Assignment Details: 

Write a proposal for your evidence-based capstone project that will be due in HCA-699. 

The proposal should be similar in format to an executive summary (a common element in typical business plans). 

The proposal should outline the nature of your project. 

Explain what type of research is needed to support your project. 

Refer to the Capstone Project Resources. 

Prepare this assignment according to the APA guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

Over view of Capstone project:

Developing and maintaining clinical databases have become increasingly involved because of the more-intricate nature of the domain of clinical information as well as its constant evolution and expansion. Specifically, the physical design of the database must be altered every time new types of data are incorporated, or current data types are modified in a standard relational database format. Clinical databases can store a wide variety of data procured from different domains, e.g., laboratory reports, medications, patient visits, test results, therapies indicated, various procedures, and diagnoses. Assuredly, clinical databases can have differing foci, e.g., clinical research, electronic patient records (EHRs), quality control, and patient management. Normally, clinical databases have myriad numbers of users, often with different needs, vis-à-vis views of the database. Understandably, the administrator wants to avoid looking at data per patient; on the other hand, nurses have the capacity to call up the current medication for a patient. Meanwhile, the researcher may wish to perform data mining on the clinical information of thousands perhaps even millions of patients, and the clinician should be able to easily look up his or her ambulatory schedule. Most clinical databases possess only some of these functionalities; however, these afore-mentioned examples demonstrate the complex requirements encountered by designers of clinical databases. Moreover, in comparison to schemas present in many other domains (e.g., public administration and finance), the logical data schemas of clinical data are constantly incomplete and evolving.

Solution: 

Certain strategies have been proposed to deal with potential problems involved in modifying existing databases to reflect the implementation of new concepts, including the following:

--Any need for frequent cross-patient data access could be fulfilled by generating backups of the production database and re-instituting them on separate hardware. Resource-intensive questions performed on the backup data will not impact the production server. In addition, the EAV data schema could be incorporated into many conventional tables upon back-up thereby simplifying query design by end-users possessing minimal SQL skills.

--In cases where complex, user-defined, attribute-centered, ad hoc questions are relevant to an application, efforts should be undertaken to ensure this desired result. The first step is building user- interfaces, whether graphical or not, to assist users in retrieving data. In fact, a user should be able to simply choose any combination of criteria and attributes. Next, the interface should translate user requests into syntactically- and semantically-valid SQL statements; moreover, when considering the user’s  point of view, whether data are stored in conventional tables or EAV tables, should not matter.  

What types of information or data would the users of the proposed system like to have compiled. What would this data provide evidence of or answer? Provide specific examples.

A comprehensive hospital solution that offers complete data integration strategy by providing standards based integration points to the Patient Management System, and EMR. The ability to share electronic health information both internally and externally with healthcare organizations thus, leads to quality information for example demographic information of patient’s, static health information, such as past medical history, lab results, previous visit, treatment history and so on that is required for quality decision making and patient care. Use of structured data is important to enable the sharing and exchange of health information with HIEs and other organizations. Data strategies and an effective data quality system that incorporate data integrity processes must be in place to ensure optimal data quality. It will not only benefit healthcare workers like physician, nurses, administrative staff, medical record, pharmacy, radiology but also will be beneficial to patient’s. For example, consider entering information such as vital signs as discrete data into correctly formatted fields, versus allowing free text entry of the vital signs into the system. No matter what system you enter a temperature or blood pressure, the format is always the same and can be more easily shared across systems. If the information was entered as free text, the formatting might be lost and the information misinterpreted.

This will improves the ability for healthcare professionals to enact evidence-based knowledge management and aids decision making for care. Integration of medical record can have a positive impact on quality of care, patient safety, and efficiencies. It will help identify pattern in patient health, or future medical needs.  Another example would be the prescription which shows history of all drugs taken by patient. However, without accurate and appropriate content in a usable and accessible form, these benefits will not be realized.

