DESIGNING A CLINICAL DATA WAREHOUSE TO STORE RELEVANT DATA FOR ASSESSMENT OF ATTENTION DEFICIT/HYPERACTIVITY DISORDER (ADHD) IN CHILDREN

DESIGNING A CLINICAL DATA WAREHOUSE TO STORE RELEVANT DATA FOR ASSESSMENT OF ATTENTION DEFICIT/HYPERACTIVITY DISORDER (ADHD) IN CHILDREN By Lillian Vanessa Ortiz Fournier DISSERTATION Presented as a requirement for the degree of Doctor in Business Administration School of Business and Entrepreneurship Universidad del Turabo Gurabo, Puerto Rico June, 2010

Copyright, 2010 Lillian V. Ortiz Fournier. All Rights Reserved.

DESIGNING A CLINICAL DATA WAREHOUSE TO STORE RELEVANT DATA FOR ASSESSMENT OF ATTENTION DEFICIT/HYPERACTIVITY DISORDER (ADHD) IN CHILDREN By Lillian Vanessa Ortiz Fournier Mysore Ramaswamy, Chair This investigation intends to design a clinical data warehouse that will keep relevant data from children of four to eight years old at the pediatricians office for screening key symptoms that may lead to the diagnosis of Attention Deficit with Hyperactivity Disorder (ADHD) with or without comorbid disorders. This data warehouse can be connected to a clinical decision support system. Researchers and physicians agree that there is a necessity for an early assessment of ADHD due to the high prevalence among children, and the existing evidence that this condition causes academic, social and somatic impairments. The ability of professionals to diagnose mental disorders is based upon years of training, research, and experience that enable them to differentiate between possible disorders and in turn prescribe appropriate treatments. Like any other business mental health care professionals, and researchers, need to analyze a vast amount of medical information for diagnosis and treatment purposes. Attention Deficit/Hyperactivity Disorder (ADHD) is found in 3-5% of all children in the United States meaning that, more than 2 iv

million children under age 18 may have the condition. In Puerto Rico 7.5% of all the children and the 26.2% of the indigent population qualify for an ADHD diagnosis (Bauermeister, et al., 2007a). As a result the design of the proposed system will contribute to a better health care and quality of life of children with and without ADHD as well as contributing to research of this condition and its associated comorbid disorders. v

RESUME EDUCATION: 2011 DBA, Management Information Systems Universidad del Turabo. 2001 MS, Open Information Systems Universidad Interamericana de Puerto Rico, Metropolitan Campus. 1994 BS, Mathematics Universidad de Puerto Rico, Cayey Campus. PROFESIONAL EXPERIENCE: 2006 Humana of Puerto Rico Application Engineer. 2004 Universidad del Turabo Information Technology Instructor. 2002 Electronic Data Processing (EDP) College Information Technology Instructor Universidad de Puerto Rico Cayey Campus Mathematics Instructor. 1995 Cooperativa de Seguros de Puerto Rico, COSVI Auxiliary Actuary. EXTRACURRICULAR & CERTIFICATIONS: 2010 2 nd Congress of Research Topics of Business and Entrepreneurship at Universidad del Turabo. Member of: the National Association of Professional Women, and the Association for Computing Machinery. 2009 SACNAS National Conference held in Dallas, Texas. 2008 Participant of the PhD Sustainability Academy sponsored by the Richard Ivey Business School of University of Western Ontario, London, Ontario, Canada. 2004 Mortality Tables Demographic Analysis Seminar at the University of Puerto Rico, Medical Sciences Campus 2003 Seminars on: Systems Auditing Techniques and Syllabus Preparation 2001 LOMA Education Program Exams 1999 Certificate in Technology and Administration of Data Bases PUBLICATIONS Ortiz-Fournier, L., Martinez, E., Flores, F.R., Rivera-Vazquez, J. & Colon, P. (2010). Integrating educational institutions to produce intellectual capital for sustainability in Caguas, Puerto Rico. Knowledge Management Research and Practice, 8(3), 203-215. Ortiz-Fournier, L., Ramaswamy, M. (2010). Healthcare knowledge collection for clinical decision support systems. Issues in Information Systems, 11(1), 538-546. Rivera-Vázquez, J., Ortiz-Fournier, L. & Flores, F.R. (2009). Overcoming cultural barriers for innovation and knowledge production. Journal of Knowledge Management, 13(5), 257-270. vi

