  Telehealth helps to facilitate access to medical professionals by enabling
remote medical services for the patients. These services have become gradually
popular over the years with the advent of necessary technological
infrastructure. The benefits of telehealth have been even more apparent since
the beginning of the COVID-19 crisis, as people have become less inclined to
visit doctors in person during the pandemic. In this paper, we focus on
facilitating the chat sessions between a doctor and a patient. We note that the
quality and efficiency of the chat experience can be critical as the demand for
telehealth services increases. Accordingly, we develop a smart auto-response
generation mechanism for medical conversations that helps doctors respond to
consultation requests efficiently, particularly during busy sessions. We
explore over 900,000 anonymous, historical online messages between doctors and
patients collected over nine months. We implement clustering algorithms to
identify the most frequent responses by doctors and manually label the data
accordingly. We then train machine learning algorithms using this preprocessed
data to generate the responses. The considered algorithm has two steps: a
filtering (i.e., triggering) model to filter out infeasible patient messages
and a response generator to suggest the top-3 doctor responses for the ones
that successfully pass the triggering phase. The method provides an accuracy of
83.28\% for precision@3 and shows robustness to its parameters.
