Healthcare Database Management for Health Informatics and Information Management Students: Challenges and Instruction Strategies—Part 1

by Ray Hylock, PhD, and Susie T. Harris, PhD, MBA, RHIA, CCS

Abstract

Enactment of the Health Information Technology for Economic and Clinical Health (HITECH) Act under the American Recovery and Reinvestment Act (ARRA) of 2009 forever altered the data management landscape in healthcare. The volume, breadth, depth, and pace at which health information is collected have surged. Effective data management is crucial for patient treatment, regulatory compliance, population health, cost management, and quality control. As a result, the health informatics and health information management disciplines have required greater educational emphasis on data management, governance, quality, and analysis. In this article, the first of a two-part series, the authors present strategies for creating a comprehensive healthcare database course. Factors influencing course design, such as themes that differ from those of traditional database courses and accreditation requirements, are addressed. The authors propose minimum and expanded construct sets for database modeling and interrogation, define a minimum feature set for database management systems used for instruction, discuss hosted and individually maintained systems, and evaluate commonly used database management systems. The result of this work is a framework and methodology for devising a healthcare database management course specifically intended for a health informatics and health information management audience.

Keywords: healthcare databases, healthcare database instruction, health information management (HIM), health informatics (HI), health information technology, database course development/design, HI/HIM instructional design, HI/HIM course development, health information technology instructional design

Introduction

Healthcare information systems have become a seemingly ubiquitous component of modern healthcare largely because of the electronic health record mandate resulting from the Health Information Technology for Economic and Clinical Health (HITECH) Act. They are instrumental in the treatment of patients, hospital administration, regulatory compliance, population health, cost management, quality control, research, and other purposes. Therefore, the accuracy, timeliness, relevancy, validity, and integrity of the information are of utmost importance.1, 2 Thus, the roles of health informatics (HI) and health information management (HIM) professionals have had an increasing focus on health information technology.3–6

At the heart of a healthcare information system is the database management system (DBMS). Virtually every critical work area defined by the American Health Information Management Association (AHIMA) HIM Workforce Study7 necessitates at least a cursory understanding of database systems. This need is echoed in the global health information technology workforce training program framework of Cortelyou-Ward et al., in which database management is cited as one of three integral elements of the data sphere.8 Although topics such as databases, data mining, and analytics obviously require in-depth knowledge, a general understanding is also important to adequately manage personnel and projects, support research and analytic initiatives, evaluate future scenarios and products, and comply with state and federal laws, regulations, and statutes.9–11 Therefore, a proper background in database management is imperative for HI and HIM students and professionals.

Of the nine HI and HIM master’s programs accredited by the Commission on Accreditation for Health Informatics and Information Management Education (CAHIIM), six require a database management course, and one allows it as an elective.12 All nine comply with the minimum database and data management requirements for accreditation, yet by necessitating a stand-alone database management course, the majority of accredited institutions have signaled the importance of these skills for the profession.

This article, the first of a two-part series, presents the methodology behind the creation of the graduate-level course in healthcare database systems at East Carolina University. The methodology is applicable to baccalaureate-level courses as well. The construction of the course draws from the authors’ 14 years of combined experience designing, implementing, and instructing on databases in professional, research, and academic settings. First, the merits of offering a healthcare-centered database course are discussed, followed by discussion of the course design itself. Every lecture is mapped to CAHIIM accreditation standards, with justification provided for each topic. Database modeling and structured query language (SQL) are elaborated upon, and minimum and expanded construct sets are proposed for each of these topics. The discussion then moves to the selection of a database management system for the course as well as the benefits and drawbacks of hosted versus individual installations. Part 2 of the series will provide greater specifics on designing a flexible course, deconstruction strategies for database modeling and interrogation instruction, and healthcare-focused sample data sets.

The Divergence of Healthcare Database Courses from Traditional Database Courses

Administrators of each HI and HIM program must decide whether to require a database course and, if the course will be required, whether to offer a healthcare-oriented course within the program or use a non-healthcare-focused course from outside the program. Common substitutes include database courses in computer science (CS) and management information systems (MIS) departments. Course content for these replacements is typically stable across colleges and universities because they rely on curriculum guidelines set forth by their respective professional organizations (e.g., Association for Computing Machinery [ACM] and IEEE for the computer science, computer engineering, information technology, and software engineering curricula;13 ACM and Association for Information Systems [AIS]for the information systems curriculum; 14 and Information Resources Management Association [IRMA] and DAMA for the information technology curriculum15). These courses generally cover database theory (e.g., relationship characterization, data modeling, functional dependencies, normalization, and concurrency controls) and database application design (e.g., database implementation and querying).16, 17 However, on the basis of experience in teaching and studying databases from CS, MIS, and healthcare perspectives, the authors of this article recommend against outsourcing database courses for the following reasons.