DEDICATION This work is dedicated to all parents, specially single mothers like me, whom have courageously faced up having a child with Attention Deficit/Hyperactivity Disorder (ADHD). The journey is challenging, and sometimes we think this is our fault in any way and we have to work with it. Guess what, there s no one s fault, and every one in the family has to understand and help. Children might not bring an instructions manual, and most of the times good intentions are not enough, but deep inside we know it will worth the effort, the sacrifices, the tears, the stops and starts, the love, among many other things. vii

ACKNOWLEDMENTS I wish to aknowledge the support of many people when preparing this work. But, I need to thank first Our Heavenly Father whom gave me the necessary strength, faith, and courage to continue working against all odds. Then, I want to thank my beloved mother that had postponed for so long the opportunity to fully enjoy her retirement. She has helped me for the last five years so I can fulfill my goals. Also, I have to thank my two children whom have waited all this time to have a full time mommy. I want to thank all my friends, specially my colleague and coauthor Juan Carlos, for been there to listen, and for all the help and support. Special thanks to my dissertation committee members, for all their help, and great ideas. I ll keep saying that I have the best committee that a student might wish, and the best committee chair that exist. I want to thank the Doctoral Program coordinators, Dra. Eulalia Márquez and Dr. Edgar Ferrer, for all their kind support and guidance. Thanks to Pablo Colón and Josefina Melgar for their great ideas and advice. Finally, to every one that in one way or the other encouraged, supported and inspired my work, and lent me a helping hand, my deepest thanks. viii

TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF APPENDIXES xii xiii xvi CHAPTER I 1 INTRODUCTION 1 Interaction between Medicine and Information Technology 2 Clinical Decision Support Systems 4 Clinical Data Warehouse 8 Attention Deficit with Hyperactivity Disorder (ADHD) and its Co-morbidities 9 The need of Information Systems for ADHD Assessment 11 ADHD and its Comorbid Disorders. 13 ADHD Guidelines for Pediatricians and Diagnosis Instruments. 14 Focus of this Study 18 Modeling a Data warehouse to keep relevant data for ADHD screening 20 Research Questions 23 Contributions of the ADHD Data warehouse 24 Contributions to Health Care. 25 Contributions to Research. 26 Summary 27 CHAPTER II 29 LITERATURE REVIEW 29 ix

Concepts of Medical Informatics 30 Clinical Decision Support Systems concepts 37 Data warehouse concepts 48 Attention Deficit/Hyperactivity Disorder (ADHD) and related concepts 59 Summary 77 Conceptual Framework 79 CHAPTER III 81 RESEARCH METHODOLOGY 81 The Delphi technique for gathering systems requirements 81 Selection of the experts. 85 Data collection procedures. 85 Summary and analysis of data. 87 Perceived utility and acceptance of the data warehouse by pediatricians 88 Selection of the sample. 88 Data collection procedures. 88 Analysis of data. 89 Validity and reliability of instruments 90 Implementing and validating the data warehouse logical schema 91 CHAPTER IV 99 DISCUSSION OF RESULTS 99 Validation of questionnaires 99 Top-down analysis 100 x

Demographic results. 100 First and second round results. 100 Bottom-up analysis 104 Demographic results. 104 First and second round results. 104 Integration analysis 107 Summary 109 Analysis of Perceived Utility and Acceptance by Pediatricians 110 Demographics. 111 Operational feasibility measure. 111 Summary 114 Validating the Data warehouse logical schema 115 Summary 128 CHAPTER V 130 SUMMARY AND RECOMMENDATIONS 130 Research questions and conceptual framework 130 Conclusions and limitations 134 Further research and recommendations 136 REFERENCES 138 APPENDIXES 154 xi

