Many infection-transmission cases are acquired and spread in hospitals. It is necessary to detect any infection early and to identify population at risk accurately and efficiently. This chapter discusses the infection tracing in i-hospital. The target environment is smart connected hospitals using Internet of Things (IoT). First, a high-level framework for collecting encounter information has been introduced. The framework utilizes the heterogeneous sensing devices in IoT to collect the encounter data, process them, and make them accessible to be utilized by various applications. Then, an infection-tracing problem is defined and how the use of encounter data can facilitate the traceback to the source of infection and identification of at-risk population; the nodes that might already be infected. Then, issues of heterogeneous sensing, communication, and infection ranges are presented, along with different encounter issues. A systemic infection tracing that utilizes IoT devices and encounter statistics during infection breakouts is presented. Traceback and filtering methods are then proposed using probabilistic forward and backward search techniques. The system evaluation metrics are described. We use extensive WLAN campus traces of six buildings with different mobility characteristics and over 34k users to drive our simulations. IoT equipment such as RFID readers and tags can apply the same tracing algorithms seamlessly. Metrics such as true positive, true negative, accuracy, and coverage are measured and presented. Our results thus far show promise, where in most cases the accuracy of correctly selecting the infected nodes that have been in the area during the epidemic period was found to reach 70–80% with population selection coverage close to the infection percentage of nodes.