Soon after hitting the healthcare industry, artificial general intelligence and large language models are reshaping the healthcare landscape. And CIOs and other health IT leaders at hospitals and health systems must fully grasp these technologies before putting them to use.
One real-world application of AI is key for service delivery organizations to understand: the use of AI-powered language models in physician-patient communication.
These models have been found to have valid feedback that simulates empathic conversations for patients, making difficult interactions easier to manage. But there are many challenges to overcome before more AI applications can move forward.
For example, one challenge is ensuring regulatory compliance, patient safety, and clinical efficiency when using AI tools.
Dr. Bala Hota is senior vice president and CIO at Tendo, a healthcare software company working in the field of artificial intelligence. We interviewed him to discuss understanding general AI and large language models, leveraging LLM for healthcare applications, real-world applications of genAI, and challenges and concerns. ethical concerns.
Ask. CIOs and other IT leaders at hospitals and health systems must understand generative AI before deploying it. What are some things about genAI that you feel are most important for these leaders to grasp?
ONE. It is important for CIOs and IT leaders to understand that genAI is just one aspect of the broader digital transformation needed in the industry, and it is essential to understand the fundamental evolution that AI has undergone In recent years.
Data generation, augmentation, and anomaly detection can significantly accelerate decision making within an organization. However, AI cannot replace human judgment and interaction. Instead, it acts as a supplement that can enhance productivity.
Semantic components of Large language models dramatically reduce the time teams across the organization spend cleaning and presenting data, allowing them to operate within their license and focus on strategic tasks. Any form of AI must ensure adequate security, compliance, and common-sense approaches to data protection and distribution. Industry must guard against technology that exceeds its practical uses.
Q. How can hospitals and health systems best leverage today’s large language models?
ONE. The use of AI is becoming increasingly important in the healthcare industry because it can help hospitals and health systems streamline decision-making processes, increase efficiency, and improve patient outcomes. core. AI has many applications, from data simplification to patient engagement, which could have a significant impact on the healthcare industry.
A significant benefit of AI in healthcare is improving the efficiency of treatment planning. Ambient voices can be used to Increase use of electronic health records. Currently, AI scribes are being deployed to support medical documentation. This allows doctors to focus on the patient while AI handles the documentation process, improving efficiency and accuracy.
Additionally, hospitals and health systems can use AI’s predictive modeling capabilities to stratify patient risk, identify patients at high or increasing risk, and determine direction best action.
In fact, AI’s cluster detection capabilities are being used increasingly in research and clinical care to identify patients with similar characteristics and determine the typical clinical course of action for them. . This could also enable virtual or simulated clinical trials to identify the most effective treatment courses and measure their effectiveness.
Ask. What are some practical applications of AI that you think will pave the way for the rest of the industry?
ONE. One real-world application of AI that points the way is the use of AI-powered language models in doctor-patient communication. These models have been found to have valid feedback that simulates empathic conversations for patients, making difficult interactions easier to manage.
This AI application can significantly improve patient care by providing faster and more efficient triage of patient messages based on the severity of their condition and messages.
Additionally, AI can be used to better stratify risk at the time of treatment. This can help healthcare providers work most effectively under their licenses by better utilizing resources. By accurately identifying patients who need more specialized care, providers can allocate their resources more effectively and improve overall patient outcomes.
This includes automating patient interactions to scale communication and increase patient engagement. AI is being used to reach patients with better reminders, tracking and engagement, leading to improved outcomes. By identifying patients who need more care, AI can help overcome barriers such as clinical inertia and poor compliance, significantly improving outcomes.
Ask. What AI challenges and ethical concerns do you feel healthcare delivery organizations must address?
ONE. One challenge when implementing AI in healthcare is ensuring regulatory compliance, patient safety, and clinical efficiency when using AI tools. While clinical trials are the standard for new treatments, there is debate over whether AI tools should follow the same approach. Some argue that mandatory FDA approval of algorithms is necessary to ensure patient protection.
Another concern is the risk of data breaches and invasion of patient privacy. Large language models trained on protected data can leak source data, posing a significant threat to patient privacy. Healthcare organizations must find ways to protect patient data and prevent breaches to maintain trust and security.
Bias in training data is also an important challenge that needs to be addressed. To avoid biased models, better methods must be devised to avoid bias in training data. It is important to develop training and academic methods that can better model training and incorporate equity in all aspects of healthcare to avoid bias.
To address these challenges and ethical concerns, healthcare delivery organizations must focus on developing datasets that accurately model healthcare data while ensuring Anonymity and de-identification.
They should also explore approaches to decentralized data, models, and experiments that use large-scale, federated data while preserving privacy. Additionally, partnerships must be established between healthcare providers, health systems, and technology companies to put AI tools into practice in a safe and thoughtful way.
By addressing these challenges, healthcare organizations can harness the potential of AI while maintaining patient safety, privacy, and fairness.
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Healthcare IT News is a publication of HIMSS Media.
