Learning System and Programs - Literature Review

In order to be able to examine the literature that is available regarding scalable learning systems and programs, it is necessary to begin with a discussion of some of the literature about what is meant by the concept of a scalable learning system. Porter, Chakrabarti, Harvey & Kenyon that a scalable learning system has the ability to change its size based on the amount of data or the number of layers that are part of the program. For example, in a learning system that involves decision trees, larger amounts of data for a given problem will be collected over time. However, as the larger amounts of data are collected, it is possible for a program to simply run out of room. A point can occur at which no more information can be added and no more information can be processed. With a scalable learning system, the algorithms that are used to process the information and to perform the tasks can be scaled based on the data that are added. In essence, there is virtually no limit to the amount of data over which algorithms and processes can occur (Li).

Another important aspect of a scalable learning system is that it must be flexible with regards to the connections that are made between nodes and the ways in which algorithms are performed (Li). This means that rather than an algorithm reading all of the data and becoming slowed down or even stopped, a learning process occurs in which algorithms only read data in batches and connect nodes that are necessary for a specific task or situation. The benefit in this is not just about speed and efficiency, but also about the ability to operate with limited memory. As the algorithms in a program examines specific batches of data, they are placed within a computer's memory. However, once those batches of data are no longer needed, they are removed frm the memory so that the program can examine other data and perform other tasks within the memory that memory (Li).

Porter, Chakrabarti, Harvey & Kenyon explained that the scale of the processing layers in scalable learning systems are essentially implied in relation to the changing size of the processes as they are performed. In this regard, the processes and algorithms that operate in such programs are indeed changing over time and are not necessarily the same size at any given time. This is indeed what makes scalable learning systems scalable in how they operate and carry out tasks and procedures. As the work that is performs changes, the programs and systems change and adapt.

The idea of a system changing and adapting based on the conditions and situations that exist is what Ma'ndziuk & Shastri noted as a system learning in increments based on smaller problems as opposed to having specific instructions to follow regardless of the situation that exists. The systems learn how to deal with smaller problems and provide information and results to users based on the problems that have been encountered. Based on the learning that occurs for one problem, then system or program will apply those results to other problems or situations that are encountered. In this way, the systems are not just scalable, but they are truly learning systems. Learning takes place by applying the information obtained from the problems faced in one situation to addressing a problem in another situation. On an overall basis, there is a learning process that occurs over time that allows the systems or programs to make connections and operate as efficiently as possible.

References

Li, X-B. A scalable decision tree system and its application in pattern recognition and intrusion detection. Decision Support Systems, 41(1), 112-130.

Ma'ndziuk, J. & Shastri, L. Incremental class learning approach and its applications to handwritten digit recognition. Proceedings of the Fifth International Conference on Neuro-Information processing, 21-23.

Porter, R., Chakrabarti, C., Harvey, N. & Kenyon, g. A scalable learning system for video recognition IEEE, March, 1-8.

1. Describe the concept of learning.

2. Distinguish between learning and performance.

3. Compare and contrast the conceptual approaches to the study of learning.

------SAMPLE RESEARCH PAPER ------

The Concept of Learning

The psychology of learning draws from both scientific disciplines such as biology, which attempt to explain how our minds work, and the discipline of philosophy, which attempts to explain why human beings behave as they do. An understanding of how and why human beings learn is a crucial component of many different disciplines; for example, educators benefit greatly from concepts of learning because these enable them to design the most effective educational programs possible; similarly, psychologists can use concepts of learning to help their patients not only in the realm of the traditional classroom but also as they encounter challenges in their working life and social interactions. It is important to remember that although learning can be simply defined as gaining knowledge, there are many types of knowledge and ways in which learning can be experienced and expressed.

As Terry points out, "knowledge must be broadly defined to include not just verbal knowledge, but also habits and skills, attitudes, and knowledge or behavior outside conscious awareness" (p. 5). When studying learning, psychologists look not only at the external and conscious behaviors of human beings, but at the conscious and subconscious behaviors of both humans and animals in order to evaluate "events as diverse as the acquisition of an isolated muscle twitch, a prejudice, a symbolic concept, or a neurotic symptom" (Terry, p. 6). Thus, the manner in which an individual acquires new knowledge or makes use of existing knowledge can tell us a great deal not only about the subject in question, but also helps psychologists to draw broader conclusions about social patterns, cultural groups, and the role that learning plays in specific sociocultural contexts (Brownlee et al.).

