DQA CAC Module

Taking Computer Assisted Coding to the next level

The DQA CAC module is more than just traditional Computer Assisted Coding (CAC). It combines practical software tools along with advanced NLP to solve the most difficult challenges faced by your coding team.

In 2016, we introduced 3terra DQA as a post-coding audit tool to identify data quality errors in medical abstracts that affect hospital funding and quality metrics. Since then, DQA has become the leading hospital data quality platform in Ontario.

In 2020, we added the CAC module to the DQA platform to improve the effectiveness of the coding process. This is a critical extension to the platform because it addresses the following issues:

There is a large and growing coder shortage in Canada. Effective tooling bridges that gap and reduces outsourcing, onboarding time, and coder burnout.

Data quality is important for hospital revenue and outcome measures. Addressing it further upstream in the coding process optimizes the overall effectiveness of your data quality initiatives.

Streamlined coding spans many areas including optimizing initial coding, effective post-coding audits, and clinical documentation improvement. The CAC module is part of the larger DQA platform that provides one place for managing and optimizing all aspects of this complex process.

The impact of Artificial Intelligence in coding often falls short of expectation because it’s only one part of a complete solution. We merge advanced AI with practical tools that impact actual productivity by focusing on integrating those tools into your existing processes.

Included in the CAC module:

Coding is a complex process requiring significant expertise. It requires a combination of tools, each suited to solve the many different challenges that coders face. The CAC module offers the following functionality:

ADVANCED SEARCH
Quickly and easily search across all documents for clinical concepts, reducing the time spent hunting for, and possibly missing, key diagnoses and procedures.

HIGHLIGHTING & ICD-10 AUTO-SUGGESTION
We use world-class Clinical Natural Language Processing (NLP) to identify key clinical concepts and auto-suggest ICD-10 codes to assist with the coding process.

DOCUMENT COMPARISON
Sifting through copy & pasted Progress Notes is extremely time consuming. We highlight differences between notes to identify what has changed over time.

AUTOMATED DATA QUALITY AUDIT
Once coding is complete, coders have the ability to immediately initiate an automated coding audit using the DQA audit engine.

ONE PLACE TO REVIEW DOCUMENTATION
All clinical documents needed for coding are available in one single view, reducing time switching between different hospital systems and reducing the risk of missing a document.

STRUCTURED DATA INTEGRATION
Import EMR data, such as diagnostic imaging exams and lab results, are brought to the coders’ attention to ensure all key activity is captured.

(Coming Q4 2022) – CODING PROCESS ANALYSIS
Monitoring and optimizing the effectiveness of your coding team is difficult. We capture various usage data and present indicators that help optimize your coding effectiveness, targeting specific problems in the process.

Frequently Asked Questions (FAQ)

Is CAC a new standalone product or part of DQA?
It is part of the DQA platform, licensed as a separate optional module. It is deeply integrated into the existing DQA platform and utilizes much of the same technology.

How have you implemented Natural Language Processing?
The last couple of years have been a watershed moment as large commercial vendors like Microsoft, Amazon, and Google have released clinical NLP as a service. This is transformational technology that enables us to implement world class clinical NLP without the costly and extremely risky task of home-growing our own clinical NLP. (See this video for more info)

What does implementation look like?
Unlike the base DQA platform which can be implemented within days, the CAC module implementation is highly dependent on the availability of clinical notes. We can extract clinical notes from EMRs via HL7, FHIR, or direct database feeds. Based on how easily available access to clinical notes is, you can expect an implementation time between 1 and 4 weeks.

What are the IT resources needed to implement the CAC module?
There are no additional hardware resources or IT assistance required if you already use the base DQA platform. All Natural Language Processing is done off-site on secure Microsoft Azure servers hosted within a geographic region of your choice. 

How long will training take? Will productivity initially decline?
Like any other new tool, there is a learning curve. However, our main differentiator compared to other platforms is a focus on user interface and tooling that will feel intuitive to your coding team. Productivity should increase almost immediately after user training, which can be completed in a day.

Does this mean coders no longer have to go into the EMR to look for documents?
That is dependent on how you’d like to configure the system and what level of documentation ingestion you’d like to setup for the CAC module. You may choose only to bring in documents that you’d like to utilize NLP on (for highlighting and auto-suggestion) while relying on the EMR for reviewing other documents.

Are the NLP translated clinical elements available for other purposes?
Yes, and this is a large differentiator between us and other vendors. Translating clinical text into standardized medical codes is valuable for purposes far beyond coding assistance. We provide database access to these translated medical concept codes for downstream use including clinical data warehouses.

What about Clinical Documentation Improvement (CDI)?
DQA has helped with CDI processes for many years and we are currently expanding our offering to include more tools to manage the entire CDI process including physician queries, automated workflow, and CDI performance monitoring tools.

Please contact us if you have any questions or would like a demo of our platform.