LIST OF TABLES TABLE 1. Examples of Computerized Decision Aids for mental Health Care 12 TABLE 2. Examples of ADHD Assessment Scales and Instruments 16 TABLE 3. Schedule of Research Activities 97 xii

LIST OF FIGURES Figure 1. The iterative activities in clinical practice. Adapted from Data Preparation for Clinical Data Mining in Developing a Problem List Proposing System, by Lin, J.H, 2008, ProQuest. (UMI No. 3302493). 5 Figure 2. The domino model of clinical decision making and process management. Adapted from Decision support and disease management: a logic engineering approach, by Fox, J. & Thomson, R., 1998, Information Technology in Biomedicine, IEEE Transactions, 2(4), 217-228. 6 Figure 3. Common Expert System Architecture. Adapted from Decision Support Systems in the 21st Century, by Marakas, G.M., 2003, 2nd Edition, Prentice Hall, Inc. 40 Figure 4. Extended process of knowledge engineering. Adapted from Web-based expert systems: enefits and challenges, Duana, Y., Edwardsb, J.S. & Xu, M.X., 2005,Information & Management, 42,799 811. 41 Figure 5. Conceptual DSS Architecture. Adapted from Decision Support and Data Warehouse Systems by Mallach, E.G., 2002, Tata Mc.Graw-Hill Edition, Mc. Graw-Hill Publishing Company. 43 Figure 6. General model of a clinical diagnostic decision support system. Adapted from Clinical Decision Support Systems Theory and Practice, by Berner, E.S., 2007, 2 nd ed.,new York, NY: Springer ScienceBusiness Media, LLC. 44 Figure 7. Getting good performance out of the data warehouse environment most out of the physical I/Os. Adapted from Building the Data Warehouse, Inmon, W.H., 2005, 4th Edition. Wiley Publishing, Inc 50 Figure 8. Data warehouse: building blocks. Adapted from Data Warehousing Fundamentals, Poniah, P., 2001, John Wiley & Sons, Inc. 51 xiii

Figure 9. Metadata drives data warehouse processes. Adapted from Data Warehousing Fundamentals, Poniah, P., 2001, John Wiley & Sons, Inc. 52 Figure 10. ADHD evaluation AAP Guidelines. Adapted from The Child with ADHD: using the AAP clinical practice guideline, by Herrerias, C.T., Perrin, J.M. and Stein, M.T., 2001, American Family Physician, 63(9), 1804-1810. 70 Figure 11. The proposed model of clinical decision making. Adapted from Decision support and disease management: a logic engineering approach, by Fox, J. & Thomson, R, 1998, Information Technology in Biomedicine, IEEE Transactions, 2(4), 217-228. 79 Figure 12. Research Design Gathering Systems Requirements Based on Bonifatti and Colleagues Approach Including the Delphi Technique. Elaborated by the author. 84 Figure 13. General process diagram of the ADHD Data warehouse. Elaborated by the author. 94 Figure 14. Detailed process flow diagram of the ADHD Data warehouse. Elaborated by the author. 95 Figure 15. Child Diagram - Staging the Assessment Process of Figure 14. Elaborated by the author. 96 Figure 16. Ideal Schema derived from psychologists answers. Elaborated by the author. 103 Figure 17. Operational Schema derived from pediatricians answers. Elaborated by the author. 106 Figure 18. Integration Schema: derived from the Ideal and Operational schemas. Elaborated by thhe author. 108 Figure 19. Box-plot of answers to perception of utility questions. Elaborated by the author. 112 xiv

