Emphasizing that it uses AI to stand for augmented intelligence, the American Medical Association lays out eight in-demand AI use cases for which the organization says it has heard physicians “express particular enthusiasm.”
The 176-year-old, 272,000-member group presents the list in a new report it prepared with the New York legal consultancy Manatt Health.
In introducing the report, Future of Health: The Emerging Landscape of Augmented Intelligence in Health Care, AMA suggests it disfavors artificial in AI because it believes AI tools and services, when properly deployed, “support rather than explicitly replace human decision-making.”
Here’s AMA’s list of nonclinical use cases to watch for—many of which are already in use, the group notes, and all of which are likely to rise in popularity over the next five to 10 years.
Access to care.
Identify optimized scheduling to minimize wait times and maximize alignment of patient needs and physician experience.
Support prior authorization process, including completion and follow-up of prior authorization documentation.
Administration and revenue cycle.
Identify appropriate billing and service codes based on medical notes.
Predict likelihood of—and identify opportunities to reduce—claims denials.
Supporting accurate coding in the context of risk-adjustment and value-based payment programs.
Predict hospital staffing volumes and requisite staffing needs.
Track inventory and utilization patterns to forecast medical supply orders.
Monitor equipment availability and predict equipment failures.
Regulatory compliance and reporting.
Automate the tracking and reporting of regulatory compliance measures, reducing administrative burden.
Analyze documentation and processes to ensure adherence to evolving health care laws and policies.
Patient experience and satisfaction analysis.
Analyze patient feedback and surveys to identify areas of improvement in patient experience.
Predict patient satisfaction trends and identify drivers of patient trust.
Quality improvement and management.
Automatically track identified quality outcomes and generate reports.
Identify gaps in quality and/or inequities in patient outcomes or services.
Monitor a clinical interaction with a model patient and provide feedback to the physician or trainee.
Based on review of physician or trainee’s experiences and skill sets, identify possible learning needs and/or recommend learning resources.
Provide automated haptic feedback during robotic training.
Predict the structures of proteins from amino-acid sequence.
Optimize research subject outreach and enrollment in clinical trials.
Analyze electronic health records at scale to identify potential human research subjects.
Elsewhere in the report, the authors reiterate that AMA’s interviews and physician survey underscored physicians’ interest in ensuring that adopted AI tools have strong data privacy protections, are safe and effective, integrate well with existing technology solutions and protect physicians from liability made by algorithmic error.
“Notably, physicians also expressed interest in being involved in the adoption of AI tools, with 86% of surveyed physicians indicating they would like to be either responsible or consulted in the process.”
More than 4 of 10 provider people believe AI is already accepted and widely used in their neck of healthcare. Only 2 of 10 of pharma folk would say the same of theirs. The Berkeley Research Group made the finding when it surveyed 150 healthcare professionals from both realms. BRG analyst Clay Willis comments that the commercial side of the pharmaceutical industry is “ripe for a push toward more effective AI use in marketing, pricing strategy and contract decisions, which have substantial upside and limited risk.” However, Willis adds, today’s uncertain regulatory landscape has led to “slower AI implementation in other key areas like drug development and clinical trials.”
That’s not to say promising things aren’t happening with AI in pharma. Case in point: This week Brand Engagement Network, aka “Ben,” announced it’s partnering with MedAdvisor Solutions. Ben is an up-and-coming provider of personalized customer engagement AI, MedAdvisor a specialist in pharmacy-based patient engagement. The pair’s strategic alliance will match conversational AI chatbots with patients filling prescriptions. Ben’s CEO, Michael Zacharski, says such AI assistance will offer an “unrushed personal conversation about medication regiments to improve the experience of the customer, the retail pharmacy chain and the pharmaceutical manufacturer.”
‘Nobody could force a doctor to practice medicine in a way they don’t want to.’ Think otherwise? Try pressuring physicians to use an iteration of AI that, in their perception, might threaten their income and their autonomy. Cardiologist-turned-venture capitalist Ronald Razmi, MD, makes the point in an interview with Newsweek. “Historically, physicians have shown they’re very good at making sure those technologies never gain adoption,” adds Razmi, who expounds on the observation in his new book, AI Doctor: The Rise of Artificial Intelligence in Healthcare.
On the other hand, physicians are only able to process about 5% of the data available to them before deciding on a particular treatment for any given patient. The other 95%? Anyone trying to manually manage it, MD degree or not, would be overwhelmed by the attempt. Dushyant Sahani, MD, chair of radiology at the University of Washington in Seattle, points this out for the Spokane-based Spokesman-Review newspaper. “In the modern world, we have so much data,” Sahani says, “but we need a better way of using it for appropriate [medical] decision-making.” Enter AI, of which Sahani seems to be an enthusiastic adopter. Read the article.
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