Split the day into morning (10:45-12:15) and afternoon (13:00-15:30) sections, with AndyN checking in to see if anyone needed any help at the start of each session and from time to time throughout.
Both teams began by asking the ‘Client’ to provide more context to the idea so that the team were all on the same page
Team Alpha: Chatbot
Questions to consider answering about how the day went
Team One Data Wranglers!
Team to add what they did, how they got on, observations, etc
Team Alpha had a quick conversation with Nazia AKHTAR as she was the client of this task.
As we transition trainees into new process they will be queries and the chatbots supports that
She threw the requirements of the chatbot on the table. She articulated that if the proposed system could handle “Categories of enquires”, “Doctors enquiry on personal data of ESR”, “Capturing the questions while admins are not able to answer instantly”, “Some queries on ARCPs process” etc.
Rob Pink mentioned that HEE has a chat system which supports for Specialty recruitment for junior doctors and others, i.e, How they can apply for a job. He also mentioned E-learning for health care which does the word search.
After having some concepts in mind that team considered few things to design the chatbot. Some of them are stated below:
Capturing inaccuracy of data
Traffic of email
less delay – save time
Responses from Admin
Conversation tree – add that in the future
Instant answer
Reporting mechanism
How intelligent the chatbot would be?
FAQ
Given the above characteristics team were researching on the net to get some hints and also design and implementation discussions were being continued. Andy Dingley came up with the ideas of implementing Amazon lex into our TSS. We found out that we had only 1 hour 30 minutes sprint/iteration to come up with the PoC.
We had only 30 minutes lunch break and we divided the dev team into front and back end part. AndyD and Jay were looking into the Amazon lex and john o and Yafang Deng were concentrating on the Front end with Amplify.
Screenshot of the TIS Self-Service implementation:
Team to add what they did, how they got on, observations, etc
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