Learning and Performance

One of the difficulties that is encountered by psychologists who study learning arises when we try to determine when and how learning occurs. As Terry (2009) notes, the process of learning occurs within the brain and beyond direct observation, requiring us to infer that learning has occurred using behavioral measures despite the fact that "there is not always a one-to-one correspondence between what the organism knows and what the organism does" (p. 10). The distinction between learning and performance means that learning can sometimes occur even when we do not immediately observe a behavioral change, as in the case of the latent learning which occurred during Tolman and Honzik's study of rats in a maze (Terry). Research conducted by Kantak and Winstein (2012) into the short and long-term development of motor skills illustrates that while the practice of certain skills can lead to immediate changes in performance which indicate that learning has occurred, there are more challenging learning situations wherein the evidence of learning is not immediately apparent even though study participants have gained new knowledge or skill sets. There are also instances where an individual's acquisition of knowledge is negatively impacted by the environment in which he or she is expected to demonstrate said knowledge acquisition, as in the case of students who are anxious about test-taking or with individuals who are inaccurately judged based on the stereotypes associated with the group to which they belong.

Conceptual Approaches to the Study of Learning

There are several conceptual approaches which have shaped our understanding of learning, including the functional, behavioral, cognitive, and neuropsychological approaches. A functional approach looks at how learning and memory help a species to survive and evolve, whereas a behavioral approach evaluates the relationship between our actions and their consequences (Terry). In the cognitive approach to learning, the emphasis is on the ways in which human beings acquire, store, and retrieve information; similarly, the neuroscience approach seeks to find biological explanations for our learning processes (Terry). While each of these approaches brings something new to our understanding of learning and memory, most researchers typically incorporate a combination of approaches into their work so as to have a broad and comprehensive way to evaluate learning processes.

For example, in their study of the learning styles and personal beliefs about learning in first-year university students, Brownlee and colleagues use both the functional and behavioral approaches to explain how university students adapt to their environment and develop techniques intended to ensure success. They suggest that new students are highly motivated to 'survive' in their new educational environment and will thus develop learning strategies such as emulating their more successful peers so as to successfully adapt into the realm of higher education (Brownlee et al.). However, although survival and adaptation are facets of a functional approach to learning, their research indicates that a behavioral approach needs to be taken into account as well, especially given that many of the behaviors assumed by these first-year students (such as imitating peers) are adopted and continued based on their consequences.

Conclusion: The Future of Learning

Although it is generally agreed upon that our experiences lead to permanent or semi-permanent changes in behavior which constitute evidence of learning, there is less consensus among researchers about the ways in which we can achieve successful learning outcomes in a variety of situations, especially given the broad range of learning styles that have been documented by researchers in the field of learning (Terry, 2009). The debates about learning which were begun with philosophers like Descartes and Locke will likely continue into the foreseeable future, as there is still no true way to determine whether our acquisition of knowledge is driven by nativism, rationalism, or empiricism (Terry). The practical applications for future work in the psychology of learning will continue to expand, especially as the means by which we acquire knowledge shifts from the traditional classroom environment to the flexible and borderless environment of the global Internet. This will result in changes regarding how we think about the process of learning and where we look for new knowledge and experiences, and will also alter the power dynamics that are associated with the possession of knowledge (Liaw). The Internet, online classrooms, and other emerging technologies will help to effectively level the educational playing field, presenting an opportunity for all learners to also become teachers, and forcing us to reconsider what it means to both learn and teach in the 21st Century.

References

Brownlee, J., Walker, S., Lennox, S., Exley, B., & Pearce, S. (2009). The first year university experience: Using personal epistemology to understand effective learning and teaching in higher education. Higher Education. doi: 10.1007/s10734- 009-9212-2

Kantak, S.S. & Winstein, C.J. (2012). Learning-performance distinction and memory processes for motor skills: A focused review and perspective. Behavioral Brain Research, 228: 219-231. doi: 10.1016/j.bbr.2011.11.028

Liaw, S.S. (2008). Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51: 864-873.

Terry, W.S. (2009). Learning and memory: Basic principles, processes, and procedures (4th ed.). Boston: Pearson/Allyn & Bacon.

Assignment's Purpose: Practice creating a project proposal for a new digital, knowledge management system for a business or organization in which the student addresses the need for such a system and what it can do for specific users within the particular business or organization, how to establish, maintain and use the system, and how to garner executive and IT support for the project.

