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Improve Radiologists Productivity in Hospitals Based on Data Mining Techniques قنياتخدام ت باستشفياتشعة في المستء ا إنتاجية أطبا تحسين تنقيبلبيانات اBy Mona Abdul-Fattah El-Sibakhi Supervised by Dr. Tawfiq Barhoom Associate prof. of Applied Computer Technology A thesis submitted in partial fulfillment of the requirements for the degree of Master of Information Technology September, 2017 الج ـ امع ـــــــــس ـة ا ـــــمي ــ ة ب غ ــ زة عمادةعليات السامي والدراعل البحث ال ك ـ ليـــــ ةعلومـــــات الم تكـــنولوجيـــــا ماجستير تكـــنولوجيــــ اعلومــــات المThe Islamic University of Gaza Deanship of Research and Postgraduate Faculty of Information Technology Master of Information Technology

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Page 1: Improve Radiologists Productivity in Hospitals Based on ... · Improve Radiologists Productivity in Hospitals Based on Data Mining Techniques تايقت مادتساب تايفت

Improve Radiologists Productivity in Hospitals

Based on Data Mining Techniques

تحسين إنتاجية أطباء األشعة في المستشفيات باستخدام تقنيات البيانات تنقيب

By

Mona Abdul-Fattah El-Sibakhi

Supervised by

Dr. Tawfiq Barhoom

Associate prof. of Applied Computer Technology

A thesis submitted in partial fulfillment

of the requirements for the degree of

Master of Information Technology

September, 2017

زةــغب ةــالميــــــة اإلســـــــــامعـالج

البحث العلمي والدراسات العليا عمادة

تكـــنولوجيـــــا المعلومـــــاتة ليــــــك

المعلومــــات اتكـــنولوجيــــ ماجستير

The Islamic University of Gaza

Deanship of Research and Postgraduate

Faculty of Information Technology

Master of Information Technology

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I

إقــــــــــــــرار

أنا الموقع أدناه مقدم الرسالة التي تحمل العنوان:

Improve Radiologists Productivity in Hospitals

Based on Data Mining Techniques

تحسين إنتاجية أطباء األشعة في المستشفيات باستخدام تقنيات تنقيب البيانات

أقر بأن ما اشتملت عليه هذه الرسالة إنما هو نتاج جهدي الخاص، باستثناء ما تمت اإلشارة إليه حيثما ورد، وأن

لنيل درجة أو لقب علمي أو بحثي لدى أي مؤسسة اآلخرينهذه الرسالة ككل أو أي جزء منها لم يقدم من قبل

تعليمية أو بحثية أخرى.

Declaration

I understand the nature of plagiarism, and I am aware of the University’s policy on

this.

The work provided in this thesis, unless otherwise referenced, is the researcher's own

work, and has not been submitted by others elsewhere for any other degree or

qualification.

:Student's name السباخي الفتاح عبد منى الطالب:اسم

:Signature التوقيع:

:Date التاريخ:

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III

Abstract

Modern radiology departments have enormous databases of images and text. Like any

databases, which are rich in data content, but poor in information content. Data Mining

is an effective tool that extracts useful information from this enormous database of

images and text which helps decision makers in departments and hospitals to take

proper decisions.

In this research, the idea investigates some problems in radiology departments at

hospitals based on applying Data Mining techniques and conducting Data Mining

model to improve radiologists productivity by assigning the appropriate cases to

appropriate radiologists within tele-radiology environment. Due to the heavy load of

work assigned to radiologists, there is significant delay in writing radiology reports by

them.

Data with seven feature sets were collected from four hospitals in Saudi Arabia

covering eight radiologists (two from each hospital) with varying productivity and

specialisation with emphasis on CT, MRI and Mammography modalities. Four

different classifiers were applied for the dataset to predict and assign the suitable cases

for each radiologist to improve radiologists productivity.

The model was evaluated by presenting its results to an expert in one of the four

hospitals for his opinion. He declared that the results of the model are very good as

they take into account the subspecialty of each procedure in assigning the cases. He

also believes that applying the model in hospitals will achieve good results and

improve the radiologists productivity.

Accuracy and F-measure evaluation performance measures were applied to compare

among the classifiers. The results show that the Naïve Bayes was the best classifier in

improving the productivity of radiologists, it improved the productivity by up to 24%

as it assigned the appropriate case to the appropriate radiologist. Naïve Bayes had the

highest value in Accuracy and F-measure by up to 8% in accuracy and 4% in F-

measure.

Keywords: Radiology, Data Mining, Classification, Productivity.

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IV

الملخص

، تكون قواعد أي قواعد بياناتلدى أقسام األشعة الحديثة قواعد بيانات ضخمة من الصور والنصوص. وكما

في محتوى البيانات ولكنها ضعيفة في استخراج المعلومات. البيانات هذه غنية

لذلك يمكن اعتبار تنقيب البيانات أداة ذات كفاءة وفعالية الستخراج المعلومات من قواعد البيانات الضخمة وتقديمها

بشكل مفيد يدعم اتخاذ القرار في تلك األقسام والمستشفيات.

اكل أقسام األشعة في المستشفيات وتقديم الحل على أساس تطبيق تستند فكرة هذا البحث على دراسة إحدى مش

بهدف تحسين إنتاجية أطباء األشعة من خالل إسناد الحاالت وإنشاء نموذج تنقيب البيانات تقنيات تنقيب البيانات

المناسبة لكل طبيب أشعة وذلك في بيئة قراءة األشعة عن بعد.

إنجاز التقارير خالل الوقت المناسب بسبب زيادة عبء بعض أطباء األشعة.تواجه أقسام األشعة حالًيا تأخيًرا في

من كل طبيبان“ثمانية أطباء تتضمنتم جمع البيانات من أربع مستشفيات مختلفة في المملكة العربية السعودية

وتمت مراعاة تفاوت األطباء في التخصص واإلنتاجية.” مستشفى

تصوير الثدي، وتضمنت لمقطعي، التصوير بالرنين المغناطيسي و اقتصر البحث على فحوصات التصوير ا

سبع صفات. البيانات

تم تطبيق أربع أدوات مختلفة لتصنيف البيانات على مجموعة البيانات، وتنبأ النموذج بالحالة المناسبة لكل طبيب

إلى توزيع الحاالت بالشكل األمثل لتحسين إنتاجية األطباء. أدىأشعة مما

بأن قالو ، النتائج على خبير في واحدة من المستشفيات األربع إلبداء رأيهم النموذج من خالل عرض تم تقيي

على األطباء الحاالت توزيعاالعتبار التخصص الفرعي لكل إجراء في بعينذ تأخألنها نتائج النموذج جيدة

جيدة وسيعمل على تحسين إنتاجية األطباء. نتائج ويعتقد أنه عند تطبيق النموذج في المستشفيات سوف يحقق

. وأظهرت النتائج أن F-measureو Accuracyم تقييم نتائج أدوات التصنيف المستخدمة باستخدام أيضا ت

Naive Bayes حيث أدت إلى تحسن أداء كانت أفضل أداة في إسناد الحاالت المناسبة لكل طبيب أشعة ،

بنسبة تصل Accuracyالوقت حصلت على أعلى نسبة في تقييم . وفي نفس%24األطباء بنسبة تصل إلى

% مقارنة بأدوات التصنيف األخرى.4بنسبة تصل إلى F-measureو% 8إلى

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V

Epigraph Page

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VI

Acknowledgment

Thanks to Almighty Allah for giving me strength and ability to understand, learn and

complete this research.

With great pleasure, I would like to express my deepest gratitude to my supervisor Dr.

Tawfiq Barhoom for his unwavering support and mentorship throughout this research.

I also greatly thank my Mum and Dad who paved the path for me and upon whose

shoulders I stand. This is dedicated to my family and the many friends who supported

me during this journey, Thank you.

Special thanks to my dear husband for his direct and indirect support to complete this

research.

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VII

Table of Contents

Declaration .................................................................................................................. I

Abstract ..................................................................................................................... III

Epigraph Page ............................................................................................................ V

Acknowledgment ...................................................................................................... VI

Table of Contents .................................................................................................... VII

List of Tables .............................................................................................................. X

List of Figures ........................................................................................................... XI

List of Abbreviations .............................................................................................. XII

Chapter 1 Introduction .............................................................................................. 1

1.1 Background and Context .................................................................................... 2

1.2 Statement of the Problem .................................................................................... 3

1.3 Objectives ........................................................................................................... 3

1.3.1 Main Objective ................................................................................................ 3

1.3.2 Specific Objectives .......................................................................................... 3

1.4 Importance of the research .................................................................................. 4

1.5 Scope and Limitations ........................................................................................ 4

1.6 Methodology ....................................................................................................... 4

1.7 Overview of research .......................................................................................... 5

Chapter 2 Background ............................................................................................... 6

2.1 Overview of Data Mining ................................................................................... 7

2.2 Rapid Miner ........................................................................................................ 8

2.3 Data Mining Classification Techniques .............................................................. 8

2.3.1 Decision Tree ................................................................................................... 8

2.3.2 Naïve Bayes ..................................................................................................... 8

2.3.3 Random Forest ................................................................................................. 9

2.4 Performance Evaluation ...................................................................................... 9

2.4.1 Confusion Matrix ............................................................................................. 9

2.4.2 Accuracy (AC) ............................................................................................... 10

2.4.3 Precision (P) ................................................................................................... 10

2.4.4 Recall ............................................................................................................. 10

2.4.5 F-measure ....................................................................................................... 10

2.5 Data Mining in Healthcare ................................................................................ 10

2.6 Data Mining versus Statistics ........................................................................... 11

2.7 Radiology Information System ......................................................................... 11

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VIII

2.8 Radiology and Tele-radiology in hospitals ....................................................... 12

2.9 Relative Value Units (RVUs) ........................................................................... 13

2.9.1 RVUs in Radiology ........................................................................................ 13

Chapter 3 Related Works ......................................................................................... 14

3.1 Radiologists Productivity Measurements ......................................................... 15

3.2 Data Mining for Measuring Indicators ............................................................. 16

3.3 Data Mining for Diagnosis Diseases ................................................................ 17

3.4 Related Work Discussion .................................................................................. 19

3.5 Summary ........................................................................................................... 19

Chapter 4 The Data Mining Model ......................................................................... 20

4.1 General View of Model .................................................................................... 21

4.2 Model Details .................................................................................................... 22

4.3 Model Iterations ................................................................................................ 22

4.3.1 The Initial Iteration ........................................................................................ 22

4.3.2 The Next Iteration (one or more) ................................................................... 25

4.4 Summary ........................................................................................................... 26

Chapter 5 Methodology ............................................................................................ 27

5.1 Methodology Steps ........................................................................................... 28

5.2 Data Acquisition and Collection ....................................................................... 28

5.3 Data Pre-processing and Feature Sets Selections ............................................. 30

5.3.1 Generating New Columns .............................................................................. 30

5.3.2 Combining Data ............................................................................................. 32

5.3.3 Feature Set Selection ..................................................................................... 32

5.4 Testing Data ...................................................................................................... 33

