Identify an analysis model that best fits your group project identified
The analysis model that best fits the implementation of the clinical information system (CIS) in five hospitals accounts for the facts that are important to the project. In addition, the analysis model acts as a foundation that will be useful to the design of the system. One analysis model that can be applied to CIS implementation is data collection. Data collection involves collecting data that is pertinent to the project, refining it to suit the needs of the project, and creating a workflow document (Johnson & FitzHenry, 2006). The data-collection analysis model also takes into account some of the basic assumptions that apply to CIS implementation, all the important tasks that apply to the project, and a record of all the other simple tasks that apply to the process.
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There are several diagrams that can be used in the data-collection model of analysis. These diagrams can be used to capture the contents of flow sheets, procedure manuals, questionnaires, observations, and interviews. An activity diagram can be used to sequence the activities that the CIS project is seeking to harmonize. Consequently, the activity diagram can be used to outline the activities that transpire during patients’ clinical visits. The activity diagram also identifies the sequence of activities in relation to patients, pharmacists, and other physicians.
Mock Draft-Sample Activity Diagram.
|Patient desk||nurse||Attending doctor||………………||Prescription|
You have been assigned as a team member for a clinic scheduling system that will be used throughout the outpatient clinics. The project leader is asking for team input into how to approach the fact-finding. Discuss how you would approach the fact-finding. Design a sample tool that could be used for fact-finding during the analysis phase.
The fact-finding mission for a clinic scheduling system begins the process of determining its importance to the clinical setting. It is also important to determine the variables that apply to clinic scheduling including “arrival and service time variability, patient and provider preferences, the available information technology, and the experience levels of the staff” (Gupta & Denton, 2008, p. 802). The actual fact-finding mission starts with the identification of a team leader, who is responsible for coming up with a team that carries out the project.
In a fact-finding mission, it is important to outline the group’s priorities throughout the duration of the project. Setting up priorities helps in determining which tasks should be expedited. In the fact-finding process, some tasks have to be performed while others can be substituted. For instance, the major priority when identifying a clinic scheduling system is determining the features that have to be included in the chosen scheme.
Furthermore, fact-finding has to account for the needs that have to be satisfied with a scheduling system. The trigger for this fact-finding mission is the need to streamline activities in various outpatient clinics. Consequently, fact-finding has to address this trigger consistently throughout the process of searching for solutions. In the course of looking for solutions, it is important to utilize case studies especially the ones that pass standard scientific thresholds. Case studies are used to determine preconditions, post-conditions, and exceptions that apply to clinic scheduling systems.
A good tool for fact-finding during the analysis phase should address three important questions that touch on expected accomplishments, positive changes, and change-identification. A sample tool that can be used in the fact-finding phase is the Triple Action Approach (TAA). TAA seeks to master the data manipulation process by stressing the need to define, measure, and analyze data. These three processes are key to successful fact-finding.
Gupta, D., & Denton, B. (2008). Appointment scheduling in health care: Challenges and opportunities. IIE transactions, 40(9), 800-819.
Johnson, K. B., & FitzHenry, F. (2006). Case Report. Journal of the American Medical Informatics Association, 13(4), 391-395.