EMR Adoption: A User Perception Study

Despite promise of significant benefits, inadequate user acceptance has frequently limited the impact of EMR implementations. Using an action research approach, our team is participating in an EMR implementation at Aravind Eye Care System (AECS), one of the largest eye hospitals in the world, to observe its current practices, measure user perceptions of EMR, plan interventions, and assess their impact. Our proximate research objective is to develop interventions based on sound conceptual foundations and empirical validation rather than in an ad hoc manner, to facilitate EMR acceptance by AECS hospital staff. The ensuing goal is to learn from the post intervention findings to develop guidelines for EMR implementations, particularly in a developing country context. In this paper we report on the first phase of this study, and these initial results show how even simple analysis of perception patterns can help to customize and shape intervention plans

Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity

In this article, we present our recent study on computational methodology for predicting the toxicity of PFAS known as “forever chemicals” based on chemical structures through evaluation of multiple machine learning methods. To address the scarcity of PFAS toxicity data, a deep “transfer learning” method has been investigated by leveraging toxicity information over the entire organic chemical domain and an uncertainty-informed workflow by incorporating SelectiveNet architecture, which can support future guidance of high throughput screening with knowledge of chemical structures, has been developed