- Do you recommend that the data analyst examine aggregate data, detailed data, or both, to investigate this quality issue? Please explain your rationale.
I think in this scenario, where they are looking for areas to perform quality improvement, the data analyst should do both – review aggregate data, then deep-dive into the detailed data. Aggregate data would give the data analyst the “big picture,” while reviewing the detailed data would provide specifics on where processes failed and why.
- 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.
A data warehouse can create standardized reports from data pulled from across many health systems, while a clinical data store pulls data from numerous sources to provide a clinical picture for a single patient (Campbell, 2018). As above, I believe it’s helpful to look at both. The data warehouse can provide a big picture across a system regarding mortality, and then the data analyst can deep-dive utilizing the clinical data store to review specifics about a patient’s individual care to identify fall-outs.
What type of tools or analytic approaches is relevant for use by this analyst? Please explain your rationale.
Power BI tools are great at taking data and categorizing it into something useful and easy to understand. We use BI within the agency to review consult timeliness metrics to help us gauge how we compare with like facilities.
I also think looking at the current process is important. Flow maps are quite helpful to give a visual of every step of the process, as the goal is to improve outcomes. As you look at each step, the analyst could list things that could go wrong at that specific step and easily identify processes that can be fixed quickly.
The analyst could also look at facility morbidity and mortality data as they relate to stroke and review historical data, then compare/contrast against current data to see trends to lock into where the challenges are.
Now, conduct a search for evidence. Select three scholarly sources of information describing the challenges of utilizing data in the clinical setting.
In a study by Vassillis (2019), the author looked at data mining as it related to drug safety. There were numerous data sources, not all standardized, creating holes in the “big picture.” For the data to be useful, it was important to ensure data definitions were standardized to provide valuable information about drug safety. In the systematic review by Cohen et. al (2018), challenges noted by them were about electronic health record (EHR) generated reports for primary care. They stated group practice managers, and process improvement personnel had significant challenges in generating the needed reports, leading to delays in quality reporting. This could be with the specific EHR cost for reporting applications as well as ease of use.
The final article by Kaulfus, et. al (2017), discusses challenges with data because of how diverse it is as it comes from numerous different sources. Another challenge was the sheer size of the files, and how the data was collected. Additional analysis was performed to standardize the information received.
Campbell, T. (2018, June 19). Clinical data repository versus a data warehouse – Which do you need? HealthCatalyst. https://www.healthcatalyst.com/insights/clinical-data-repository-data-warehouse/ (Links to an external site.)
Cohen, D. J., Dorr, D. A., Knierim, K., DuBard, C., Hemler, J. R., Hall, J. D., Marino, M., Solberg, L. I., McConnell, K., Nichols, L. M., Nease, D. E., Edwards, S. T., Wu, W. Y., Pham-Singer, H., Kho, A. N., Phillips Jr., R. L., Rasmussen, L. V., Duffy, F., & Balasubramanian, B. A. (2018). Primary care practices’ abilities and challenges in using electronic health record data for quality improvement. Health Affairs, 37(4), 635–643. https://doi.org/10.1377/hlthaff.2017.1254 (Links to an external site.)
Kaulfus, A., Alexander, S., Zhao, S., Oster, R. A., O’Keefe, L. C., & Bartolucci, A. (2017). The inherent challenges of using large data sets in healthcare research. CIN: Computers, Informatics, Nursing, 35(5), 221–225. https://doi.org/10.1097/cin.0000000000000359 (Links to an external site.)
Koutkias, V. (2019). From data silos to standardized, linked, and fair data for pharmacovigilance: Current advances and challenges with observational healthcare data. Drug Safety, 42(5), 583–586. https://doi.org/10.1007/s40264-018-00793-z
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