5.5 Implementation ................................................................................................. 34

5.5.1 Tools .............................................................................................................. 34

5.6 Evaluation ......................................................................................................... 35

5.7 Summary ........................................................................................................... 35

Chapter 6 Results, Discussion and Evaluation ....................................................... 36

6.1 Classification Methods Settings ........................................................................ 37

6.2 Experimental Results ........................................................................................ 37

6.3 Evaluation ......................................................................................................... 40

6.3.1 Performance Evaluation Results .................................................................... 40

6.4 Summary ........................................................................................................... 42

Chapter 7 Conclusions and Future Work .............................................................. 43

7.1 Conclusion ........................................................................................................ 44

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IX

7.2 Future Work ...................................................................................................... 45

References .................................................................................................................. 46

References .................................................................................................................. 47

Appendices ................................................................................................................. 50

Appendix A: Reported Cases Statistics .................................................................. 51

Appendix B: Sample of Exam Code Dictionary ..................................................... 53

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X

List of Tables

Table (3.1): Summary of the Most Related Works to this Work ............................... 19

Table (4.1): Previous Productivity ............................................................................. 24

Table (4.2): Joined Data ............................................................................................. 24

Table (4.3): Calculating Current Productivity ........................................................... 24

Table (4.4): The Work of the Loop ............................................................................ 25

Table (5.1): Description of Figure 5.2 Columns ........................................................ 29

Table (5.2): FDA Age Classifications (FDA, 2014) .................................................. 31

Table (6.1): Classifiers Settings ................................................................................. 37

Table (6.2): Auto Assigned Cases ............................................................................. 39

Table (6.3): Performance Evaluation Results ............................................................ 40

Table (6.4): Wrongly Assigned Cases ....................................................................... 41

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XI

List of Figures

Figure (2.1): Confusion Matrix .................................................................................... 9

Figure (4.1): The Model ............................................................................................. 21

Figure (4.2): The Initial Iteration of the Model ......................................................... 23

Figure (4.3): The Next Iteration of the Model ........................................................... 26

Figure (5.1): Methodology Steps ............................................................................... 28

Figure (5.2): Data Before Pre-processing .................................................................. 29

Figure (5.3): Exam Code Dictionary ......................................................................... 30

Figure (5.4): Generate Age Group ............................................................................. 30

Figure (5.5): Generate Time to Report ...................................................................... 31

Figure (5.6): Generate Reporting Time ..................................................................... 31

Figure (5.7): Generate Exam Day .............................................................................. 32

Figure (5.8): Combined Data ..................................................................................... 32

Figure (5.9): Training Data Set .................................................................................. 32

Figure (5.10): Testing Data Before Pre-processing ................................................... 33

Figure (5.11): Combined Data ................................................................................... 33

Figure (5.12): Testing Data ........................................................................................ 34

Figure (5.13): Rest Data ............................................................................................. 34

Figure (6.1): The Initial Iteration in Rapid Miner ..................................................... 38

Figure (6.2): Auto Assigned Cases ............................................................................ 38

Figure (6.3): Next Iteration in Rapid Miner .............................................................. 39

Figure (6.4(: Productivity Comparison ...................................................................... 40

Figure (6.5): Performance Evaluation Results ........................................................... 41

Figure (6.6): Naive Bayes Classifier Results ............................................................. 41

Figure (7.1): Productivity Comparison ...................................................................... 45

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XII

List of Abbreviations

AC Accuracy

ANN Artificial Neural Network

CAD Coronary Artery Disease

CT Computerized Tomography

CV Coefficient of Variation

FDA Food and Drugs Administration

FTE Full-Time Equivalent

HIS Hospital Information Systems

KDD Knowledge Data Discovery

KNN K-Nearest Neighbour

LAD Left Anterior Descending

LCX Left Circumflex

LOS Length of Stay

L-RVU Local Relative Value Units

MRI Magnetic Resonance Imaging

P Precision

PACS Picture Archiving and Communication System

RCA Right Coronary Artery

RIS Radiology Information System

ROC Receiver Operating Characteristic

RVUs Relative Value Units

SCI Spinal Cord Injuries

SVM Support Vector Machines

TASH Total Available Staffed Hours

TCM Traditional Chinese Medicine

US Ultrasonography

X-Ray Conventional Radiography

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Chapter 1

Introduction

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Chapter 1

Introduction

1.1 Background and Context

Nowadays Healthcare industry produces massive amounts of complex data about

patients, hospitals resources and disease diagnosis. This massive amount of data is a

key resource to be processed and analyzed for knowledge extraction that enables

support for cost-savings and decision making (Desikan, Hsu, & Srivastava, 2011).

Previous studies solved the problems that concentrated on the prediction and diagnosis

of heart diseases and breast cancer in addition to measure RVUs and productivity for

radiologists. Each study has its own Data Mining techniques which gives it points of

strength but at the same time some limits. In this research, the idea investigates some

problems in radiology departments at hospitals with the aim to construct a model to

improve radiologists productivity which helps in identifying the hospitals that are in

actual need for radiologists and to apply auto assigning in Tele-radiology. Due to the

heavy load of work assigned to radiologists, there is significant delay in writing

radiology reports by them and some reports have been pending for days or weeks.

However, the conventional Tele-radiology procedure provided by hospitals is limited

and has never overcome such delay. Data Mining has been utilized to improve the

radiologists productivity by assigning the appropriate cases to targeted radiologist. The

main objective of this research is to construct a model to improve radiologists

productivity in hospitals. This research focuses on applying Data Mining techniques

on data from different hospitals in different areas of Saudi Arabia. Data Mining brings

a set of tools and techniques that can be applied to the processed data to discover

hidden patterns that provide healthcare professionals with an additional source of

knowledge for making decisions (Desikan, Hsu, & Srivastava, 2011). Radiologists

productivity was measured, then all cases were reassigned to all radiologists to apply

auto assign Tele-radiology environment, Tele-radiology means that the radiologist

could conduct writing reports for different hospitals.

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1.2 Statement of the Problem

Nowadays, radiology has a vital role in medical diagnosis process. Computerized

Tomography (CT), Magnetic Resonance Imaging (MRI) and Mammography are the

most popular modalities of radiology. Radiologists encountered delays in writing

radiology reports and some reports were kept pending for a long time, mainly those

related to CT, MRI and Mammography. Radiologists productivity percentages vary

and may be above the acceptable range. The conventional Tele-radiology procedure

provided by hospitals is limited and has never overcome such delays. Data Mining has

been utilized to improve the radiologists productivity by assigning the appropriate

cases to targeted radiologist.

1.3 Objectives

1.3.1 Main Objective

The main objective of this research is to construct a model to improve radiologists

productivity in hospitals.

1.3.2 Specific Objectives

The specific objectives of the research are:

1. Data aggregation from different hospitals.

2. Pre-processing and Analyzing data to prepare it for implementing Data Mining

techniques.

3. Data Modelling: implementing Data Mining classification techniques such as

Decision Tree, Naïve Bayes, K-NN and Random Forest.

4. Measuring Radiology productivity percentage by using the traditional way before

applying Data Mining techniques.

5. Assigning appropriate radiology procedure for appropriate radiologist by applying

Data Mining classification techniques within Tele-radiology environment

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4

7. Evaluating the model by presenting the results to an expert from one of the four

hospitals for his opinion about the model. Also, an evaluation to classification

techniques was done by using different evaluation measures to evaluate the

performance and to compare among them.

1.4 Importance of the research

Despite the differences and inconsistency in approaches, the radiology system is in

more need for Data Mining today. There are some arguments that could support the

use of Data Mining in the radiology system (D. & Jr., 2009). Radiologists who must

write radiology reports delay writing such reports or keep them pending for quite a

long time. Together with the radiologists productivity problems, this led to implement

Data Mining techniques which have ability to improve the productivity of radiologists.

1.5 Scope and Limitations

The research focused on applying Data Mining techniques on data for eight

radiologists from four different hospitals in different areas of Saudi Arabia.

Radiologists productivity was measured, then all cases were reassigned to all

radiologists to apply auto assigned Tele-radiology environment.

Hospitals face a lot of problems and delay of reports in CT, MRI and Mammography

because of the large number of cases and patients. So, in this research the proposed

model concentrates on these modalities.

The radiologists workload is limited to clinical work only, other work like

administration, teaching and conferences are not considered.

1.6 Methodology

The methodology of this research consists of the following phases:

Phase 1: Data Acquisition and Collection

Data were collected for eight radiologists from four hospitals in Saudi Arabia; one data

set for a year was collected for training data and another for a month for testing data.

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The training and testing datasets contained data of the three modalities which the

research concentrated on, i.e. CT, MRI and Mammography.

Phase 2: Data Pre-processing

Data were pre-processed, and a dataset of training and testing data was built. Seven

feature sets were created: Radiologist as a label, Visit_Class, Age_Group, Body_Part,

Reporting_time, Exam_Day and wRVU. For training data, 13,142 records were

selected for a year and for testing data, 1,290 records were selected for a month.

Phase 3: Implementation

Rapid Miner Programme was used to implement Data Mining techniques. Decision

Tree, Naïve Bayes, K-NN and Random Forest classification Data Mining techniques

were applied, data for a year used as training data set and data for a month used as

testing data set.

Phase 4: Evaluation

After applying classification methods, the model was evaluated by presenting the

results to an expert from one of the four hospitals for his opinion about the model

Accuracy and F-measure evaluated the performance of the model.

1.7 Overview of research

The research is divided into seven chapters; chapter one includes the Introduction,

chapter two provides Literature Review, chapter three includes related works in Data

Mining healthcare, chapter four provides the model and its component, chapter five

provides description of the methodology, chapter six includes the analysis of

experiments results and evaluation and chapter seven talks about conclusion and future

work.

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Chapter 2

Background

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7

Chapter 2

Background

This chapter presents the background and theoretical concepts of the Data Mining

techniques applied in this research. It starts by discussing the importance of Data

Mining techniques in healthcare domain, and clarifies the statistics versus Data

Mining. The last part presents a background about Radiology Systems in hospitals,

Tele-radiology system and Relative Value Units (RVUs).

2.1 Overview of Data Mining

Data Mining is considered to be a recently developed methodology and technology,

starting in 1994 aiming to identify valid, new, useful, and understandable data

correlations and patterns (Koh & Tan, 2005).

It can be considered as the process of extracting important information from large set

of data utilizing the relationship between the data. It is also exemplified as Knowledge

Data Discovery (KDD) and many consider that it is almost impossible to distinguish

between the two. Many also consider Data Mining to be a very vital step in KDD (

Tapedia & Wagh , 2016).

Data Mining is a variety of methods and techniques utilized in different analytical

patterns to address a range of organizational needs (SAS Institute Inc, 2012).

Descriptive Modelling: It reveals similarities or groupings in historical data to know

success or failure causes such as Clustering which groups similar records together and

Association Rule Learning which shows relationships between records (SAS Institute

Inc, 2012).

