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Workforce shortages, regulatory complexities, and economic challenges have put hospitals and health systems under immense pressure to improve operational efficiency, accelerate payments, and reduce costs. In 2022, over half of hospitals operated at a loss, and the situation in 2023 wasn’t any better, due to a 12.4% economy-wide inflation surge that outpaced Medicare reimbursements for hospital inpatient care. [1] Failure to confront these issues could lead to hospital closures, further limiting access to care — a fate that has already befallen many hospitals across the nation.
Intelligent healthcare technology, particularly medical coding automation, holds immense promise in solving ongoing challenges faced by healthcare providers, making it crucial for healthcare leaders to understand these technologies and their potential. Let’s examine the inner workings of this technology.
Modern medical coding automation technology using artificial intelligence (AI) has been proven to significantly improve operational efficiency and financial performance, while reducing reliance on human labor. AI-based medical coding utilizes various machine learning (ML) models, including deep learning (DL) and natural language processing (NLP), to analyze medical records. Done correctly coding automation can manage more than 200,000 medical codes – ICD-10, CPT, HCPCS, HCC, MIPS, and Modifiers – ensuring accurate, efficient, and consistent application, while remaining compliant with payer contracts and healthcare regulations. [2]
ML involves learning from large datasets, extracting knowledge from these datasets, and training algorithms to make predictions and decisions without explicit programming. Traditional programming works well when the algorithms that produce outcomes from the input are known. However, ML is more effective when there are many variables involved. The ML model continuously refines itself through feedback, and its accuracy is influenced by the size of the dataset and the learning algorithm used. Often, ML by itself is not sufficient to derive the final codes and may require further algorithmic reduction, combination, calculation, sequencing, and validation with provider and payer policies. Therefore, a complete and comprehensive AI-based automated coding platform requires a judicious combination of ML and traditional software comprising algorithms, rules, and tables.
In healthcare, ML models predict outcomes and automate tasks by extracting key information from the clinical record using NLP. Large language models (LLMs) improve this process by understanding and generating text based on vast data sets, leading to enhanced reliability and accuracy.
Autonomous coding systems derive codes without requiring user intervention. Adding automation means that the autonomous codes are reliable enough to proceed directly to billing without human intervention or review, referred to as “touchless.” Using an AI-driven coding automation platform means hospitals can increase the speed of their entire revenue cycle, improve coding accuracy with little to no human intervention, and lower administrative costs.
Adopting our PULSE Coding Automation Technology™ increases coding productivity by 7x, processing millions of charts daily. The system’s AI models, all discussed here, complete coding tasks in seconds, and the entire process in minutes, reducing workload, accelerating revenue cycles, and improving cash flow. This improvement allows hospitals to manage resources for scalable coding operations, moving coders into higher-level roles like auditors or system trainers.
In one case, PULSE demonstrated significant revenue cycle improvements for a major healthcare system, including a 23% rise in net patient revenue and a 90% automation rate in chart coding. This translates to substantial financial and operational benefits. Our commitment to continuous innovation, backed by our collaboration with a leading tech university, ensures our partners receive the most advanced AI-driven technology solutions, delivering dependable results and strategic advantages for long-term success.
For more details on how our technology transforms healthcare coding read the full case study.
Partnering with us offers healthcare providers more than just technological solutions; it provides a partnership with dedicated, clinically-led experts committed to innovation and your success.
[2] Note: Effective autonomous coding does not result directly from AI but rather from the complete and correct implementation of AI, leveraging datasets, AI models, supporting software, and implementation that respects customers’ special needs.
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