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According to a report by L.E.K Consulting, challenges are unlike other industries’, and adoption of artificial intelligence may be slow. Telemedicine accelerated by COVID-19 could drive uptake.
Artificial intelligence (AI) is poised to disrupt the hospital industry, according to a new report from global management consulting firm L.E.K. Consulting.
Machine-learning and decision-support systems may soon play more of a role in diagnosis, disease monitoring, treatment and prevention, patient self-management and compliance, as well as the day-to-day operations of hospitals, clinics and practices.
The coronavirus pandemic and the resulting upsurge in telemedicine will increase the pace of AI adoption as hospitals and practitioners work to streamline and integrate clinical information and reduce operating costs.
According the report, AI faces bigger hurdles in healthcare than in other industries as a result of privacy restrictions and the challenge of integrating multiple IT systems.
These are among some of the key findings in Artificial Intelligence and Hospitals: Separating Myth from Reality, a collaborative report between L.E.K. Consulting and Ruchin Kansal, founder and Managing Director of Kansal & Company. The report details the contributions hospital AI is likely to make and the hospital-industry-specific barriers it will have to overcome.
“AI applications will support, not replace, healthcare professionals,” says Jonas Funk, L.E.K. managing director and an author of the report. “But we are beginning to see both clinical and nonclinical advancements in AI technology that indicate that healthcare is rapidly approaching an inflection point for substantial change.”
Privacy concerns may limit hospital AI, however, cost pressures, the need for outcomes data, and the demands of COVID-19 may drive adoption.
Adoption of AI is likely to be more challenging in healthcare than in other industries, according to the report. Among the healthcare-specific barriers are privacy restrictions like HIPAA that limit data access and make it hard to “train” AI; complex legacy IT systems that are difficult to integrate and limited buy-in from staff that see AI as a threat to their jobs.
Although, there are also industry-specific factors likely to drive adoption. These include cost pressures, provider shortages that demand more efficiency, healthcare consumerism that drives demand for more data and the shift to value-based care – healthcare delivery and reimbursement programs that are focused on and driven by clinical results.
“The shift to telemedicine, an increased focus on ambulatory care and the need for large-scale data analysis were already major features of the healthcare landscape before the pandemic,” says Monish Rajpal, L.E.K. managing director and a report co-author. “But the pandemic has escalated those needs, and AI can make all of them more effective and efficient.”
The first hospital AI solutions will support clinical decisions, patient outreach and the streamlining of day-to-day operations
What form will healthcare AI take? According to the report, initial applications will be somewhat subtle. Early healthcare applications will have three areas of focus:
“While most AI-enabled healthcare solutions are still in their infancy, we expect rapid growth in the market over the next three years,” said Rajpal.
Administrators should focus AI initiatives on maximum strategic impact
Hospital administrators planning AI initiatives should treat them as they would any other major investment, the report says.
“It’s essential to evaluate each AI project for its strategic value,” said Ruchin Kansal, founder and managing director of Kansal & Company and a report co-author. “The highest strategic impact results from initiatives that allow the health system to expand into an opportunity that is adjacent to its core business model, for example expanding its ambulatory services or enhancing its patient outreach. The highest financial impact will result from new revenue opportunities rather than just cost reductions. Finally, it’s important to consider the organization’s readiness to adopt the solution – in terms of both technology and willingness to implement AI into workflow.“