Understanding the prognosis of older adults is a big challenge in healthcare research, especially since very little is known about how different comorbidities interact and influence the prognosis. Recently, a electronic healthcare records dataset of 24 patient attributes from Northwestern Memorial Hospital was used to develop predictive models for five year survival outcome. In this study we analyze the same data for discovering hotspots with respect to five year survival using association rule mining techniques. The goal here is to identify characteristics of patient segments where the five year survival fraction is significantly lower/higher than the survival fraction across the entire dataset. A two-stage post-processing procedure was used to identify non-redundant rules. The resulting rules conform with existing biomedical knowledge and provide interesting insights into prognosis of older adults. Incorporating such information into clinical decision making could advance person-centered healthcare by encouraging optimal use of healthcare services to those patients most likely to benefit.