Predictive Modelling: This modelling classifies future events or estimates unknown

outcomes – for example, using credit scoring to determine an individual's likelihood

of repaying a loan. Similar to Decision Tree, it is tree-shaped diagrams in which each

branch represents a probable occurrence and Support Vector Machine which is

supervised learning models with related learning algorithms (SAS Institute Inc, 2012).

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2.2 Rapid Miner

Rapid Miner is a data science software developed to provide a cohesive environment

for data preparation, machine learning, deep learning, text mining, and predictive

analytics. It used for business and commercial applications and for research, education,

training. Rapid Miner supports all steps of the machine learning process including data

preparation, results visualization, model validation and optimization.

2.3 Data Mining Classification Techniques

Classification is the most commonly applied technique in Data Mining, which utilizes

a set of pre-classified examples to develop a model through which the population of

records can be classified at large. This method usually employs Decision Tree or

Neural Network-based classification algorithms. The data classification process

involves learning and classification. Learning analyses the training data by

classification algorithm. Classification uses test data to estimate the accuracy of the

classification rules. These pre-classified examples are used by the classifier-training

algorithm to determine the set of parameters required for proper discrimination. These

parameters are then encoded by the algorithm into a model called a classifier (Mlambo

, 2016). In this research, a variety of classification methods with different feature sets

were used such as: Decision Tree, Naïve Bayes, K-NN and Random Forest.

2.3.1 Decision Tree

Decision Tree is a graphical representation of the relations existing between the data

and is used for data classification. The result is displayed as a tree, hence the name of

this technique. Decision Tree is a simple and a powerful way of representing

knowledge and is mainly used in the classification and prediction. The models

obtained from the decision tree are represented as a tree structure (Milovic & Milovic,

2012).

2.3.2 Naïve Bayes

The Bayesian Classification represents a supervised learning method and a statistical

method for classification. It supposes a probabilistic model and allows to capture

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uncertainty about the model by determining probabilities of the outcomes. It solves

diagnostic and predictive problems (Hachesu, Ahmadi, Alizadeh, & Sadoughi, 2013).

2.3.3 Random Forest

The Random Forest is a group of unpruned classification trees. It generates many

classification trees where each tree is constructed by a different sample from the

original data using a tree classification algorithm. After the forest is formed, a new

object that needs to be classified is put down each tree in the forest for classification.

Each tree gives a vote indicating its decision about the class of the object. Then the

forest chooses the class with the most votes for the object (Al Mehedi , Nasser, Pal, &

Shamim, 2014).

2.4 Performance Evaluation

Performance Evaluation aims to provide an equitable measurement of a model to

produce accurate evaluation and to obtain a high level of quality and quantity in the

results produced (Capko, 2003).

2.4.1 Confusion Matrix

A confusion matrix is a simple performance analysis tool used in supervised learning.

It is used to represent the test result of a prediction model. Each row of the matrix

represents the instances in a predicted class, while each column represents the

instances in an actual class (M, 2012).

Figure (2.1): Confusion Matrix

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2.4.2 Accuracy (AC)

Accuracy is the proportion of the total number of predictions that were correct. It is

determined using the equation:

(2.1)

2.4.3 Precision (P)

Precision is the proportion of the predicted positive cases that were correct. It is

calculated using the equation:

(2.2)

2.4.4 Recall

The recall is the positive cases that were correctly identified. It is calculated using the

equation:

(2.3)

2.4.5 F-measure

A measure that combines precision and recall is the harmonic mean of precision and

recall. It is calculated using the equation:

(2.4)

2.5 Data Mining in Healthcare

Many organizations have used Data Mining, Data Mining becoming very popular in

healthcare. Its applications can greatly benefit all those involved in the healthcare

True Positive (TP): If the instance is positive and it is classified as positive

False Positive (FP): If the instance is negative but it is classified as positive

True Positive (TP): If the instance is positive and it is classified as positive

False Negative (FN): If the instance is positive but it is classified as negative

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industry. They can help healthcare insurers detect fraud and abuse, healthcare

organizations make customer relationship management decisions, physicians identify

effective treatments and best practices and patients receive better and more affordable

services. Healthcare transactions produce great amounts of data which are usually

complicated and huge in size. It is difficult to process and analyze these data by usual

methods. Data Mining can provide the means and technology to change the data into

useful information to help decision makers to take proper decisions (Koh & Tan,

2005).

2.6 Data Mining versus Statistics

Data Mining is the process of extracting unidentified information from large databases

and using it to make decisions. It is a set of methods used in the knowledge discovery

process to distinguish previously unknown relationships and patterns within data. Data

Mining that provides the tools and analytics techniques for dealing with huge amounts

of data contains statistics. It is the science of learning from data and includes

everything from collecting and organizing to analyzing and presenting data. Data

Mining and Statistics are related to learning from data. They are about discovering and

identifying structures in them, thus aim to turn data to information. Both techniques

have different approaches. Although their aims overlap. Statistics is only about

quantifying data. While it uses tools to find relevant properties of data, it is very much

like math. It provides the tools necessary for Data Mining. On the other hand, Data

Mining builds models to detect patterns and relationships in data, particularly from

large data bases (GS, 2015).

2.7 Radiology Information System

A Radiology Information System (RIS) is the main system for the management of

imaging departments. Its major functions include scheduling patients, managing

resources, tracking examination performance, interpreting examinations, distributing

results, and billing procedure. RIS complements Hospital Information Systems (HIS)

and Picture Archiving and Communication System (PACS), and is critical to efficient

workflow to radiology practices.

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Radiology in healthcare provides diagnostic imaging services for patients using

Computed Tomography (CT), Magnetic Resonance Imaging (MRI), radionuclide

imaging (nuclear medicine), UltraSonography (US), Conventional Radiography (X-

ray) and interventional procedures using advanced image-guided techniques. These

types of images are archived in PACS and radiology workflow is managed by RIS.

2.8 Radiology and Tele-radiology in hospitals

The Department of Radiology has an ongoing programme for monitoring, evaluating,

and assuring quality services by the department. The programme has integrated in the

hospital's overall quality assessment and improvement plan. The Radiology

Department has a quality system that ensures compliance with accreditation

requirements. Quality planning and evaluating the effectiveness and efficiency of the

Quality system is conducted through scheduled leadership reviews. The Hospital

Director is very firmly committed to support all activities, participations, and

implementation of the Quality system. The department supports the Quality system

and contributes data collection for quality reports. The Department provides services

like conventional X-Rays, Fluoroscopy, Colour Doppler Studies, and Ultra-

Sonography (Radiology | Al Yousuf Hospital, Al Khobar, KSA, 2013).

Tele-Radiology is the electronic transmission of radiographic images from one

geographical location to another for interpretation and consultation. It allows various

users in different locations to view images simultaneously. The main applications of

Tele-Radiology provide radiological expertise at remote sites more quickly than would

otherwise be possible (Ahmed & Aldosh, 2014).

Need of Tele Radiology in Saudi Arabia: Saudi Arabia, faces a variety of health

challenges. There is a shortage of Community Health Centres and specialists at these

Centres and hospitals. Particularly, Radiology departments are understaffed where

staffing shortages are occurring at a time when radiology volume generally is

increasing. The gap between demand and supply of quality radiologist is always

increasing. There are various underlying reasons why the supply of radiologists is

insufficient to meet the demand in many areas. Tele-Radiology services in Saudi

Arabia are going to setup across various district hospitals in the southern region where

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Tele-Radiology solution will help to manage the data and streamline the image flow

from all district hospitals. Tele-Radiology system, offers a comprehensive enterprise

class hospital. (Ahmed & Aldosh, 2014).

2.9 Relative Value Units (RVUs)

Relative value unit (RVU) is a measure of value used in the United States Medicare

for radiologist services, it is considered to be the primary measure of a radiologist’s

productivity. In the past, there were several ways of calculating radiologist

productivity, nowadays these ways focus on models based on Relative Value Units

(RVUs). RVUs which reflect the relative level of time and skill required of a

radiologist to provide a given service. RVUs are a method for calculating the volume

of work or effort expended by a radiologist in treating patient (MERRITT HAWKINS

an AMN Healthcare Company, 2011).

2.9.1 RVUs in Radiology

In the past, tracking productivity was not a big issue due to the surplus of radiologists.

Today radiology practices aim to have the most qualified staff to interpret the volume

of images and not lose money to competitors. RVUs have a correct and accurate

measurement on radiologists (Forrest, 2007). There are three components of a

Medicare RVU: Work RVU (wRVU) ≈ 52%, which is Relative time, effort, and skill

needed by a radiologist in providing a procedure, Practice Expense RVU (peRVU) ≈

44%, it is Costs associated with maintaining a practice, and Malpractice Expense RVU

(mRVU) ≈ 4% m, it is Professional liability insurance (Kuehn, 2009).

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Chapter 3

Related Works

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Chapter 3

Related Works

Many of researches works concentrate on Data Mining in Healthcare. Data mining is

becoming increasingly popular and essential (Koh & Tan, 2005). The Following are

related works that uses Data Mining techniques in Healthcare.

3.1 Radiologists Productivity Measurements

Researchers (dora , Faccin , & Fogliatto , 2016) developed a local RVU (L-RVU)

system to measure radiologists reporting productivity and workload focusing on CT

exams. A method to normalize exams according to the anatomical region (Body Part)

was developed. A time-based measure of radiologist reporting workload was built, a

query that searched for all CT reports (from July 1st, 2013 to February 28th, 2015)

was performed from Radiology Information System (RIS). The query resulted in

42,382 instances for 24 tests performed by the CT Unit. A list of 17 categories

(anatomical region) was proposed, then these categories of tests were normalized with

the shortest reporting time as the reference test. The result total RVUs (Productivity)

did not have target value.

Researchers (Cowan, MacDonald, & Floyd, 2013) focused on measuring radiologists

RVUs based on reporting times using data from a Radiology Information System (RIS)

for all reports generated from 1 January 2010 to 30 June 2012 including CT, MRI, US

and X-Ray modalities. A technique for semi-automated measurement of radiologist

reporting time and measuring the time required for radiologists to produce reports

during normal work was created. A sample of reporting times was recorded by the

Radiology Information System using voice recognition system with the description of

each examination and placed in a database. The study was limited to consultant

radiologists. Relative Value Units (RVUs) were calculated using the reporting time for

a single view chest X-ray of 1 min 38 s. The researchers categorized the data based on

modality and exam description e.g. CT abdomen pancreas, an examination is defined

by a single modality in the same visit, even though it covered more than one anatomical

area. So, CT of head plus chest is one examination. This led to wrong RVUs results.

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The researcher (Brady, 2011) reported a survey of Consultant Radiologist workload

in Ireland in 2009 for measuring radiologist workload, Relative Value Units (RVUs)

were assigned. Hospitals’ data were collected for the full calendar year of 2009. The

2006 Australian survey recommended 40,000 RVU per radiologist. In 2009 the same

methodology to measure RVU in a larger and broader sample was applied, the results

found that the RVU level had risen to 45,000 RVU per radiologist. On the other hand,

the researcher recalculated the RVU value and his results showed that the value was

57,659.1, but with taking into account the non-clinical work (teaching and

administration), the value rose to 103,987. These results showed that radiologist

staffing levels were already more than appropriate international value of RVU. This

survey did not suggest any solutions to overcome the overload of radiologists.

