Improving Complete Access To Data Within Collaborative Systems In Healthcare Domain.
IMPROVING COMPLETE ACCESS TO DATA WITHIN
COLLABORATIVE SYSTEMS IN HEALTHCARE DOMAIN
FATHIN NABILLA BINTI MD LEZA
MASTER OF SCIENCE IN INFORMATION AND
COMMUNICATION TECHNOLOGY
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Faculty of Information and Communication Technology
IMPROVING COMPLETE ACCESS TO DATA WITHIN
COLLABORATIVE SYSTEMS IN HEALTHCARE DOMAIN
FATHIN NABILLA BINTI MD LEZA
Master of Science in Information and Communication Technology
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IMPROVING COMPLETE ACCESS TO DATA WITHIN COLLABORATIVE SYSTEMS IN HEALTHCARE DOMAIN
FATHIN NABILLA BINTI MD LEZA
A thesis submitted
in fulfillment of the requirements for the degree of Master of Science in Information and Communication Technology
Faculty of Information and Communication Technology
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1. DECLARATION
I declare that this thesis entitle “Improving Complete Access to Data within Collaborative Systems in Healthcare Domain” is the result of my own research except as cited in the references. The thesis has not been accepted for any degree and is not concurrently submitted in candidature of any other degree.
Signature :
Name : Fathin Nabilla binti Md Leza
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2. APPROVAL
I hereby declare that I have read this thesis and in my opinion, this thesis is sufficient in terms of scope and quality for the award of Master of Science in Information and Communication Technology.
Signature :
Name : Dr. Nurul Akmar Emran
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3. DEDICATION
Every challenging work needs self-efforts as well as
guidance of elders especially those who were very closed to our hearts My humble effort I dedicate to my loving;
Father and Mother,
Whose affection, love, encouragement and du‟a of day and night make me able receive such success and honor
Alhamdulillah
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4. ABSTRACT
The problem of accessing complete data especially involving different sources had been a major concern in many fields. Due to the absence of data model that become the barrier to access and manage complete datasets, data providers within the collaborative environment may affect users to have fast and seamless accessibility. There had been many data accessibility improvements methods such as: data replication, data sharing, cloud computing and much more. This dissertation had presented the critical review of these methods based on their criteria of accessibility. However, studies to measure complete access to data were limited. A data accessibility model has been proposed within a collaborative environment named as Collaborative Integrated Database System (COLLIDS), in order to overcome the issue to complete access to data. COLLIDS aims to improve complete access to data in multi-data provider‟s context in a case study. The highlighted feature in COLLIDS which is the completeness analyzer provides relative completeness analysis among data provider. Population-based Completeness (PBC) was applied in COLLIDS completeness analyzer to measure the completeness for the dimension of interest, among the population of data providers. It has been hypothesized that there will have an increment in the ratio of completeness of data accessible by data providers. Therefore, COLLIDS have been evaluated in terms of increment of data completeness for all data providers. Further analysis also has been conducted to compute the completeness cases of data providers. In this dissertation, healthcare domain has been chosen as the case study in order to explore the problem of accessing complete data seamlessly. Sample data have been collected from 106 healthcare providers called as „panel clinics‟. PBC have been used in this dissertation to measure the reference population which is the union of patient datasets collected from all the clinics. Two groups of datasets have been measured; „As-Is Completeness‟ and „ Completeness Increment‟ in normality test and Wilcoxon-Sign Rank test, in order to know the significance differences between both groups. The outcome of this dissertation will have to contribute towards understanding for the practical analysis and the evaluation of COLLIDS data model, in measuring complete access to data within multiple data providers‟ environments.
