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Use Computer Assisted Coding (CAC) and Artificial Intelligence (AI) to reduce manual errors and turnaround time taken for accurate coding for billing and revenue purposes.

Problem statement

Apart from patient care, billing and revenue are extremely crucial for any hospital or healthcare organization. The dependency on manual intervention for accurate coding not only results in human error but also delayed turnaround time and additional effort. The introduction and subsequent migration to ICD-10 codes have only made the coding system more complex.

However, the quality of the codes directly affects the billing and hence the revenue. Mistakes can potentially lead to legal hassles as well. The feedback loop for wrong codes does in no way impact the quality of the codes – putting potential revenue at risk.

aciana aims to decrease manual intervention for coding to a very large extent by using CAC not only to assist but in decision making as well based on the AI algorithms.

Our Features

  1. Early detection of diseases
  2. Improved accuracy levels of risk stratification of patients based on clinical data
  3. Accuracy of readmission rate
  4. Denial Analytics
  5. Underpayment Analysis
  6. Improving bed utilization
  7. Accurate Shift rotation and support staff requirements

Fact sheet

$228 M

is spent on unnecessary lab tests

$228 M

is spent on unnecessary lab tests


Computer Assisted Coding and Artificial Intelligence ensures faster and accurate coding. Code accuracy in turn results in revenue protection. CAC can also potentially reduce administrative intervention time during auditing. The CAC product from aciana is geared to take up both ICD-9 as well as ICD -10 codes. The product assures consistent and accurate coding even before the billing thus ensuring the right revenues for the healthcare organizations.

The CNLP will be embedded within the CAC for aiding healthcare organizations in a multitude of business use cases. These use cases are solely focused on increasing the value based care for the organization. 

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