First, whether the course is offered in a CS or MIS department, HI and HIM students will constantly be seeking healthcare analogs to the examples and problems presented in the textbook and lectures. Although this task appears innocuous, it adds an undue burden to the student and can lead to confusion and even incorrect application. Second, most CS textbooks portray examples and definitions in the form of equations, proofs, and set theory, which are unfamiliar to typical HI and HIM students, decreasing retention and discernment.18–20 Third, healthcare laws and regulations pertaining to data and information security, such as the Health Insurance Portability and Accountability Act (HIPAA) and the HITECH Act, are not discussed in non-healthcare-related courses. Although these topics may be addressed in other courses, applying these rules to actual data sets solidifies the concepts for the student. Lastly, the data sets, databases, and data warehouses used in these courses are not as complex and standardized as those used in healthcare. That is, healthcare databases store highly diverse and heterogeneous data, maintain standard data sets for reporting and compliance, utilize various data standards and vocabularies, transmit data using different standards (e.g., HL7), require immense privacy and security oversight, and are typically an amalgamation of multiple sources. Combined, these shortcomings provide a strong fundamental argument in favor of a healthcare-focused database course. However, if offering such a course is not feasible or practical, then a MIS database course is generally a suitable alternative. In fact, of the seven CAHIIM-accredited programs with a database option, three outsource the course to MIS; the remaining programs offer a healthcare-focused course.

Course Design

The course design follows the minimalist theory of database instruction.21 The name of this theory is a misnomer because it does not imply less substantive content; rather, it refers to a course progression that builds on self-contained minimal activities, using error recognition and recovery to reinforce concepts in realistic, interactive environments. Such environments include the concepts of active learning, theory-to-practice, and scaffolding, which have been repeatedly shown to increase student comprehension and retention of database material.22–28 Mohtashami and Scher refer to this pedagogical theory as a transition from “sage on the stage” to “guide on the side”29—that is, from rote lecturing to hands-on topic immersion and discovery. Minimalist objectives incorporate pre- and postmodule learning outcomes. For instance, one would first describe single-relation operations prior to describing multirelation operations. Error recognition and recovery can be a powerful pedagogical tool in this area.

Error recognition and recovery is a process of providing positive and negative examples, forcing the student beyond problem solving to solution finding. To continue the previous example, the demonstration of single-relation anomalies segues seamlessly into normalization and multirelation operations. Additionally, one could demonstrate the perils of an unconstrained database by adding, for example, encounters for patients who do not exist, and then trying to produce a bill. These errors reinforce the concepts of referential integrity. Stated simply, the goal of error recognition and recovery is to move from simply solving a problem to identifying errors (whether logical or syntactic) and providing solutions.

Conforming to Accreditation Standards

Accreditation signals to the public that a program has met and maintained an acceptable level of quality as determined by an external accrediting body in the field of study. CAHIIM provides this function for the HI and HIM disciplines. CAHIIM produces curriculum competencies for associate through master’s degrees in HIM, master’s degrees in HI, and five specializations.30 As a HI master’s program (in candidacy) offering RHIA eligibility (accredited), East Carolina University must comply with both the master’s in HI (MHI) and baccalaureate in HIM (BHIM) requirements. Thus, although this article focuses on the graduate-level curriculum, it is also applicable to baccalaureate healthcare database course design.

Table 1 presents lecture topics for the Database Systems in Health Care course at East Carolina University. Several factors contributed to the material and its order. First, the minimum content level was derived from the explicit database, data management, data quality, and modeling requirements of the accreditation standards. From there, a rough course outline was prepared and expanded to include concepts such as privacy and security, de-identification, database merging, concurrency controls, and NoSQL (nonrelational) databases. Each topic is mapped to the BHIM, MHI, and, for completeness, master’s in HIM (MHIM) curriculum requirements using the current versions of the requirements (year denoted in parentheses in the table). Adjacent to the domain or facet is the portion of the standard covered. It is the responsibility of each program to ensure that competencies are met following the guidelines specified in the accreditation documents. Therefore, the competencies may be considered to be met if full consideration of the section is included in instruction. A justification for each featured competency is given in Table 2, which was compiled on the basis of the authors’ years of experience teaching and working with database management systems, as well as input from the program’s advisory board.

