Roan Writer 3 | - Freelance Writer
Nov 06, 2014 | #1
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.
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
-------------
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).
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.
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
-------------
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.