HOW TO USE MACHINE LEARNING IN HEALTHCARE CHATBOTS AND MESSAGING SYSTEMS TO INCREASE STAFF PRODUCTIVITY AND IMPROVE PATIENT SATISFACTION

The ultimate goals of healthcare messaging systems should be to increase patient education, answer questions in a timely manner, improve patient outcomes, reduce patients’ administrative burdens, and increase staff productivity by allowing staff to focus on communicating with the neediest patients. The unintended results of many healthcare messaging systems have, unfortunately, been to accidentally increase staff workloads and reduce productivity for some because patients often chat more with staff willing to message with them. The good news is that there are machine learning (ML) techniques available to allow machine responses with auto-triage and auto-escalation and simulate human responses so that some or many patient and staff communiques can be answered via chatbots and passing some or many messages to humans only when necessary. Join Shahid Shah, who’s built many healthcare chatbots and messaging systems, lead an interactive session on what works and what doesn’t.
Shahid Shah, Publisher, Netspective Media