Database Modeling and SQL Constructs

Research has repeatedly shown that database modeling (topic 3) and interrogation (topics 5 to 7) provide the greatest difficulty for students and instructors alike.31–34 From the students’ perspective, modeling requires the production of interconnected, abstract, logically related elements, while interrogation (typically through SQL) necessitates procedural thinking. The literature focuses on students in computer science, information systems/technology, and related technical fields, where students have more exposure to these topics through ancillary courses than HI or HIM students typically have because of their varied backgrounds (e.g., business, biology, chemistry, nursing, psychology, and information technology). Thus, the students’ difficulties in HI and HIM courses are generally greater than the research suggests. This difficulty exacerbates instructional challenges.

The instructor must provide a minimum level of education not only to meet explicit accreditation requirements but to prepare the student for work in the field and future classes (e.g., project management, policy evaluation and creation, data governance and analysis, systems analysis and design, and decision support), while reducing extraneous features that may be less frequently utilized and generally confusing for students in introductory courses. Thus, the exact set and depth of constructs to cover is not always clear. In the following sections, a detailed breakdown of constructs for each topic and justification for inclusion of the topic in the minimum and/or expanded construct sets is given. A minimum construct set defines the narrowest collection of constructs conveyed to students to satisfy accreditation and basic system utilization requirements. The expanded construct set presents additional features that are desirable to include if time and student progress permit.

Database Modeling

A data model is a collection of concepts used to describe the structure of a system, and is widely considered to be the most important component of systems development. Data models characterize the facts, rules, and integrity controls of a system; analyze data rather than processes (which are the most complex element in modern information systems); provide a guide for information inquiry, analysis, and summary; and are generally stable compared to business processes, experiencing greater lifetimes and usefulness.35 Thus, data modeling is an integral component of systems analysis and design, project management, software engineering, and database development, and other areas, as well as in HI and HIM education.

Pertaining to database design, the most pervasive model is the entity-relationship (ER) model. Standards of ER diagramming do not exist; therefore, many forms are available.36–38 Regardless of the chosen form, all support the defined minimum and expanded construct sets (Table 3 and Table 4).

Structured Query Language

SQL is the de facto database language standard. As such, it is the most commonly taught language for database interrogation. SQL is generally deconstructed into three main languages. The first is Data Definition Language (DDL). DDL commands define a database by creating, altering, and dropping schemas, tables, attributes, constraints, indices, and views. Without knowledge of such commands, students would be unable to design and manage databases. Table 5 and Table 6 present the proposed minimum and expanded construct sets related to DDL.

The next language is the one typically associated with SQL—Data Manipulation Language (DML). DML commands are those that query and maintain data; these functions are referred to as CRUD (create, read, update, delete) operations. HI and HIM students should be familiar with at least the minimum construct set presented in Table 7 to populate, manipulate, and extract information from databases. Time and student success permitting, elements from the expanded construct set in Table 8 can be incorporated.

The final language, Data Control Language (DCL), administers a database. These commands control a database and transactions by way of administering privileges, bounding transactions, and committing data, to name a few. DCL commands are essential functions to ensure data integrity and database security; thus they are mandatory for instruction. Table 9 and Table 10 present the minimum and expanded construct sets for DCL statements.

 

Recommended Database Management System Features

Many DBMSs are available for use in learning environments. Common systems include Microsoft Access, Oracle, MySQL (owned by Oracle Corporation), Microsoft SQL Server, and PostgreSQL. In this section, a minimum DBMS feature set for instruction is proposed. This list considers CAHIIM accreditation requirements and draws on the authors’ years of experience teaching and utilizing various DMBSs in professional, instructional, and research capacities.

The DBMS should include the following features:

 

