AI-Powered Symptom Checkers can Help Healthcare Systems Deal with the COVID-19 Burden

AI-powered symptom checkers can potentially reduce the number of people going to in-person clinics during the pandemic, but first, researchers say, people need to know they exist.

COVID symptom checkers are digital self-assessment tools that use AI to help users identify their level of COVID-19 risk and assess whether they need to seek urgent care based on their reported symptoms. These tools also aim to provide reassurance to people who are experiencing symptoms that are not COVID-19 related.

Most platforms, like Babylon and Isabel, are public-facing tools, but the University of California, San Francisco (UCSF) has one of the first COVID-19 symptom checkers that is fully integrated with the users' medical records, allowing for immediate appointment scheduling.

A mixed-methods study at the University of Waterloo found that 18- to 34-year-old young adults, typically the first age group to adopt new technologies, were unaware of the existence of these platforms.

"Young adults are usually eager adopters of technology, so we were a little surprised by this finding," said lead researcher Stephanie Aboueid, a PhD candidate in the School of Public Health and Health Systems. "Symptom checkers have the potential to reduce the burden on health-care systems and the risk of person-to-person infection, so we wanted to find out how to improve these platforms so more people use them."

Of the 22 university students interviewed in winter and spring of 2020, nine of them did not know the tools exist. The researchers also conducted a survey on general symptom checkers in winter 2021, and data suggested 88 per cent (1,365 out of 1,545) of participants did not use one in the past year.

Findings from the smaller qualitative study suggest that three-quarters of those who had used government-issued symptom checkers were satisfied with them. Those who used non-government symptom checkers found the experience suboptimal, citing a lack of trust and credibility.

"One of the findings was that users wanted more personalization and were less trusting of tools that gave the same results to everyone," Aboueid said. "The UCSF system was able to reduce the number of visits while taking into account underlying conditions along with the symptom checks and booking follow-up appointments when needed."

Besides more personalization, the researchers found that other improvements to existing symptom checkers include providing users more information about the creators of the platform, providing more explanation of the symptoms in lay language, more language options and the option to get tested at a nearby location.

Stephanie Aboueid,corresponding author Samantha B Meyer, James R Wallace, Shreya Mahajan, Teeyaa Nur, Ashok Chaurasia.
Use of symptom checkers for COVID-19-related symptoms among university students: a qualitative study.
BMJ Innov. 2021. doi: 10.1136/bmjinnov-2020-000498

Most Popular Now

AI-Powered CRISPR could Lead to Faster G…

Stanford Medicine researchers have developed an artificial intelligence (AI) tool to help scientists better plan gene-editing experiments. The technology, CRISPR-GPT, acts as a gene-editing “copilot” supported by AI to help...

Groundbreaking AI Aims to Speed Lifesavi…

To solve a problem, we have to see it clearly. Whether it’s an infection by a novel virus or memory-stealing plaques forming in the brains of Alzheimer’s patients, visualizing disease processes...

ChatGPT 4o Therapeutic Chatbot 'Ama…

One of the first randomized controlled trials assessing the effectiveness of a large language model (LLM) chatbot 'Amanda' for relationship support shows that a single session of chatbot therapy...

AI Tools Help Predict Severe Asthma Risk…

Mayo Clinic researchers have developed artificial intelligence (AI) tools that help identify which children with asthma face the highest risk of serious asthma exacerbation and acute respiratory infections. The study...

AI Model Forecasts Disease Risk Decades …

Imagine a future where your medical history could help predict what health conditions you might face in the next two decades. Researchers have developed a generative AI model that uses...

AI Distinguishes Glioblastoma from Look-…

A Harvard Medical School–led research team has developed an AI tool that can reliably tell apart two look-alike cancers found in the brain but with different origins, behaviors, and treatments. The...

Smart Device Uses AI and Bioelectronics …

As a wound heals, it goes through several stages: clotting to stop bleeding, immune system response, scabbing, and scarring. A wearable device called "a-Heal," designed by engineers at the University...

AI Model Indicates Four out of Ten Breas…

A project at Lund University in Sweden has trained an AI model to identify breast cancer patients who could be spared from axillary surgery. The model analyses previously unutilised information...

Overcoming the AI Applicability Crisis a…

Opinion Article by Harry Lykostratis, Chief Executive, Open Medical. The government’s 10 Year Health Plan makes a lot of the potential of AI-software to support clinical decision making, improve productivity, and...

Dartford and Gravesham Implements Clinis…

Dartford and Gravesham NHS Trust has taken a significant step towards a more digital future by rolling out electronic test ordering using Clinisys ICE. The trust deployed the order communications...