CREATE A NEW USER INTERFACE (UI)

General Information- Business or organization for whom you're preparing the proposal:

Digital knowledge management system you're proposing:

Why are you proposing this system?

What is the need you're trying to address?

How will it benefit the business or organization? (Don't tell me that it will. Tell me how.)

- Decision-making
- Profits
- Productivity
- Efficiency
- Add to overall knowledge
- Other

Within the business or organization, are there specific people who will benefit from your system?

- If so, who are they? (departments or job titles) (This is your target market.)

Please add the above section into the paper.

Pitch

To whom will you pitch or give your proposal?

Why did you select this person or group of people?

What arguments can be used to convince decision-makers to implement BI systems?

How will you get initial executive support for the project?

- Which executive(s) is most appropriate to approach for support, and why?
- How will you continue to garner executive support after the project's implementation?

Creation

Will you have your BI system custom-made or will you buy BI tools from vendors?

How will the system be created?

Who will create it?

What qualifications will the creators need to have?

Which data analytics tools will be applied to your system?

How will you use agile development in creating your system?

What kinds of front-end tools will be developed for people who use the system?

Implementation

Which department will you start with or will you implement the BI system throughout the enterprise? What the pros and cons of departmental versus enterprise-wide BI development?

How long will it take to implement the system?

Budget

How much will the system cost?

- Hardware
- Software
- Implementation, including testing
- Training
- Upgrades
- On-going/recurring costs such as maintenance
- Outside consultants (if any)
- Wages/salaries
- Other (specify)

Based on the above, what is the overall budget for this knowledge management system?

Where will the money come from to fund your proposed system?

Management

Will IT be involved in the system in any way?

- How will you get buy-in for the project from IT?

Who will test the system?

- What are the criteria for such a test?

- Who will resolve problems uncovered during the test?

How will the system be maintained?

Who will maintain it?

Will you create a BI lab? Why?

Will you create a BI competency center? Why?

Does the organization/company already have a data governance committee?

If not, what advice will you provide on forming a data governance committee?

- Who will serve on the data governance committee?
- What responsibilities with the data governance committee have?
- To whom will the data governance committee report?

Users

When (or under what circumstances) will the system be used?
- Who will use the system?
- Who will have access to it?

Responsibility

Who will have overall responsibility for the system?
- Will you put the BI system in the IT department?
- Who will select members of the BI steering committee?
- What qualifications must members have?

Security

How will the information knowledge management system be kept secure?

- Measuring Success

How will you measure the system's success?
- How will you define success?
- How often will you measure success?
- Who will be responsible for measuring success?
- What will you do if/when you discover problems or inefficiencies?

System

What metadata schema will be used in your system? Use XML

- Why did you select this particular schema?

What will be the sources of data for your system? Select an appropriate analytical tool

- How will you evaluate whether that data is "good" data?
- How will you define "good" data?
- What is relevant data and how will you decide what's relevant to which groups of people?
- Will your system use, analyze or store big data?

Who will decide which users get which kinds of front-end tools?

Future

How will advances in technology and software impact the future of the BI system you implement now?

Who will be responsible for monitoring for advances in technology and software that might improve or replace your current BI system?

Reading Reference:

Howson - Chapter 6
Howson - Chapter 10

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Project Proposal: CREATING A NEW USER INTERFACE (UI). Digital Knowledge Management System

General Information

Geisinger Health System is a medium sized provider of healthcare services in the Eastern Pennsylvania area (Geisinger, 2014). This proposed digital knowledge management system will collect patient health information from existing medical records, patient feedback, and physical observation, and will allow all parties to access this information for healthcare purposes.

This system is being proposed in response to rising healthcare costs and the difficulty of recordkeeping in an environment with many physicians and three separate regional providers. This system is intended to reduce healthcare costs and improve patient outcomes by streamlining access to knowledge of both individual patients and the general population served. This will allow patients, physicians, and providers to track trends in service access, procedure outcomes, and patient records. It will also allow information to be easily shared with other regional providers when patients access care outside the network.

Organizational Benefits

This system will improve decision making by allowing care providers access to accurate, up to date patient information. It will allow hospitals to track care trends so that they can more easily decide what types of care are in the greatest demand and identify what procedures are meeting outcome goals (IBM, 2012). This system will also allow the network to identify which physicians, hospitals, and procedures are most effective and how the network can better serve the patient population (IBM, 2012). Finally, by providing information to patients on the outcomes and costs of different procedures, the system will allow them to make better-informed choices regarding their own health (IBM, 2012).

