Project implementation is tedious and requires massive support and facilitation from health care teams and stakeholders. The current project is resilience training to help the nursing staff overcome the adverse effects of the nursing shortage. Like other projects, the implementation should be evaluated to determine whether the project achieved the expected results. As a result, data collection resources and processes should be functional and appropriate. Change proponents should also use effective data analysis approaches to avoid evaluation and dissemination flaws. The purpose of this assignment is to describe the data collection and analysis plan.
Data collection is a continuous process in project implementation. It requires combining effective tools and methods to give accurate qualitative and quantitative insights (Seebacher, 2021). The data collected provide insights into the project’s impacts in four main areas; financial, internal, client, and innovation. Regarding the financial aspects, the focus is on how the project reduces adverse effects that increase health care costs. The internal dimension concerns the areas requiring excellence and the project’s impacts on the same. The data gathered in the client’s dimension is on the outcomes affecting patient care. Such impacts include working for longer hours without exhaustion and engaging in self-care. Innovation entails intervention necessary for better output.
Data analysis involves analyzing the collected data to extract insights that guide decision-making. The data collected can be analyzed both quantitatively and qualitatively. An effective quantitative approach is a comparative (numerical) analysis of the differences in the reported cases of burnout, stress, and uncivil behaviors before and after the project implementation. Qualitatively, the descriptive analysis would suit the current project. It turns raw data from various sources into valuable insights through manipulation and interpretation (Seebacher, 2021). For instance, an interview with the nurses on attitude change and their current self-care plan will provide accurate insights into the project’s effects on behaviors.
Performance denotes the completion of a proposed activity. Mailat et al. (2019) described balanced scorecards as valuable tools for performance evaluations for reducing ambiguities and increasing congruence between various organization groups. The current scorecard is functional and effectively measures the performance aspects it was developed to measure. It has targets and measures for each performance component- financial, internal, client/customer, and innovation/growth. Key performance indicators under each component help to measure the project’s outcomes both qualitatively and quantitatively.
The balanced scorecard and performance dashboard should be combined for detailed insights and cost-effective health care delivery. Dashboards are enterprising tools that provide a clear view of business performance indicators to enable leaders to make competent decisions (Victor & Farooq, 2021). I am managing my dashboard through progressive review and making updates where necessary. Doing so ensures that the visualized information is current. I am also combining descriptive and visual illustration tools to ensure that the dashboard is detailed and illustrates data comprehensively. Other management strategies will be incorporated as situations oblige.
Change leaders collect massive data during project implementation. Accurate and adequate data is crucial for flawless evaluation and dissemination. As explained in this paper, the current data can be analyzed through comparative performance assessment and descriptive analysis. The implication is that it will be analyzed both quantitatively and qualitatively. As further explained, the balanced scorecard is functional and effectively measures the elements it was developed to measure.
Mailat, D., Stoica, D. A., Surgun, M. B., Traistaru, N. I., & Vranceanu, A. (2019). Balanced scorecard vs. dashboard: Implications and managerial priorities. Academic Journal of Economic Studies, 5(1), 170-174. https://web.archive.org/web/20200727213534id_/http://www.ajes.ro/wp-content/uploads/AJES_article_1_242.pdf
Seebacher, U. (2021). Predictive intelligence for data-driven managers. Springer International Publishing.
Victor, S., & Farooq, A. (2021). Dashboard visualisation for healthcare performance management: Balanced scorecard metrics. Asia Pacific Journal of Health Management, 16(2), 28-38. doi: 10.24083/apjhm.v16i2.625