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.