AI improves patient outcomes: Is AI our best healthcare worker yet?

• Author:
• Author name

  Quantumrun Foresight
• December 13, 2023

Insight summary

The US healthcare system, amidst challenges like an aging population and staff shortages, is increasingly adopting AI and value-based care to improve patient outcomes and manage costs. As healthcare spending is set to reach $6 trillion by 2027, AI is being used to enhance diagnoses, treatment planning, and operational efficiency. However, this shift also brings risks like regulatory challenges and potential patient harm due to AI errors. This evolution in healthcare raises critical questions about the future role of healthcare workers, insurance policies for AI, and the necessity for more stringent government oversight on AI's application in healthcare.

AI improves patient outcomes context

The US healthcare spending is forecast to reach USD $6 trillion by 2027. However, healthcare providers are not able to keep up with the increasing demands of an aging population and mass resignations in the industry. The Association of American Medical Colleges reported that there could be a deficit of about 38,000 to 124,000 physicians by 2034. Meanwhile, the hospital workforce has decreased by almost 90,000 since March 2020, according to the US Bureau of Labor Statistics. To combat these alarming numbers, the healthcare sector is turning to AI. In addition, according to a survey of healthcare executives conducted by provider Optum, 96 percent believe AI can enable health equality goals by ensuring consistent quality of care.

Platforms and tools leveraging AI technologies are well-positioned to support and increase the productivity of healthcare providers while improving patient outcomes. These technologies include automated systems that enhance visual perception, diagnoses and predictions, and seamless data processing. Using patient information, AI can identify those at most risk and recommend treatments based on medical records and history. AI also can help clinicians make better judgments, and it has aided drug development, customized medicine, and patient monitoring.

Disruptive impact

AI has many benefits for patient care. First, AI can help doctors digest and streamline data, allowing them to focus on their patients’ histories and potential needs. AI has also been incorporated into electronic health records (EHR) systems to identify, evaluate, and reduce threats to patient safety. The technology can also target unique symptoms and stratify risk severity for each patient, ensuring they receive the best possible treatment plan. Finally, AI can measure the quality of care being delivered to patients, including identifying gaps and areas for improvement. Interpreting patient data through AI may also assist hospitals in speeding up responses to therapies, streamlining processes, and allowing staff to spend less time on time-consuming procedures and manual activities. In addition, enhanced efficiency lowers costs, resulting in more dedicated patient care, efficient hospital administration, and reduced stress for all medical staff.

However, as AI is increasingly utilized in healthcare, several risks and difficulties may surface at the personal, macro-level (e.g., regulation and policies), and technical levels (e.g., usability, performance, data privacy, and security). For example, a widespread AI failure may result in significant patient injuries compared with a small number of patient injuries resulting from a provider’s error. There have also been cases when conventional analytical methods outperformed machine learning approaches. Thus, it is critical to understand both AI’s beneficial and damaging effects on patient safety outcomes because AI has such a wide range of efficacy.

Wider implications of AI improving patient outcomes

Possible implications of AI improving patient outcomes may include: 

• More healthcare-related businesses and clinics relying on AI to automate as many repetitive tasks as possible so healthcare workers can focus on providing higher-value care.
• Healthcare workers increasingly reliant on AI tools to assist and guide them in decision-making and patient care management.
• Doctors becoming healthcare consultants that focus on crafting treatments instead of primarily diagnosing patients since AI will eventually be able to accurately determine illnesses through machine learning.
• Insurance companies adding the option of insuring against AI failures like misdiagnoses.
• Increased government regulatory oversight on how AI is used in healthcare and the limits of its diagnosis capabilities.

Questions to comment on

• Would you be okay with AI overseeing your healthcare procedures?
• What are the other potential challenges in implementing AI in healthcare?