I read from http://math.arizona.edu/~jwatkins/N_unbiased.pdf
We know the Fisher information is $$I(\theta)=E\bigg[\bigg(\frac{\partial \log f(X) }{\partial \theta}\bigg)^2\bigg]. $$
Cramer-Rao lower bound is $\frac{(h'(\theta))^2}{I(\theta)}$ where $h'(\theta)=E\bigg[d(X) \frac{\partial \log f(X) }{\partial \theta}\bigg]. $
Then in page 214, example 14.15 considered the independent Bernoulli random variables with unknown success probability $\theta$. So density function is $\theta^x (1-\theta)^{1-x}$. In this case $I(\theta)=\frac{1}{\theta\left(1-\theta\right)}$.
Now if we take $d(X)=\bar X,$ then $h'(\theta)=E\bigg[\bar X \frac{\partial \log f(X) }{\partial \theta}\bigg]$. Then how to calculate $h'(\theta)$?
It seems $E\bigg[\bar X \frac{\partial \log f(\bar X) }{\partial \theta}\bigg]= E(\bar X \frac{\bar X -\theta}{\theta (1-\theta)})= \frac{1}{\theta(1-\theta)} E( \bar X ^2 - \theta \bar X)= \frac{1}{\theta (1-\theta)}Var (\bar X)= \frac{1}{n}$.
We know $Var(\bar X)=\frac{\theta(1-\theta)}{n}$. Also $I(\theta)=\frac{1}{\theta (1-\theta)}$. If $h'(\theta)=1$ then $Var(\bar X) < \frac{(h'(\theta))^2}{I(\theta)},$ contradicts the CR bound.