Health Analytics Lead to More Questions: A Comorbidity Lens Approach
2016
- 131Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage131
- Downloads86
- Abstract Views45
Conference Paper Description
As we amass more data, we have an opportunity to analyze a pseudo-population to better understand differences in health across groups. For example, comorbidity is a medical condition when a patient develops more than one disease simultaneously. The way patients belonging to different population groups develop comorbidities can have a major impact on their health outcomes. Therefore, there is a strong need to know these differences in comorbidities across population groups. In this study, we apply the grounded theory methodology lens to compare the comorbidities across population groups. First, we create a comprehensive network for each population group and then compare their structural properties. This leads to developing multiple research questions that need to be explored in the future research. The interesting findings and theoretical implications are discussed.
Bibliographic Details
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