When the COVID-19 pandemic hit, people had a lot of questions – What are the symptoms? What should I do if I have been exposed? Where can I get tested?
Call centers and care practices across Penn Medicine bore the brunt of the increased volume of inquiries, resulting in long wait times for patients and an increased burden on frontline clinicians.
In analyzing a database of COVID-19-related telephone encounters and patient-submitted secure messages to our patient portal, we found that many of the questions coming in could be addressed with standardized answers reflecting the most up-to-date knowledge and guidelines about the virus.
Across many industries, chatbots are leveraged as an efficiency-enhancing way for institutions to interact with target audiences. Chatbots leverage machine learning and natural language processing to understand user intent and reply with appropriate answers.
We designed a public-facing chatbot in partnership with Google and Verily that was equipped to provide appropriate and timely responses to questions about COVID-19 and triage symptomatic patients to the right level of care at Penn Medicine.
Content for the chatbot came from the COVID-19 FAQ, an interactive app continuously updated by students at the Perelman School of Medicine and validated by experts in infectious disease, occupational medicine, women's health, operations, and oncology.
Two weeks after our initial planning meeting, the chatbot went live.
The chatbot and patient triage tool we created enabled patients to get answers to their COVID-19 questions instantly, 24 hours a day, seven days a week during the height of the pandemic. It also reduced call center volume and offloaded work from frontline clinicians. In early 2021, when COVID-19 cases in Philadelphia and demand for the chatbot began to decline, we decided to retire the resource.
While building the tool, we learned more about mapping patient language to intent, introducing logic to disambiguate intent, and inserting automated, self-serve tools into workflows to address call volume and clinical workloads. These insights will help inform ongoing algorithmic care efforts at Penn Medicine.