Figure 20. Box-plot of answers to perception of acceptance questions. Elaborated by the author. 113 Figure 21. Patient record page 1 Demographics, and History of mothers health when pregnant and child s birth. Elaborated by the author. 117 Figure 22. Patient record page 2 History of development milestones. Elaborated by author. 118 Figure 23. Patient record page 3 Evaluation, Diagnosis and Procedures. Elaborated by the author. 119 Figure 24. Patient record page 4 History of treatments, prescriptions and visits. Elaborated by the author. 120 Figure 25. Conner s Scoring Sheet. Elaborated by the author. 122 Figure 26. SNAP-IV Scoring Sheet Report ADHD-Inattentive subscale. Elaborated by the author. 123 Figure 27. SNAP-IV Scoring Sheet Report ADHD-Impulsive subscale. Elaborated by the author. 124 Figure 28. SNAP-IV Scoring Sheet Report ADHD-Impulsive subscale. Elaborated by the author. 125 Figure 29. SNAP-IV Scoring Sheet Report ADHD- Aggressive/Defiant subscale. Elaborated by the author. 126 Figure 30. Guidelines for ADHD referral report. Elaborated by the author. 127 xv

LIST OF APPENDIXES APPENDIX #1 Cuestionario de Cernimiento para Profesionales de la Salud Mental Sobre colección de Datos Relevantes al Trastorno por Déficit de Atención/Hiperactividad (TDAH) 155 APPENDIX #2 Cuestionario de Experiencia para Pediatras Sobre colección de Datos Relevantes al Cernimiento del Trastorno por Déficit de Atención/Hiperactividad (TDAH) 171 APPENDIX #3 Cuestionario de Percepción de Utilidad y Aceptación de un Almacén de Datos ( Data Warehouse ) para Cernimiento del Trastorno por Deficit de Atención/Hiperactividad (TDAH) 187 APPENDIX #4 Plantilla de Evaluación del Cuestionario de Experiencia para Profesionales de la Salud Mental Sobre colección de Datos Relevantes para Cernimiento del Trastorno por Déficit de Atención/Hiperactividad (TDAH) 190 APPENDIX #5 Plantilla de Evaluación del 207 Cuestionario de Experiencia para Pediatras Sobre colección de Datos Relevantes para Cernimiento del Trastorno por Déficit de Atención/Hiperactividad (TDAH) 207 APPENDIX #6 Plantilla de evaluación del Cuestionario de Percepción de Utilidad y Aceptación de un Almacén de Datos para colección de Datos Relevantes al Cernimiento del Trastorno por Déficit de Atención/Hiperactividad (TDAH) 224 APPENDIX #7 Institutional Review Board Letter of Approval 228 xvi

1 Chapter I Introduction The ability of professionals to diagnose mental disorders is based upon years of training, research, and experience that enable them to differentiate between possible disorders and in turn prescribe appropriate treatments. While being consistent in its reasoning, a decision support system can be a valuable asset to any process that demands a high degree of diagnostic correctness and correlation (Moreno & Plant, 1993). Although clinical reasoning is imperfect, it is the only instrument psychiatrists use to diagnose their patients in clinical practice. In the diagnostic decision context, the problem is not to process quantity but to identify the quality and relevance of information within a changeable context (Razzouk, Mari, Shirakawa, Wainer, & Sigulem, 2006). Like any other business mental health care professionals and researchers need to analyze a vast amount of medical information for diagnosis and treatment purposes. Decision aids are available in a variety of formats print publications, decision boards, videos, audio-guided workbooks, and Web applications and help to clarify choices by providing information about conditions and possible treatment options, probabilities of relevant outcomes, exercises to clarify values, and coaching in the steps of decision making. They offer clinicians a validated format for presenting facts that surpasses conventional advice in terms of balance, accuracy, and consistency (Woolf et al., 2005). Researchers agree that when a physician has access to all patient information they may commit less errors diagnosing and ordering an accurate treatment. This is true for assessing a possible diagnosis that may need further referral to a better trained physician or specialist. For this purpose a decision support tool