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5. ABSTRAK
Masalah mengakses data yang lengkap terutama melibatkan sumber-sumber yang berbeza adalah satu kebimbangan utama dalam pelbagai bidang. Oleh kerana ketiadaan data model telah menjadi halangan untuk mengakses dan menguruskan sebuah dataset yang lengkap, khususnya kepada pembekal data dalam lingkungan kolaboratif, sejurus boleh menjejaskan pengguna untuk mempunyai akses cepat dan lancar. Terdapat banyak penambahbaikan akses kepada data kaedah seperti; replikasi data, perkongsian data, pengkomputeran awan dan sebagainya. Disertasi ini telah membentangkan kajian kritikal kaedah berdasarkan kriteria kaedah-kaedah akses kebolehcapaian data. Walau bagaimanapun, kajian untuk mengukur akses penuh kepada data adalah terhad. Model akses data telah dicadangkan dalam lingkungan kolaboratif yang dinamakan sebagai Sistem Pangkalan Data Bersepadu Kolaboratif (COLLIDS), untuk mengatasi isu akses data yang lengkap. COLLIDS bertujuan untuk meningkatkan akses penuh kepada data dalam konteks pembekal data yang berbilang dalam sebuah kajian kes. Ciri yang diketengahkan dalam COLLIDS adalah penganalisis lengkap yang menyediakan analisis lengkap data berhubung kalangan para pembekal data. Populasi Lengkap (PBC) telah digunakan dalam penganalisis lengkap COLLIDS untuk mengukur lengkap data bagi dimensi yang tertentu antara para pembekal data. Ia telah dihipotesiskan bahawa terdapat kenaikan dalam jangkaan nisbah lengkap data yang diakses oleh para pembekal data. Oleh itu, COLLIDS dinilai dari segi kenaikan data lengkap untuk semua pembekal data. Analisis lanjut juga turut dijalankan untuk menentukan kes lengkap untuk pembekal data COLLIDS. Dalam kajian ini, bidang kesihatan telah dipilih sebagai kajian kes untuk meneroka masalah akses data yang lengkap dengan lancar. Sampel data telah dikumpulkan daripada 106 pusat kesihatan dipanggil sebagai 'klinik panel’. PBC kemudiannya digunakan untuk mengukur populasi rujukan yang merupakan kesatuan antara semua dataset pesakit yang dikumpul daripada semua klinik. Dua kumpulan dataset telah diukur; iaitu ‘sedia ada lengkap’ dan ‘kenaikan lengkap’ dalam ujian normal dan juga ujian Wilcoxon-Sign Rank, demi mengetahui perbezaan dan juga signifikasi antara kedua-dua kumpulan. Hasil daripada kajian ini akan menyumbang ke arah pemahaman analisis praktikal dan penilaian model COLLIDS, dalam mengukur akses penuh kepada data dalam lingkungan pembekal data yang kolaboratif.
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6. ACKNOWLEDGEMENT
First and foremost, I would like to thank Allah for the will and spirit He has given me, to further complete my study. Thank you to my family especially my beloved father Md Leza Salleh and mother Suraya Shafiee for full support they had given me throughout my study.
Finally I would like to thank my supervisor, Dr. Nurul Akmar Emran who has provided excellent guidance to me throughout my research especially for her support and facilities along this research study. I also would like to thank you to Dr. Yogan Jayakumar, Dr. Samad Hassan Basari and Dr. Gede Pramudya Ananta for guiding me in statistical analysis for this thesis. Also, special thank you to Miss Nuridawati Mustafa that have helped me in giving support and courage when I was having hard time. Not forgotten, to other Faculty of Information Communication and Technology (FTMK) lecturers and my friends who also have been supporting me during this study. Lastly, thank you too, to all my UIC DDPZ students for always making me smiles during my cloudy days. Thank you so much.
