In my last article, I provided background for preparing your business cases to transition from a conventional computer-assisted coding (CAC)/natural language processing (NLP) environment to a technology platform to support coding and/or clinical documentation integrity (CDI) fueled by advanced artificial intelligence (AI), of which I defined new features and underlying technology requirements, along with the current state of features and advancements. This article will provide you and your organization with examples of where other organizations have delivered on the promise of AI to truly revolutionize CDI as we know it today. This will provide you and your stakeholders the opportunity to examine the early results of advanced AI to determine if it is worth the investment to migrate to, installing features on your current platform, or to select a new AI partner/system.
Historically, solving CDI problems meant increasing people power – the theory being that more reviewers led to more compliant operations and consistent revenue. This reactive approach tackled immediate issues without addressing root causes, focusing on capturing complications and comorbidities (CCs) and major CCs (MCCs) to boost the case mix index (CMI) and reimbursement. Over time, automated tools improved efficiency, but remained largely transactional.
In many organizations, the objective is to achieve a 35-percent query rate. CDI specialists (CDISs) typically handle around 25 cases daily, aiming for a 90-percent physician response rate and an 85-percent agreement rate on queries. While these metrics gauge productivity, they do not identify areas needing improvement to accurately depict a patient’s acuity, and this leads to missed opportunities to capture revenue and assess risk accurately. This outdated approach to CDI results in inconsistent revenue and compliance performance.
This means that hospitals must transition from outdated tactical approaches to a modern, strategic approach. A strategic model is efficient, data-driven, and monitored with analytics. It is also supported by clinical audits and ensures consistent compliance and revenue integrity. By clearly defining problems and aligning projects with strategic objectives, hospitals can use resources efficiently and effectively to improve compliance, financial health, and overall patient care quality.
The challenges for CDI in healthcare are innumerable, associated with ensuring accurate, reliable, and compliant operations across the continuum. Denial management challenges also stem from poor documentation and inaccurate coding. Furthermore, there is often a lack of standardization and effective processes for managing appeals. The goal is to leverage technology to optimize CDI staff time, reduce burnout, ensure that our staff work at the top of their license, and reduce our reliance on contract backfill. Also, in non-acute-care settings, providers coding their own cases using non-standard templates and code capture can adversely impact the integrity of the documentation and code capture of the social determinants of health (SDoH) codes and other critical codes. In the acute-care setting, timely, complete, and specific documentation is critical to reflect the true patient population and complete code capture.
The CDI value proposition that advanced AI technology needs to encapsulate includes the entire continuum below to mitigate the above challenges, as we continue our transition from fee-for-service care to full capitation, as depicted in the diagram below:
CDI must be poised to transition to advanced AI to enable healthcare systems to instantly select, score, and prioritize high-impact cases for review to:
The important point with the migration to advanced AI is that it augments, not replaces, the CDIS, enabling the CDI team to:
A CDI program’s financial impact relies on identifying revenue-increase and revenue-decrease opportunities, revealing vulnerabilities and educational needs. Aligning critical metrics to revenue goals is challenging, even with convention CAC/NLP tools, which measure the impact of queries. However, many queries are critical to the capture of value-based care and comparative quality system drivers, and do not carry a financial weight.
CMI is a popular metric, but it doesn’t fully address performance due to factors like surgery schedules, patient acuity, and external events. A refined approach should enable hospitals to benchmark their CMI against comparable institutions, adjusting for patient acuity and care complexity. This method provides a clearer picture of actual performance by highlighting discrepancies between clinical evidence and recorded data. For instance, while a standard CMI might show a decline, this technology could reveal that the decline is due to an improved observation rate, not poor documentation or coding. This is just one of the many functions that enable this technology to drive real financial impact.
Again, advanced AI systems enable the analysis of top-performing peers to track your program’s progress. Organizations can start with cohort benchmarking to identify areas for improvement. Ensure that metrics are comparable within the cohort, and communicate the benchmarking process across teams, particularly with physicians, to foster understanding and engagement. Ideally, the aim to reach the top quartile of one’s peer group by comparing the 50th and 75th percentiles is an achievable target with this technology. Organizations must be committed to regularly revisiting and adjusting their cohort to maintain relevance as performance evolves.
Advanced AI technology will also enable the analysis to uncover root causes of documentation discrepancies to further drive improvements. For instance, consistent physician query responses of “unable to determine” often indicate the need for targeted education on specific diagnosis codes or general query responses. Regularly reviewing these data points reveals common areas to achieve the greatest ROI.
Program improvement should be communicated in terms of dollars, differentiating between revenue-increase opportunities and revenue-decrease opportunities, as they represent different vulnerabilities, risks, or areas that require education, coupled with observed versus expected values resulting from their comparative quality applications. Again, this requirement is a feature of advanced AI CDI systems, without a technology vendor tacking on an analytics charge, requiring a background team.
Specific questions about data help identify what to track for improvement, and this should be facilitated by the analytics feature. These include questions like “Where do we have changes?” “What diagnosis codes are we using?” “Was it a principal diagnosis?” “Was it a secondary diagnosis?” and “What’s the nature of the change?” The metrics will help uncover root causes and identify program process improvements for targeted education across teams.
Lastly, evaluate the effectiveness of the review process itself. Are the right cases being selected for review? Is there a good hit rate that justifies the continued performance of these reviews, ensuring that they pay for themselves and generate additional value?
