How Predictive Analytics is Shaping the Next Era of Payment Integrity

In the often-tumultuous domain of healthcare payments, predictive analytics is emerging as an innovative new tool for payers to help ensure payment integrity and accuracy. This advanced capability leverages historical and real-time data to forecast future events, enabling health plans to preemptively identify and address payment inaccuracies.

The adoption of predictive analytics in healthcare payment has brought about a paradigm shift, moving away from traditional reactive methods to a more proactive and efficient model. This shift not only helps avoid unnecessary costs and increase savings but can also benefit the relationship between payers and providers by reducing friction, increasing transparency, and enhancing the efficiency of payment processes. For example, by identifying and preventing inaccuracies before payments are made, payers can avoid the cumbersome process of post-payment recovery, thus significantly improving their return on investment (ROI). This aligns with the trend of health plans increasingly moving beyond the historical perception of being pure financial intermediaries and rebranding as holistic health companies.

Predictive models are used to augment pre-pay editing rules and identify payment integrity issues or inaccuracies that would otherwise be found post payment as a part of data mining activities. They are also used to predict events creating a window of opportunity for various interventions in advance of services being performed or claims getting submitted.

The Data Cornerstone

Establishing a robust data foundation is essential to deriving the attributes or features needed to build, train, and test predictive models –– and to retrain them as needed. This foundation is built from diverse data sources, including claims data, clinical records, and member eligibility information, to accurately determine responsibility for payments and identify fraudulent activities. 

The integration of post-event data, such as the outcomes of claims identified as fraudulent, is vital. This historical data enhances the predictive model’s accuracy, enabling it to identify potentially fraudulent activities more effectively in future claims. Over time, as the model is exposed to more data, its ability to discern between fraudulent and non-fraudulent claims becomes more nuanced, improving the overall efficacy of predictive analytics in payment integrity.

Maximizing ROI

Practical applications of predictive analytics have demonstrated its effectiveness in improving payment integrity. For example, pre-pay predictive models trained on historical claims data can flag potential issues such as fraud, errors, and duplications for review before payment, leading to considerable savings and efficiency improvements. 

Reviewing claims that are likely to be inaccurate or have integrity issues is much more efficient than manually reviewing all high-cost claims. Additionally, models can predict the likelihood of other circumstances that, when addressed, can help reduce administrative burden, increase revenue, and boost member and provider experience. Examples include: 

• Identifying members who are likely to have eligibility discrepancies.
• Flagging claims associated with a work related or a vehicle accident.
• Avoiding potential provider abrasion situations associated with PI capability deployment.
• Recognizing situations where additional provider education can help avoid incorrect billing.
• Distinguishing which VBC provider a member will likely choose as a primary, allowing the plan to engage with the provider on an appropriate care strategy. 

By ensuring that providers are reimbursed accurately and efficiently, particularly in value-based arrangements, predictive analytics can help patients receive the care they need when they need it—fostering trust and engagement. And by facilitating more accurate and efficient predictive analytics helps align the interests of payers and providers, leading to better care quality and outcomes for patients.

Implementation Challenges

Health plans seeking to integrate and leverage predictive analytics to enhance payment integrity must have proper strategy, planning, and execution. While the promise of improved efficiency, fraud detection, and cost savings is significant, several implementation challenges must be navigated carefully:

Ensuring Data Quality

High-quality data is the cornerstone of effective predictive analytics. Inconsistent, incomplete, or inaccurate data can lead to misleading predictions, impacting decision-making and outcomes.

Validating Model Effectiveness

A predictive model must prove effective not just on historical training data but also under real-world conditions. This requires ongoing validation to ensure predictions remain accurate over time.

Knowing When to Retrain Models

Predictive models can become outdated as new data emerges and patterns change. Determining the optimal time to retrain models is crucial for maintaining their accuracy and relevance.

Having an Orchestration Capability

To use predictive models effectively, health plans need an orchestration layer that can seamlessly trigger the appropriate actions based on predictions, integrating these insights into operational workflows.

Ability to Integrate with Existing Workflows

Integrating predictive analytics into existing workflows without causing disruption is a delicate balance, requiring systems and process modification to accommodate new inputs and actions.

Ensuring Responsible AI

As health plans adopt AI and predictive analytics, they must navigate concerns related to privacy, data use, fairness, transparency, and safety, ensuring that their implementations respect ethical guidelines and regulations.

Overcoming the implementation challenges of predictive analytics in health plans requires a strategic blend of robust data management, rigorous model validation, and the seamless integration of insights into existing operational processes. 

Advanced AI/ML platforms play a crucial role in this context, facilitating the analysis of complex datasets to extract actionable insights. These insights, grounded in high-quality data, empower health plans to make informed decisions that bolster payment integrity. Moreover, integrating these predictive insights into daily operations is critical for realizing their full potential. Insights must drive actionable improvements, highlighting the importance of not only detecting potential issues but also implementing preventive measures to enhance efficiency and accuracy in payment processes. 

Ensuring the responsible use of AI, with a focus on transparency, fairness, and data privacy, is essential for maintaining trust and ethical standards. Through these strategic approaches, health plans can leverage predictive analytics to address key challenges, leading to tangible enhancements in payment integrity and operational effectiveness.

The Road Ahead for Payers and Predictive Analytics

Emerging technologies like generative AI are set to further enhance the capabilities of predictive analytics in healthcare payment. These advancements will decrease manual review processes, provide detailed explanations related to model predictions, and personalize provider education, among many other benefits.

Health plans must stay abreast of future trends and emerging capabilities, such as real-time adjudication, to advance their agendas of increasing savings, reducing fraud and waste, and boosting efficiency in all related administrative processes. To this end, partnering with AI-driven platform providers and starting with focused pilots will help further refine models and processes. Emphasizing the balance between automation and human oversight is crucial to maximizing the benefits of predictive analytics in transforming healthcare payment accuracy.

The power of predictive analytics in healthcare payment accuracy offers a promising path forward for healthcare payers. By embracing this technology, payers can not only enhance payment integrity and efficiency but also contribute to better healthcare outcomes and stronger payer-provider relationships.