2 is necessary for primary care physicians such as pediatricians to give a better advice to a child s parents. Clinical information systems with decision support and the capacity to assess and monitor care can improve outcomes and enable more innovative, efficient use of physicians time and practice resources (Schoen et al., 2006). Since the mid-1980s data warehouses have been developed and deployed as an integral part of a modern decision support environment. A data warehouse provides an infrastructure that enables businesses to extract, cleanse, and store vast amounts of corporate data from operational systems for efficient and accurate responses to user queries and empowers knowledge workers with information that allows them to make decisions based on a solid foundation of fact (Nemati, Steiger, Iyer & Herchel, 2002). It is a repository of data from a wide variety of sources that will organize and integrate these data to facilitate rapid ad-hoc analysis and reporting. Clinical data repositories (CDRs) are large, usually relational, databases that receive a variety of clinical and administrative data from primary electronic sources that are used to collect comprehensive data on large patient cohorts, assembled and stored over time, which not only permit these institutions to examine trends in utilization and outcomes, but also to perform sophisticated quality assurance and medical management queries independent from the systems that collect the data such as laboratory, management systems, among others (Mullins, Siadaty, Lyman, Scully, Garrett, et al., 2005). Interaction between Medicine and Information Technology Medical informatics is the field concerned with the management and use of information in health and biomedicine. Like the rest of Health Information Technology (HIT), is a

3 heterogeneous field with a diversity of backgrounds, skills, and knowledge, which is probably beneficial for the complex task of working at the intersection of health care and technology (Hersh, 2002, 2006). It is also, a dynamic field of medical information, technology, and practical application, all combined in an attempt to improve health outcomes, lower healthcare costs, and educate healthcare providers and patients (Rohm & Rohm, 2007). Medical professional societies everywhere point to medical informatics as an essential element of medical practice (Musen, 2002). As stated by Chiasson, Reddy, Kaplan and Davidson (2006) medical informatics (MI) developed as a research field focused on realizing the potential to use computer and information technologies in health care, which has produced a valuable body of knowledge on health care IT. In addition, MI s core goal is to directly improve clinical care through the use of information technology. To do so, MI research examines the design of IT applications to address the practicalities of health care delivery, with a focus on clinical users (e.g., physicians, nurses, pharmacists). Although, as these authors present, MI s studies tend to report system effects, such as specific changes in physician behavior due to a clinical decision support system (CDSS), without delving into sociological or psychological explanations for those changes, or without using models or theory to predict outcomes. What is important about building this huge body of knowledge is that when developed properly information systems may improve patient health care by giving physicians better informed alternatives to backup their decisions and, protect hospital health care professionals from malpractice demands.

4 According to Blois (1984) it is not the building of artifacts or the deployment of information technology in clinical settings per se that made medical informatics fitting for scientific inquiry but the elucidation of the underlying medical knowledge required to build clinical information systems that demanded a theoretical foundation which is the subject of active scholarly investigation. Pantazzi, Arocha & Moehr (2004) describe medicine and medical informatics as having as a primary purpose not only distilling data and observations into general knowledge, but are also concerned with the implementation details and with the application of theories to individual problem solving (e.g., diagnosis and treatment of real patients). Then, the need to build information systems that could acquire, store, and communicate clinical data sparked the emergence of many of the great academic groups in medical informatics. The very essence of creating a knowledge-based system requires the construction of a model of human problem solving and the representation of human knowledge in a computational form (Musen, 2002). Clinical Decision Support Systems The principal purpose of computerized decision support systems in clinical practice is to support clinical judgment and to provide the structures underlying continuing care. They may support the primary care consultation in the following ways (Delaney, Fitzmaurice, Riaz, & Hobbs, 1999): 1. By providing ready access to appropriate knowledge or protocols via patient specific prompts; 2. By providing a rational aid to diagnosis or probable outcome on the basis of patient specific data; and