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7. TABLE OF CONTENTS
PAGE DECLARATION
APPROVAL DEDICATION
ABSTRACT i
ABSTRAK ii
ACKNOWLEDGEMENT iii
TABLE OF CONTENTS iv
LIST OF TABLES vi
LIST OF FIGURES vii
LIST OF ABBREVIATIONS ix
LIST OF APPENDICES xi
LIST OF RELATED PUBLICATION xii CHAPTER
1. INTRODUCTION
1.1 Research Motivation 1
1.1.1 Factors Affecting Data Accessibility 3
1.2 Research Aim and Objectives 7
1.3 Research Contributions 8
1.4 Research Scope 12
1.5 Thesis Outline 12
2. LITERATURE REVIEW
2.1 Background 13
2.2 Data Accessibility Definitions 14
2.3 Data Accessibility Methods 16
2.3.1 Providing Secure Access 16
2.3.2 Providing Seamless Access 20
2.3.3 Improving Speed of Access 30
2.3.4 Acquiring Complete Datasets 32
2.4 Conclusion 38
3. METHODOLOGY
3.1 Research Design 40
3.2 Conceptual Model of COLLIDS 43
3.3 Architecture of COLLIDS 45
3.3.1 Data Cleaning Layer in COLLIDS 49
3.3.2 Application Layer in COLLIDS 50
3.4 Implementation of COLLIDS 59
3.4.1 Collaborative System Environment 59
3.4.2 COLLIDS Implementation Background and Requirements 62
3.5 Evaluation Method 64
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4. PRACTICAL ANALYSIS
4.1 A COLLIDS System Implementation: A Case Study in Healthcare 70
4.1.1 QR Code Applications Related Work 73
4.2 Development of COLLIDS prototype 77
4.3 Implementation of COLLIDS Architecture Setup in Healthcare Domain 78 4.3.1 Data Cleaning Implementations in COLLIDS 82 4.4 Implementation of COLLIDS Application Layer in Healthcare Domain 83 4.5 The Practical Analysis of COLLIDS Implementation 89
4.6 Conclusion 91
5. RESULT AND DISCUSSION
5.1 Data Completeness Cases 92
5.2 Results and Discussion 100
5.2.1 Normality Test Result 101
5.2.2 Wilcoxon Test Result Validation 104
5.2.3 Analysis of Data Providers‟ Completeness 109
5.3 Benefits of COLLIDS 110
5.4 Conclusion 112
6. SUMMARY AND CONCLUSION
6.1 Concluding Remarks 114
6.2 Research Contributions 115
6.2.1 The Result of Practical Analysis 116
6.2.2 The Result of Completeness Evaluation Analysis 117
6.3 Future Work 119
REFERENCES 120 APPENDICES 143
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vi
8. LIST OF TABLES
TABLE TITLE PAGE
1.1 Research Outlines 9
2.1 Summary of Criteria Data Accessibility Methods 36 3.1 Implementation Requirements of COLLIDS 63
4.1 QR Codes Applications 75
5.1 Case of Completeness 98
5.2 Description of Data 100
5.3 Normality test result for „As-Is Completeness‟ 102 5.4 Normality test result for „Completeness Increment‟ 103
5.5 Ranks 106
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9. LIST OF FIGURES
FIGURES TITLE PAGE
3.1 Research Flowchart 41
3.2 A Conceptual Model of COLLIDS 44
3.3 COLLIDS Architecture during data preparation for Data providers 46 3.4 COLLIDS Architecture during data preparation for Administrators,End users 47 3.5 COLLIDS Architecture during data retrieval for all users 48 3.6 Data Cleaning Steps Involved in Data Preparation 49 3.7 Flowchart of Data Authentication Module in COLLIDS 51 3.8 Flowchart of Data Manipulation Module in COLLIDS 52 3.9 Flowchart of Data Viewing Module in COLLIDS 53 3.10 Flowchart of Completeness Analysis Module in COLLIDS 54
3.11 Steps of validation of COLLIDS 68
4.1 Global Schema of COLLIDS 80
4.2 SampleQR Code 83
4.3 Main Page of COLLIDS 83
4.4 Modules Selection Page 84
4.5 Receive login credential page 84
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4.8 Update patient treatment details by healthcare providers 86 4.9 Notification dialog box for successful update 87
4.10 Completeness Analyzer Main Page 87
4.11 The Results of Completeness in Tabular Form 88 4.12 The Results of Completeness in Graphical Form 88
5.1 p1 is the superset of p2 94
5.2 p1 is the subset of p2 95
5.3 p1 is equal to p2 96
5.4 p1 and p2 are disjoint 98
5.5 Normality graph for „As-Is Completeness‟ 102 5.6 Normality graph for „Completeness Increment‟ 103 5.7 Distribution of healthcare providers by Completeness Increment 109
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10. LIST OF ABBREVIATIONS ABE AES AHIMA CDEC CDSS COLLIDS CTL CWA DBMS DP DQAF DWR- EHR ETL GIS HIS HRM - - - - - - - - - - - - - - - - - -
Attribute Advanced Encryption Standard Based Encryption
American Health Information Management Association California Data Exchange Center
Collaborative Data Sharing System Collaborative Integrated Database System Clover Transformation Language
Close World Assumption Database Management System Data Provider
Data Quality Assessment Framework Department of Water Resources Electronic Health Records Extract, Transform, Load
Geographical Information System Hospital Information System Hierarchical Replication Model