  • Full support of the DDL, DML, and DCL minimum and expanded construct sets outlined in Tables 5, 6, 7, 8, 9, and 10.
  • A database catalog (also referred to as metadata tables): This feature provides information about database objects such as relations, indices, constraints, and views, and system data such as statistics and privileges.
  • Concurrent user support: The DBMS should provide an environment where user-based and role-based access controls (DCL) are necessary.
  • Aggregate functions: At a minimum, the standard five aggregate functions (average, count, max, min, and sum) are necessary, but support for basic measures of spread for analysis of populations and samples (e.g., standard deviation and variance) are desirable to assist with integrity controls, quality monitoring, data analytics, and reporting.
  • A robust SQL interface with profiling support (e.g., explaining and tracing queries): Drag-and-drop query design is suitable in basic situations. However, many healthcare queries involve complex joins (e.g., nested outer and recursive unary joins—refer to Table 7 and Table 8 for more details) and subqueries, requiring manual SQL programming. A robust query interface allows for quick connections, database navigation, syntax highlighting, query profiling, and error detection and reporting.
  • Large data set support: Healthcare data by nature are historical and verbose, necessitating vast quantities of storage. This need for storage translates into secondary systems as well (e.g., registries, indices, and de-identified data sets). Support is generally measured at the file (e.g., table) and database level.
  • Entire curriculum support: The chosen DBMS must be capable of supporting the activities of the educational program in general. That is, the choice of DBMS cannot be limited to the needs of a single course. If the chosen system fails to meet the requirements of other courses or topics (e.g., quality, data analytics, and decision support), student learning opportunities will be lost to software training in different courses.
  • System compatibility: The software must be compatible with student hardware and must be affordable (free if possible). Most students use low to midrange laptops of the Windows or, with increasing frequency, Mac variety; occasionally, a student will use some form of the Linux operating system. All platforms, therefore, should be supported unless the academic program specifically states that a student must own or have access to a particular platform, for example, a Windows-based machine.

Table 11 compares five common database systems intended for individual users on the basis of the features identified. Here, the Express editions of Oracle and SQL Server are discussed. The results indicate that MySQL and PostgreSQL are prime contenders for course selection. Although Oracle XE and SQL Server Express are free, neither supports the full range of potential operating systems (Windows and Mac at a minimum), and the imposed hardware restrictions can degrade performance with larger data sets. In the authors’ experience (as both instructor and student), it is difficult to fully uninstall Oracle because it leaves residual files in the system. SQL Server Express is an attractive solution because of the prevalence of SQL Server implementations in healthcare. However, the increased presence of Mac computers on campuses removes it from consideration. Third-party SQL interfaces such as Oracle SQL Developer39 and SQuirreL SQL40 are available, but their use limits the benefits of training on a particular system. For instance, relying on SQL Developer to interact with SQL Server restricts the student’s exposure to the SQL Server ecosystem (e.g., database browsing, built-in functions, database linkages, and profiling tools unique to SQL Server Management Studio), diminishing the intended effect.

Microsoft Access requires a more detailed examination. This software is widely used to teach databases in non–computer science settings. Many basic database courses or those with a database module rely on Access to provide a user-friendly tool with a shallow learning curve. In these environments, the use of Access is acceptable or even encouraged. However, a comprehensive healthcare database course typically exceeds the limits of Access.41–43 Supported operating systems aside, Access is designed to take a one-database-to-a-file approach and, as of version 2010, does not support user-level security. From a database management and administration instructional perspective, the lack of a database catalog and the lack of privileges to manipulate make this software less than desirable. Additionally, query performance in Access diminishes rapidly as the number of tuples (records) increases to even moderate sizes. Finally, to invoke the query profiler, the user must modify the Windows registry, which is not to be attempted by novice users; furthermore, this hack cannot be used with the cloud version of the software. Therefore, Access should be avoided in a comprehensive healthcare database course.

Hosting a database for a course is also an option. An instructor might consider doing so, for example, to make use of a commercial tool for which the university, college, or department has available licenses; to maintain control of shared DBMS resources (e.g., adding and removing data, altering schemas, and having a central “grading” repository); or to eliminate the need for student installation (thereby alleviating the need for the instructor to provide technical support). Table 12 represents the same feature set comparison between DBMS server options. It should be noted that Table 12 compares the commercial versions of Oracle and SQL Server, not their Express versions. An additional attribute—client operating systems—is added to report operating systems supported by the native interface of the DBMS. Here, only SQL Server, which supports only Windows clients, is ruled out as a viable option. Because Oracle requires a commercial license, MySQL and PostgreSQL are again the top contenders.

As instructors, the authors have experimented with both hosted and user-based DBMSs in various database courses; a list of observations regarding key installation and support activities is presented in Table 13. In the authors’ experience, the choice generally comes down to the instructor’s preference. Most institutions have the capability to host databases and provide some level of support. After attempting both methodologies, the authors prefer a user-based approach, mostly out of the desire to maintain control. That is, utilizing hosting sources almost certainly requires the instructor to relinquish administrator privileges, limiting his or her ability to, for example, troubleshoot small problems (those that are less pressing from the standpoint of the information technology department) and grant or revoke privileges (from a security and instructional point of view).

