Summary: Stocklytics.com reports that 15% of healthcare workforce hours will be automated by 2030, driven by AI adoption due to increased patient volumes and a shortage of specialists.
Key Takeaways:
- The COVID-19 pandemic highlighted the need for healthcare modernization, accelerating AI adoption to manage growing patient volumes and reduce provider burnout.
- Machine learning currently leads AI applications in healthcare, enhancing diagnosis, treatment, and overall service quality.
According to the latest analysis by Stocklytics.com, 15% of the current healthcare workforce hours will be subject to automation by 2030. The growing reliance on AI is primarily due to the swelling patient population and a shorter pool of health specialists.
COVID-19 Pandemic as a Catalyst
The COVID-19 pandemic’s impact on patient volumes served as a critical wake-up call, underscoring the urgent need to modernize and upgrade the healthcare system, according to Stocklytics financial analyst Edith Reads.
AI’s Role in Alleviating Workload
The adoption of AI is expected to alleviate the workload on healthcare providers, potentially reducing burnout rates and improving job satisfaction. By automating mundane tasks, healthcare professionals can dedicate more time to patient care, research, and specialized medical procedures, thereby enhancing the overall quality of healthcare services.
Widespread AI Adoption
According to the company, nearly 90% of healthcare workers, life science companies, and tech vendors use AI in some capacity. AI implementation in healthcare systems can be categorized into machine learning, natural language processing, computer vision, and context-aware computing.
So far, machine learning has taken the lead in most AI-driven solutions by integrating AI and robotics in diagnosis and treatment. The full story and statistics can be found here.