What kinds of reports would the users of the proposed system like to be able to generate.

Proposed system should generate accurate and complete data content to facilitate clinical decision making by providing information tailored to a specific patient’s treatment and care. System should alert the provider to potential drug interactions, pre-existing conditions, and other types of safety issues, and provide tools and aides that enhance the care and safety of the patient. Documentation must be complete to ensure that appropriate information is available to abstract and report quality measures. This information is also used to help define and develop evidence-based care protocols.

System must ensure continuity between those caring for the patient today and those who will care for the patient in the weeks or years to come. Effective health information exchange can reduce or eliminate duplication of diagnostic tests, redundancy of processes to obtain information, and the risk of treatment errors. This leads to higher quality patient care, cost savings, and helps to eliminate duplicative processes. Clinical decision support is a core function of the proposed system and will justify the patient’s admission status, continued stay, and any therapies, treatments or procedures that are provided.

3) What is the feasibility of the proposal? Do you think the proposal is feasible or possible? Why or why not? What possible problems or barriers do you foresee? Are there any specific assumptions which need to be made?

 This proposal is feasible because it can help organization connect better with the people they serve. It can transform data into actionable ideas. It can help healthcare to see new opportunities. It can help to increase evidence based practice. Standard development organizations provide documentation that can help guide the development of facility data standards. The healthcare industry must move toward a set of standards that ensure the most consistent guidelines for providers for all uses of health information. In response to these challenges, the Agency for Healthcare Research and Quality (AHRQ), in partnership with the Foundation of Research and Education (FORE) of the American Health Information Management Association (AHIMA) and the Medical Group Management Association Center for Research (MGMA CFR) has initiated data strategies and an effective data quality program that incorporate data integrity processes that must be in place at every healthcare setting to ensure optimal data quality.

Some areas to consider addressing in an overall plan for data quality monitoring and improvement include: Thorough training for all front-end users—especially those in registration and scheduling roles—and proactive surveillance by data integrity analysts for any patient identification errors should be given the utmost attention to ensure proper patient identification.

Project Assumptions/Challenge

While the data is there, getting that data into a format meaningful to the organization or operational managers and users has required more IT levels. Reports can be sorted fairly easily by users, but are not easily built or created by end-users. Several iterations of a given report that is created and tweaked with input from the operational manager are not uncommon.

While the integration is very positive in a lot of ways, it also created some issues. For example, if a patient was registered incorrectly, it would have widespread negative impacts for billing and the ability to drop a bill and ultimately receive payment until there was issue resolution by registration. These types of errors have required departments that previously did not communicate often to restructure communication channels so they can more effectively and efficiently resolve integration issues. Develop a centralized communication platform for users; allow flexible/creative training opportunities. As sites are implemented, it is recommended:

- To have a reduction of patient visits until providers are comfortable with the application. 

- Maximize training opportunities prior to site “go live”. 

- Have individual super users on-site to assist with clinical application questions. 

- Decrease schedules as necessary to facilitate implementation activities. Individuals with access  to the EMR have instant access to all records in the database. 

- Develop/Approve Confidentiality Guidelines & processes. 

- Regular reporting and investigation by site managers of chart access by providers/staff.

                                                                   References

Regan. M & Randall-Lewis, R. (2008). Collaboration: The power of data aggregation.       http://www.healthleadersmedia.com/content/TEC-209949/Collaboration-The-Power-of-Data-%20Aggregation.html

HealthIT.gov. (2014). Benefits of electronic health records (EHRs). Retrieved from  http://www.healthit.gov/providers-professionals/benefits-electronic-health-records-ehrs

Gungor,F. (2011). Disadvantagesofelectronicmedicalrecords. Retrievedfrom  http://www.onesourcedoc.com/blog/bid/71535/Disadvantages-of-Electronic-Medical-Records

http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_050085.hcsp?dDocName=bok1_050085

http://academic.regis.edu/spsugmod/capstone.htm