Conclusions

The high-volume, data-intensive healthcare landscape has thrust data management to the forefront of the HI and HIM professions. As a result, preparatory programs have placed a greater emphasis on instruction in data management. In this article, the design of a graduate-level course in healthcare database management is presented, along with discussion of the importance of offering a healthcare-specific version of the course rather than outsourcing it to another department. The course is mapped in its entirety to CAHIIM accreditation standards for BHIM, MHI, and MHIM programs to indicate topic coverage if sufficiently implemented. Data modeling and SQL constructs are discussed in finer detail because of the elusive nature of balance in HI and HIM courses resulting from the diversity of student backgrounds. Minimum and expanded construct sets are proposed for these topics to support the course and perceived needs within an accredited program at large. Lastly, a minimum DBMS feature set for instruction is proposed, and traditional DBMSs used in the classroom in individual and hosted environments are compared. The authors of this article recommend using either MySQL or PostgreSQL, with hosted or individually managed configurations at the discretion of the instructor.

The second part of this series will present instructional strategies used in East Carolina University’s healthcare database course. Tactics for providing a dynamic course, deconstructing complex problems, and ensuring advanced student engagement will be detailed. Additionally, the article will provide access to multiple data sets that the authors have constructed and curated specifically for healthcare database education.

 

 

Ray Hylock, PhD, is an assistant professor in the College of Allied Health Sciences at East Carolina University in Greenville, NC.

Susie T. Harris, PhD, MBA, RHIA, CCS, is an associate professor and director of the Master of Science in Health Informatics and Information Management Program at East Carolina University in Greenville, NC.

 

 

