SMITH BRAIN TRUST – Complaints about the U.S. healthcare system aren’t exactly new. But the coronavirus pandemic has thrown the long-standing and chronic deficiencies of the U.S. healthcare system into sharp relief, says Maryland Smith’s Ritu Agarwal. And it’s likely to spark some changes.
“COVID-19 was the healthcare system’s wake-up call, reminding us of the underfunded and inadequate public health infrastructure, the inability to capture, analyze and share data to make timely and evidence-based decisions, the deeply disturbing healthcare apartheid in society, and the enormous price-tag associated with the system,” says Agarwal, senior associate dean and distinguished university professor of the University of Maryland’s Robert H. Smith School of Business.
Amid the pandemic, even the conversations have changed, says Agarwal, the Robert H. Smith Dean’s Chair of Information Systems and co-director of the Center for Health Information and Decision Systems. More than ever before, there’s consensus on the need for greater use of digital technologies and data to lubricate decision making and break down silos. “Digital healthcare may finally have reached a tipping point,” she says.
Two technologies in particular, she says, will “remain sticky post-COVID” – telemedicine and artificial intelligence (AI)/machine learning (ML) .
Telemedicine: The technology has been with us awhile, but the use of it in healthcare has never caught on in the United States quite like this. “Healthcare workers’ safety, patients’ inability to travel because of COVID restrictions, and a growing patient volume necessitated a quick pivot from the traditional in-person healthcare encounter to a virtual visit enabled by telemedicine,” Agarwal says. Telehealth use might have been adopted as a temporary solution to the pandemic crisis, but it will continue, she says, as patients have become increasingly comfortable with virtual visits and providers have learned to utilize them more effectively. “Telemedicine can reduce costs, mitigate inefficiencies, and perhaps make healthcare more accessible and available to vulnerable and underserved populations.”
Artificial intelligence (AI): This technology is emerging and evolving, says Agarwal. But AI, and in particular, ML, is already beginning to transform healthcare. “AI offers the promise of enabling greater leverage of one the scarcest resource in healthcare: clinical knowledge and expertise, while also supporting efficiencies and improving quality and consistency in care delivery,” she says. Machine learning, or the ability for systems to learn automatically from data, had already demonstrated robust potential pre-pandemic in healthcare that is awash with data, catching the attention of doctors, hospital systems, and insurance companies. The pandemic sparked many applications using AI/ML, for example, predicting the risk of being infected by the virus so hospital workers can triage patients more effectively, or forecasting the emergence of new clusters of infection. Post-pandemic, attention will shift to continued exploration of where AI/ML offers the most value across a range of use cases such as medical image analysis, pattern detection and new clinical discoveries in medical charts, and greater personalization of medical interventions. “We have just scratched the tip of the iceberg” Agarwal says.
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