As healthcare organizations work to improve efficiency in their revenue cycle operations, it is crucial for leaders to thoroughly evaluate medical coding automation technology solutions. This evaluation helps manage expectations regarding the most effective strategies. Implementing the right automation approach can enhance accuracy, streamline workflows, and ease the challenges posed by rising volumes, staffing shortages, and complex reimbursement processes. Understanding key milestones to a future state is critical, but the road to building an automation strategy will be different based on the individual needs of the organization.
Evaluating Medical Coding Automation Technologies
Traditional medical coding methods are labor-intensive and prone to errors, and automation can help alleviate challenges in the three key areas where healthcare organizations struggle, including volume, staffing, and denials management.
- Rising volume: McKinsey & Company estimates a 5% to 10% growth rate in key healthcare segments between 2023–2028.
- Staffing shortages: The American Medical Association (AMA) reports a 30% deficit in medical coders, and an American Health Information Management Association (AHIMA) survey showed 48% of healthcare organizations experienced decreased reimbursement and slower claim processing due to understaffing.
- Denials and reimbursements: Coding-related denials remain a top concern for hospital executives, exacerbated by shifting payer policies and regulatory changes.
There are three primary technologies driving coding automation at a facility and professional revenue cycle management (RCM) level, though one solution may not necessarily be equally effective for both settings. Available automation technologies include:
- Computer-Assisted Clinical Documentation Improvement (CDI): Automated query suggestions
- Computer-Assisted Coding (CAC) and Computer-Assisted Professional Coding (CAPC): Automated suggestions
- Autonomous Coding: No human intervention coding
To successfully implement automation, healthcare organizations must also assess:
- SMART worklist: Intelligent worklist prioritization
- Audit processes: Collaboration across teams
- Priority management: Alerts and notifications
- Advanced analytics: Leveraging data to optimize coding accuracy and provider behavior
True autonomous coding involves a reality that requires addressing challenges associated with handling fall-out charts that automation cannot process. High-volume, low-complexity specialties are most suitable for early adoption and a single, closed-loop feedback system accelerates results, drives accountability, and provides a pathway to autonomous production.
Implementation Readiness Checklist
Assessing a healthcare organization's readiness is crucial for developing the right coding automation strategy. Before deploying coding automation, teams should assess:
- Volume by specialty analysis
- Specialty complexity rating
- Staffing constraints/ consideration
- Infrastructure basics (digital housing of clinical documentation)
- Security assessment criteria
- Project and implementation management availability
- Meaningful baseline metrics
- Physician champion engagement
Technology Assessment Criteria
It is important to work with a technology partner that offers solutions along the automation continuum for both facilities and professionals. Five key areas to consider when assessing coding automation technology include:
- Platform uniformity: Ensure a cohesive user experience and streamlined communication across vendors, enhancing data aggregation and implementation efficiency.
- Technology foundation: Distinguish between legacy rules-based technology and advanced natural language processing (NLP) and machine learning-based solutions that provide flexibility to adjust to changing healthcare environments and regulations.
- Partnership expertise: Collaborate with a strategic focus on both coding expertise and technology to handle all encounters, regardless of those ideal for immediate technology adoption.
- Innovation: Align solutions with scalability, workload allocation, and streamlined audit feedback to advance autonomous capabilities.
- Artificial Intelligence (AI) transparency: Understand the origins of code suggestions and ensure clear feedback loops to enhance the learning process of the technology.
The medical coding automation journey requires careful planning, strategic investments, and ongoing evaluation. A road map to maturity in medical coding automation provides a solid foundation for healthcare organizations to navigate the path to success. By leveraging AI-driven solutions in a phased approach, healthcare organizations can improve accuracy, reduce administrative burdens, and optimize revenue cycle performance.
To learn more about the RCM automation continuum, watch our webinar on Navigating the Medical Coding Automation Journey: Roadmap to Maturity.
Katy Morgan
Vivek Menon