Researchers (Radiology, 2013) measured the required RVU which should be done by

radiologist per a staffed hour. The study took into account the radiologists vacations,

non-clinical work and on-call duties. Some definitions and formulas were applied to

get results, the result of the calculations yielded to some indexes: productivity index is

the average professional component work RVUs per available staffed hour,

availability index is a measure of the time of radiologists availability relative to the

number of working hours in a business year, and intensity indicator is a measure of the

degree of difficulty of the procedures performed by the practice. The study did not

consider the time taken to write reports.

3.2 Data Mining for Measuring Indicators

The researcher (Lai, 2015) analyzed the use of TCM by employed Complete datasets

of Traditional Chinese medicine (TCM) outpatient from 2005 to 2007, the

characteristics of TCM patients, and the disease categories that were treated by TCM

in Taiwan. The result of this study showed that female use TCM more frequently than

male. The reasons for this female majority were not fully explained in previous reports.

It was suggested that independent females or females of good social status, had higher

expectations and belief in TCM in respect of postpartum conditions, menopause and

chronic diseases. The age distribution of TCM users peaked in the 20-29 group,

followed by the 10-19 group and 31-39 group.

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Researchers (Hachesu, Ahmadi, Alizadeh, & Sadoughi, 2013) provided a model to

predict the Length of Stay (LOS) of heart patients. Data were collected from patients

with Coronary Artery Disease (CAD). Records of 4,948 patients who had suffered

from CAD were included in the analysis. Classification techniques were used with

three algorithms: Decision Tree, Support Vector Machines (SVM) and Artificial

Neural Network (ANN). LOS was the target variable. The overall accuracy of SVM

was 96.4% in the training set. single patients had an LOS ≤5 days with percentage of

64.3%, whereas 41.2% of married patients who had an LOS >10 days.

3.3 Data Mining for Diagnosis Diseases

Researchers (Shukla, Gupta, & Prasad, 2016) presents the importance and usefulness

of different Data Mining techniques such as Classification, Clustering, Decision trees

and Naïve Bayes. Comparison is done of different Data Mining techniques used for

prediction of cancer disease with different accuracy. The techniques are effective for

identifying hidden cancer aggregation pattern and for classification of familiar risk by

the help of providing better accuracy in many cases as compared to other techniques.

Researchers (Dubey & chandrakar , 2015) presented a systematic review of the

application of Data Mining methods to solve the problems in healthcare domain, the

research aimed to use Data Mining techniques for the diagnosis and prognosis of

different heart diseases. The study discussed how different types of Data Mining

techniques were used for diagnosis of heart diseases and how to perform better results

when it applied on different data sets. Each technique was unique, which might be

suitable for different applications. Hybrid Data Mining techniques showed promising

results in the diagnosis of heart diseases.

Researchers ( Bellaachia & Guven , 2006) Used Data Mining techniques, Experiments

to predict the survivability rate of breast cancer was presented. Experiments were

conducted using Naïve Bayes, Neural Network and the C4.5 Decision Tree algorithms.

The accuracy of three Data Mining techniques was compared. The goal was to have

high accuracy, besides high precision and recall metrics, C4.5 algorithm had a much

better performance than the other two techniques ( Bellaachia & Guven , 2006)

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In (Lundina , et al., 1999) ANN was applied on 951 instances dataset of Turku

University Central Hospital and City Hospital of Turku to evaluate the accuracy of

neural networks in 5, 10 and 15 years’ for predict breast cancer specific survival. The

values of Receiver Operating Characteristic ROC curve for 5 years were evaluated as

0.909, for 10 years 0.086 and for 15 years 0.883. These values were used as a measure

of accuracy of the prediction model. They found that ANN predicted survival with

higher accuracy.

Researchers (Choi, Han, & Park, 2009) compared the performance of an Artificial

Neural Network, a Bayesian Network and a Hybrid Network used to predict breast

cancer prognosis. The hybrid Network combined both ANN and Bayesian Network.

The accuracy of ANN (88.8%) and Hybrid Network (87.2%) were very similar and

they both outperformed the Bayesian Network. The proposed Hybrid model can also

be useful to take decisions.

In (Shouman, Turner, & Stocker, 2012) KNN was applied to help healthcare in the

diagnosis of heart disease. It was also integrated with voting to enhance the accuracy

in the diagnosis of heart disease patients. The results showed that KNN achieved a

higher accuracy more than neural network in the diagnosis of heart disease patients.

The results also showed that applying voting could not enhance the KNN accuracy in

the diagnosis of heart disease.

The study in (Alizadehsani, et al., 2013). aimed to use Data Mining algorithms to

predict the stenosis of arteries. Among many people who were referred to hospitals

due to chest pain, a great number of them were normal and as such did not need

angiography. The objective of this study was to predict patients who were most

probably normal using features with the highest correlations with coronary artery

disease (CAD) with a view to obviate angiography costs and complications, Bagging

and C4.5 classification algorithms were applied to analyse the data, the accuracy rates

of 79.54%, 61.46%, and 68.96% for the diagnosis of the stenosis of the Left Anterior

Descending (LAD), Left Circumflex (LCX), and Right Coronary Artery (RCA),

respectively. The accuracy to predict the LAD stenosis was attained via feature

selection.

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3.4 Related Work Discussion

Table (3.1): Summary of the Most Related Works to this Work Research Name Description Short come

The use of relative value

units to monitor

radiologists’ reporting

productivity and workload

This work aimed to develop

local RVU (L-RVU) system to

measure radiologists reporting

productivity and workload.

The result total RVUs

(Productivity) did not have

target value.

Measuring and managing

radiologist workload

This study focused on

measuring radiologists RVUs

based on reporting times using

data from a Radiology

Information System (RIS),

concentrated on CT, MRI, US

and X-Ray modalities. A

technique measuring the time

required for radiologists to

produce reports during normal

work was created.

The researchers categorized

the data based on modality

and exam description e.g

CT abdomen pancreas, an

examination was defined by

a single modality in the

same visit, even though it

covered more than one

anatomical area. So, CT of

head plus chest was one

examination, this will may

be led to wrong RVUs

results.

Measuring Consultant

Radiologist workload:

method and results from a

national survey

This study reported a survey of

Consultant Radiologist

workload in Ireland in 2009 for

measuring Radiologist

workload, Relative Value

Units (RVUs) were assigned.

This survey did not suggest

any solutions to overcome

the overload of radiologists.

Radiologist Productivity

Measurement

This study aimed to measure

the required RVU which

should be done by radiologist

per a staffed hour. The study

took into account the

radiologists vacations, non-

clinical work and on-call

duties.

The study did not consider

the time taken to write

reports.

3.5 Summary

This chapter presents a number of related works in radiology departments and Data

Mining in healthcare. Table (3.1) shows the most related works to this work. These

works concentrated on calculating RVUs and measuring productivity of radiologists

but suffer from some gaps which differentiate between them and the work of this

research. The related works calculated the RVUs without considering reported times

and did not provide solutions for the overload of radiologists. These gaps are

considered in this research that overcome the overload of radiologists.

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Chapter 4

The Data Mining Model

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Chapter 4

The Data Mining Model

In this chapter, a Data Mining model of this work is presented. It aims to improve the

productivity of radiologists by assigning the appropriate case to the appropriate

radiologist. Figure 4.1 shows the model; data from different hospitals were collected

and contained radiology cases which were assigned using the Data Mining model to

different radiologists in different hospitals based on Tele-radiology system. The model

was applied four times with different classification method in each one. The four

classification methods which were applied are: Decision Tree, Naïve Bayes, K-NN,

Random Forest.

4.1 General View of Model

Hospital B

Hospital A Hospital C

Radiologists in Hospitals

Assigning Cases

Assigning Cases

Assigning Cases

Figure (4.1): The Model

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4.2 Model Details

Data from different hospitals were collected, it contained radiology cases to be

assigned to the radiologists using the Data Mining model. The model consists of some

iterations, the need of iterations is to assign the cases, the initial iteration assign the

cases to the appropriate radiologists, the need to more iterations is to reassign the cases

which caused the radiologists productivity to exceed 100 to the radiologists who did

not reach 100. The model was applied four times with using four classification

techniques. In this research, each classification technique has four iterations to assign

all cases to the radiologists.

4.3 Model Iterations

The model consists of some iterations to assign the cases. The initial iteration assign

the cases to the appropriate radiologists. The cases which caused the radiologists

productivity to exceed 100 need to more iterations to reassign them to the radiologists

who did not reach 100. The iterations will continue until all cases are distributed.

4.3.1 The Initial Iteration

The flow chart of the initial iteration is shown in Figure 4.2. Training and Testing Data

are inputs for apply model to apply the classification method to assign the appropriate

cases to the appropriate radiologists. The previous productivity for other modalities

was calculated from the Rest Data then it was joined to the assigned data to calculate

the total productivity of the radiologists after auto assigning. The total productivity

joined a loop and was checked one by one; if it is <=100 the cases which were assigned

will be added to a final result, and if the total productivity exceeds 100 the cases which

cause such exceeding will be added to reassign data as input in the next iteration.

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Figure (4.2): The Initial Iteration of the Model

• Training Data which contain radiologists cases for one year including three

modalities CT, MRI and Mammography.

• Testing Data which contain radiologists cases for one month including three

modalities CT, MRI and Mammography.

• Apply Model which applied the classification technique e.g. Decision Tree to

auto assign cases to all radiologists.

• Assigned Cases which are the result of apply model (the assigned cases).

• Rest Data which contain radiologists cases for the same month of Testing Data

including other modality types such as (US, X-Ray, Fluro. …etc.).

• Calculate Productivity which calculates the radiologists productivity for the

modalities in Rest Data in order to achieve the target of total productivity that

the radiologist must reach.

• Previous Productivity which is the result of calculating productivity

(radiologists productivity in Rest Data).

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Table (4.1): Previous Productivity

Radiologist Previous Productivity

Radiologist 2 98.3

Radiologist 4 13.9

• Joined Data which attach the value of previous productivity for each

radiologist to the assigned cases which are the result of apply model.

Table (4.2): Joined Data

Case ID Radiologist wRVU Previous Productivity

Case 1 Radiologist 2 0.6 98.3

Case 2 Radiologist 4 0.4 13.9

Case 3 Radiologist 2 0.6 98.3

Case 4 Radiologist 2 0.3 98.3

Case 5 Radiologist 4 0.2 13.9

The Loop:

The need for the loop is to identify which cases will be reassigned and which cases

will go to the final result by checking the total productivity. Table 4.4 shows the work

of the loop.

• Current Productivity is the value of RVU for the current assigned case +

RVUs of the previous assigned cases for the same radiologist.

