[PDF][PDF] CONSENSUS: a Shiny application of dementia evaluation and reporting for the KU ADC longitudinal Clinical Cohort database
P Sharma, RN Montgomery, RS Graves, K Meyer… - JAMIA …, 2021 - academic.oup.com
JAMIA open, 2021•academic.oup.com
Abstract Background The University of Kansas Alzheimer's Disease Center (KU ADC)
maintains several large databases to track participant recruitment, enrollment, and capture
various research-related activities. It is challenging to manage and coordinate all the
research-related activities. One of the crucial activities involves generating a consensus
diagnosis and communicating with participants and their primary care providers. Process To
effectively manage the cohort, the KU ADC utilizes a combination of open-source electronic …
maintains several large databases to track participant recruitment, enrollment, and capture
various research-related activities. It is challenging to manage and coordinate all the
research-related activities. One of the crucial activities involves generating a consensus
diagnosis and communicating with participants and their primary care providers. Process To
effectively manage the cohort, the KU ADC utilizes a combination of open-source electronic …
Background
The University of Kansas Alzheimer’s Disease Center (KU ADC) maintains several large databases to track participant recruitment, enrollment, and capture various research-related activities. It is challenging to manage and coordinate all the research-related activities. One of the crucial activities involves generating a consensus diagnosis and communicating with participants and their primary care providers.
Process
To effectively manage the cohort, the KU ADC utilizes a combination of open-source electronic data capture (EDC) (i.e. REDCap), along with other homegrown data management and analytic systems developed using R-studio and Shiny.
Process evaluation
In this article, we describe the method and utility of the user-friendly dashboard that was developed for the rapid reporting of dementia evaluations which allows clinical researchers to summarize recruitment metrics, automatically generate letters to both participants and healthcare providers, which ultimately help optimize workflows.
Conclusions
We believe this general framework would be beneficial to any institution that build reports and summarizing key metrics of their research from longitudinal databases.
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