Friday 10 August 2012

Engineering Research Paper Summary Project


Using the time and motion method to study clinical work processes and workflow:methodological inconsistencies and a call for standardized research
Author: Kai Zheng

Objective To identify ways for improving the consistency of design, conduct, and results reporting of time and motion (T&M) research in health informatics.

Materials and methods We analyzed the commonalities and divergences of empirical studiespublished 1990e2010 that have applied the T&M approach to examine the impact of health IT implementation on clinical work processes and workflow. The analysis led to the development of a suggested ‘checklist’ intended to help future T&M research produce compatible and comparable results. We call this checklist STAMP (Suggested Time And Motion Procedures).

Results STAMP outlines a minimum set of 29 data/ information elements organized into eight key areas, plus three supplemental elements contained in an ‘Ancillary Data’ area, that researchers may consider collecting and reporting in their future T&M endeavors.

Discussion T&M is generally regarded as the most reliable approach for assessing the impact of health IT implementation on clinical work. However, there exist considerable inconsistencies in how previous T&M studies were conducted and/or how their results were reported, many of which do not seem necessary yet can have a significant impact on quality of research and generalisability of results. Therefore, we deem it is time to call for standards that can help improve the consistency of T&M research in health informatics. This study represents an initial attempt.

INTRODUCTION
While there has been a widely acknowledged consensus that health IT implementation often introduces radical changes to clinical work processes and workflow, 1e4 it remains unclear what these changes are and how they impact actual clinical efficiency, team coordination, and ultimately quality of care and patient safety. Developing such an understanding requires rigorously conducted research that can generate compatible and comparable results to inform effective technology designs and implementation approaches.
                           Methods for studying changes to work processes and workflow vary widely depending on research contexts and research objectives. In this paper, we focus on quantitative approaches for measuring the impacts of such changes by means of quantifying clinicians’ time utilization and delineating how their time is allocated to different types of clinical and non-clinical activities. Among several approaches commonly used to date, time and motion (T&M), which involves continuous and independent observation of clinicians’ work, is generally regarded as the most reliable method compared to alternative methods such as work sampling and time efficiency questionnaires. In recent years, informatics research utilizing T&M has grown substantially thanks to a series opioneering paper7 8 that led to a widely-used T&M data acquisition tool made available through the Agency for Healthcare Research and Quality.9.

METHOD
As the focus of this research is on informatics studies conducted in health and healthcare domains, we limited the literature search to PubMed/MEDLINE only. The search was also restricted to empirical studies published in English within the last 20 years (1990e2010). Further, we applied two salient inclusion/exclusion criteria in study selection, namely: (1) the quantitative method employed by the study must be T&M, and the T&M method used must conform to the following definition: ‘independent and continuous observation of clinicians work to record the time required to perform a series of clinical or non-clinical activities.’ 5 6 With this definition, we excluded work sampling research, ‘time’ studies for collecting efficiency measures on isolated events (eg, medication turnaround time),
DISCUSSION
In addition to the issues mentioned above, our review of theliterature also raised two other concerns. First, that reports common reasons for paper exclusion, the term‘time and motion’ may have been overused as it appears frequently in studies that did not actually use the T&M methodaccording to the prevalent ; examples include work sampling studies, studies on turnaround time, and questionnaire surveys. This issue could create chaos in knowledge accumulation and cause unnecessary difficulties for researchers conducting systematic reviews and meta-analyses. Second, there does not seem to be a standard way to train T&M observers or to calibrate inter-observer disagreements (if applicable). This fact may exacerbate potential observer biases and undermine the validity of cross-study research synthesis. Developing a standard observer training set, possibly in the form of video recordings of typical clinical sessions annotated by T&M experts, would therefore be highly valuable.

CONCLUSION
We reviewed existing empirical studies that have applied the T&M approach to evaluate the impact of health IT implementation on clinical work processes and workflow. Based on the results, we developed a suggested ‘checklist’ intended to help standardize the design, conduct, and results reporting of future T&M studies. We believe that the resulting checklist, STAMP (Standard Time And Motion Procedures), may contribute to improving the methodological and results reporting consistency of T&M research, so that meaningful insights can be derived from across-study synthesis and health informatics, as a field, will be able to accumulate knowledge from these studies.
                                          
                                                                                                                  Mamta Sahare 44

Design and Production Process Project


Providing Security against SQL Injection Attack in Web Applicatio 
            
                                                                                       MAMTA SAHARE 44
Algorithm of the Needleman-Wunsch Algorithm
1. Initialization
C(0, 0) = 0
C(0, i) = −i * d
C(j, 0) = −j * d

2. Main Iteration
For each i = 1 . . . M
For each j = 1 . . . N
C(i, j) = max
                       { C(i − 1, j − 1) + s(xi, yj), case 1
                         C(i − 1, j) − d , case2
                      C(i, j − 1) − d , case3
                     }
Ptr(i, j) =
                { DIAG , if case 1
                LEFT , if case 2
               UP , if case 3
              }
Where C is the Score Matrix and Ptr is the Traceback Matrix.
Suppose there are two sequences
e.g.               Normal:- AM EQ (profile amino acid code)
                     Attacked:- AM EQ AM
The Needleman-Wunsch algorithm consists of three steps
1.    Initialization of the Score Matrix.
Score Matrix is a matrix which is used in Needleman wunsch algorithm to calculate best score. It also helps to construct a matrix which in turn helps to deduce best alignment between two sequences.
2.    Calculation of scores and Filling the Traceback matrix.
Traceback Matrix is used to deduce a Traceback path. This Traceback path helps to find best alignment.
3.    Deducing the alignment from the Traceback matrix.
 Initialization of the Score Matrix
The cells of the Score Matrix are labeled C(i, j) where
i = 1; 2; …..;N and j = 1; 2;….;M