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x IDE IODE IRWM IWRIS LAN NBC ODP OGC OTP PBC QC QR RDF SBC SQL TBC TC USGS UTeM WHO - - - - - - - - - - - - - - - - - - - - -
Problems 10th Revision
Integrated Development Environment
The International Oceanographic Data and Information Exchange Integrated Regional Water Management
Integrated Water Resources Information System Local Area Network
Null-based Completeness Ocean Data Portal
Open Geospatial Consortium Web Services One-Time Password
Population-based Completeness Query Completeness
Quick-Response
Resource Description Framework Schema-based Completeness Standard Query Language Tuple-based Completeness Table Completeness
United States Geological Survey Universiti Teknikal Malaysia Melaka World Health Organization
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11. LIST OF APPENDICES
APPENDIX TITLE PAGE
A Data Cleaning Process in CloverETL 143
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12. LIST OF RELATED PUBLICATION
1. M.Leza, F.N. and Emran, N.A., 2014. Data Accessibility Model Using QR Code for Lifetime Healthcare Records. World Applied Sciences Journal (Innovation Challenges in Multidiciplinary Research & Practice), vol 30, pp.395–402.
2. M.Leza, F.N., Emran, N.A., and MKA. Ghani, 2014. Review of Data Accessibility Methods In Healthcare. Science International, vol 26 , issue 5, pp.1733–1737
3. M.Leza, F.N., Emran, N.A., and MKA. Ghani, 2013. Improving Data Accessibility Using QR Code in Healthcare Domain. In: e-Proceeding of Software Engineering Postgraduates Workshop ( SEPoW ) Innovative Software Engineering for Creative and Co-Organizer. pp.119–123.
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CHAPTER 1
1. INTRODUCTION
In this chapter, research background and summary of research work, research goals and the significance of the research work are presented. Section 1.1 explains the research background that provides the motivation of the work. Section 1.2 presents research aim and objectives followed by research contributions in Section 1.3. Research scope is also provided in Section 1.4. Finally, Section 1.5 presents the thesis outline.
1.1 Research Motivation
In many database operating environment, the fundamental operation is data accessibility. Data accessibility is an operation triggered by users in order to retrieve desired information. Data accessibility, accessibility (in the context of access to data), or data access have similar definitions in many contexts/aspects.
According to Clio (2008), data accessibility is the ability to access the data regardless of natural or man-made disasters, hardware failures or in any circumstances. In terms of data quality, according to Pipino et al. (2002), data accessibility is claimed as the range to which data is available or easily and quickly retrievable. The term of “data accessibility” also can be referred as other terms; “accessibility” or “data access” which
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2
The definitions of data accessibility so far signifies its‟ importance in data retrieval. Ideally, the retrieved data is complete and the process of retrieval is fast and seamless in many domains. The inability to retrieve complete data is not only frustrating but also may jeopardize crucial operation such as decision making. For instance, the use of data in data warehousing to support decision making for one company can be affected due to incomplete data, as reported by Eckerson (2002) and Ballou and Tayi (1999).
Incomplete data also can affect data analysis. For example, according to white paper report from SPSS (2009), incomplete data can be a serious problem as it can lead to misleading results of analysis (i.e. cannot run factor analysis, and results may not be statistically significant). In the end, the result obtained due to this problem is not accurate as the analysis ended up not analyzing what supposed to be measured (SPSS, 2009).
Incomplete data due to restricted access can result in to time-consuming procedures. For example, in healthcare domain, incomplete data may cause repeatable procedures where patients are required to undergo the same process (i.e. registration process, blood test and urine test). In another example, incomplete data causes many organizations (i.e. telecommunication firms, insurance company, regional bank, information services firm and global chemical company) to suffers huge losses in terms of profit as mentioned by Eckerson (2002). Accessing complete data in fast and seamless1 manner is challenging
especially when involving multiple data providers. This is due to several factors that become the challenges to gain access to complete data. This problem will be described in next sub-section.
