Reflection on learning

2

Week 7: Data Analytics

Student’s name

Instructor

Course

Date

Do you recommend that the data analyst examine aggregate data, detailed data, or both, to investigate this quality issue? Please explain your rationale.

As a data analyst, I believe that in this situation, when the goal is to enhance quality, the analyst should analyze aggregate data as well as more specific data. The "large picture" may be gained through aggregating data (Campbell, 2018). Big thinkers notice possibilities and take advantage of them. For the sake of profit, they're prepared to take risks. Detailed data analysis would reveal where and why procedures failed. It is considerably more intriguing to look at transactional data than it is to put them into demographic categories (Campbell, 2018).

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.

According to Campbell (2018), medical trial data collection is currently a time-consuming, error-prone, and sometimes incomplete process due to the complexity of the data. To increase data quality and minimize data collecting times, new and more reliable procedures are required if information is dispersed across numerous data sources. Rather of relying on disparate health systems for the creation of a coherent clinical picture, the data analyst should instead create standard reports from a centralized data warehouse that contains data from several sources (Campbell, 2018). If you look at both, you'll have a better picture of what's going on. If the data warehouse can offer an overall picture of mortality in a system, an analyst may utilize the clinical database to examine the intricacies of a patient's treatment and discover fall-out.

What type of tools or analytic approaches is relevant for use by this analyst? Please explain your rationale.

When it comes to classifying and making sense of data, Power BI capabilities are excellent. We utilize BI at the agency to analyze metrics related to the amount of time it takes for us to get back to our patients.

It's also critical, in my opinion, to consider how things are currently going. Flow diagrams are a great way to visualize every phase of the process in order to enhance results (Campbell, 2018). When looking at each stage, the analyst might list things that could go wrong and rapidly find procedures that can be changed.

Using facility morbidity and mortality data as a starting point, the analyst may examine historical data and then compare/contrast current data to identify patterns that indicate where the issues are (Campbell, 2018).

Now, conduct a search for evidence. Select three scholarly sources of information describing the challenges of utilizing data in the clinical setting.

Vassillis (2019) examined data mining in relation to medication safety in his work. The "big picture" was missing because of a lack of standardization in the data. Accordingly, it was imperative that the definitions of variables be standardized in order to offer usable information on medication safety. Reports created using electronic health records (EHRs) were found to be problematic by Cohen et al (2018) in their systematic evaluation. As a result of difficulties in creating the required reports, quality reporting was delayed, according to those in charge of group practices. Depending on the individual EHR costs and ease of use, this might be the case.

Kaulfus et. al. (2017), in their concluding paper, highlight the difficulties in using data that is so varied since it originates from so many various sources. It was also difficult to work with such large files because of how the data was acquired. It was necessary to do more research in order to standardize the collected data. These obstacles must be overcome for medical big data to deliver on its promise of improving patient outcomes.

References

Campbell, T. (2018, June 19). Clinical data repository versus a data warehouse - Which do you need? Health Catalyst. https://www.healthcatalyst.com/insi ghts/clinical-data-repository-data-warehouse/ (Links to an external site.)

Cohen, D. J., Dorr, D. A., Knierim, K., DuBard, C. A., Hemler, J. R., Hall, J. D., ... & 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.

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

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