D-SNP/MLTSS plans face unique challenges in growing while managing complex member populations, especially in new markets. Siftwell offers a practical tool to ease this process, built by experienced MCO operators who deeply understand these challenges.

Author: Siftwell Team

Siftwell hosted a webinar with MLTSS Association in partnership with ACAP to discuss how D-SNP/LTSS/MA Plans can use AI to level up.

Here’s a quick overview of the discussion, but watch the full recording on-demand above.


“I encourage everyone within community health plans to start exploring AI, even if it’s as basic as implementing voice-to-text tools for your care workers. This technology is tangible and has real-world applications, which I’ve had the privilege to witness firsthand. I recently came across a quote, perhaps a meme, that resonated with me: “AI won’t take away jobs from humans, but humans who leverage AI will surpass those who don’t.”

This sentiment applies to health plans as well. Our country’s safety net plans, including all the not-for-profit, governmental, and quasi-governmental plans affiliated with Mary’s (MLTSS Association) and Christine’s (ACAP) organizations, play a crucial role in our healthcare ecosystem. In other states, we’ve seen these plans disappear without return once market share is taken by well-resourced national plans.

States are increasingly considering AI, as evident in requests for proposals (RFPs). When comparing the response capabilities of larger plans to smaller ones, it’s clear that AI-equipped plans have a significant advantage. During my tenure running a plan, I experienced this firsthand.

By amalgamating industrial and informational elements, AI has the potential to significantly impact healthcare, improving member health, community well-being, and overall plan success. So, if you’re a health plan in the audience, I urge you to take proactive steps toward leveraging AI. We’re here to assist with our expertise and connections in the AI field, ensuring a seamless and successful integration into your operations,” Trey Sutten, CEO & Co-Founder, Siftwell.

The webinar identified critical challenges and the corresponding Siftwell solutions tailored for D-SNP/LTSS/MA plans. The discussion revolved around member retention, value-based contracting, leveraging AI for improved outcomes, and more.

Member Retention Challenges:

  • Plans invest in populations, but immediate impacts on services are not always evident.
  • Alignment of Medicare and Medicaid benefits poses challenges for member retention.
  • Dually eligible populations require specialized strategies due to unique policy complexities.

Siftwell Solutions for Member Retention:

  • Implementing targeted campaigns and interventions based on data analytics.
  • Psychographic modeling helps predict member behavior for specific healthcare actions.
  • Effective data collection and analysis lead to personalized outreach strategies.

Impact of AI on Healthcare Outcomes:

  • AI-driven interventions result in a significant increase in member retention rates.
  • Leveraging AI for predictive modeling enhances proactive healthcare management.
  • Addressing psychosocial determinants alongside physical health improves overall outcomes.

Transition from Institutionalization:

  • Focus on transitioning individuals from institutions to home and community-based settings.
  • Importance of supporting successful post-transition outcomes for improved long-term care.
  • Integrating AI for risk assessment and targeted support accelerates community reintegration.

Value-Based Contracting Challenges and Solutions:

  • Defining value within MLTSS populations requires balancing cost savings and quality-of-life improvements.
  • AI assists in identifying high-risk members and customizing interventions for value-based care.
  • Collaborative efforts between AI technologies and human expertise drive effective value-based contracting.

The Implications and Recommendations

The webinar highlighted the transformative potential of AI in revolutionizing managed care and improving healthcare outcomes for complex populations. Key recommendations stemming from the discussion include:

  • Embracing AI technologies to enhance member retention strategies and personalized healthcare interventions.
  • Prioritizing data-driven decision-making and predictive modeling for proactive healthcare management.
  • Strengthening partnerships between AI solution providers and healthcare organizations to drive innovation and value-based care initiatives.
  • Investing in continuous training and education for healthcare professionals to leverage AI tools effectively.

Watch the full discussion