Sample Answer for NUR 630 Topic 7 DQ 2 Included After Question
You are going to present data that has been collected to your administrative group. The focus is on outcome measures and the data collected is unplanned readmission rates at two different hospitals. What format would you choose to display your data and why? What information would you include with the data?
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A Sample Answer For the Assignment: NUR 630 Topic 7 DQ 2
Title: NUR 630 Topic 7 DQ 2
Data presentation is a critical research process. Data analysts often ensure that the result of analysis is presented so as to whole team of audiences can link the objectives with the research outcomes. Usually, data presentation takes different format depending on the researchers. Some of the common data presentation techniques may include graphs, tables, charts, and texts. Some researchers may use word document or Microsoft Excel to display data resulting from the processes of analysis (Rayat, 2018). Choosing the accurate method in the data presentation processes is essential given the desire to communicate precise outcomes of the research.
While presenting the data on the unplanned readmission rates at two different hospitals, I would choose tabular format to enhance clarity and understanding of the outcomes. In most cases, tabular presentation incorporate different aspects of the data including variables, types of analysis as well as the actual outcomes. Tabular format is generally used to differentiate categories of data and relate different datasets (Aidley, 2018). The tables can take simple pros and cons format, as well as data with the corresponding values such as bank statements, annual GDP, as well as monthly expenditure (Trauth & Sillmann, 2018). The presentation of data in a tabular format is also easier, the clarity is achieved, and as a result, the audience may understand different concepts that are being presented. With the columns and rows, it is also easy for the audience to follow and understand whatever is being investigated.
While presenting data in a tabular format, I would incorporate variables with their characteristics. I would also include data types as well as the measures that were recorded during data collection and analysis. Finally, I will include descriptive statistics which incorporate the summary of data as well as the measures of central tendency.
References
Aidley, D. (2018). Introducing Quantitative Methods: A Practical Guide. Macmillan International Higher Education.
Rayat, C. S. (2018). Applications of Microsoft Excel in Statistical Methods. In Statistical Methods in Medical Research (pp. 139-146). Springer, Singapore.
Trauth, M. H., & Sillmann, E. (2018). Collecting, Processing and Presenting Geoscientific Information: MATLAB® and Design Recipes for Earth Sciences. Springer.
Data visualization is increasingly important in the communication of quality improvement data. Effective use of data visualization with graphics, dashboards, and other tools requires an understanding of why this approach works and how to optimize its effect. The most important part of any data strategy is making sure the data can be used by the people who can benefit. In healthcare visualization can be taken further by adding a narrative, telling a story with the data and graphics rather than presenting a static representation (AHC MEDIA, 2019). Outcome measures reflect the impact of the health care service or intervention on the health status of patients. An outcome is the result of numerous factors, many beyond providers’ control. Risk-adjustment methods; mathematical models that correct for differing characteristics within a population, such as patient health status which can help account for these factors. However, the science of risk adjustment is still evolving (Agency for Healthcare Research and Quality, 2015).
Quality improvement is characterized as the ongoing efforts of all healthcare professionals, patients and their families, researchers, payers, planners, and educators to improve patient outcomes, system performance, and professional growth (Banerjee et al., 2019). A hospital readmission occurs when a patient has an unplanned admission to a hospital within a specific time period of discharge from an earlier or initial hospital stay. Preventable readmissions have turned into a critical challenge for the healthcare system globally, and hospitals seek care strategies that reduce the readmission burden. Some countries have developed hospital readmission reduction policies, and in some cases, these policies impose financial penalties for hospitals with high readmission rates (Lahijanian, Alvarado, 2021).
Decision models are needed to help hospitals identify care strategies that avoid financial penalties, yet maintain balance among quality of care, the cost of care, and the hospital’s readmission reduction goals. We develop a multi-condition care strategy model to help hospitals prioritize treatment plans and allocate resources. The stochastic programming model has probabilistic constraints to control the expected readmission probability for a set of patients (Lahijanian, Alvarado, 2021). The model determines which care strategies will be the most cost-effective and the extent to which resources should be allocated to those initiatives to reach the desired readmission reduction targets and maintain high quality of care. A sensitivity analysis was conducted to explore the value of the model for low- and high-performing hospitals and multiple health conditions. Model outputs are valuable to hospitals as they examine the expected cost of hitting its target and the expected improvement to its readmission rates (Lahijanian, Alvarado, 2021).
References
Agency for Healthcare Research and Quality. (2015). Types of health care quality measures. Ahrq.Gov. https://www.ahrq.gov/talkingquality/measures/types.html
AHC MEDIA. (2019). Optimize Data Visualization to Improve Communication About Quality Improvement: Case Management Advisor, 30(8), N.PAG.
Banerjee, A., Stanton, E., Lemer, C., & Marshall, M. (2019). What can quality improvement learn from evidence-based medicine? Journal of the Royal Society of Medicine, 105(2), 55–59. https://doi.org/10.1258/jrsm.2011.110176
Behshad Lahijanian, & Michelle Alvarado. (2021). Care Strategies for Reducing Hospital Readmissions Using Stochastic Programming. Healthcare, 9(940), 940. https://doi-org.lopes.idm.oclc.org/10.3390/healthcare9080940