Table (4.3): Calculating Current Productivity

Case ID Radiologist wRVU Sum wRVU Prev. Productivity

Case 1 Radiologist 2 0.6 0.6 98.3

Case 2 Radiologist 4 0.4 0.4 13.9

Case 3 Radiologist 2 0.6 1.2 98.3

Case 4 Radiologist 2 0.6 1.8 98.3

Case 5 Radiologist 4 0.2 0.6 13.9

• Sum Previous Productivity and Current Productivity which calculates the

current productivity then sums its value with the previous productivity of the

radiologist to produce the total productivity.

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• Checking Total Productivity: The total productivity was checked; if it is

<=100 the cases will be added to a final result, otherwise it will be added to

reassign data as input in the next iteration.

Table (4.4): The Work of the Loop

Case ID Radiologist wRVU Sum

wRVU

Previous

Prod.

Total

Prod. Condition

Case 1 Radiologist 2 0.6 0.6 98.3 98.9 Final

Case 2 Radiologist 4 0.4 0.4 13.9 14.3 Final

Case 3 Radiologist 2 0.6 1.2 98.3 99.5 Final

Case 4 Radiologist 2 0.6 1.8 98.3 100.1 Re-Assign

Case 5 Radiologist 4 0.2 0.6 13.9 14.5 Final

4.3.2 The Next Iteration (one or more)

Figure 4.3 shows the flow chart of the next iteration of the model; the testing data are

replaced with reassigned data (the result of the previous iteration) and the Training

Data are filtered from the radiologists whose productivity became around 100. The

iterations will continue until all cases are distributed.

The inputs of the next iteration:

• Training Data which were filtered from the radiologists whose productivity

became around 100

• Reassigned Cases which were the result of the initial iteration (cases which

were assigned to radiologists and made their productivity >100).

• Rest Data in addition to initial iteration result.

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Figure (4.3): The Next Iteration of the Model

4.4 Summary

This chapter presents the Data Mining model of this work and describes its

components. The model consists of iterations which will continue until all cases are

distributed. In this research, each classifier of the four applied classifiers had four

iterations to assign all cases to the radiologists. The model aims to improve the

productivity of radiologists in hospitals utilizing Data Mining classification

techniques.

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Chapter 5

Methodology

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Chapter 5

Methodology

In this chapter, the methodology for enhancing radiologists productivity in hospitals is

presented. The chapter is divided into six sections, section one introduces the

methodology steps, section two contains the process of collection and acquisition of

data which were collected from different hospitals, section three contains data pre-

processing, section four provides feature sets selections and extraction, section five

provides the classifications algorithms which were used and section six provides the

evaluation of the model.

5.1 Methodology Steps

Figure (5.1): Methodology Steps

5.2 Data Acquisition and Collection

Data were collected for eight radiologists from different four hospitals in Saudi Arabia.

Two data sets were collected: One for training data for a year (From 1 May 2016 to 30

April 2017) and another for testing data for a month (May 2017). The training and

testing data sets contain data of the three modalities which the research concentrated

on, i.e. CT, MRI and mammography. Figure 5.2 shows a data set for training from a

hospital before pre-processing, EXM_NUMBER is a unique number for each

radiology procedure, Visit_Class is the class of the patient where there are three classes

for a patient (OutPatient, InPatient, Emergency). Radiologist is the radiologist name

and PAT_DOB is the patient Date of Birth. EXM_DONE_STAMP is the exam date

and time when sent to the radiologist to write report, EXM_APPROVED_STAMP is

Data Acquisition

Data Preprocessing

and Building up Dataset

Feature Sets Selection

Training Process Implementation Evaluation

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the date and time of writing the report by radiologist, and EXM_CODE is exam

procedure identification.

Figure (5.2): Data Before Pre-processing

Table (5.1): Description of Figure 5.2 Columns

Column Name Description

EXM_NUMBER number of exam procedure

Visit_Class class of the patient (OutPatient, InPatient, Emergency).

Radiologist Radiologist Name

PAT_DOB patient Date of Birth

EXM_DONE_STAMP exam date and time when sent to the radiologist to write

report.

EXM_APPROVED_STAMP date and time of writing the report

EXM_CODE exam identification procedure.

Figure 5.3 shows the exam code dictionary; it is a schedule to describe radiology

procedure with details of body parts and modality type. Code is the exam identification

procedure, Description is a full description for radiology procedure, Modality is the

radiology machine type (CT, MRI …… etc.), Body part is the part of body is exposed

to radiation, Subspecialty is the exact specialization of the radiology procedure,

Exam_Group is a radiology procedure grouped by modality type and (body_part or

Subspecialty, wRVU is it is Relative time, effort, and skill needed by a radiologist in

providing a report (Kuehn, 2009).

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Figure (5.3): Exam Code Dictionary

5.3 Data Pre-processing and Feature Sets Selections

In this phase, data have been pre-processed, a data set of training and testing was built

and six feature sets were created: Radiologist as a label, Visit_Class, Age_Group,

Body_Part, Reporting_time, and Exam_Day. training data were selected for a year was

selected (13,142 records) and testing data were selected for a month (1,290 records).

Data named as rest data to be taken into account.

5.3.1 Generating New Columns

Using Excel 2016 Age column was generated using EXM_DONE_STAMP and

PAT_DOB columns in Figure 5.2, then according to Food and Drugs Administration

(FDA) age classifications, age was grouped into four groups (Infant, Child,

Adolescent, Adult) (FDA, 2014). Table 5.2 shows FDA age classifications.

Figure (5.4): Generate Age Group

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Table (5.2): FDA Age Classifications (FDA, 2014)

Pediatric Subgroup Approximate Age Range

Infant greater than 1 month to 2 years of age

Child greater than 2 to 12 years of age

Adolescent greater than 12 through 21 years of age

Adult Greater than 22

Time to Report was generated by subtracting EXM_DONE_STAMP from

EXM_APPROVED_STAMP. Figure 5.5 shows generate Time to Report.

Figure (5.5): Generate Time to Report

Reporting Time was generated from Time to Report column, if Time to report <=24

then the Reporting Time is 24, in case it is <=48 then frame is 48 and if it >48 the

frame is 100. Figure 5.6 shows generate Reporting Time.

Figure (5.6): Generate Reporting Time

Exam_Day was generated from EXM_DONE_STAMP column and the Date is

converted into Day. Figure 5.7 shows generate Exam Day.

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Figure (5.7): Generate Exam Day

5.3.2 Combining Data

Using Excel 2016 data from figure 5.2 and 5.3 were combined by comparing

EXM_CODE in figure 5.2 with Code in figure 5.3 and taking the value of

EXAM_GROUP.

Figure (5.8): Combined Data

5.3.3 Feature Set Selection

After data combining, seven feature sets were selected from figure 4.9 to use as

training data in rapid miner programme. Figure 5.9 shows the final training data set

with seven feature sets.

Figure (5.9): Training Data Set

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Radiologist is the name of the radiologist who writes the report, Visit_class is the

patient class (OutPatient, InPatient, Emergency), Age_Group is the patient age,

Reporting Time is the time which the radiologist takes to write the report, Exam_Day

is the day on which the radiology procedure was done and wRVU is the time, effort,

and skill needed by a radiologist to provide a report.

5.4 Testing Data

Testing Data were collected for the same eight radiologists for one month (May 2017).

Figure 5.10 show testing data before pre-processing.

Figure (5.10): Testing Data Before Pre-processing

After generating new columns and combining figure 5.10 with 5.3, figure 5.11 was

obtained.

Figure (5.11): Combined Data

Data in Figure 5.11 contains all types of modalities which the research concentrated

on, i.e. CT, MRI and Mammography in addition to other types such as US, X-RAY,

Fluoroscopy…. etc.) which were taken into account. Therefore, data were divided into

two data sets: Figure 5.12 shows the first dataset which contains the research

modalities (Testing Data) and Figure 5.13 shows the second dataset which contains

other modalities and which is named (Rest Data).

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Figure (5.12): Testing Data

Figure (5.13): Rest Data

ID column is a unique number to distinguish each row and Reporting Time column is

generated based on Visit Class. The radiological studies are reported by the radiologist

within defined time limits. Urgent cases (Inpatient, Emergency) are reported within 24

hours and routine cases (Outpatient) are reported within 48 hours ((CBAHI), 2016).

5.5 Implementation

After preparing the data sets with seven different feature sets, four types of

classification methods were applied using Rapid Miner programme. The model

consists of some iterations which will be explained in the next chapter.

5.5.1 Tools

The tools which were used to apply implementation are:

• Rapid Miner: it was used to apply classifiers of this model, also it was used to

test the model with different performance measures.

• Microsoft Excel 2016: it is a spreadsheet developed by Microsoft for Windows,

Mac OS X, and iOS. Its features are calculation, graphing tools a pivot tables.

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5.6 Evaluation

After applying the model, the results of the model were presented to an expert from

one of the four hospitals and his feedback about the model was obtained. Also,

accuracy and F-measure evaluate the performance of the classification methods to

compare among them.

5.7 Summary

In this chapter, a methodology of this work was presented. Data were collected from

different four hospitals contained eight radiologists, data were pre-processed by

generating new columns to extract useful data sets. Data after pre-processing were

combined, seven feature sets were selected for training and testing data sets to apply

the model and classification methods.

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Chapter 6

Results, Discussion and

Evaluation

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Chapter 6

Results, Discussion and Evaluation

In this chapter, the classification methods settings and the results of the model are

presented, also the evaluation which were conducted on the model is provided,

accuracy and F-measure performance evaluation were used to evaluate the model.

6.1 Classification Methods Settings

Table (6.1): Classifiers Settings

Classifier Property Value

K-NN The k value of the

classifier

1

Naïve Bayes Laplace Correction unchecked

Decision Tree

Criterion gain_ratio

Minimize size of split 4

Minimal leaf size 2

Minimal gain 0.1

Maximal depth 20

Confidence 0.25

Pruning and pre-pruning checked

Random Forest

Number of trees 10

Criterion gain_ratio

Maximal depth 20

Confidence 0.25

Minimal gain 0.1

Minimal leaf size 2

Minimal size for split 3

6.2 Experimental Results

The model consists of iterations which are applied to four classification methods.

Figure 6.3 shows the initial iteration in Rapid Miner.

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Figure (6.1): The Initial Iteration in Rapid Miner

After applying the initial iteration, Figure 6.4 shows the cases which are auto assigned

to different radiologists.

Figure (6.2): Auto Assigned Cases

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Figure (6.3): Next Iteration in Rapid Miner

Table 6.2 shows the number of cases distributed in each iteration in different

classifiers. Decision Tree, Random Forest and K-NN assigned all cases but in Naïve

Bayes 61 cases are still not assigned.

