the Gaussian), standard normal, Student’s t, F, chi-square, and Weibull distributions. Lesser known but useful and interesting distributions are also included in this chapter: the uniform, triangular, gamma, Erlang, exponential, Rayleigh, lognormal, beta, and Cauchy. In addition, key theorems such as the law of large numbers and Chebyshev’s inequality are presented and explained. At the end of the chapter, a summary of the distributions appears for quick reference. After studying the material in Chapters 4 and 5 and completing the practice problems in the companion workbook, the reader will be able to select the appropriate distribution for a wide range of scenarios, state the formulas for the mean and variance for various distributions, and correctly evaluate probability statements.

The appendices contain the distribution road map, a graphic of all the probability distributions presented in the text and how they are related. Probability tables for the binomial and Poisson distributions as well as cumulative probability tables for the binomial, Poisson, standard normal, Student’s t, chi-square, and F distributions are also provided.

As extensive as the list of rules, theorems, and distributions covered in the text happens to be, this book is by no means comprehensive! The distributions presented in the text were carefully chosen for their applicability to the types of problems that arise in the quality field. Univariate distributions not covered include the Laplace and extreme value distributions, as well as the Pearson series of distributions. There also exists a multitude of multivariate distributions in which arrays of random variables are modeled. These distributions include the Dirichlet, multivariate normal, Hotelling’s T2, and the Wishart and require a working knowledge of matrix algebra. To learn more about these distributions, you can consult a thicker and more densely written text!

Even though my outline was carefully crafted, I did experience scope creep in the writing process. Just as soon as I would finish one section, I would invariably have an idea in the shower of yet another formula, relationship, example, or interesting fact to add. Finishing the book was becoming a Sisyphean task. In order to send a completed manuscript to the publisher, I had to either stop showering or decide that, as it stood, the book more than covered what was necessary. To my family’s great relief, I chose the latter option.

It is my hope that as you read this book you underline new terms, highlight formulas, write in its margins, and refer to it often. It would be gratifying to see dog-eared copies of The Probability Handbook on office shelves or opened up during certification exams.

Feel free to contact me with comments or questions about the book or to learn more about courses based on the book. Visit
Nice that she includes Bayes and Chebyshev. Color me both Bayesian and Chebyshev-ist (pdf).

Expensive book, at $99 retail and $60 ASQ member price. I personally don't need it: been there, done that (and I already have a huge stash of advanced stats books in my stacks).

For someone prepping for one of the ASQ certification exams, however, it's probably well worth the money.
The lottery has been characterized as a tax on the mathematically naive. Consider a player who uses a “system” to carefully curate his picks based on his anniversary date, his child’s age, and the current phase of the moon. Unfortunately, all the superstition in the world can’t overcome the tyranny of random chance: a player choosing the numbers 1 2 3 4 5 has the same probability of winning as our player using his “system.” As the popular financial advisors on television tell us, in the long run it would be better to invest the dollar than to spend it on a lottery ticket. But what would be the fun in that?

The lure of easy money is nothing new. For centuries, gamblers have tried to outsmart other players, as well as fate, in the hopes of scoring the big win. Not surprisingly, the study of probability traces its origins to games of chance. Unlike the lottery, which is based on pure luck, many games involve decision making and strategy that can be crafted by using probability concepts. Girolamo Cardano (1501–1576), by turns quite a successful professional gambler, mathematician, and physician, wrote the first treatise on winning betting strategies for cards and dice using the concepts of probability. The work was published posthumously almost a century later in 1663. At about this same time, the mathematicians Blaise Pascal and Pierre de Fermat were conducting a lengthy correspondence concerning the solution to the “Problem of Points,” in which the stakes in an unfinished game of chance involving coin flips must be fairly divided between two players.

Probability has since evolved beyond rolls of the dice and flips of a coin to influence almost every aspect of our lives. Medical researchers, meteorologists, and even online dating sites use probability to estimate disease risk, create weather forecasts, and match clients, respectively...
Nothing in the table of contents regarding "design of experiments." I'd be looking to bone up on areas of "Clinical Study Design." Beyond books, there's a ton of freely available stuff out on the 'net. e.g.,

Another great free stats resource: "StatSoft has freely provided the Electronic Statistics Textbook as a public service since 1995."

There's a good bit of relatively non-technical discussion of probability issues in Dr. Hatch's book, which I first referred to here, on June 8th.

I originally envisioned Snowball in a Blizzard as a book that would focus on methodological aspects of human-subjects research, mainly the difficulties of study design and the subtleties of statistical interpretation. When, for instance, does a relative risk value diverge from an odds ratio, and why are the two often confused? What is a Type I versus Type II error? How do we “power” studies? A few years ago, as I was struggling with these kinds of issues in my professional work, I thought that they would be ideal subjects to illuminate to a general audience. I can see now that these fairly technical matters were unlikely to help nonspecialists have a more thorough understanding of clinical research, and it is probably why I received fairly tepid responses from literary agents. 

Over time, I realized that more was to be gained by telling stories about the consequences of these issues, and that I could occasionally sprinkle the text with brief explanations of the more essential methodological points. For instance, I thought it absolutely critical to explain the concept of positive predictive value in order to show why the USPSTF does not universally recommend mammograms for women under age fifty. One can’t easily grasp the justification for the task force’s reasoning without being acquainted with the notion of positive predictive value; once one understands the concept and sees the truly lousy predictive value of a positive screening mammogram in this age group, it’s hard to understand why there was (and is) so much fuss in the first place. However, I shelved the idea of devoting entire chapters, say, to the difference between nested case-control and case-cohort studies or the beauty inherent in the Mann-Whitney U test. Such subjects, fascinating though they can be to epidemiologists, would probably be valuable to nonspecialists only as a soporific. 

Thus, I elected to prioritize narration over technical explanation to describe these points, and whether I have succeeded or failed at that task, I leave for you, the reader, to judge. However, I do believe that there is one statistical concept worth exploring in a little more detail than the structure of this book allowed for because so much of what I have discussed in the previous pages relies on it: significance. I can’t speak for the basic research scientists, but for clinicians statistical significance is in many ways the yardstick by which we measure relevance in medical knowledge...

Hatch, Steven (2016-02-23). Snowball in a Blizzard: A Physician's Notes on Uncertainty in Medicine (pp. 241-242). Basic Books. Kindle Edition.

In the wake of Orlando,

In December 2012, a gunman walked into Sandy Hook Elementary School in Newtown, Connecticut, and killed 20 children, six adults, and himself. Since then, there have been at least 1,000 mass shootings, with shooters killing at least 1,140 people and wounding 3,942 more.

The counts come from the Gun Violence Archive, a database that tracks events since 2013 in which four or more people (not counting the shooter) were shot at the same general time and location...

More to come...

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