5 3. By involving patients explicitly in the decision making process. However, these systems are as good as its underlying knowledge base which changes rapidly as medical science evolves (Purcell, 2005). In clinical practice, a physician examines the patient s condition through various assessments, makes a diagnosis, and plans how to treat the patient (Lin, 2008). Figure 1 presents the iterative process followed by physicians in their practice. Figure 1. The iterative activities in clinical practice. Adapted from Data Preparation for Clinical Data Mining in Developing a Problem List Proposing System, by Lin, J.H, 2008, ProQuest. (UMI No. 3302493). Doctors collect relevant information concerning the health of their patients such as family history of conditions, former treatments, results from laboratory, radiology and/or previous physical examinations, among others. This information is used to make an empirical diagnosis based on acquired knowledge and experience and arrive to the better treatment for the diagnosed illness or condition. As Lin (2008) explains this process repeats until the patient recovers. The diagnosis and the treatment reflect how the physician s judgment is driven by the domain knowledge and the clinical context on which this process takes place. However, and despite

6 most doctors are doing their best, it is too much to do and too little time to ensure that all their decisions attain an optimal balance between achieving the best care for their patients and the most efficient use of resources. As described by Fox & Thomson (1998) the domino model (Figure 2) is an abstraction of the various decision types explored in applications projects designed for different medical environments which presents the functions required for enacting practice guidelines, therapy protocols and other clinical procedures such as, oncology, radiology, and clinical trials. They also argued that the model can be used to specify the knowledge required in medical decision making and the management of clinical procedures. It is also observed, how the physicians decisions are based on the interaction between clinical goals, possible solutions and patients data leading to care plans and actions which at the same time influences patients outcomes, clinical goals and possible solutions, making the process of decision making in a clinical setting a cyclic one. Figure 2. The domino model of clinical decision making and process management. Adapted from Decision support and disease management: a logic engineering approach, by Fox, J. & Thomson, R., 1998, Information Technology in Biomedicine, IEEE Transactions, 2(4), 217-228.

7 Computer based applications are being developed to help clinicians integrate a patient's preferences (values) with scientific evidence, the patient's history, and local constraints. Decision aids differ from information aids mainly in that they contain explicit components to help users clarify their values: the patient's personal values and the utility or importance of the risks and benefits of each alternative are elicited (Eysenback, 2000). Kawamoto, Houlihan, Andrew-Balas & Lobach (2005, p. 1) define a clinical decision support system as any electronic or non-electronic system designed to aid directly in clinical decision making, in which characteristics of individual patients are used to generate patient-specific assessments or recommendations that are then presented to clinicians for consideration. Harrison & Palacio (2006) states that clinical information systems are increasingly being used in health care organizations, and can improve efficiency as well as quality in the health care system. The recent American Health Insurance Portability and Accountability Act of 1996 (HIPAA) provided a standard electronic framework for electronic claims submission, thereby encouraging the adoption of clinical information systems in the health care industry. As patients become increasingly involved in decisions about their own care, an improved understanding of lay reasoning and decision making will become essential for developing effective systems targeted at patient as well as health care provider populations (Patel & Kushniruk, 1998). However, as stated by Dugas, Schauer, Volk & Rau (2002) clinical decisions should be taken in the foreseeable future by doctors and patients, not by machines. This means that the design of the decision aid may provide the physician with the most complete and accurate information in order to provide a correct assessment, but the interpretation as well as the treatment planning made by the clinician will always be necessary.

