VirtualHealth & AI: Harnessing AI in HELIOS to Accelerate CM and UM Value

Artificial intelligence (AI) is a trending thing in healthcare, and it’s why HELIOS already has built-in integrations powered by AI that clients can leverage. AI is threaded throughout the HELIOS platform in a variety of ways. Specifically for this blog, we’ll focus on how AI is leveraged to power predictive analytics for population health and help automate decisioning and authorizations.

VirtualHealth is actively integrating Artificial Intelligence (AI) technologies into its HELIOS platform intending to accelerate outcomes its customers care most about, which are to enhance efficiencies, improve patient outcomes, and streamline operations. By leveraging machine learning models, secured LLMs (Large Language Models), and AI-powered tools owned by partners, VirtualHealth seeks to optimize Utilization Management (UM), Care Management (CM), and HELIOS implementation processes. A commitment to security, partnership, and continuous innovation underpins this approach.

AI is leveraged alongside configured automations to help accelerate communications between payers and providers, automate prior authorizations, expedite care delivery, and help payers better understand their population. See below for some highlighted use cases of AI in HELIOS.

Highlighted AI Use Cases in HELIOS

Improving UM Cycle/Turnaround Times

  • Automate Prior Authorization Processes: Leveraging AI technologies to accelerate communication between payers and providers for prior authorizations while ensuring the accurate application of medical policies in a swift, secure, and cost-saving manner.
  • Automate Determination Recommendations: Utilizing machine learning to auto-generate determination recommendations based on authorization characteristics, while preemptively identifying potential compliance or reporting issues.
  • Enhanced Data Capture with Closed System LLMs: Automating the summarization of medical director reviews and other note compilation tasks in HELIOS for improved data accuracy and efficiency.

Managing High-Cost Populations

  • Proactive Prevent High-Cost Outcomes: Leveraging AI technologies and HELIOShub to predict future high-cost claimants, identify individual-level risk for conditions and procedures, and recommend the next best action. These AI-powered predictive analytics help healthcare organizations transform care from reactive to proactive and support proactive care approaches and real-time care management initiatives. 

Predictive analytics gained via AI in HELIOS can be applied to guide decisions across the healthcare ecosystem. Some of the results of using these insights include:

  1. Improving population healthcare management
  2. Better informing and supporting care and treatment decisions
  3. Identifying patterns in patient behaviors
  4. Better patient outcomes
  5. Greater efficiency in care delivery
  6. Factoring in SDOH data to predict and address health risks and obstacles sooner
  7. Creating a safer home environment to prevent readmissions
  8. Reducing the costs associated with readmissions and unnecessary interventions
  9. Lowering staff burnout and increasing time and focus on care
  10. Improving patient engagement and satisfaction

With AI for predictive analytics in HELIOS, healthcare becomes more proactive and precise, leading to healthier, happier individuals. Learn more about HELIOS for population health and using predictive analytics.

Enhancing HELIOS Care Manager Satisfaction

  • Streamlined Data Capture and Interaction Summarization: Leveraging LLMs to condense HELIOS data and phone call interactions, saving time and improving user experience.
  • Medication Reconciliation & Adherence Automation: Using AI-powered chatbots for efficient medication management across member caseloads.
  • 24/7 Support via Virtual Assistants: Deploying AI chatbots and assistants for around-the-clock patient support, enhancing satisfaction and personalized care.

Accelerating HELIOS Customer Go-Live Time to Value

  • Automated HELIOS Configuration: Train machine learning models to expedite the configuration process in HELIOS, significantly reducing the go-live time for customers and facilitating easier business evolution on the platform.


VirtualHealth’s Next Steps and Roadmap for Further AI Development

The roadmap for the future is ambitious and focuses on leveraging existing and new partnerships, sequencing and storing HELIOS-specific data stores effectively, and staying at the cutting edge of AI in healthcare population management.

  • Leverage AI Partners: Continue to leverage AI experts in the industry to actualize AI-enable solutions in the short term as we continue to make integrations available to HELIOS users out-of-the-box vs via integration.
  • Sequencing & Harvesting HELIOS Data: Continuing to sequence and harvest HELIOS source data and associated metrics as future training data for VH-owned, secure machine learning models.

VirtualHealth’s strategic focus on AI-driven solutions underscores its commitment to keeping pace with industry advancements and leading the charge in transforming healthcare management. Through innovative use cases and a clear vision for the future, VirtualHealth continues to use emerging technologies to improve outcomes its customers care about most.


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