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The Crucial Role of Quality Data in Advancing AI

The Crucial Role of Quality Data in Advancing AI in Workers' Compensation

by Bill Zachry, SCIF Board Member

There is a paramount importance of quality data in the context of Risk Management / Workers’ Compensation and driving advancements in artificial intelligence (AI) within the industry. There is an urgent need for a standardized data glossary and data collection practices.

Reliable data will have a direct impact on the development and implementation of AI solutions. Due to a lack of consistent data definitions or a data glossary, there are significant challenges

associated with the current industry data quality, embedded biases, privacy concerns, and the complex process of harmonizing data definitions across various stakeholders. Consistent data definitions will have profound benefits that quality data can bring to workers’ compensation, from industry-wide research to improved decision- making and regulatory compliance.

The Data Dilemma in Workers’ Compensation

In the rapidly evolving landscape of workers’ compensation, the term “data” has emerged as a crucial asset for the industry’s evolution.

However, the lack of consistent data definitions (or a data glossary) and standardized data collection practices has posed significant challenges, hindering the industry’s ability to harness the potential of AI. One reason for the lack of common definitions is that there are more than fifty different workers’ compensation systems each with their own set of definitions. One example of the lack of a consistent definition is that of a “medical only” claim. In Tennessee there is a seven day waiting period and in California there is a three day waiting period.

The Need for Standardization:

The workers’ compensation industry currently operates without a centralized oversight body that sets data definition or collection standards. This absence of standardized data definitions or a comprehensive data dictionary has resulted in data fragmentation and inconsistency. There are subsets of data in the industry where definitions and processes are standardized. Two examples of this are the MediCare process and data definitions created by the NCCI and other rating organizations.

Benefits of Standardized Data: Industry-Wide Impact

The standardization of data definitions and data collection processes would have far-reaching benefits for the workers’ compensation

industry. It would enable companies, researchers, and jurisdictions to:

Perform industry-wide research.

  • Make administrative cost and performance comparisons.
  • Identify and promote best practices.
  • Improve claims administration efficiency.
  • Enhance timely and accurate benefit provision.
  • Drive legislative agendas based on comprehensive data insights. Identify and remove system biases.

Internal Benefits: Empowering Organizations

Standardized data would empower individual companies within the workers’ compensation industry by allowing them to:

  • Conduct internal analysis of staff productivity.
  • Compare claims results and benefit provision.
  • Evaluate underwriting results.
  • Assess the performance and expense of vendors.
  • Enable timely and accurate decision-making.
  • Enhance training programs for claims supervisors and managers.
  • Identify and eliminate fraud.
  • Identify and eliminate examiner, attorney, and medical provider biases.

Challenges in Standardizing Data Collection

Despite the clear benefits, standardizing data collection in the workers’ compensation industry presents significant challenges:

  • Data collection can be expensive, necessitating justification through benefits.
  • Legacy systems may lack important data elements.
  • Changing or modifying data elements in older systems can be costly.
  • Data integrity must be ensured, especially for less immediately relevant elements.
  • Gaps can be introduced during system migration.
  • Integrating vendor data into payer systems can be problematic. Lack of standard database software and design.
  • Different data needs for insurance companies, self-insured entities, and third-party administrators (TPAs).
  • Proprietary ownership of data forms and formats.
  • Privacy concerns and regulatory compliance.

Overcoming Challenges: The Path Forward

Addressing these challenges requires a collaborative effort and careful consideration of various factors:

  • Encourage automated data collection from multiple sources.
  • Implement standardized data definitions.
  • Provide immediate feedback to data input personnel.
  • Foster a culture of accountability for data accuracy.
  • Recognize the importance of data accuracy in decision-making.
  • Consider the impact of data collection on staff retention.
  • Leverage newer systems that use images for data.
  • Scrutinize data migration during system changes.
  • Encourage direct input from relevant parties.
  • Align data collection with evolving workers’ compensation systems.

Data Privacy and Compliance

While striving for data standardization, it is essential to remain mindful of data privacy regulations such as HIPAA, the California Confidentiality and Privacy Act, and identity theft risks. Striking a balance between data privacy and research needs is crucial to ensure compliance and maintain public trust.

The Data-Driven Future of Workers’ Compensation.

The workers’ compensation industry stands at a critical juncture where the quality of data will determine its ability to leverage AI for better decision-making and industry-wide improvements. Standardized data definitions and data collection practices are imperative to unlock the potential of AI in this sector. While challenges exist, the benefits of quality data far outweigh the obstacles, paving the way for a data- driven future in workers’ compensation.

Call to Action

The need for standardized data in the workers’ compensation industry is clear and pressing. To drive meaningful change and unlock the potential of AI, we call upon industry stakeholders, including insurers, claims administrators, regulators, and technology providers, to join forces. Here are some actionable steps:

  • Advocate for Data Standardization: Encourage industry associations and regulatory bodies to prioritize the development of standardized data definitions and data collection practices.
  • Invest in Technology: Embrace modern data collection and management technologies that facilitate the harmonization of data across systems.
  • Educate and Train: Invest in training programs to enhance data accuracy and promote a culture of accountability among claims personnel.
  • Collaborate: Foster collaboration between industry players, researchers, and regulators to develop data standards that benefit all stakeholders.
  • Stay Informed: Stay updated on evolving data privacy regulations and ensure compliance while advocating for standardized data.

By taking these actions, we can collectively transform the workers’ compensation industry into a data-driven ecosystem that harnesses the power of AI for the benefit of all stakeholders. The time for change is now, and together, we can shape the future of workers’ compensation through data standardization and innovation.

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