8 non-clinical AI applications that doctors are particularly passionate about, according to the AMA

Emphasis on leveraging AI to represent enhanced The American Medical Association lists eight in-demand AI use cases that it says it has heard doctors are “expressing particular enthusiasm for.”

The 176-year-old organization, which has 272,000 members, presents the list in a new report prepared in collaboration with New York legal consultancy Manatt Health.

In introducing the report, The Future of Healthcare: The Emerging Landscape of Augmented Intelligence in HealthcareAMA suggests not favorable artificial Because we believe that AI tools and services, when deployed properly, “support human decision-making, rather than explicitly replace it.”

Below is a list of notable non-clinical uses of AMA. Many are already in use, and all could become popular over the next five to 10 years, the group notes.

1. Access to care.
   • Identify optimized schedules to minimize wait times and maximize alignment of patient needs and physician experience.
   • Support the pre-approval process, including completion and follow-up of pre-approval documentation.
2. Management and revenue cycles.
   • Identify appropriate billing and service codes based on medical records.
   • Predict the likelihood of claim denials and identify opportunities to reduce claims.
   • Supports accurate coding in the context of risk adjustment and value-based payment programs.
3. operation.
   • Forecast hospital staffing levels and required staffing needs.
   • Track inventory and usage patterns to predict medical supplies orders.
   • Monitor equipment availability and predict equipment failure.
4. Regulatory Compliance and Reporting.
   • Automate the tracking and reporting of regulatory compliance measures to reduce administrative burden.
   • Analyze documents and processes to ensure compliance with evolving health care laws and policies.
5. Analysis of patient experience and satisfaction.
   • Analyze patient feedback and surveys to identify improvements in patient experience.
   • Predict trends in patient satisfaction and identify drivers of patient trust.
6. Quality improvement and management.
   • Automatically track identified quality results and generate reports.
   • Identify gaps and inequities in patient outcomes and quality of services.
7. education.
   • Monitor clinical interactions with model patients and provide feedback to physicians or residents.
   • Identify possible learning needs and recommend learning resources based on a review of the physician or resident's experience and skill set.
   • Provide automated haptic feedback during robot training.
8. the study.
   • Predict protein structure from amino acid sequence.
   • Optimize research patient reach and clinical trial enrollment.
   • Analyze electronic medical records at scale to identify potential human research subjects.

Elsewhere in the report, the authors find that AMA interviews and a survey of physicians show that the AI ​​tools employed have strong data privacy protections, are safe and effective, and integrate well with existing technology solutions. , reiterated that physicians' interest in protecting them from liability due to algorithmic errors was emphasized. .

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“Notably, physicians have also expressed interest in being involved in the implementation of AI tools, with 86% of physicians surveyed saying they would like to be responsible or consulted in the process. doing.”

The full report will be published free of charge.