Question: Who wrote a book in 1954 finding statistical methods superior to diagnostic methods?
About the article "Medicine", section "Criticism of modern medicine".

Candidate passages:
According to Paul Farmer, the main problem for modern medicine is lack of access in poor regions.
There is an "outcome gap" between the rich and poor that is most noticeable with expensive-to-treat diseases like AIDS and tuberculosis.
The majority of medical resources and therapies are concentrated in the rich, low-incidence regions such as the West.
On the other hand, countries in the developing world have high rates of HIV but lack the necessary resources to treat them.
Medical errors and overmedication and other forms of iatrogenesis (harms caused by medical treatment) are also the focus of complaints and negative coverage.
Practitioners of human factors engineering believe that there is much that medicine may usefully gain by emulating concepts in aviation safety, where it is recognized that it is dangerous to place too much responsibility on one "superhuman" individual and expect him or her not to make errors.
Reporting systems and checking mechanisms are becoming more common in identifying sources of error and improving practice.
Clinical versus statistical, algorithmic diagnostic methods were famously examined in psychiatric practice in a 1954 book by Paul E. Meehl, which found statistical methods superior.
A 2000 meta-analysis comparing these methods in both psychology and medicine found that statistical or "mechanical" diagnostic methods were, in general, although not always, superior.
Disparities in quality of care given among local demographics are often an additional cause of controversy.
For example, elderly mentally ill patients received poorer care during hospitalization in a 2008 study.
Rural poor African-American men were used in an infamous study of syphilis that denied them basic medical care.
Statistical Methods for Research Workers (ISBN 0-05-002170-2) is a classic 1925 book on statistics by the statistician R.A. Fisher.
It is considered by some to be one of the 20th century's most influential books on statistical methods, together with his "The Design of Experiments" (1935).
According to ,Ronald A. Fisher was "interested in application and in the popularizationof statistical methods and his early book "Statistical Methods for Research Workers", published in 1925, went through many editions andmotivated and influenced the practical use of statistics in many fields ofstudy.
His "Design of Experiments" (1935) statistical technique and application.
In that book heemphasized examples and how to design experiments systematically froma statistical point of view.
The mathematical justification of the methodsdescribed was not stressed and, indeed, proofs were often barely sketchedor omitted altogether ..., a fact which led H. B. Mann to fill the gaps with a rigorous mathematical treatment in his well known treatise, ."
Despite their superior performance over least squares estimation in many situations, robust methods for regression are still not widely used.
Several reasons may help explain their unpopularity (Hampel et al.
1986, 2005).
One possible reason is that there are several competing methods and the field got off to many false starts.
Also, computation of robust estimates is much more computationally intensive than least squares estimation; in recent years however, this objection has become less relevant as computing power has increased greatly.
Another reason may be that some popular statistical software packages failed to implement the methods (Stromberg, 2004).
The belief of many statisticians that classical methods are robust may be another reason.
Although uptake of robust methods has been slow, modern mainstream statistics text books often include discussion of these methods (for example, the books by Seber and Lee, and by Faraway; for a good general description of how the various robust regression methods developed from one another see Andersen's book).
Also, modern statistical software packages such as R, Stata and S-PLUS include considerable functionality for robust estimation (see, for example, the books by Venables and Ripley, and by Maronna et al.
). 
Parametric statistics is a branch of statistics which assumes that the data has come from a type of probability distribution and makes inferences about the parameters of the distribution.
Most well-known elementary statistical methods are parametric.
Generally speaking parametric methods make more assumptions than non-parametric methods.
If those extra assumptions are correct, parametric methods can produce more accurate and precise estimates.
They are said to have more statistical power.
However, if assumptions are incorrect, parametric methods can be very misleading.
For that reason they are often not considered robust.
On the other hand, parametric formulae are often simpler to write down and faster to compute.
In some, but definitely not all cases, their simplicity makes up for their non-robustness, especially if care is taken to examine diagnostic statistics. 
There are a number of methods or techniques that can be used in a diagnostic procedure, including performing a differential diagnosis or following medical algorithms.
In reality, a diagnostic procedure may involve components of multiple methods.