By eliminating knowledge gaps between hospitals, physicians, and provider networks, the system will reduce costs associated with poor procedure success, which leads to more time spent in-network and higher access to services. It will also enable hospital managers to understand the needs of their population and understand their relative performance, so that they can identify which training, infrastructure, and service programs are most likely to reduce resource use and serve patient needs. Finally, the system will reduce the risk of failed procedures and the costs associated with them to networks, hospitals, and physicians.

In regard to patient health, this system will allow physicians and other care providers access to patient information shared between stakeholders. By making comprehensive patient and population information easily available and quickly accessible, the system will reduce wait time for record requests. Also, by improving understanding of patient and procedure outcomes, the system will allow care providers to better understand patient needs and reduce diagnosis errors. This will result in lower care times and the ability to serve a greater number of patients in existing facilities.

There are a number of individuals and departments who will benefit from this system. First, administrators of the healthcare network are interested in providing the highest level of care at the lowest cost, and look for ways to achieve this at the strategic level. Executives in charge of Information, Science and Technology, and Resource Development will see a benefit from this system, and will be able to demonstrate reduced costs and better patient care. Also, Quality Improvement and Transformation Officers will benefit from an increased ability to identify areas for improvement. Finally, individual physicians who contract their services to hospitals but also operate private practices will benefit from the system by gaining improved access to patient records through their ability to access patient information. Collaborating with these individuals is also important for system development because they have access to information that currently exists outside the network's records.

Pitch

Executive level of sponsorship is important for project success (Howson, 2014), and there are many administrators who would benefit from this project. As a result, this project proposal would be delivered to multiple potential sponsors. Ideally, the CEO or similar C-level executives, should receive the proposal, along with CIOs and Improvement or Development officers. Individuals will see the most direct benefit from the project and will have the greatest likelihood of understanding how knowledge management can improve the organization. The Analytics Officer may fill this role in the organization (Howson, 2014), and if this is the case this person would make a better candidate for the proposal. The other administrative roles listed above are also good choices to include, particularly if their input is highly regarded by other decision makers.

Specific arguments to convince decision makers are those that demonstrate need and solve a problem. These arguments can generally be aligned with the benefits listed above. Reduced costs, better patient outcomes, and a greater understanding of the market are all central to the interests of administrators. As a result, explaining the benefits of knowledge management and supporting them with specific examples, such as the reduced rate of service access by healthcare plan members that results from a higher procedure success rate, can be used to convince the decision makers.

Finally, if there are any specific frustrations that administrators are experiencing, such as difficulty in sharing information with physicians or a low success rate for certain procedures, explaining how this system can solve these problems will provide a clear solution to their concerns (Howson, 2014).

Gaining Executive Support

Just as proposing the project to multiple positions is important, gaining support from multiple sponsors improves the likelihood of project success. The CEO is an excellent target for sponsorship, however, the COO is also likely to see the benefit of this project, and their sponsorship is likely to provide the necessary drive for carrying it forward. Other likely sponsors are Vice Presidents of Quality, Innovation, or Development. Finally, while Information Officers may not have strong influence with other departments, they are often referred to on technical projects, and so their support is still useful for indirect influence.

Continuing support depends on ensuring that sponsors understand the process of development and are informed of any progress (Howson, 2014). Maintaining their investment in the project requires frequent communication, explaining the importance of any developments, and providing evidence that the project if moving forwards successfully. Equally important is creating clear definitions of the project scope at the outset and ensuring that sponsors are aware of what is and is not involved, so that their expectations align with the process of development.

Creation

For this project, the most cost effective route is to purchase tools from vendors. There are a number of well-developed tools in existence that can fulfill the needs of this project, and these tools are designed to be user friendly for the large and diverse group of data users. For example, IBM's Cognos tool is widely used for business intelligence in healthcare, and has a wide range of applications while being well regarded for its usability.

Because poor data quality is a concern when using these tools, a focus on quality reporting and comprehensive recordkeeping should follow implementation. This can also be easily supported by existing tools, such as those that prompt physicians and nurses to record patient care information and record lapses in this reporting.

Creation will be handled through a third-party developer familiar with the tools that are identified as best-fitting the organization's needs. The creators will need some experience implementing the tools that are selected as best fitting the project. It is also preferred that the creators have some familiarity with the nature of healthcare reporting and data collection, as this is necessary for understanding how to adapt the tools used to the healthcare industry. Further, an understanding of the regulatory environment will be useful for understanding what data reporting is necessary and what data can be shared with which stakeholders.