Notes

  1. Warner, Diana. “IG 101: What Is Information Governance?” Journal of AHIMA, December 4, 2013. Available at http://journal.ahima.org/2013/12/04/ig-101-what-is-information-governance/.
  2. Warner, Diana. “IG 101: Enterprise Information Governance.” Journal of AHIMA, December 11, 2013. Available at http://journal.ahima.org/2013/12/11/ig-101-enterprise-information-governance/.
  3. Bates, Mari, et al. “Perceptions of Health Information Management Educational and Practice Experiences.” Perspectives in Health Information Management (Summer 2014).
  4. Palkie, Brooke. “The Perceived Knowledge of Health Informatics Competencies by Health Information Management Professionals.” Educational Perspectives in Health Informatics and Information Management (Winter 2013).
  5. Fenton, Susan H., et al. “Health Information Technology Employer Needs Survey: An Assessment Instrument for HIT Workforce Planning.” Educational Perspectives in Health Informatics and Information Management (Winter 2013).
  6. Warner, Diana. “IG 101: Information Asset Management.” Journal of AHIMA, December 13, 2013. Available at http://journal.ahima.org/2013/12/13/ig-101-information-asset-management/.
  7. American Health Information Management Association. “Embracing the Future: New Times, New Opportunities for Health Information Managers. Summary Findings from the HIM Workforce Study.” 2005. Available at http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_027397.hcsp?dDocName=bok1_027397.
  8. Cortelyou-Ward, Kendall, Summerpal Kahlon, and Alice Noblin. “Competencies for Global Health Informatics Education: Leveraging the US Experience.” Educational Perspectives in Health Informatics and Information Management (Winter 2013).
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  10. American Health Information Management Association. “Embracing the Future: New Times, New Opportunities for Health Information Managers. Summary Findings from the HIM Workforce Study.”
  11. White, Susan, and Sandra Nunn. “Two Educational Approaches to Ensuring Data Quality.” Journal of AHIMA 85, no. 7 (July 2014): 50–51.
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  13. Association for Computing Machinery. “Curricula Recommendations.” Available at https://www.acm.org/education/curricula-recommendations (accessed October 1, 2015).
  14. Ibid.
  15. Cohen, Eli B. “Rationale for the IRMA/DAMA 2000 Model Curriculum.” Information Resource Management Association International Conference (2001): 612–16.
  16. Mohtashami, Mojgan, and Julian M. Scher. “Application of Bloom’s Cognitive Domain Taxonomy to Database Design.” Proceedings of the Information Systems Education Conference (2000).
  17. Connolly, Thomas M., and Carolyn E. Begg. “A Constructivist-based Approach to Teaching Database Analysis and Design.” Journal of Information Systems Education 17, no. 1 (2005): 43–53.
  18. Garcia-Molina, Hector, Jeffrey D. Ullman, and Jennifer D. Widom. Database Systems: The Complete Book. Upper Saddle River, NJ: Prentice Hall, 2001.
  19. Silberschatz, Abraham, Henry Korth, and S. Sudarshan. Database System Concepts. 5th ed. Boston: McGraw-Hill, 2005.
  20. Ullman, Jeffrey D., and Jennifer Widom. A First Course in Database Systems. 3rd ed. Upper Saddle River, NJ: Pearson/Prentice Hall, 2008.
  21. Kwan, A. C. M. “Towards a Minimalist Approach to Lesson Planning of an Introductory Database Course.” 2013 IEEE International Conference on Teaching, Assessment and Learning for Engineering (2013): 782–85.
  22. Boyeena, M., and P. Goteti. “Promoting Active Learning through Case Driven Approach: An Empirical Study on Database Course.” 2010 IEEE Students’ Technology Symposium (2010): 191–95.
  23. Etlinger, Henry A. “Adding a Contributing Student Pedagogy Component to an Introductory Database Course.” In SIGCSE ’13: Proceedings of the 44th ACM Technical Symposium on Computer Science Education. New York: ACM, 2013, 299–304.
  24. Kwan, A. C. M. “Towards a Minimalist Approach to Lesson Planning of an Introductory Database Course.”
  25. Rashid, T. A., and R. S. Al-Radhy. “Transformations to Issues in Teaching, Learning, and Assessing Methods in Databases Courses.” 2014 International Conference on Teaching, Assessment and Learning (2014): 252–56.
  26. Wang, Jingmin, and Haoli Chen. “Research and Practice on the Teaching Reform of Database Course.” In International Conference on Education Reform and Modern Management (ERMM 2014). Paris, France: Atlantis Press, 2014, 229–31.
  27. Sastry, Musti K. S. “An Effective Approach for Teaching Database Course.” International Journal of Learning, Teaching and Educational Research 12, no. 1 (2015): 53–63.
  28. Chen, Hsuan-Hung, Yau-Jane Chen, and Kim-Joan Chen. “The Design and Effect of a Scaffolded Concept Mapping Strategy on Learning Performance in an Undergraduate Database Course.” IEEE Transactions on Education 56, no. 3 (2013): 300–307.
  29. Mohtashami, Mojgan, and Julian M. Scher. “Application of Bloom’s Cognitive Domain Taxonomy to Database Design.”
  30. Commission on Accreditation for Health Informatics and Information Management Education. “Welcome to CAHIIM.”
  31. Connolly, Thomas M., and Carolyn E. Begg. “A Constructivist-based Approach to Teaching Database Analysis and Design.”
  32. Kung, Hsiang-Jui, and Hui-Lien Tung. “A Web-based Tool for Teaching Data Modeling.” Journal of Computing Sciences in Colleges 26, no. 2 (2010): 231–37.
  33. Brusilovsky, Peter, et al. “Learning SQL Programming with Interactive Tools: From Integration to Personalization.” Transactions on Computing Education 9, no. 4 (2010): 19:1–19:15.
  34. Kwan, A. C. M. “Towards a Minimalist Approach to Lesson Planning of an Introductory Database Course.”
  35. Elmasri, Ramez, and Shamkant Navathe. Fundamentals of Database Systems. 6th ed. Boston: Addison Wesley, 2010.
  36. Ibid.
  37. Hoffer, Jeffrey A., Mary B. Prescott, and Fred R. McFadden. Modern Database Management. 6th ed. Upper Saddle River, NJ: Prentice Hall, 2002.
  38. Ullman, Jeffrey D., and Jennifer Widom. A First Course in Database Systems. 3rd ed.
  39. Oracle Corporation. “Oracle SQL Developer.” Available at http://www.oracle.com/technetwork/developer-tools/sql-developer/overview/index-097090.html (accessed October 1, 2015).
  40. “SQuirreL SQL.” Available at http://squirrel-sql.sourceforge.net/ (accessed October 1, 2015).
  41. White, Susan. A Practical Approach to Analyzing Healthcare Data. 2nd ed. Chicago: American Health Information Management Association, 2013.
  42. Fenton, Susan H., and Sue Biedermann. Introduction to Healthcare Informatics. Chicago: American Health Information Management Association, 2013.
  43. Hoffer, Jeffrey A., Mary B. Prescott, and Fred R. McFadden. Modern Database Management. 6th ed.

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