Reviews for Beginnings Count: The Technological Imperative in American Health Care

The average rating for Beginnings Count: The Technological Imperative in American Health Care based on 2 reviews is 4.5 stars.

Review # 1 was written on 2018-02-18 00:00:00 Richard Hartnett Professionally, I'm not in finance, nor am I an actuary. I'm a software engineer. I consider myself a practical and simple person. I don't do a lot of theory. I do something and then I observe what effect it had. Call it intuitive hypothesis testing. Call it eyeball statistics. I did take some basic statistics at University, and I have read a bit of Shewhart and Deming, but that's the extent of my statistical training. Control charts are the most advanced tool I use in my line of work, and even then only rarely, because (a) they answer only a small set of questions, and (b) I haven't figured out a way to make them work with the fat tailed reality of software development. I was under the impression that if you were going to do anything meaningful with statistics, you had to bring out the big guns and do very advanced analysis. I don't know where I got the idea, but I was convinced the most simple statistical tools are no better than intuition and eyeballing plots. Then I started stumbling over a growing mountain of evidence that even the most naïve of statistical techniques outperform, well, intuition and eyeballing plots. This is a complete 180 in my world view. Highly trained professionals perform much worse than plain old ordinary least squares linear regression. This is a result that has been replicated again and again, in all sorts of fields. It's really bad. For a brief summary, see the classic paper Clinical Versus Actuarial Judgment. For more modern review, maybe Superforecasters or Expert Political Judgment is a start. So here we are. I've probably been performing worse than linear regression my whole life. Linear regression, for God's sake! The stupidest and dumbest of models. There are, of course, several reasons for this. An obvious one is that humans are biased and noisy and make mistakes. A much more important reason, I think, is that phrasing your decision in the form of a statistical model means you have to propose your hypothesis up front (!), and it will be painfully obvious when it is falsified, and give you reason to re-think your position. When you eyeball statistics you unconsciously and continuously adapt your hypothesis to match what you see, all the while both overfitting it, and coming up with causal links that aren't there. Okay, so why care? It's not immediately obvious to me that it is worth investing effort in getting better at explaining and predicting things. Sure, it sounds good, but at what cost? Is something any engineer should ask themselves. But prediction is literally about seeing into the future. It's a slice of clairvoyance. It is insanely valuable in almost any line of work. In light of this, the least I can do is brush up on these basic statistical techniques, and use them as often as I can. I don't know which book is best for that, but this one is good. It goes through the theory in sufficient detail for it to stick in my memory, it has plenty of practical examples and exercises to aid retention. It surveys the most important simple tools of the trade, with references both to more advanced techniques and more elaborate works on the simple tools. This book is exactly what someone in my position needs.

Review # 2 was written on 2008-07-22 00:00:00 DE Red In this book, insurance lawyer Eric D. Gerst defends his stance on the need for federal regulation of the insurance industry. His argument is swift and compelling, and he backs all claims with significant research into the insights of industry leaders as well as the anecdotal experience of many consumers. To say that the material in this book is quite shocking would be a gross understatement, unless you or someone you know has ever been burned by the insurance industry. Sadly, too many Americans have had negative experiences with their insurers, making the included anecdotes that much more compelling, as they stand for a common experience rather than an anomaly. Perhaps the most valuable element of this book is its inclusion of a viable solution and plan of implementation. Rather than simply identifying the current issues, a point to which many authors seem to limit their analysis, Gerst includes and supports his opinion of the best possible solution. The book is thus quite biased, but not undeservedly so. Because Gerst is able to so strongly defend his position, his platform becomes a good launch point for further discussion and, hopefully, eventual implementation of insurance industry reform (whatever that reform may ultimately be). One of Gerst's greatest strengths as a writer is his conciseness, even brevity. At the end of chapter 9, rather than offering a lengthy summary of everything he has discussed in the preceding pages, he concludes by stating simply, "The options are on the table. We need to act." His agenda is clear throughout the text, and he does not mince words trying to make anyone feel good about useless options. I admire that approach. Even though it may not win him friends among his opposition, it is ultimately a far more utilitarian approach for reducing confusion and wasting less time. This book is also especially valuable as a teaching tool for those that may be unfamiliar with many of the intricacies of the insurance industry. Gerst writes with simple yet compelling prose that offers a wealth of specific details in a straightforward and uncomplicated manner. While industry experts may find the inclusion of such detail somewhat tedious and redundant, it provides a significant boon for the average reader not previously familiar with the complexities of industry laws and regulations. This book forces readers to take a good hard look at the current state of affairs in the insurance industry, and contemplate the specific types of reform that will help the industry avoid meltdown and move toward success for insurers, the insured, and state and federal regulators alike.