Table (6.2): Auto Assigned Cases

Iteration 1 Iteration 2 Iteration 3 Iteration 4 Total

Decision Tree 789 217 161 123 1290

Random Forest 408 296 401 185 1290

K-NN 725 288 104 173 1290

Naive Bayes 773 335 93 28 1229

The 61 cases consist of six exam groups which are CT abd-pelvis, CT brain, CT Chest,

CTA, MR brain and MR MSK. The radiologists which have the lowest productivity

are Radiologist 4 and Radiologist 6. From the training data Radiologist 4 and

Radiologist 6 did not write reports for these exam groups. Therefore, the Naïve Bayes

classifier did no assign any of these cases to them whereas in other classifiers some of

these cases were assigned to those radiologists

Figure 6.4 shows the differences between productivity of radiologists before and after

Data Mining with the four different classification methods, the differences between

productivity in different classifiers are may due to the wrongly assigned cases which

will be explained in table 6.4

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Figure (6.4(: Productivity Comparison

6.3 Evaluation

Model Evaluation is an essential part of model development process. It helps to select

the best model that represents the data and how well the selected model will work in

the future (sayad, 2014).

6.3.1 Performance Evaluation Results

The model was evaluated by presenting its results to an expert in one of the four

hospitals for his opinion. He declared that the results of the model are very good as

they take into account the subspecialty of each procedure in assigning the cases. He

also believes that applying the model in hospitals will achieve good results and

improve the radiologists productivity. Also, an evaluation to classification techniques

was done by using different evaluation measures to evaluate the performance and to

compare among them.

The performance evaluation of the classifiers was based on matching the assigned

cases to radiologists with their last year cases (training data). Four classification

methods were applied to a data set. The number of samples was 1,290 in all classifiers

except Naïve Bayes, the Naïve Bayes classifier was higher in both accuracy & F-

measure, as shown in table 6.3 and Figure 6.7

Table (6.3): Performance Evaluation Results

Classifier Accuracy F-measure

Decision Tree 93.88 96.84

K-NN 88.68 94.00

Random Forest 86.74 92.90

Naïve Bayes 100 100

Naïve Bayes* 95.27 97.58

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Naïve Bayes*: Performance measurements with the unassigned 61 cases.

Figure (6.5): Performance Evaluation Results

Figure (6.6): Naive Bayes Classifier Results

The performance evaluation was measured based on the assigned cases. Naïve Bayes

has the highest value in both accuracy and F-measure because it did not have any

wrongly assigned cases but other classifiers have wrongly assigned cases which means

that the classifiers assigned new cases to the radiologists although they did not deal

with these cases in the last year (training data). Table 6.4 shows the number and the

percentage of the wrong cases in each classifier.

Table (6.4): Wrongly Assigned Cases

Classifier Wrong assigned cases Percentage

Decision Tree 79 6.12%

K-NN 146 11.31%

Random Forest 171 13.25%

Naïve Bayes 0 0%

80

82

84

86

88

90

92

94

96

98

100

Decision Tree K-NN Random Forest Naïve Bayes

Accuracy F-measure

9293949596979899

100101

Accuracy F-measure

Naïve Bayes (with unassinged cases)

Naïve Bayes (without unassigned cases)

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6.4 Summary

In this chapter, the experimental and evaluation results were reviewed. Four classifiers

were applied for the data set. To evaluate the model the results were presented to an

expert from one of the four hospitals, he declared that the results of the model are good

as they take into account the subspecialty of each procedure in assigning the cases and

he believes that applying the model in hospitals will achieve good results. Accuracy

and F-measure performance evaluation were used to compare among these classifiers.

The results show that the Naïve Bayes was the best classifier by up to 8% in accuracy

and 4% in F-measure due to its way of assigning cases. It assigned the appropriate case

to the appropriate radiologist. Naïve Bayes had the highest value in accuracy and F-

measure.

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Chapter 7

Conclusions and Future

Work

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Chapter 7

Conclusions and Future Work

7.1 Conclusion

Today, radiology departments have moved away from a patient-focused to concentrate

on other areas as well. Several processes of procedures need interpretation by the

radiologists.

The idea of this research investigates some problems in radiology departments at

hospitals based on the delay of writing reports by radiologists which is due to the heavy

load of work assigned to them. A Data Mining model was conducted to overcome this

problem and improve the radiologists productivity by assigning the appropriate cases

to the appropriate radiologists within Tele-radiology procedure.

Data were collected from four hospitals in different areas in Saudi Arabia covering

eight radiologists (two from each hospital) with varying productivity and

specialization with emphasis on CT, MRI and Mammography modalities. The data

were pre-processed and combined to produce data sets to apply the model, one data

set for a year for training data and another for a month for testing data. The training

and testing datasets contained data of the three modalities which the research

concentrated on, i.e. CT, MRI and Mammography.

Data Mining model was conducted to improve the productivity of radiologists by

assigning the appropriate case to the appropriate radiologist. Data from different

hospitals were collected and contained radiology cases which were assigned using the

model to different radiologists in different hospitals based on Tele-radiology system.

Four different classifiers were applied, each classifier has four iterations to predict and

assign the suitable cases to each radiologist to improve radiologists productivity.

The results showed the differences between radiologists productivity before and after

Data Mining model with the four different classification methods, these differences

are may due to the wrongly assigned cases. The productivity of each radiologist has

improved as the aim of this research is to ensure that the productivity of each

radiologist is within an acceptable range (around 100) and that he has a fair load.

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Figure (7.1): Productivity Comparison

Naïve Bayes did not have any wrongly assigned cases but other classifiers had wrongly

assigned cases which means that the classifiers assigned new cases to the radiologists

although they did not deal with these cases in the last year (training data). The accuracy

and F-measure performance evaluation were used to compare among the four

classifiers. The results showed that the Naïve Bayes was the best classifier due to its

way of assigning cases. It assigned the appropriate case to the appropriate radiologist

and had the highest value in accuracy and F-measure.

7.2 Future Work

This model which is conducted for the first time achieved its objectives and improved

radiologists productivity.

The following operations can be carried out to improve the performance of the model:

Unassigned cases in Naïve Bayes results are due the limited number of radiologists.

This led to help the decision makers to determine the accurate need of radiologists and

their specialties when applying the model in hospitals.

Improving the model requires bigger training dataset, which can be obtained from the

historical data in hospitals and from daily workload.

Improving RVU calculations to take into account non- clinical and teaching work of

radiologists.

The model can be applied on one or several hospitals in case of Tele-radiology by

making it as a part of RIS "Radiology Information Systems". The model can also be

developed as a layer on top of these systems since they are provided by different

vendors.

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References

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Appendices

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Appendix A: Reported Cases Statistics

Hospital 1 CT MR US MG Grand Total

Delayed 506 353 180 107 1146

On-Time 4810 975 4693 170 10648

Grand Total 5316 1328 4873 277 11794

Hospital 2 CT MR US MG Grand Total

Delayed 565 591 104 8 1268

On-Time 11175 3777 5771 112 20835

Grand Total 11740 4368 5875 120 22103

Hospital 3 CT MR US MG Grand Total

Delayed 4314 1313 1466 65 7158

On-Time 9217 1692 12302 697 23908

Grand Total 13531 3005 13768 762 31066

Hospital 4 CT MR US MG Grand Total

Delayed 6042 1962 1972 200 10176

On-Time 11540 3311 10837 73 25761

Grand Total 17582 5273 12809 273 35937

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Table A.1: Delayed Reported Cases Percentage

CT MR MG US

Hospital 1 9.52% 26.58% 38.63% 3.69%

Hospital 2 4.81% 13.53% 6.67% 1.77%

Hospital 3 40.01% 33.77% 37.43% 1.63%

Hospital 4 34.36% 37.21% 73.26% 15.40%

Figure A.1: Delayed Cases Chart

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

Hospital 1 Hospital 2 Hospital 3 Hospital 4

CT MR MG US

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Appendix B: Sample of Exam Code Dictionary

CODE DESCRIPTION MOD BODYPART SUBSPECIALTY Exam_Group wRVU

CT0001 CT Abdomen w/ + w/o Contrast CT ABDOMEN ABDOMINAL CT abd-pelvis 0.278

CT0002 CT Abdomen w/ Contrast CT ABDOMEN ABDOMINAL CT abd-pelvis 0.278

CT0003 CT Abdomen w/o Contrast CT ABDOMEN ABDOMINAL CT abd-pelvis 0.278

CT0004 CT Angiography Abd Aorta +

Iliofemoral CT AORTA CTANGIO CTA 0.442

CT0005 CT Angiography Abdomen CT ABDOMEN CTANGIO CTA 0.442

CT0006 CT Angiography Aorta CT AORTA CTANGIO CTA 0.442

CT0007 CT Angiography Chest w/ + w/o

Contrast CT CHEST CTANGIO CTA 0.442

CT0008 CT Angiography Head w/ + w/o

Contrast CT BRAIN CTANGIO CTA 0.442

CT0009 CT Angiography Lower

Extremity Bilat CT BLEXTREMITY CTANGIO CTA 0.442

CT0010 CT Angiography Lower

Extremity Left CT LLEXTREMITY CTANGIO CTA 0.442

CT0011 CT Angiography Lower

Extremity Right CT RLEXTREMITY CTANGIO CTA 0.442

CT0012 CT Angiography Neck w/ + w/o

Contrast CT NECK CTANGIO CTA 0.442

CT0013 CT Angiography Pelvis w/ + w/o

Contrast CT PELVIS CTANGIO CTA 0.442

CT0014 CT Angiography Upper

Extremity Bilat CT BUEXTREMITY CTANGIO CTA 0.442

CT0015 CT Angiography Upper

Extremity Left CT LUEXTREMITY CTANGIO CTA 0.442

CT0016 CT Angiography Upper

Extremity Right CT RUEXTREMITY CTANGIO CTA 0.442

CT0017 CT Ankle w/ + w/o Contrast

Bilateral CT BANKLE MSK CT MSK 0.189

CT0018 CT Ankle w/ + w/o Contrast Left CT LANKLE MSK CT MSK 0.189

CT0019 CT Ankle w/ + w/o Contrast

Right CT RANKLE MSK CT MSK 0.189

CT0020 CT Ankle w/ Contrast Bilateral CT BANKLE MSK CT MSK 0.189

CT0021 CT Ankle w/ Contrast Left CT LANKLE MSK CT MSK 0.189

CT0022 CT Ankle w/ Contrast Right CT RANKLE MSK CT MSK 0.189

CT0023 CT Ankle w/o Contrast Bilateral CT BANKLE MSK CT MSK 0.189

CT0024 CT Ankle w/o Contrast Left CT LANKLE MSK CT MSK 0.189

CT0025 CT Ankle w/o Contrast Right CT RANKLE MSK CT MSK 0.189

CT0026 CT Aspiration CT ASPIRATION ASPIRATION CT chest 0.227

CT0027 CT Aspiration Renal Left CT ASPIRATION ASPIRATION CT chest 0.227

CT0028 CT Aspiration Renal Right CT ASPIRATION ASPIRATION CT chest 0.227

CT0029 CT Biopsy CT BIOPSY BIOPSY CT abd-pelvis 0.278

CT0030 CT Biopsy Abdomen CT BIOPSY BIOPSY CT abd-pelvis 0.278

CT0031 CT Biopsy Bone CT BIOPSY BIOPSY CT abd-pelvis 0.278

CT0032 CT Biopsy Liver CT BIOPSY BIOPSY CT abd-pelvis 0.278

CT0033 CT Biopsy Lung Left CT BIOPSY BIOPSY CT abd-pelvis 0.278

CT0034 CT Biopsy Lung Right CT BIOPSY BIOPSY CT abd-pelvis 0.278

CT0035 CT Biopsy Pancreas CT BIOPSY BIOPSY CT abd-pelvis 0.278

CT0036 CT Biopsy Pleura Left CT BIOPSY BIOPSY CT abd-pelvis 0.278

CT0037 CT Biopsy Pleura Right CT BIOPSY BIOPSY CT abd-pelvis 0.278

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CT0038 CT Biopsy Renal Left CT BIOPSY BIOPSY CT abd-pelvis 0.278