Analytic Tools

Analysis and benchmarking tools will need to measure aggregate patient data, such as the average rate of healthcare service use for specific illnesses, demographics, and plan types, as well as quality control information such as rates of readmission, incidence of mortality, and recovery times. Metadata, such as reporting completion rates will also be needed to identify ways to improve reporting. Finally, the ability to compare these rates across healthcare providers, such as different private practices, will be needed to determine the effectiveness of care providers in different areas of medicine. These measures will also need to be analyzed on a cost-per-procedure basis to determine how changes to operations impact profitability.

Agile Involvement

Agile will be used on a continuous development basis after core requirements have been met. Because healthcare regulation is still developing standards for what data must be reported and when providers must be able to satisfy these regulations (IBM, 2012), the Agile model will be used to continuously respond to the changing needs, as well as to implement data reporting that is not considered immediately necessary.

Front End Tools

Front end tools for care providers will include data display for comprehensive patient profiles, including medical history, service access patterns, and demographic information. Providers will also be able to query information on different medical procedures, including their average cost, success rate, chance of complication, and information regarding patterns of occurrence in the population. For administrators, tools will provide displays on the cost of procedures and admission, as well as information on the prescriptions and results of physicians in the network.

Implementation

Implementation will begin with the existing regulatory requirements to provide information for physicians and other care providers. These include electronic recordkeeping for patient profiles and demographic information such as average incidence of diseases. This will then be supported with the development of live reporting and knowledge bases for different procedures and demographic information. Once these are in place, the information system can integrate financial and performance analytics for administrators, such as procedure costs, care access times and outcomes, and physician performance. After these development stages are complete, an agile model will be used for continuous development in response to new regulations. Implementation of each stage listed above is estimated at approximately three months, though full integration will likely take substantially longer. This means that full implementation can be achieved within one year.

Budget

The integration of electronic health records is estimated at approximately $35,000 in the first year (HealthIT.gov, 2014). Demographic information is already held by the health system, and will follow a similar implementation path, at a similar estimated cost. Analytic tools for administrators are expected to cost approximately $50,000 in the first year, plus costs of upkeep. Hardware costs are estimated at approximately $4,000 for small providers; however as a health system with dozens of participating physicians, these costs are expected to increase to an estimated cost of $20,000. In one case study, training time takes on average 130 hours for care providers to be comfortable using the system with patients, and this comes at an estimated cost of $2,000 per trainee, of which there will be approximately fifty.

Taking the above information into account, the estimated cost for this system implementation is $500,000. After the initial expenditure, financial projects show that based on the experiences of other health systems, the cost savings over five years will represent the breakeven point for the cost of this project.

Management

The information technology (IT) unit of the Geisinger Healthcare system will be involved in the digital knowledge management system that will collect and store health information data from patients. The project identifies a gap in the current IT capabilities of the Healthcare system and the commitment of internal stakeholders such as the IT department is crucial to the proper implementation of the digital knowledge management system. A buy-in and commitment from internal stakeholders is possible only when it's possible to ensure that all stakeholders are involved in the process. The IT department has to be consulted and engaged early for the implementation of the digital knowledge management system. The benefits of the project have to be explicitly implied and the risks have to be managed and identified by the project management unit and shared with the IT division. Listening and communicating with all members of the IT staff and giving a clear justification and rationale for introduction of a system could help gaining their support. The IT department will also be the first to test the system by initially using data strictly within the healthcare divisions of Geisinger. When this is successful, the system can be taken further for external applications. The IT division and the project management divisions of Geisinger will have the joint responsibility of maintaining the new digital knowledge management system. A BI lab will also be created solely to maintain the digital knowledge management system and this will be an affiliate of the IT division.

Data Governance

A data governance committee will be formed from the internal stakeholders of Geisinger Healthcare system and this will include the Director or IT systems at Geisinger, the CIO and Chief of BI Lab and Director of Project management unit. The data governance committee will meet monthly on issues related to maintenance of the digital knowledge system and on collection and storage of data (Sarsfield, 2009). Issues that will be discussed at the committee meetings are security and safety of data, data breach and confidentiality of patient information. Adequate and safe data storage and use are some of the concerns. The data governance committee is responsible for safe storage and use of data and reports directly to the CEO and Board of Directors.

