From Multiple Code books to Simplified Chart Review

The global market size of artificial intelligence (AI) in the healthcare industry is expected to reach more than $28 billion by 2025.¹

According to the latest survey that was conducted to understand the importance of AI by 2031 amongst clinicians, sixty-four per cent of them in the Asia Pacific and South America regions consider AI-Based Tools will help make a majority of decisions, while a little less than fifty per cent of clinicians surveyed Europe and North America agreed that majority of their decisions in ten years will be based on it.2

With the above statistics, we can understand how AI is gaining traction in the healthcare ecosystem.

AI is already helping risk adjustment chart retrieval and coding review organizations streamline multiple administrative tasks, including clinical natural language processing (NLP) for medical records retrieval and retrospective chart review risk adjustment. 

In the blog, we will know more about how you can switch from a codebooks review workflow to a Three-Click Chart review.

Workflow of risk adjustment chart retrieval process

In the United States, the Centers for Medicare and Medicaid Services (CMS) and insurance companies are using the Risk adjustment models to calculate risk adjustment factor (RAF) scores and Medicare Advantage. 

Millions of Medicare Advantage’s medical records are retrieved and reviewed manually in order to ensure completeness of patient’s health status.

Traditional hierarchical condition category (HCC) coding and risk adjustment chart retrieval process lack the tools that are required to predict and prioritize chart review solutions in risk adjustment, including to support unreported diagnosis codes.

Optum, an American pharmacy benefit manager and health care provider, conducted a study to understand how many chart retrieval solutions providers for risk adjustment supported unreported diagnosis codes. More than 50 per cent of medical records reviewed and retrieved did not support unreported diagnosis codes, Optum’s study said.

How do you conduct a chart review process?

Usually, chart images are manually transferred in the form of paper or via electronic health records (EHR). The patient chart collection can be a number of pages, now imagine the next step is for the coder who receives those large charts which can be disorganized. Then, coders or HCC coding companies in the United States perform the medical record review and ensure supporting documentation to add the correct diagnosis codes.

How AI is helping coders and coding companies to perform modern risk adjustment chart retrieval tasks?

By using AI-Enabled Risk Adjustment Solution for your chart review process, you will automatically eliminate the risk of human-prone errors and assist with appropriate in-built codebooks to avoid inefficiency and inaccuracy.

Data/Codes can feed into the AI and can be customized to detect specific diagnosis code suspects while performing patient’s medical records retrieval for risk adjustment score calculation task.

AI-Enable Risk Adjustment Chart Retrieval Solution makes it easy to capture unreported diagnosis codes and eliminates the need of depending on multiple vendors for handling one or more of your retrospective risk adjustment process.

In addition, AI-Enabled Risk Adjustment Chart Retrieval Solution automates the manual coding workflow, from multiple codebooks to a three-click chart review, proving to offer quality risk adjustment solutions, while being the latest innovation to boost RAF scores and Medicare Advantage.

Ending note

Adoption of an AI-Enabled Risk Adjustment chart retrieval solution will allow your coding organization to decrease the overall chart review timings for coders, dependency on multiple vendors, and waste in the healthcare ecosystem. It will automatically positively impact the return on investment (ROI) and productivity of your coding company.

More charts reviewed with accuracy in lesser time give proper, smooth, and error-free Risk score derivation for Value-Based Patient care.

Source: 

1https://www.statista.com/statistics/826993/health-ai-market-value-worldwide/

2https://www.statista.com/statistics/1298955/clinicians-views-on-ai-use-to-make-decisions-by-2031-by-region/

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Disclaimer: All the information, views, and opinions expressed in this blog are those of the authors and their respective web sources and in no way reflect the principles, views, or objectives of RAAPID.