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Deep Dive: Artificial Intelligence for Payer Use Cases

PAGES: 19 Pages
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This IDC Perspective takes a deep dive into artificial intelligence for payer use cases. In the past decade, health insurance companies have been looking to artificial intelligence (AI) and machine learning (ML) to identify at-risk individuals and reduce rising costs in the healthcare sphere.It's important to note that a majority of the price consumers pay when enrolling in health insurance goes into risk prediction and risk management. By using AI to create a system that can create more accurate risk models and predict which individuals need specific types of care, health insurance providers can spend more money on their beneficiaries and less on those processes. Platforms that can take in, study, and learn from data; refine judgments; and generate intelligent insight can also greatly reduce the need for expensive human data analysts.When AI is injected into ways of working across the payer workforce, employees can be liberated from more tactical and mundane work. They can be redirected to more strategic tasks, tapping into previously unavailable internal and external data for more insight-driven ways of working. This can translate into new products and services that improve service and member health while supporting new growth strategies for the business."The use of AI in health insurance is quickly driving improvements and changing the relationship between payers, providers, and members. It helps enhance and streamline the member/patient experience and make internal operations easier," says Jeff Rivkin, research director, Payer IT Strategies at IDC Health Insights. "While there is still plenty of room to enlarge scope of this technology, it is clear that AI has a foothold in payers."

Product Code: US49995322

Executive Snapshot

Situation Overview

  • Digital Patient Experience
    • Preventing Adverse Drug Events
    • Prescription Benefit Management
    • Medication Adherence
    • Chronic Disease Management/Alerting Providers to Patient Deterioration
  • Population Health
    • Early Disease Identification - Identifying Undiagnosed Conditions and Intervention Support
    • Population Risk Modeling/Stratification
  • Digital Administrative Experience
    • Prior Authorization
      • The Triage Engine
      • The Authorization Automation Engine
    • Conversational AI
      • Sales Center Conversational AI
      • Service Center Conversational AI
      • Call Routing
    • Claims Settlement
    • Fraud Detection/Payment Integrity
    • Billing
    • Service Clinical Review
    • Coordination of Benefits
    • Underwriting
    • Enrollment - Personalized Health Insurance Policies
  • Adoption of AI in Payer Organizations
  • Drawbacks of Using Artificial Intelligence in Health Insurance
    • Interpretability
    • Data Privacy
    • Security
    • Ethical/Equity Concerns
    • Intellectual Property

Advice for the Technology Buyer

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  • Related Research
  • Synopsis
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Jeroen Van Heghe

Manager - EMEA



Christine Sirois

Manager - Americas


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