Use and Responsibility

The IT department, its affiliate the BI lab, the division of Project management will have the overall responsibility of using and managing or maintaining the new digital knowledge management system. The uses will be primarily storage of information with access to data for healthcare and research purposes. The BI lab will be a part of the IT division and its members will be IT and project management professionals who are directly responsible for IT and Project management systems at Geisinger healthcare.

Security

The security of the information and adequate and safe storage of data are important issues that will be taken up regularly during data governance meetings and the data governance committee would be primarily responsible for the new digital knowledge management systems and ensure that all patient data including health records are kept secure (McInerney, 2002). This is done by ensuring safe access to data, password protected data storage units and systems and separate data storage facilities with restricted access.

Measuring Success

The success of the new digital knowledge management system and its implementation would be measured with the aid of surveys and patient feedback. Interviews with staff at Geisinger and healthcare professionals would also help gauge the level of change that the newly introduced system was able to bring. The system's success is measured with quantified or qualified changes such as better and faster access to patient records, improved healthcare facilities, reduced healthcare costs and detailed clinical information on patients and treatment options. The digital knowledge management system is expected to bring positive change in the storage and access to healthcare related information for research and clinical management purposes (McInerney, 2002). Annual checks, internal and external surveys and quantified data on healthcare information will help in measuring success of the system and lower scores or poor feedback in surveys would mean problems or inefficiencies in the system.

System

The Metadata Schema used for this data management system is Dublin Core. The Dublin Core is a metadata element set that facilitates discovery of electronic resources and originally conceived for author generated resources. The Dublin Core is characterized by Simplicity; Semantic Interoperability; International Consensus; Extensibility; and Metadata Modularity on the Web. Dublin Core Metadata Elements include Identifier, Language, Creator, Title, Publisher, Date, Source, Format, Description, Contributor, Subject, Rights and Relation (Beynon-Davies, 2004).

Dublin Core is an Innovative Interfaces Meta-Source and describes a suite of tools that allows libraries to manage their digital collections. The new healthcare digital management system if focused on storing and management of information presented as digital collections. The storage and analytic tools could be made up of three components including Millennium Media Management, XML Harvester and Metadata Builder. These tools could help in the creation and storage of media objects such as images, sound files, and audio files and would also include information on Copyright and Access components and this information is important in handling the licensing and copyright issues of digital collections (Connolly, 2002; Date, 2003). The XML Harvester is used for gathering the XML records from any server and also creates records on the Innovative digital knowledge management system. The Metadata Builder Stores XML in the metadata scheme of choice and good and bad data are evaluated based on the accessibility and usability of the information available. Good data is defined as data that is accessible and usable and can provide a range of information on healthcare and patient medical records. The front end tools are given to users according to their special skills and abilities and they handle this data based on their level of expertise and the requirements of the data access or storage systems.

Future

It is important to determine how advances in technology and software will impact the future of the BI system that is being implemented now. The advances in technology would be primarily based on developing the information and knowledge management systems in healthcare and other industries and the future involves regular and responsible monitoring of software applications and in this case implementation of improved versions of the new digital knowledge management system (Maier, 2007). The business intelligence model as used for this proposal may be replaced in the future with a better model that will focus on not just patient information, but would even predict data trends and new research directions. The IT division, project management departments and data governance committee would be responsible for monitoring data storage and changes in the knowledge management systems.

References

Beynon-Davies P. (2004). Database Systems 3rd Edition. Palgrave, Basingstoke, UK

Connolly,T and Carolyn B. Database Systems (2002). New York: Harlow, 2002.

Date, C. J. An Introduction to Database Systems, Fifth Edition. Addison Wesley.

Geisinger. About Geisinger.

Howson, C. (2014). Successful Business Intelligence. New York: McGraw.

HealthIT.gov. (2014). How Much Will This Cost?

Hopwood, Peter (June 2008). "Data Governance: One Size Does Not Fit All". DM Review Magazine. Retrieved on 2014-11-03.
IBM Institute for Business Value. (2012). The Value of Analytic in Healthcare.

Maier, R. (2007). Knowledge Management Systems: Information And Communication Technologies for Knowledge Management (3rd edition). Berlin: Springer.

McInerney, Claire (2002). "Knowledge Management and the Dynamic Nature of Knowledge". Journal of the American Society for Information Science and Technology 53 (12): 1009-1018.

Sarsfield, Steve (2009). "The Data Governance Imperative", IT Governance.