Study design:
We used a coding group established for clinical coding in Telegram. We used this group for COVID-19 coding consultations. The analysis was done on the consultations (messages were exchanged between coders). The inclusion criteria were messages which contained consultation requests about COVID-19 coding, while all messages about general news of COVID-19, prevention protocols and guidelines about access to COVID medical records were excluded.
Some factors such as message numbers, types, and contents were recorded. Also, the purpose of messages, response times (time until the first reply), total time of consultation (lengths of data transmission), and number of persons involved in consultations were evaluated. Other noted fields were final conclusion (whether consultations were entirely responded to), the time of questions and replies and their compliance with office hours.
We also sent a survey to participants in COVID-19 consultations and collected information about coders themselves. Information including age, gender, workload, educational level, as well as province and city of residence were collected. Moreover, we asked about the problems coders encountered during COVID-19 clinical coding and their opinion on whether these consultations were effective. Finally, participants indicated whether they agreed to use their personal information as a research report. Issues mentioned in the surveys by coders were analysed and the associated themes were extracted.
Participants were asked to read and observe group instructions. If these instructions were violated, they would be warned for the first time and removed from the group following a first warning. These instructions included not sending irregular messages, observing ethical and professional principles in conversations, not sending messages containing links, and avoiding using extra compliments to keep the group busy. Discussions about the codes were free, but three expert coders always checked the final answers for accuracy.
We used some bots to manage sent messages and remove potential advertising messages.