CT0039 CT Biopsy Renal Right CT BIOPSY BIOPSY CT abd-pelvis 0.278

CT0040 CT Brain Perfusion Study CT BRAIN NEURO CT brain 0.189

CT0041 CT Bronchoscopy CT CHEST CHEST CT Chest 0.227

CT0042 CT Consultation Outside Film CT OUTFILMS CT Chest 0.227

CT0043 CT Dental CT DENTAL DENTAL CT head & neck 0.253

CT0044 CT Drainage - Abscess or Cyst CT DRAINAGE DRAINAGE CT abd-pelvis 0.278

CT0045 CT Drainage Liver CT DRAINAGE DRAINAGE CT abd-pelvis 0.278

CT0046 CT Drainage Lung Bilateral CT DRAINAGE DRAINAGE CT abd-pelvis 0.278

CT0047 CT Drainage Lung Left CT DRAINAGE DRAINAGE CT abd-pelvis 0.278

CT0048 CT Drainage Lung Right CT DRAINAGE DRAINAGE CT abd-pelvis 0.278

CT0049 CT Drainage Pancreas CT DRAINAGE DRAINAGE CT abd-pelvis 0.278

CT0050 CT Drainage Peritoneal CT DRAINAGE DRAINAGE CT abd-pelvis 0.278

CT0051 CT Drainage Renal Bilateral CT DRAINAGE DRAINAGE CT abd-pelvis 0.278

CT0052 CT Drainage Renal Left CT DRAINAGE DRAINAGE CT abd-pelvis 0.278

CT0053 CT Drainage Renal Right CT DRAINAGE DRAINAGE CT abd-pelvis 0.278

CT0054 CT Drainage Retroperitoneal

Abscess CT DRAINAGE DRAINAGE CT abd-pelvis 0.278

CT0055 CT Drainage

Subdiaphragm/Subphrenic CT DRAINAGE DRAINAGE CT abd-pelvis 0.278

CT0056 CT Elbow w/ + w/o Contrast

Bilateral CT BELBOW MSK CT MSK 0.189

CT0057 CT Elbow w/ + w/o Contrast

Left CT LELBOW MSK CT MSK 0.189

CT0058 CT Elbow w/ + w/o Contrast

Right CT RELBOW MSK CT MSK 0.189

CT0059 CT Elbow w/ Contrast Bilateral CT BELBOW MSK CT MSK 0.189

CT0060 CT Elbow w/ Contrast Left CT LELBOW MSK CT MSK 0.189

CT0061 CT Elbow w/ Contrast Right CT RELBOW MSK CT MSK 0.189

CT0062 CT Elbow w/o Contrast Bilateral CT BELBOW MSK CT MSK 0.189

CT0063 CT Elbow w/o Contrast Left CT LELBOW MSK CT MSK 0.189

CT0064 CT Elbow w/o Contrast Right CT RELBOW MSK CT MSK 0.189

CT0065 CT Femur w/ + w/o Contrast

Bilateral CT BFEMUR MSK CT MSK 0.189

CT0066 CT Femur w/ + w/o Contrast

Left CT LFEMUR MSK CT MSK 0.189

CT0067 CT Femur w/ + w/o Contrast

Right CT RFEMUR MSK CT MSK 0.189

CT0068 CT Femur w/ Contrast Bilateral CT BFEMUR MSK CT MSK 0.189

CT0069 CT Femur w/ Contrast Left CT LFEMUR MSK CT MSK 0.189

CT0070 CT Femur w/ Contrast Right CT RFEMUR MSK CT MSK 0.189

CT0071 CT Femur w/o Contrast

Bilateral CT BFEMUR MSK CT MSK 0.189

CT0072 CT Femur w/o Contrast Left CT LFEMUR MSK CT MSK 0.189

CT0073 CT Femur w/o Contrast Right CT RFEMUR MSK CT MSK 0.189

CT0074 CT Fistula or Sinus Tract

Abscess Study CT FISTULA CT MSK 0.189

CT0075 CT Foot w/ + w/o Contrast

Bilateral CT BFOOT MSK CT MSK 0.189

CT0076 CT Foot w/ + w/o Contrast Left CT LFOOT MSK CT MSK 0.189

CT0077 CT Foot w/ + w/o Contrast

Right CT RFOOT MSK CT MSK 0.189

CT0078 CT Foot w/ Contrast Bilateral CT BFOOT MSK CT MSK 0.189

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CT0079 CT Foot w/ Contrast Left CT LFOOT MSK CT MSK 0.189

CT0080 CT Foot w/ Contrast Right CT RFOOT MSK CT MSK 0.189

CT0081 CT Foot w/o Contrast Bilateral CT BFOOT MSK CT MSK 0.189

CT0082 CT Foot w/o Contrast Left CT LFOOT MSK CT MSK 0.189

CT0083 CT Foot w/o Contrast Right CT RFOOT MSK CT MSK 0.189

CT0084 CT Forearm w/ + w/o Contrast

Bilateral CT BFOREARM MSK CT MSK 0.189

CT0085 CT Forearm w/ + w/o Contrast

Left CT LFOREARM MSK CT MSK 0.189

CT0086 CT Forearm w/ + w/o Contrast

Right CT RFOREARM MSK CT MSK 0.189

CT0087 CT Forearm w/ Contrast

Bilateral CT BFOREARM MSK CT MSK 0.189

CT0088 CT Forearm w/ Contrast Left CT LFOREARM MSK CT MSK 0.189

CT0089 CT Forearm w/ Contrast Right CT RFOREARM MSK CT MSK 0.189

CT0090 CT Forearm w/o Contrast

Bilateral CT BFOREARM MSK CT MSK 0.189

CT0091 CT Forearm w/o Contrast Left CT LFOREARM MSK CT MSK 0.189

CT0092 CT Forearm w/o Contrast Right CT RFOREARM MSK CT MSK 0.189

CT0093 CT Guidance Tissue Ablation CT GUIDANCE INTERVENTIONAL CT MSK 0.189

CT0094 CT Guide for Stereotactic Loc CT GUIDANCE INTERVENTIONAL CT MSK 0.189

CT0095 CT Hand w/ + w/o Contrast

Bilateral CT BHAND MSK CT MSK 0.189

CT0096 CT Hand w/ + w/o Contrast Left CT LHAND MSK CT MSK 0.189

CT0097 CT Hand w/ + w/o Contrast

Right CT RHAND MSK CT MSK 0.189

CT0098 CT Hand w/ Contrast Bilateral CT BHAND MSK CT MSK 0.189

CT0099 CT Hand w/ Contrast Left CT LHAND MSK CT MSK 0.189

CT0100 CT Hand w/ Contrast Right CT RHAND MSK CT MSK 0.189

MA0001 MA Additional Projections Left MG LMAMMARY BREAST Mammography 0.189

MA0002 MA Additional Projections

Right MG RMAMMARY BREAST Mammography 0.189

MA0003 MA Breast Ndl Loc Placement

Bilat MG BMAMMARY BREAST Mammography 0.189

MA0004 MA Breast Ndl Loc Placement

Left MG LMAMMARY BREAST Mammography 0.189

MA0005 MA Breast Ndl Loc Placement

Right MG RMAMMARY BREAST Mammography 0.189

MA0006 MA Consultation Outside Film MG OUTFILMS Mammography 0.189

MA0007 MA Core Biopsy Breast L MG LMAMMARY BREAST Mammography 0.189

MA0008 MA Core Biopsy Breast R MG RMAMMARY BREAST Mammography 0.189

MA0009 MA Ductogram or Galactogram

Multi Bilat MG BMAMMARY BREAST Mammography 0.189

MA0010 MA Ductogram or Galactogram

Multi Left MG LMAMMARY BREAST Mammography 0.189

MA0011 MA Ductogram or Galactogram

Multi Right MG RMAMMARY BREAST Mammography 0.189

MA0012 MA Ductogram or Galactogram

Single Bilat MG BMAMMARY BREAST Mammography 0.189

MA0013 MA Ductogram or Galactogram

Single Left MG LMAMMARY BREAST Mammography 0.189

MA0014 MA Ductogram or Galactogram

Single Right MG RMAMMARY BREAST Mammography 0.189

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MA0015 MA Mammogram Diagnostic

Bilateral MG BMAMMARY BREAST Mammography 0.189

MA0016 MA Mammogram Left MG LMAMMARY BREAST Mammography 0.189

MA0017 MA Mammogram Right MG RMAMMARY BREAST Mammography 0.189

MA0018 MA Mammogram Routine

Screening Bilat MG BMAMMARY BREAST Mammography 0.189

MA0019 MA Radiological Specimen MG MASPECIMEN BREAST Mammography 0.189

MA0020 MA Stereotactic Localization

Bilateral MG BMAMMARY BREAST Mammography 0.189

MA0021 MA Stereotactic Localization

Left MG LMAMMARY BREAST Mammography 0.189

MA0022 MA Stereotactic Localization

Right MG RMAMMARY BREAST Mammography 0.189

MA0023 MA Previous Films for

Comparison MG Mammography 0.189

MA0023 MA Stereotactic VAB Right MG RMAMMARY BREAST Mammography 0.189

MA0024 MA Stereotactic VAB Left MG LMAMMARY BREAST Mammography 0.189

MA0025 MA Stereotactic VAB Bilateral MG BMAMMARY BREAST Mammography 0.189

MA0026 MA Stereotactic FNA Right MG RMAMMARY BREAST Mammography 0.189

MA0027 MA Stereotactic FNA Left MG LMAMMARY BREAST Mammography 0.189

MA0028 MA Stereotactic FNA Bilateral MG BMAMMARY BREAST Mammography 0.189

MR0001 MRA Abdomen MR AORTA MR-

ANGIOGRAPHY MRA 0.568

MR0002 MRA AO with LE Run off MR AORTA MR-

ANGIOGRAPHY MRA 0.568

MR0003 MRA Aortic Arch w + w/o

Contrast MR AORTA MR-

ANGIOGRAPHY MRA 0.568

MR0004 MRA Aortic Arch w/ Contrast MR AORTA MR-

ANGIOGRAPHY MRA 0.568

MR0005 MRA Aortic Arch w/o Contrast MR AORTA MR-

ANGIOGRAPHY MRA 0.568

MR0006 MRA Chest MR AORTA MR-

ANGIOGRAPHY MRA 0.568

MR0007 MRA Head w/ + w/o Contrast MR BRAIN MR-

ANGIOGRAPHY MRA 0.568

MR0008 MRA Head w/ Contrast MR BRAIN MR-

ANGIOGRAPHY MRA 0.568

MR0009 MRA Head w/o Contrast MR BRAIN MR-

ANGIOGRAPHY MRA 0.568

MR0010 MRA Iliac Vessels w/ Contrast MR PELVIS MR-

ANGIOGRAPHY MRA 0.568

MR0011 MRA Lower Extremity Bilat MR BLEXTREMITY MR-

ANGIOGRAPHY MRA 0.568

MR0012 MRA Lower Extremity Left MR LLEXTREMITY MR-

ANGIOGRAPHY MRA 0.568

MR0013 MRA Lower Extremity Right MR RLEXTREMITY MR-

ANGIOGRAPHY MRA 0.568

MR0014 MRA Lower Extremity w/ + w/o

Bilat MR BLEXTREMITY MR-

ANGIOGRAPHY MRA 0.568

MR0015 MRA Lower Extremity w/ + w/o

Left MR LLEXTREMITY MR-

ANGIOGRAPHY MRA 0.568

MR0016 MRA Lower Extremity w/ + w/o

Right MR RLEXTREMITY MR-

ANGIOGRAPHY MRA 0.568

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MR0017 MRA Lower Extremity w/ Bilat MR BLEXTREMITY MR-