8 Clinical Data Warehouse The clinical system captures a tremendous amount of valuable data on many patients, but it is structured to support and direct the care on a patient by patient basis (Wyderka, 1999). A Data Warehouse was described by Inmon (2005) as a repository of integrated information, available for querying and analysis. It is also defined as "a subject-oriented, integrated, nonvolatile and time-variant collection of data in support of management s decisions" (Inmon, 2005, p. 29). Then, it is a driving force for consolidation and integration of data structure designed for swift information access in almost all disciplines. A Clinical Data Warehouse (CDW) is a place where healthcare providers can gain access to clinical data collected in the patient care process. Data acquisition and information dissemination in a knowledge-intensive and time-critical environment presents a challenge to clinicians, medical professionals, statisticians and researchers (Sahama & Croll, 2007). As in any other business taking medical data from different sources for analysis and correlations is really useful to get a better understanding of an illness or condition as well as developing best practices for providers and better treatments for patients. In a health care setting a lot of important information is gathered by different providers, on different formats, and for different purposes. This is one of many reasons to develop and implement a data warehouse architecture to harness and keep all this medical information. Moreover, a decision support system requires the construction of a data warehouse to complete its life cycle. Thus, data warehousing is a process requiring a set of hardware and software components that can be used to better analyze the massive amounts of data that organizations, companies and research disciplines are accumulating, and may be considered a proactive approach to information integration, for purposes of analysis and actionable knowledge (Sahama & Croll, 2007).

9 Finally, a decision support system connected to a data warehouse provides support to patient specific clinical decision making and comprise a set of knowledge-based tools fully integrated with both clinician workflow and a repository of complete, accurate, patient-specific clinical data (Ledbetter & Morgan, 2001). Attention Deficit with Hyperactivity Disorder (ADHD) and its Co-morbidities The National Institute of Mental Health (NIMH) describes Attention Deficit with Hyperactivity Disorder (ADHD) as a condition that becomes apparent in some children in the preschool and early school years. It is hard for these children to control their behavior and/or pay attention. Children diagnosed as having ADHD manifest developmentally inappropriate levels of hyperactivity, impulsiveness, and inattention that typically have an onset in early to middle childhood, are relatively pervasive across settings, and produce impairment in major life activities (Barkley, Fischer, Smallish & Fletcher, 2004). The literature has shown the difficulties that children with ADHD experience during their lifespan. Also, the Center for Disease Control and Prevention recognizes the condition as: (a) a serious public health problem because of the large estimated prevalence of the disorder; (b) significant impairment in the areas of school performance and socialization; (c) the chronicity of the disorder; (d) the limited effectiveness of current interventions to attend to all the impairments associated with ADHD; and (e) the inability to demonstrate that intervention provides substantial benefits for long-term outcomes (Lesesne, Abramowitz, Perou & Brann, 2000). The Diagnostic and Statistical Manual of Mental Disorders 4 th Edition (DSM-IV (American Psychiatric Association, 1994)) establish that diagnosis of ADHD requires that six or more of the inattention,

10 or the hyperactivity-impulsivity symptoms are present with an intensity that is not consistent with the child s developmental level. These symptoms had to be expressed before the age of seven and cause impairment in two or more settings such as school (or work), home, or with friends and relatives. ADHD is associated with impairments in health related quality of life parameters, including academic performance, behavior at school, peer relations and family function, and may also affect the health and ability to work of family members. In addition, ADHD was found to interfere even to a greater extent than asthma with the daily lives of children, parents and families while the coexisting psychiatric conditions add to the impairment primarily in areas related to psychosocial functioning (Hakkaart-van Roijen et al., 2007). Literature defines the following three types of ADHD. 1. Predominantly Inattentive Type requires six or more of nine inattention symptoms. 2. Predominantly Hyperactive Type requires six or more of nine hyperactivityimpulsivity symptoms. 3. Combined Type requires a combination of inattention and hyperactivity-impulsivity symptoms. ADHD is the most common psychiatric disorder in children and adolescents and it is associated with high rates of existing (co-morbid) conditions such as: antisocial conduct, oppositional defiant disorder, depression, anxiety disorders, learning problems, aggression, and poor peer relations. Biological factors (abnormalities in brain development) are most related to the cause of ADHD while studies indicate genetic contribution to these abnormalities one that