1 Seamless is defined as smooth and continuous which is without difficulty, with no apparent gaps or spaces
between one part and the next. Retrieved from Oxford Dictionaries at
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1.1.1 Factors Affecting Data Accessibility
Each data provider enforces different security requirements, and access control procedures. As a result, to retrieve data from these data providers one must fulfill different data access requirements and different security levels (less stringent or more stringent). For example, sensitive and confidential data require more stringent security requirements (i.e. bypassing firewalls, acquire exclusive access privileges) because exposure of these data can affect organization operational and financial due to data leakage. According to McCarty (2015) there have been reports that confidential data has been exposed 330 times, in Internet Corporation for Assigned Names and Numbers (ICANN) (an Internet Domain Name organization). This security breach unfortunately has impacted 96 applicants whose confidential data have been compromised (McCarthy, 2015). Similar incidents have been reported by ICANN several times which causes delay of the organization‟s landmark program launching.
Secondly, fast and seamless access to complete data is hindered by the nature of data management structure. The best scenario to observe this problem is in healthcare environment. In healthcare, a patient commonly seeks treatment from more than one healthcare provider. As a result, his /her treatment records are stored in multiple healthcare providers. Getting complete treatment records require manual records integration which is not only impractical, but also time consuming. Due to this problem, a patient is usually required to experience painful tiring and time-consuming medical procedure in order to get complete crucial treatment records such as immunizations, medical screening, ante-natal
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4
Thirdly, access to data that are stored in database systems usually requires network connectivity. Some network connectivity such as ad-hoc networks (especially mobile connections) are unstable (Rani et al., 2013). The speed and reliability of mobile connections (Rani et al., 2013) can vary from network to network and might lead to unstable connections that hinder data accessibility. Within multiple data-providers environment, fast and seamless access to complete data can be affected due to network problems inherent in data provider‟s network systems. Since data providers‟ network connectivity may vary, complete data retrieval depends on successful access to data. Therefore, stable network connectivity is a requirement to retrieve complete data seamlessly.
Fourthly, heterogeneous access methods complicate fast and seamless access to complete data. Within multiple data providers‟ environment, it is unlikely to find data providers who use the same access method that is familiar to all users. Even though web-based method is a common access method used by many data providers nowadays, each data provider maintain its own database in isolation. Consequently in order to acquire complete data, users must gather and integrate the data manually. Some data providers who uses biometric systems requires the users to fulfill biometric authentication before access to data can be granted. In addition, access methods which rely on the use of devices such as code reader (i.e. barcode, QR Code), and smart card reader requires the users to setup the devices prior to data access. Unless users are willing to adapt with access methods heterogeneity, seamless access to complete data can be achieved.
Finally, the fifth challenge in retrieving complete data seamlessly is to deal with the quality of data (Olson, 2003). As data are residing at multiple data providers‟ databases, the quality of it is subject to data quality imitative taken by the data providers. Common data problems are inconsistency (i.e. format heterogeneity and conflicting values, data
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duplication), accuracy and missing values (Eckerson, 2002). For example, format heterogeneity normally caused when different data providers have different database structures that have different data formats. Data that are of different formats are considered as „dirty‟ and are not fit for use. In order to use the data, a data cleaning process is required. The process of data cleaning that most commonly used today is ETL (Extract, Transform, Load) (Rahm and Do, 2000). Through ETL process, which provided with tools helps in cleaning the data into desired data formats thus reducing the errors. This data cleaning process delays the effort to acquire complete data and therefore fast and seamless access to data is hindered.
The challenges presented in this section are the obstacles in retrieving complete datasets in fast and seamless manner, especially in multi-data provider‟s context. Due to the challenges, several proposals can be found in the literature that attempt to deal with the problems. For example, data integration methods (i.e. data sharing (Smith, 1994; Ives et al., 2008a; Tang et al., 2011), data replication (Hara, 2001; Hara and Madria, 2006; Lu et al., 2010), and data federation (Lans, 2010; Danyaro et al., 2014)) are proposed to improve data completeness where different datasets are integrated into single unified view of all data (Lans, 2010). Nevertheless, these methods do not aim to provide fast and seamless access to complete data. Consequently, issues regarding the practicality of these methods remain uncovered. In addition the way completeness is measured is missing from the work. Hence, the limitations in the current studies of data accessibility and data completeness are the motivation of this research. Specifically, the following are the research problems (RPs) that have been identified:
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RP1: There is limited comprehensive studies on how complete data can be accessed, particularly in collaborative systems in healthcare domain
RP2: Lack of data accessibility models that put emphasis on accessing complete data using multiple access methods. Therefore, understanding of the practicality and implementation is limited.