ANGIOGRAPHY MRA 0.568

MR0018 MRA Lower Extremity w/ Left MR LLEXTREMITY MR-

ANGIOGRAPHY MRA 0.568

MR0019 MRA Lower Extremity w/ Right MR RLEXTREMITY MR-

ANGIOGRAPHY MRA 0.568

MR0020 MRA Lower Extremity w/o Bilat MR BLEXTREMITY MR-

ANGIOGRAPHY MRA 0.568

MR0021 MRA Lower Extremity w/o Left MR LLEXTREMITY MR-

ANGIOGRAPHY MRA 0.568

MR0022 MRA Lower Extremity w/o

Right MR RLEXTREMITY MR-

ANGIOGRAPHY MRA 0.568

MR0023 MRA Neck w/ + w/o Contrast MR NECK MR-

ANGIOGRAPHY MRA 0.568

MR0024 MRA Neck w/ Contrast MR NECK MR-

ANGIOGRAPHY MRA 0.568

MR0025 MRA Neck w/o Contrast MR NECK MR-

ANGIOGRAPHY MRA 0.568

MR0026 MRA Pelvis MR PELVIS MR-

ANGIOGRAPHY MRA 0.568

MR0027 MRA Pelvis w/ + w/o Contrast MR PELVIS MR-

ANGIOGRAPHY MRA 0.568

MR0028 MRA Pelvis w/ Contrast MR PELVIS MR-

ANGIOGRAPHY MRA 0.568

MR0029 MRA Pelvis w/o Contrast MR PELVIS MR-

ANGIOGRAPHY MRA 0.568

MR0030 MRA Popliteal w/ Contrast MR KNEE MR-

ANGIOGRAPHY MRA 0.568

MR0031 MRA Popliteal w/+w/o Contrast MR KNEE MR-

ANGIOGRAPHY MRA 0.568

MR0032 MRA Popliteal w/o Contrast MR KNEE MR-

ANGIOGRAPHY MRA 0.568

MR0033 MRA Portal Vessels w/contrast MR LIVER MR-

ANGIOGRAPHY MRA 0.568

MR0034 MRA Pulmonary Vessels w/

Contrast MR CHEST MR-

ANGIOGRAPHY MRA 0.568

MR0035 MRA Renal Artery w/+w/o

Contrast MR RENAL MR-

ANGIOGRAPHY MRA 0.568

MR0036 MRA Renal Artery w/ Contrast MR RENAL MR-

ANGIOGRAPHY MRA 0.568

MR0037 MRA Renal Artery w/o Contrast MR RENAL MR-

ANGIOGRAPHY MRA 0.568

MR0038 MRA Spinal Canal + Contents MR SPINE MR-

ANGIOGRAPHY MRA 0.568

MR0039 MRA Subclavian Artery w/+w/o

Contrast MR CHEST MR-

ANGIOGRAPHY MRA 0.568

MR0040 MRA Subclavian Artery w/o

Contrast MR CHEST MR-

ANGIOGRAPHY MRA 0.568

MR0041 MRA Subclavian Artery w/

Contrast MR CHEST MR-

ANGIOGRAPHY MRA 0.568

MR0042 MRA Superior Mesenteric

Vessels w/ Contr MR ABDOMEN MR-

ANGIOGRAPHY MRA 0.568

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MR0043 MRA Superior Mesenteric

Vessels w/+w/o C MR ABDOMEN MR-

ANGIOGRAPHY MRA 0.568

MR0044 MRA Superior Mesenteric

Vessels w/o Cont MR ABDOMEN MR-

ANGIOGRAPHY MRA 0.568

MR0045 MRA Upper Extremity Bilat MR BUEXTREMITY MR-

ANGIOGRAPHY MRA 0.568

MR0046 MRA Upper Extremity Left MR LUEXTREMITY MR-

ANGIOGRAPHY MRA 0.568

MR0047 MRA Upper Extremity Right MR RUEXTREMITY MR-

ANGIOGRAPHY MRA 0.568

MR0048 MRI Face w/o Contrast MR FACE MR body 0.379

MR0049 MRI Abdomen w/ + w/o

Contrast MR ABDOMEN ABDOMINAL MR body 0.379

MR0050 MRI Abdomen w/ Contrast MR ABDOMEN ABDOMINAL MR body 0.379

MR0051 MRI Abdomen w/o Contrast MR ABDOMEN ABDOMINAL MR body 0.379

MR0052 MRI Adrenal Gland w/ +w/o

Contrast MR ADRENAL ABDOMINAL MR body 0.379

MR0053 MRI Adrenal Gland w/ Contrast MR ADRENAL ABDOMINAL MR body 0.379

MR0054 MRI Adrenal Gland w/o

Contrast MR ADRENAL ABDOMINAL MR body 0.379

MR0055 MRI Ankle w/ + w/o Contrast

Bilateral MR BANKLE MSK MR MSK 0.253

MR0056 MRI Ankle w/ + w/o Contrast

Left MR LANKLE MSK MR MSK 0.253

MR0057 MRI Ankle w/ + w/o Contrast

Right MR RANKLE MSK MR MSK 0.253

MR0058 MRI Ankle w/ Contrast Bilateral MR BANKLE MSK MR MSK 0.253

MR0059 MRI Ankle w/ Contrast Left MR LANKLE MSK MR MSK 0.253

MR0060 MRI Ankle w/ Contrast Right MR RANKLE MSK MR MSK 0.253

MR0061 MRI Ankle w/o Contrast

Bilateral MR BANKLE MSK MR MSK 0.253

MR0062 MRI Ankle w/o Contrast Left MR LANKLE MSK MR MSK 0.253

MR0063 MRI Ankle w/o Contrast Right MR RANKLE MSK MR MSK 0.253

MR0064 MRI Axilla w/ Contrast MR AXILLA CHEST MR body 0.379

MR0065 MRI Axilla w/+w/o Contrast MR AXILLA CHEST MR body 0.379

MR0066 MRI Axilla w/o Contrast MR AXILLA CHEST MR body 0.379

MR0067 MRI Brachial Plexus w/

Contrast MR NECK NEURO

MR brain/spine + additional

sequences 0.505

MR0068 MRI Brachial Plexus w/+w/o

Contrast MR NECK NEURO

MR brain/spine + additional

sequences 0.505

MR0069 MRI Brachial Plexus w/o

Contrast MR NECK NEURO

MR brain/spine + additional

sequences 0.505

MR0070 MRI Brain CSF Flow Study MR BRAIN NEURO MR brain 0.316

MR0071 MRI Brain Neuronavigation MR BRAIN NEURO MR brain 0.316

MR0072 MRI Brain Perfusion MR BRAIN NEURO MR brain 0.316

MR0073 MRI Brain Stroke MR BRAIN NEURO MR brain 0.316

MR0074 MRI Brain w/ + w/o Contrast MR BRAIN NEURO MR brain 0.316

MR0075 MRI Brain w/ Contrast MR BRAIN NEURO MR brain 0.316

MR0076 MRI Brain w/o Contrast MR BRAIN NEURO MR brain 0.316

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MR0077 MRI Breast w/ + w/o Contrast

Bilateral MR BMAMMARY BREAST MR breast 0.568

MR0078 MRI Breast w/ + w/o Contrast

Left MR LMAMMARY BREAST MR breast 0.568

MR0079 MRI Breast w/ + w/o Contrast

Right MR RMAMMARY BREAST MR breast 0.568

MR0080 MRI Breast w/ Contrast

Bilateral MR BMAMMARY BREAST MR breast 0.568

MR0081 MRI Breast w/ Contrast Left MR LMAMMARY BREAST MR breast 0.568

MR0082 MRI Breast w/ Contrast Right MR RMAMMARY BREAST MR breast 0.568

MR0083 MRI Breast w/o Contrast

Bilateral MR BMAMMARY BREAST MR breast 0.568

MR0084 MRI Breast w/o Contrast Left MR LMAMMARY BREAST MR breast 0.568

MR0085 MRI Breast w/o Contrast Right MR RMAMMARY BREAST MR breast 0.568

MR0086 MRI Calf Left w/ Contrast MR LTIBFIB MSK MR MSK 0.253

MR0087 MRI Calf Left w/+w/o Contrast MR LTIBFIB MSK MR MSK 0.253

MR0088 MRI Calf Left w/o Contrast MR LTIBFIB MSK MR MSK 0.253

MR0089 MRI Calf Right w/ Contrast MR RTIBFIB MSK MR MSK 0.253

MR0090 MRI Calf Right w/+w/o Contrast MR RTIBFIB MSK MR MSK 0.253

MR0091 MRI Calf Right w/o Contrast MR RTIBFIB MSK MR MSK 0.253

MR0092 MRI Cardiac Function Complete MR CARDIAC CARDIOLOGY MR cardiac 0.758

MR0093 MRI Cardiac Function Limited MR CARDIAC CARDIOLOGY MR cardiac 0.758

MR0094 MRI Cardiac Morphology w/

Contrast MR CARDIAC CARDIOLOGY MR cardiac 0.758

MR0095 MRI Cardiac Morphology w/o

Contrast MR CARDIAC CARDIOLOGY MR cardiac 0.758

MR0096 MRI Cardiac Velocity Flow

Mapping MR CARDIAC CARDIOLOGY MR cardiac 0.758

MR0097 MRI Cervico-Thoracic Spine w/

Contrast MR CTSPINE NEURO MR spine 0.253

MR0098 MRI Cervico-Thoracic Spine

w/+w/o Contra MR CTSPINE NEURO MR spine 0.253

MR0099 MRI Cervico-Thoracic Spine

w/o Contrast MR CTSPINE NEURO MR spine 0.253

MR0100 MRI Cervix w/ Contrast MR PELVIS GYNECOLOGY MR body 0.379