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RAAPID Pre-Encounter CDI Prospective NLP Analytics for HCC Capture Optimization

The business of healthcare delivery requires a combination of learned, attentive patient care and thorough documentation for subsequent encounter reimbursement. Both goals can be enhanced with automated pre-visit chart reviews delivered through Pre-Encounter CDI-like NLP analytics, proving that the best way forward can often be achieved by beginning with a quick look back.

The double-edged sword of electronic health record EHR systems is the sheer volume of data they record and hold. Every diagnosis and procedure involved with the care of an individual patient, along with the narrative details required to justify their inclusion in the revenue cycle management RCM system are part of the patient’s recorded history and available for review by healthcare providers prior to patient visit.

Given the sheer volume of patients seen daily, along with the demands to document more thoroughly, providers are under tremendous pressure and already report spending more time each day in front of computer screens than face to face with their patients. Asking them to review complete patient histories more thoroughly prior to every encounter so they can proactively capture all potential HCC assignments is simply unrealistic. 

This documentation workload conflict has inherently relegated comprehensive CDI initiatives for HCC capture to retrospective reviews, necessitating additional post-encounter documentation touches by providers when improvement opportunities are identified. It’s no wonder Administrative Burdens is the number one cause of provider burnout. CDI efforts can improve the quality and completeness of clinical documentation, but only after the documents are completed, forcing such records to be handled twice before being closed. 

Conversely, automated pre-charting through Pre-Encounter CDI-like proactive input cures this dilemma with succinctly compiled prospective analytics to ensure comprehensive HCC capture.  Such pre-visit NLP engine generated output identifies the appropriateness and potential outcomes of planned interventions or actions in advance of the patient visit, highlighting improved care and optimal healthcare revenue cycle management impacting concerns for the provider to address with the patient and in the subsequent documentation.

May Blog 3 The Key to Better Physician Reviews Simplifying Chart Prep with AI Solutions inner image

Prospective Review: Pre-Visit Automated Analytics Workflow

NLP analytics can comprehensively review all patient records within a provider’s EHR system and deliver cleanly organized condition details, prioritized risk concerns, and proactive care recommendations pre-visit. By presenting such a comprehensive review of all records on file, healthcare providers are better oriented for the visit, resulting in improved care and better healthcare revenue cycle management outcomes that are optimized for accuracy, workflow efficiency, and justifiable reimbursements.  

Prospective review pre-visit automated workflow deliver several benefits to the patients, providers, and the RCM objectives of the healthcare organization:

  • Patients feel healthcare providers are more personally engaged when they follow up on conditions unrelated to the reason for that particular visit
  • Providers no longer have to rely on memory or manual chart reviews to make certain they address and document what is required for HCC compliance and comprehensive care
  • Providers are relieved of the majority of their post-visit CDI-driven documentation efforts, minimizing their documentation related administrative burdens 
  • RCM workflow is enhanced through proactive CDI, maximizing supporting documentation for encounter diagnoses, procedures, and required HCC references
  • Justifiable reimbursements are optimized when healthcare providers have access to succinct pre-encounter CDI details, prompting more meaningful discussions and documentation

Adding automated pre-encounter CDI-like efforts with prospective NLP Analytics for HCC capture improve encounter results for the patient, the provider, and the healthcare provider organization’s revenue stream. They effectively support proactive patient care, decision-making, risk assessment, and ensuring optimal justified reimbursement.

To learn more about RAAPID’s Pre-Encounter CDI and the impact it can have on care delivery, documentation, and justifiable revenue results through improved HCC capture, speak to an expert now!


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Disclaimer: All the information, views, and opinions expressed in this blog are inspired by Healthcare IT industry trends, guidelines, and their respective web sources and are aligned with the technology innovation, products, and solutions that RAAPID offers to the Risk adjustment market space in the US.