RP3: Studies that aim to improve data completeness do not cover ways to measure completeness within multi-data provider environment.
The motivation of this research is therefore to answer the following fundamental research questions (RQs) that address the research problem stated above:
RQ1: What are data accessibility methods available and why these methods are proposed?
RQ2: How to design data accessibility model that can improve completeness? RQ3: How to evaluate the proposed data accessibility model?
In the next section will provide the aim and objectives of the research that are formulated to answer the above questions.
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CHAPTER 1
1. INTRODUCTION
In this chapter, research background and summary of research work, research goals and the significance of the research work are presented. Section 1.1 explains the research background that provides the motivation of the work. Section 1.2 presents research aim and objectives followed by research contributions in Section 1.3. Research scope is also provided in Section 1.4. Finally, Section 1.5 presents the thesis outline.
1.1 Research Motivation
In many database operating environment, the fundamental operation is data accessibility. Data accessibility is an operation triggered by users in order to retrieve desired information. Data accessibility, accessibility (in the context of access to data), or data access have similar definitions in many contexts/aspects.
According to Clio (2008), data accessibility is the ability to access the data regardless of natural or man-made disasters, hardware failures or in any circumstances. In terms of data quality, according to Pipino et al. (2002), data accessibility is claimed as the range to which data is available or easily and quickly retrievable. The term of “data accessibility” also can be referred as other terms; “accessibility” or “data access” which brings similar definitions in different domains.
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The definitions of data accessibility so far signifies its‟ importance in data retrieval. Ideally, the retrieved data is complete and the process of retrieval is fast and seamless in many domains. The inability to retrieve complete data is not only frustrating but also may jeopardize crucial operation such as decision making. For instance, the use of data in data warehousing to support decision making for one company can be affected due to incomplete data, as reported by Eckerson (2002) and Ballou and Tayi (1999).
Incomplete data also can affect data analysis. For example, according to white paper report from SPSS (2009), incomplete data can be a serious problem as it can lead to misleading results of analysis (i.e. cannot run factor analysis, and results may not be statistically significant). In the end, the result obtained due to this problem is not accurate as the analysis ended up not analyzing what supposed to be measured (SPSS, 2009).
Incomplete data due to restricted access can result in to time-consuming procedures. For example, in healthcare domain, incomplete data may cause repeatable procedures where patients are required to undergo the same process (i.e. registration process, blood test and urine test). In another example, incomplete data causes many organizations (i.e. telecommunication firms, insurance company, regional bank, information services firm and global chemical company) to suffers huge losses in terms of profit as mentioned by Eckerson (2002). Accessing complete data in fast and seamless1 manner is challenging especially when involving multiple data providers. This is due to several factors that become the challenges to gain access to complete data. This problem will be described in next sub-section.
1 Seamless is defined as smooth and continuous which is without difficulty, with no apparent gaps or spaces
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1.1.1 Factors Affecting Data Accessibility
Each data provider enforces different security requirements, and access control procedures. As a result, to retrieve data from these data providers one must fulfill different data access requirements and different security levels (less stringent or more stringent). For example, sensitive and confidential data require more stringent security requirements (i.e. bypassing firewalls, acquire exclusive access privileges) because exposure of these data can affect organization operational and financial due to data leakage. According to McCarty (2015) there have been reports that confidential data has been exposed 330 times, in Internet Corporation for Assigned Names and Numbers (ICANN) (an Internet Domain Name organization). This security breach unfortunately has impacted 96 applicants whose confidential data have been compromised (McCarthy, 2015). Similar incidents have been reported by ICANN several times which causes delay of the organization‟s landmark program launching.