A
M
E
Q
A
M
C(1,1)
C(1,2)
C(1,3)
C(1,4)
C(1,5)
C(1,6)
C(1,7)
A
C(2,1)
C(2,2)
C(2,3)
C(2,4)
C(2,5)
C(2,6)
C(2,7)
M
C(3,1)
C(3,2)
C(3,3)
C(3,4)
C(3,5)
C(3,6)
C(3,7)
E
C(4,1)
C(4,2)
C(4,3)
C(4,4)
C(4,5)
C(4,6)
C(4,7)
Q
C(5,1)
C(5,2)
C(5,3)
C(5,4)
C(5,5)
C(5,6)
C(5,7)

SCORE MATRIX
This is the initialization of first row and first column of the score matrix. This can be done by any value. Here the initialization is done with the values 0, -1,-2, …………..     
A
M
E
Q
A
M
 0  
-1
-2
-3
-4
-5
-6
A
-1
M
-2
E
-3
Q
-4
                                                                
TRACEBACK MATRIX
This is the initialization of first row and first column of Traceback Matrix.
First row is initialized with a value ‘left’ and first column is initialized with a value ‘up’. This will help to deduce Traceback Path.
A
M
E
Q
A
M
done
left
left
left
left
Left
left
A
up
M
up
E
up
Q
up




3. Scoring
Formula for calculating the identity score
The score of any cell C(i , j) is the maximum of:
    qdiag = C(i -1, j - 1) + S(i ; j)                       
qup = C(i -1,j) + g
qleft = C(i , j - 1) + g                                    ----------------(1)
Where S(i , j)   is a score given to whether i-th element and j-th element of alignment are identical or not. Gap is a cut in score for insertion of blank, which fills matrix C. The score lower right end of matrix C is the maximum score of global alignment.
 The calculation for the cell C(2 , 2):-
           i=2, j=2, g=(-1)
qdiag = C(1, 1) + S(A,A) = 0 + 1 = 1
qup = C(1, 2) + g = -1 + (-1) = -2
qleft = C(2, 1) + g = -1 + (-1) = -2
Here the maximum value is 1 so we put C(2,2) =1 in score matrix and fill diag into the Traceback matrix in the cell Ptr(2,2)=diag.
SCORE MATRIX
In this matrix the value for the cell c(2,2) is filled as calculated with the  formula 1. 
A
M
E
Q
A
M
0
-1
-2
    -3
-4
-5
-6
A
   -1
  1
M
   -2
E
   -3
Q
   -4

TRACEBACK MATRIX
Since the value calculated for the cell c(2,2) has come from qleft. Thus ptr(2,2)=diag is filled in the Traceback Matrix.
A
M
E
Q
A
M
 done
left
left
left
left
left
Left
A
up
diag
M
up
E  
up
Q
up

This process is continued until all the blank cells are fulfilled.
SCORE MATRIX     
Here the values are calculating for all the cells of score matrix. The completely filled matrix is given below
A
M
E
Q
A
M
 0
-1
-2
-3
-4
-5
-6
A
-1
1
0
-1
-2
-3
-4
M
-2
0
2
1
0
-1
0
E
-3
-2
1
3
2
1
0
Q
-4
-3
0
2
4
3
2




TRACEBACK MATRIX
After filling Score Matrix, different cells of Traceback Matrix are to be filled according to their values in Score Matrix. The final Traceback Matrix obtained is given below
A
M
E
Q
A
M
done
left
left
left
left
left
Left
A
up
diag
left
left
left
left
Left
M
up
up
diag
left
left
left
Diag
E
up
up
up
diag
left
left
left
Q
up
up
up
up
diag
left
Left

For deducing best alignment, Traceback path is to be formed. For calculating Traceback path, start from rightmost lower corner. In the first cell i.e. c(5,7)=left is written, so now move in the leftward direction. In the next cell again a left is found, so again move in leftward direction. Now in the next cell a diag is found, so now move in diagonal direction. Proceeding in the similar manner, traverse until done is found.
The path formed in this manner is called Traceback path. The final matrix formed is given below 
A
M
E
Q
A
M
done
left
left
 left
left
left
left
A
up
diag
left
Left
left
left
left
M
up
up
diag
 left
left
left
diag
E
up
up
up
diag
left
left
left
Q
up
up
up
up
diag
left
left

For deducing best alignment, start traversing Traceback path. Start again from rightmost lower corner. The rule to be followed for deducing the alignment is as follows
1. If a left is encountered, place a gap in leftward sequence.
2. If a up is encountered, place a gap in upward sequence.
3. If a diag is encountered, then it means that the two characters are aligned. 
With the help of given formulas, traverse the Traceback path to introduce gap in the sequences.
AM EQ AM
AM EQ
Best score is=2
Step 5 Measure similarity of Na Code Amino and Aa Code Amino
Alignment identity I for measuring similarity to detect whether Pa Code Amino and Aa Code Amino are injected is computed as shown in formula.

I = (alignment match code (Pa Code Amino, Aa Code Amino)/
Aa Code Amino length + gap)) %100

The identity for the measurement of similarity is computed by dividing identity scores of two sequence alignments by sum of blank length and whole sequence length. Namely, upon calculating similarity, score is computed in consideration of sequence length both in the case of long sequence length with high identity score and in the case of short sequence length with low identity score, which secures balance. Alignment is compared in order to judge which alignment is superior. If the code sequences are 100% identical then this query is allowed to be executed otherwise this is rejected. The identity is calculated for given two sequences