Jill,
Consider the scenario above to address the following.
Do you recommend that the data analyst examine aggregate data, detailed data, or both, to investigate this quality issue? Please explain your rationale.
Aggregate data is a summary of the population being evaluated and does not consider individualized data. Detailed data evaluates individualized information, which can eliminate potential biases that may exist in aggregated data. Tierney et al. (2020) concluded that aggregate data were more likely to agree with detailed data when the information size was large. Since this is a quality issue and the information provided does not state the sample size, I recommend evaluating the aggregate and detailed data to ensure enough information is captured.
Do you recommend that the data analyst use a retrospective data warehouse, clinical data store, or both, to investigate the mortality rate? Please explain your rationale.
To investigate the mortality rate, the analyst should evaluate the retrospective data warehouse. The retrospective data warehouse provides aggregate and detail-level data, whereas the clinical data store manages operational and clinical data to assist clinicians at the point of care. Aggregate data in the retrospective data warehouse may contain a mortality rate, risk-adjusted mortality rate, and risk of mortality for specific patient populations (McBride, 2019).
What type of tools or analytic approaches is relevant for use by this analyst? Please explain your rationale.
First, this analyst will need a spreadsheet, such as Microsoft Excel, that allows the data to be organized and sorted into various charts and graphics. BI tools are software applications that assist in the multidimensional analysis of clinical data in organizations (McBride, 2019). Statistical packages assist the analyst in organizing the data, performing data cleaning, and validating the data. Statistical tests such as t-test, correlations, and regression are often used.
Now, conduct a search for evidence. Select three scholarly sources of information describing the challenges of utilizing data in the clinical setting.
Data utilization in the healthcare industry presents many challenges. One challenge is proprietary or determining who owns the data, the facility, or the patient (Kruse et al., 2016). The patient’s ability and ease of accessing their information is another concern. Patient security is a significant challenge in data management (Galetsi et al., 2019; Kruse et al., 2016; Ristevski & Chen, 2018). According to Galetsi et al. (2019), future research is directed towards the standardization of data systems to allow for the safe extraction of patient data from all relevant organizations. Advanced encryption algorithms and pseudo-anonymization of personal data should be used to avoid patient security and privacy issues (Ristevski & Chin, 2018; Galetsi et al., 2019).
References
Galetsi, P., Katsaliaki, K., & Kumar, S. (2019). Values, challenges and future directions of big data analytics in healthcare: A systematic review. Social Science & Medicine, 241, 112533. https://doi.org/10.1016/j.socscimed.2019.112533 (Links to an external site.)
Kruse, C., Goswamy, R., Raval, Y., & Marawi, S. (2016). Challenges and opportunities of big data in health care: A systematic review. JMIR Medical Informatics, 4(4), e38. https://doi.org/10.2196/medinform.5359 (Links to an external site.)
McBride, S. (2019). Nursing informatics for the advanced practice nurse: Patient safety, quality, outcomes, and interprofessionalism (2nd ed.). Springer Publishing Company.
Ristevski, B., & Chen, M. (2018). Big data analytics in medicine and healthcare. Journal of Integrative Bioinformatics, 15(3). https://doi.org/10.1515/jib-2017-0030 (Links to an external site.)
Tierney, J. F., Fisher, D. J., Burdett, S., Stewart, L. A., & Parmar, M. B. (2020). Comparison of aggregate and individual participant data approaches to meta-analysis of randomised trials: An observational study. PLOS Medicine, 17(1), e1003019. https://doi.org/10.1371/journal.pmed.1003019