Secondly, fast and seamless access to complete data is hindered by the nature of data management structure. The best scenario to observe this problem is in healthcare environment. In healthcare, a patient commonly seeks treatment from more than one healthcare provider. As a result, his /her treatment records are stored in multiple healthcare providers. Getting complete treatment records require manual records integration which is not only impractical, but also time consuming. Due to this problem, a patient is usually required to experience painful tiring and time-consuming medical procedure in order to get complete crucial treatment records such as immunizations, medical screening, ante-natal and post-natal events (Abd Ghani et al., 2008). Therefore, data sharing is not in practice even though these healthcare providers are supposed to work collaboratively. Practically, data providers (healthcare providers) are still operating in silos.
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Thirdly, access to data that are stored in database systems usually requires network connectivity. Some network connectivity such as ad-hoc networks (especially mobile connections) are unstable (Rani et al., 2013). The speed and reliability of mobile connections (Rani et al., 2013) can vary from network to network and might lead to unstable connections that hinder data accessibility. Within multiple data-providers environment, fast and seamless access to complete data can be affected due to network problems inherent in data provider‟s network systems. Since data providers‟ network connectivity may vary, complete data retrieval depends on successful access to data. Therefore, stable network connectivity is a requirement to retrieve complete data seamlessly.
Fourthly, heterogeneous access methods complicate fast and seamless access to complete data. Within multiple data providers‟ environment, it is unlikely to find data providers who use the same access method that is familiar to all users. Even though web-based method is a common access method used by many data providers nowadays, each data provider maintain its own database in isolation. Consequently in order to acquire complete data, users must gather and integrate the data manually. Some data providers who uses biometric systems requires the users to fulfill biometric authentication before access to data can be granted. In addition, access methods which rely on the use of devices such as code reader (i.e. barcode, QR Code), and smart card reader requires the users to setup the devices prior to data access. Unless users are willing to adapt with access methods heterogeneity, seamless access to complete data can be achieved.
Finally, the fifth challenge in retrieving complete data seamlessly is to deal with the quality of data (Olson, 2003). As data are residing at multiple data providers‟ databases, the quality of it is subject to data quality imitative taken by the data providers. Common data problems are inconsistency (i.e. format heterogeneity and conflicting values, data
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duplication), accuracy and missing values (Eckerson, 2002). For example, format heterogeneity normally caused when different data providers have different database structures that have different data formats. Data that are of different formats are considered as „dirty‟ and are not fit for use. In order to use the data, a data cleaning process is required. The process of data cleaning that most commonly used today is ETL (Extract, Transform, Load) (Rahm and Do, 2000). Through ETL process, which provided with tools helps in cleaning the data into desired data formats thus reducing the errors. This data cleaning process delays the effort to acquire complete data and therefore fast and seamless access to data is hindered.
The challenges presented in this section are the obstacles in retrieving complete datasets in fast and seamless manner, especially in multi-data provider‟s context. Due to the challenges, several proposals can be found in the literature that attempt to deal with the problems. For example, data integration methods (i.e. data sharing (Smith, 1994; Ives et al., 2008a; Tang et al., 2011), data replication (Hara, 2001; Hara and Madria, 2006; Lu et al., 2010), and data federation (Lans, 2010; Danyaro et al., 2014)) are proposed to improve data completeness where different datasets are integrated into single unified view of all data (Lans, 2010). Nevertheless, these methods do not aim to provide fast and seamless access to complete data. Consequently, issues regarding the practicality of these methods remain uncovered. In addition the way completeness is measured is missing from the work. Hence, the limitations in the current studies of data accessibility and data completeness are the motivation of this research. Specifically, the following are the research problems (RPs) that have been identified:
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RP1: There is limited comprehensive studies on how complete data can be accessed, particularly in collaborative systems in healthcare domain
RP2: Lack of data accessibility models that put emphasis on accessing complete data using multiple access methods. Therefore, understanding of the practicality and implementation is limited.
RP3: Studies that aim to improve data completeness do not cover ways to measure completeness within multi-data provider environment.
The motivation of this research is therefore to answer the following fundamental research questions (RQs) that address the research problem stated above:
RQ1: What are data accessibility methods available and why these methods are proposed?
RQ2: How to design data accessibility model that can improve completeness?
RQ3: How to evaluate the proposed data accessibility model?
In the next section will provide the aim and objectives of the research that are formulated to answer the above questions.