Artificial Intelligence has made significant advancements to payment integrity and the DRG reviews process. By leveraging AI technologies, such as natural language processing (NLP) and machine learning (ML) algorithms, healthcare organizations can streamline the review process, improve accuracy, enhance efficiency, and better manage the cost of quality care for their constituents.
Building the Model: Preprocessing and Feature Engineering
The foundation of AI-powered DRG reviews relies on robust preprocessing and feature engineering techniques. Preprocessing enhances the quality and legibility of unstructured medical records and scanned documents. Image enhancement techniques, including contrast adjustment, noise reduction, and sharpening, improve the visual clarity and readability of the documents, enabling better text extraction.
Feature engineering is critical in identifying relevant features associated with specific DRG concepts. AI techniques are utilized to extract information from clinical narratives like diagnoses or impressions within radiology reports. NLP methods, including named entity recognition (NER), enable the identification and extraction of clinical concepts, such as symptoms, diagnoses, treatments, and observations.
Combined, preprocessing and feature engineering create the model that powers the streamlined, AI-driven DRG review process.
Model Evaluation and Deployment
Once built, a model undergoes rigorous evaluation to assess its performance across different medical record formats and claims. Coders and clinicians evaluate the model’s output for accuracy, providing valuable feedback for refinement. The algorithm is adjusted, additional filters are incorporated, and new features are added until the desired accuracy threshold is achieved.
Once the model achieves satisfactory performance, it is deployed into the production environment for the clinical team to leverage for that specific DRG concept. The model is integrated into an intuitive interface that allows users to validate the output against the source medical record. Transparency is provided by presenting each incorporated feature to the reviewer, enabling detailed feedback and continuous improvement.
Benefits of AI in DRG Predict: Efficiency and Accuracy
One of the key advantages of integrating AI into DRG reviews is the improvement in review efficiency. Traditional manual reviews often require reviewers to review hundreds of pages of medical records, leading to time-consuming and labor-intensive processes. Conversely, AI-powered models narrow down the review process by focusing on key clinical indicators, significantly reducing the time and effort required to complete a review.
Another benefit is streamlining the review process by prioritizing cases most likely to have an impact and reducing cases that are not false positives. By leveraging AI technologies, organizations can choose which claims require closer attention, improving the findings rate for DRG concepts.
Additionally, AI-powered models generate insights and analytics that help detect patterns and trends across a provider network, enabling proactive measures to address potential issues. AI in the DRG review process improves consistency by standardizing the review approach. Reviewers may interpret guidelines differently, leading to variations in review outcomes. AI algorithms ensure a consistent and standardized approach, reducing provider abrasion and increasing the accuracy and reliability of the review process.
Moving Towards Pre-Pay: Enhancing Payment Accuracy
Health plans aim to pay claims correctly the first time, reducing the need for post-payment reviews. AI-powered DRG reviews provide the necessary processes and technologies to shift toward pre-payment accuracy. These models are designed to handle large amounts of data, allowing organizations to quickly analyze and pay claims appropriately. By identifying potential billing errors upfront, healthcare organizations can enhance payment accuracy and minimize the need for post-payment recovery.
The Future of AI in DRG Review: Continuous Improvement and Adaptability
As technology evolves, AI-powered DRG review systems will continue to advance. Continuous maintenance and improvement are crucial to ensure the accuracy and relevance of the models. User feedback loops allow for adjustments and modifications to the features presented, enabling the model to pick up on trends and patterns to drive the output. Additionally, regular evaluation of industry guidelines ensures that the models remain accurate and up to date.
Looking ahead, the development of a re-trainable OCR engine and text spotting modules will further enhance the data extraction capabilities of AI-powered DRG reviews. These advancements will enable the detection of words appearing in multiple rotations on the same page, reducing manual effort and improving accuracy.
AI-powered DRG reviews have revolutionized the healthcare industry by streamlining the review process, improving efficiency, and enhancing payment accuracy. Through robust preprocessing, feature engineering, and model evaluation, AI algorithms can accurately extract clinical data from unstructured medical records. Continuous maintenance and user feedback loops ensure that the models remain up-to-date and adaptable to changing guidelines. As healthcare organizations increasingly adopt AI technologies, the future of DRG reviews looks promising, offering improved accuracy, efficiency, and consistency in reimbursement processes.
How Advent Can Help
Advent Health Partners’ CAVO® payment integrity platform was built to support DRG reviews. CAVO uses a variety of AI methods to intelligently digitize medical record images and then extract relevant clinical data and pull specific codes driving the DRG. CAVO DRG Predict incorporates user experience and feedback to continually improve performance. A use case-specific, user-friendly interface is able to adjust and modify core features presented, tailored to the desired determination or output. Please get in touch to discover how Advent Health Partners can help your organization tackle its business challenges.