Advanced AI for CDI will enable the case review process to be more efficient, reducing the workload for CDI, HIM, and coding teams, incorporating CDI methodologies into proprietary algorithms based on comprehensive CDI insights, DRG methodology, and clinical knowledge. Also, the application should include encounter data characteristics, review outcomes, and changes in coding rules and guidelines. Finally, it should possess an extensive knowledge base, which allows the scoring engine to pinpoint smaller clinical populations accurately, identifying opportunities efficiently and flagging cases for expert reviews.
Although it’s difficult to demonstrate the technology in an article, a demo from available technology vendors should adequately demonstrate the easing of the documentation and review burden by both the provider and the CDIS, utilizing nudge technology, leveraging either direct documentation in the electronic medical record (EMR) or speech/ambient recognition/listening.
Advances with documentation captured through ambient listening interoperability with the EMR and the CDI application is a game-changer. Also, many organizations have reported successful strategies with discrete field population utilizing these techniques, which will further amplify data analytics capabilities described above.
Below is a depiction of the high-level interaction from the provider’s documentation, dictation (captured successfully now via speech recognition or ambient listening technology):
The next article (Part IV) will highlight use cases on the coding front to mitigate current technology and resource challenges, and the final article (Part V) will provide a blueprint for preparing your business case and setting up your implementation for success.
Programming note:
Listen to Cassi Birnbaum report this story live today during Talk Ten Tuesday, 10 Eastern, with Chuck Buck and Dr. Erica Remer.
Coding in the post-acute care world has taken on a new significance in the past five years. For those of us who are coders, this
Election Day has finally arrived – however, some physicians may not be celebrating the new 2025 Medicare Physician Fee Schedule (PFS) Final Rule that also
Please log in to your account to comment on this article.
Join Beth Wolf, MD, CPC, CCDS, for an in-depth webcast on the FY2025 spinal fusion MS-DRG updates. Discover key changes in DRG classification, understand impacts on documentation and CMI, and learn strategies to ensure compliance.
Join Angela Comfort, DBA, MBA, RHIA, CDIP, CCS, CCS-P, as she presents effective strategies to strengthen collaboration between CDI, coding, and quality departments in acute care hospitals. Angela will also share guidance on implementing cross-departmental meetings, using shared KPIs, and engaging leadership to foster a culture of collaboration. Attendees will gain actionable tools to optimize documentation accuracy, elevate quality metrics, and drive a unified approach to healthcare goals, ultimately enhancing both patient outcomes and organizational performance.
Optimize your outpatient clinical documentation and gain comprehensive knowledge from foundational practices to advanced technologies, ensuring improved patient care and organizational and financial success. This webcast bundle provides a holistic approach to outpatient CDI, empowering you to implement best practices from the ground up and leverage advanced strategies for superior results. You will gain actionable insights to improve documentation quality, patient care, compliance, and financial outcomes.
Enhancing outpatient clinical documentation is crucial for maintaining accuracy, compliance, and proper reimbursement in today’s complex healthcare environment. This webcast, presented by industry expert Angela Comfort, DBA, RHIA, CDIP, CCS, CCS-P, will provide you with actionable strategies to tackle complex challenges in outpatient documentation. You’ll learn how to craft detailed clinical narratives, utilize advanced EHR features, and implement accurate risk adjustment and HCC coding. The session also covers essential regulatory updates to keep your documentation practices compliant. Join us to gain the tools you need to improve documentation quality, support better patient care, and ensure financial integrity.
Dr. Ronald Hirsch provides critical details on the new Medicare Appeal Process for Status Changes for patients whose status changes during their hospital stay. He also delves into other scenarios of hospital patients receiving custodial care or medically unnecessary services where patient notifications may be needed along with the processes necessary to ensure compliance with state and federal guidance.
Healthcare organizations face complex regulatory requirements under the No Surprises Act and Price Transparency rules. These policies mandate extensive fee disclosures across settings, and confusion is widespread—many hospitals remain unaware they must post every contracted rate. Non-compliance could lead to costly penalties, financial loss, and legal risks. Join David M. Glaser Esq. as he shows you how to navigate these regulations effectively.
Protect your facility from unwanted audits! Join Becky Jacobsen, BSN, RN, MBS, CCS-P, CPC, CPEDC, CBCS, CEMC, and take a deep dive into both the CMS and AMA guidelines for reporting post operative pain blocks. You’ll learn how to determine if the nerve block is separately codable with real life examples for better understanding. Becky will also cover how to evaluate whether documentation supports medical necessity, offer recommendations for stronger documentation practices, and provide guidance on educating providers about documentation requirements. She’ll include a discussion of appropriate modifier and diagnosis coding assignment so that you can be confident that your billing of post operative pain blocks is fully supported and compliant.
During this RACmonitor webcast Dr. Ronald Hirsch spotlights the areas of the OIG’s Work Plan and the findings of their most recent audits that impact utilization review, case management, and audit staff. He also provides his common-sense interpretation of the prevailing regulations related to those target issues. You’ll walk away better equipped with strategies to put in place immediately to reduce your risk of paybacks, increased scrutiny, and criminal penalties.
Copyright © 2024 ICD10monitor. Powered by MedLearn Media.
Prepare for the 2025 CMS IPPS Final Rule with ICD10monitor’s IPPSPalooza! Click HERE to learn more
Get 15% OFF on all educational webcasts at ICD10monitor with code JULYFOURTH24 until July 4, 2024—start learning today!
