Abstract: FR-OR010

The Use of a Medical Application Improves the Identification and Classification of AKI

Session Information

Category: Nephrology Education

  • 1301 Educational Research


  • Claure-Del Granado, Rolando, Universidad Mayor de San Simon, School of Medicine, Cochabamba, Bolivia, Plurinational State of
  • Iturricha caceres, Maria Fernanda, Hospital Obrero #2 - C.N.S., Cochabamba, Bolivia, Plurinational State of
  • Macedo, Etienne, UCSD, San Diego, California, United States
  • Mehta, Ravindra L., University of California San Diego Medical Center, San Diego, California, United States

The use of mobile devices by health care professionals (HCPs) has transformed many aspects of clinical practice. Mobile devices and apps provide many benefits for HCPs, perhaps most significantly they increase the access to point-of-care tools, which has been shown to support better clinical decision-making and improved patient outcomes. In this study we tested the hypothesis that the use of an app specifically designed for recognition and management of AKI will help HCPs to better identify and classify AKI.


We included 20 AKI cases from our center that were part of the 0by25 Global Snapshot study report. Twenty clinical vignettes of these patients (including baseline serum creatinine (sCr) and a second sCr that was measure in a 7 day period) were presented to 50 last year medical students and ask two simple questions: 1) Did the patient develop AKI? and 2) To classify the stage of AKI; before and after providing them with an app that was developed for early identification, classification and management of AKI (IRA SLANH app, Island of the Moon® V.1, 2014; Cochabamba-Bolivia). We analyzed if the use of a medical app could improve correct identification and stage classification of AKI.


Before the IRA SLANH app was introduce to the 50 medical students, the mean number of correct answers were 14.7±4.7 with a minimum of 3 correct answers and a maximum of 20 correct answers; and only in 6.7±4.4 of the cases the correct stage of AKI was identified. After the app was presented to the medical students the number of correct answers improved to 20 and in all cases AKI stage was correctly classified. Before the medical app was presented to the medical students, only 22% of them were able to correctly identify all AKI cases, and 0% of them could correctly classify all cases of AKI.


Medical applications are useful tools in the practice of evidence-based medicine at the point of care. The use of a medical application specifically developed for the identification and staging of AKI could play a very important role in early identification and correct classification of AKI potentially allowing earlier intervention with preventive and treatment strategies to reverse kidney injury and improve recovery.