To learn more about how AI can be applied in proactive and personalized healthcare strategies for members and patients, check out Part 1 of this blog series.
AI-powered solutions, such as predictive analytics, generative AI, and conversational AI, are revolutionizing how healthcare organizations engage with patients and members to promote preventive care.
- Predictive analytics enable proactive identification of health risks within populations, allowing for targeted interventions tailored to individual needs.
- Generative AI can help streamline clinical decision support and content creation, facilitating personalized care recommendations and communication at scale.
- Conversational AI, with its natural language processing (NLP) capabilities, can deliver automated, targeted interventions that help individuals overcome barriers to accessing preventive care.
These AI-driven approaches not only enhance efficiency and scalability of healthcare organizations’ initiatives but can also foster better engagement and outcomes. By leveraging AI technologies, healthcare organizations can help patients and members navigate the complexities of the healthcare landscape, empower them to take control of their health, and ultimately, pave the way for a healthier future.
Risks & Mitigations: Combating Bias in AI for Healthcare
Within the public discourse on AI’s many benefits, there has also been a robust debate around ethical concerns, namely the potential for bias in training data sets and model design. Because these models are developed by people, they could end up replicating the biases of their creators. If AI bias is present in preventive care practices or treatment recommendations, this could pose a risk for unfair outcomes, perpetuating and compounding health disparities.
Healthcare leaders can help put safeguards in place by working with technology providers or internal AI solution developers to ensure training data and algorithmic transparency. Decision Point by mPulse mitigates the risk of bias and representation of diverse groups in training data sets through detailed model governance procedures, recurring audits, and rigorous documentation – all of which serves to also facilitate transparency with partners.
Navigating Reliability and Accuracy in AI Applications
Another risk surrounding AI that has graced headlines for the past several months is reliability and accuracy of results produced through AI. Reliability and accuracy are of the foremost importance in preventive care, therefore healthcare organizations will need to consider context, use case and type of AI applied.
Due to challenges in the variability of outputs, using Generative AI-powered chat bots to run conversations with patients and health plan members would not be an advisable context or use case for this tool as it could pose an ethical and legal risk if the chatbots and their recommendations are not closely supervised by their human counterparts.
To ensure reliability of its conversational AI technology, mPulse conducts ongoing monitoring by its in-house team and maintains an over 93% accuracy in response handling. Because mPulse’s conversational AI leverages natural language understanding (NLU) that is rules-based, it is easy to adjust should any updates be needed or if inaccuracy in content is identified.
It does not rely on large language models (LLMs) that would need to be retrained. mPulse’s NLU is also programmed with “autoresponders” such as “sorry” or “contact us” that can be triggered if the intent of a member or patient response is not understood. mPulse’s AI technology has been developed through 10+ years of working solely in healthcare contexts.
It has also been fine-tuned to healthcare use cases and designed with safeguards in place such as escalations and alerts to client team members should any safety or self-harm risks be identified.
Healthcare Regulatory Compliance and Data Security in AI
Regulatory compliance and data security is another area that has caused apprehension in adopting AI into a healthcare organization’s daily practices. In the highly regulated industry of healthcare – any technology that doesn’t meet mandates for data privacy, security and ethical use is a nonstarter.
- Safeguarding Risks with AI Designed for Healthcare: Utilizing AI technology specifically tailored for healthcare use-cases is one way that organizations can help mitigtate risks. mPulse has focused exclusively on supporting healthcare organizations since its inception, committing to rigorous HIPAA data protection practices and upholding HITRUST, SOC 2 Type 2 regulatory compliance certifications. This ensures the data from healthcare partners and the members and patients they serve is safely managed.
- Challenges with Generative AI: Contrarily, generative AI technologies is have yet to universally achieve HIPAA compliance due to their uncontrolled outputs, posing potential risks for accidental Protected Health Information (PHI) exposure. The large language models (LLM) that the technology utilizes, require vast amounts of training data that can obfuscate data sources and whether or not these contained PHI.
- Collaboration for Industry Standards: As AI becomes more integrated into healthcare operations, it’s imperative for healthcare leaders, technology experts, and regulatory bodies to collaborate, establishing industry standards and risk mitigation best practices.
- The Upside of AI Adoption: Leveraging AI in healthcare promises significant benefits, from enhanced patient outcomes to operational cost savings. These advantages can directly improve financial performance, and offer the potential to reallocate funds to support further resources and services for those in need.
In summary, navigating the complexities of AI adoption requires consideration of compliance and security standards and evaluating the best use cases for different forms of AI, particularly in a field as sensitive as healthcare. By prioritizing these aspects, healthcare organizations can harness AI’s potential responsibly and effectively.
