Powering Change in the US Healthcare with Artificial Intelligence, Machine Learning (AI/ML)

The Healthcare Crossroads: Why AI and ML Matter Now

The U.S. healthcare ecosystem stands at a defining moment. Despite being one of the most advanced systems globally, it continues to battle high costs, inefficiencies, and accessibility gaps. Post-pandemic realities have made it clear that innovation is not optional — it’s essential for survival.

Artificial Intelligence (AI) and Machine Learning (ML) are now driving a new era of transformation. From automating manual operations to enabling predictive decision-making, these technologies are helping payers, providers, and regulators streamline workflows, reduce administrative costs, and improve patient outcomes.

According to Morgan Stanley Research, 94% of healthcare organizations plan to integrate AI/ML in daily operations by 2024, with AI budgets doubling from 5.5% in 2022 to 10.5% in 2024. The message is clear: the next frontier of healthcare success lies in intelligent automation and data-driven insights.

The Need for Innovation

Traditional healthcare processes are manual, repetitive, and error-prone — resulting in:

• Long resolution times for appeals and claims.
• Higher administrative costs.
• Regulatory compliance risks.
• Fragmented data and lack of real-time visibility.

By 2025, the U.S. AI healthcare market is expected to reach $6.6 billion, growing at an impressive 48% CAGR, signaling strong momentum toward digitization.

AI and ML can address these challenges head-on by automating complex workflows, extracting insights from unstructured data, and empowering faster, more accurate decision-making.

Key Areas Where AI/ML Is Transforming Healthcare

1. Appeals and Grievances (A&G)

A&G processes are critical for payer compliance and member satisfaction. They involve addressing disputes around coverage, claims, or service quality — areas prone to manual delays.

AI/ML innovations enable:

• Automated workflow management for faster case resolution.
• Natural Language Processing (NLP) to analyze unstructured text from complaints and detect trends or patterns.
• Chatbots and virtual assistants to guide members through appeals in real time.
• Predictive analytics to assess which cases need urgent intervention.
• Fraud detection by identifying unusual claim patterns.

By combining automation with intelligence, health plans can resolve grievances faster and enhance transparency while maintaining regulatory compliance.

2. Provider Lifecycle Management (PLM)

AI and ML are revolutionizing Provider Lifecycle Management, spanning credentialing, contracting, and ongoing data maintenance.

2.1. Provider Credentialing

• Automated verification by cross-referencing provider data with medical boards, education databases, and licensing registries.
• ML-powered anomaly detection to identify inconsistent credentials.
• Predictive analytics to forecast provider performance and compliance risks.
• Chatbots for provider support, reducing administrative workload.

2.2. Provider Contracting

• AI-driven contract review extracts key terms and identifies redlining inconsistencies.
• NLP-based risk detection to flag unfavorable clauses.
• Real-time negotiation support powered by data insights and historical trends.
• Predictive forecasting of contract performance and financial outcomes.

2.3. Provider Data Maintenance

• AI-powered cleansing and deduplication for maintaining clean, standardized provider data.
• Continuous monitoring for changes in credentials or licenses.
• Integration with data sources like CAQH, NPPES, and CMS for real-time accuracy.
• NLP-based extraction of information from unstructured text (e.g., PDFs, forms).

By automating PLM, payers reduce manual effort, ensure compliance, and strengthen provider relationships — a key differentiator in the value-based care era.

3. Claims Processing and Adjudication

Claims management is one of the most resource-intensive operations in healthcare. AI and ML are enabling intelligent, end-to-end claims automation that enhances accuracy and reduces turnaround time.

AI/ML enables:

• Automated claim intake and verification with minimal manual intervention.
• ML-based adjudication that detects coding errors and predicts claim outcomes.
• Fraud detection by identifying anomalies in billing and claim submissions.
• Faster reimbursements, improving both provider and member satisfaction.

The result — fewer denials, lower costs, and significantly improved compliance and member experience.

4. Risk Assessment and Underwriting

AI/ML models are redefining how payers assess and manage risk.

• Analyze demographic and medical data to forecast healthcare utilization.
• Identify high-risk members early to enable preventive interventions.
• Predict claim volumes and loss ratios for more accurate premium pricing.

This data-driven underwriting allows payers to stay profitable while ensuring fairness and transparency in policy pricing.

5. Member Engagement and Personalization

Personalization is the future of healthcare engagement. AI-powered systems analyze member behavior, preferences, and feedback to deliver contextual, real-time experiences.

Applications include:

• Predicting member needs based on claim and usage patterns.
• Recommending wellness programs, benefits, or preventive screenings.
• Automating communication through chatbots and virtual assistants.
• Improving satisfaction by proactively addressing grievances.

AI transforms engagement from one-way communication into personalized, value-based interaction that builds trust and loyalty.

6. Provider Network Optimization

AI-driven network analysis helps payers design high-performing, cost-effective provider networks.

Key capabilities:

• Evaluate provider performance and utilization metrics.
• Identify underperforming or non-compliant providers.
• Recommend network expansion or restructuring for better coverage.
• Forecast network impact based on new regulations or demand patterns.

This ensures optimized care access, improved quality, and balanced provider workloads.

How Newgen’s Data Science Platform Powers AI/ML Transformation?

Newgen’s AI/ML Data Science Platform is purpose-built to help healthcare payers operationalize intelligence across the enterprise — seamlessly and securely.

Platform Highlights:

• End-to-End Integration: Manage data preprocessing, model training, deployment, and monitoring through a unified interface.
• Low-Code, Drag-and-Drop Interface: Empower users to build AI workflows without extensive coding.
• Comprehensive Functionality: Supports automated data pipelines, model lifecycle management, and analytics.
• Cross-Functional Collaboration: Enables data scientists, business teams, and developers to co-create intelligent solutions.
• Adaptive Learning: Allows retraining and optimization of ML models as data evolves.
• Accelerated Time-to-Value: Reduces implementation cycles and speeds up ROI.

With these capabilities, Newgen enables healthcare organizations to scale AI adoption across use cases — from A&G and PLM to claims, risk, and engagement — all while maintaining compliance and transparency.

The Business Impact

Healthcare payers leveraging AI and ML achieve:

• 50–70% faster turnaround on key processes.
• Up to 60% reduction in manual workload.
• Improved compliance and reduced fraud.
• Enhanced member and provider satisfaction.
• Smarter, data-backed decision-making.

AI and ML are not just tools, they are strategic enablers driving the future of healthcare efficiency, personalization, and innovation.

Begin Your AI-Driven Healthcare Transformation

Healthcare transformation begins with intelligent automation. If your organization still relies on manual workflows, now is the time to take the next step.