Artificial intelligence has revolutionized the healthcare industry in recent years. Although the technology has potential to improve patient care and streamline operations, it poses significant risks.
Pharmaceutical manufacturers could leverage AI to speed up research, conduct more efficient clinical trials, and make data-driven decisions–but it poses ethical, safety, and regulatory challenges that could undermine compliance and patient trust.
How is AI delivering value in healthcare?
According to IBM, “Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy.” The term “AI” describes a wide range of technologies from natural language processing (NLP) to physical robots. Healthcare providers have been leveraging these technologies in a variety of ways.
AI healthcare applications:
- Machine learning drives diagnosis: Healthcare providers can share large data sets with machine learning systems to predict which protocols may be more effective for their patients. The AI looks for patterns from past patients to predict outcomes. As they develop new drugs, pharma companies can use ML to identify new drug candidates by analyzing millions of compounds in a simulated setting.
- Natural language processing organizes documents: NLP technologies can ingest messy clinical notes, unstructured reports, or other documents and create structured documentation–improving worker efficiency.
- Robots handle repetitive tasks: Health systems use robotic process automation to complete repetitive tasks like prior authorization or billing–no human worker required. This could empower a nurse to reclaim the 25% of their work time they spend on admin activities to care for patients, for example.
Medical benefits of AI may include prediction and prevention of disease, enhanced surgical outcomes, and better decision-making. Administrative and cost benefits may include lightening the administrative burden for providers and cost savings. The applications of AI in healthcare are virtually limitless.
AI healthcare applications:
- Machine learning drives diagnosis: Healthcare providers can share large data sets with machine learning systems to predict which protocols may be more effective for their patients. The AI looks for patterns from past patients to predict outcomes. As they develop new drugs, pharma companies can use ML to identify new drug candidates by analyzing millions of compounds in a simulated setting.
- Natural language processing organizes documents: NLP technologies can ingest messy clinical notes, unstructured reports, or other documents and create structured documentation–improving worker efficiency.
- Robots handle repetitive tasks: Health systems use robotic process automation to complete repetitive tasks like prior authorization or billing–no human worker required. This could empower a nurse to reclaim the 25% of their work time they spend on admin activities to care for patients, for example.
Medical benefits of AI may include prediction and prevention of disease, enhanced surgical outcomes, and better decision-making. Administrative and cost benefits may include lightening the administrative burden for providers and cost savings. The applications of AI in healthcare are virtually limitless.
In summary
Although AI is emerging as an intelligent assistant to healthcare workers, it needs careful oversight: protecting patient data and monitoring the quality of AI outputs will be critical. Research suggests that regulatory uncertainties and operational challenges will slow widespread adoption. “…we expect to see limited use of AI in clinical practice within 5 years and more extensive use within 10.”
The healthcare industry may be slow to change. But health systems, providers, administrators, and other workers could benefit from increasing their knowledge and familiarity with AI technologies–by setting up tightly regulated pilots. When formal guidance catches up, they’ll be ready to embrace change.
