The most powerful Coding Assistance and Data Quality platform available

Without a data compilation strategy, it is simply not possible for clinical coders and automated systems to be fully effective.

Any robust coding solution needs to include a frictionless way of introducing new structured and unstructured data into the process without requiring substantial technical integration effort or customization from vendors. This includes automated EMR data feeds (HL7/FHIR), clinical note repositories, data warehouses, and even spreadsheets of manually compiled data.

Hospitals need to be able to easily bring in their own data without external vendor assistance, therefore DQA was designed from the outset with data integration in mind. The platform includes data interfaces that allow hospitals to import new data sources themselves.

The recent availability of world class clinical AI engines have opened up limitless new opportunities in healthcare. DQA utilizes sophisticated commercial clinical NLP engines to translate clinical text into a semi-structured data format. Improving the coding process is one of many practical applications for this AI interpreted clinical text.

In addition to integrating this world-class clinical NLP, we’ve spent over five years developing a library of machine learning algorithms and data quality rules to target specific coding issues where translated medical text is insufficient. Flexibility in creating new data quality rules has been a key driver of DQA’s success. We often say DQA was built by coders across the province because, in many ways, it was. The flexible rule engine has allowed us to integrate new intelligence rapidly based on techniques and approaches proven by Health Records departments across many hospitals. This also ensured that rules specific to Canadian and Ontario coding were properly represented in the platform.

Coders need to access and digest a large amount of data from different information systems when coding patient encounters. It is often not even possible to gather the necessary information from all relevant data sources across the hospital. Without the proper tools, clinical coding can become a highly labor-intensive investigative exercise.

Compilation of data has always been a key part of the DQA data quality audit process, and this data is now available to the coders at the time of coding to improve baseline data quality.

To help improve the efficiency of your coding team, we provide a unified user interface of all structured and AI-interpreted clinical notes. These tools help you consume data faster as well as reduce cognitive fatigue through visualization techniques such as highlighting and color coding. The AI also immediately identifies and labels coded elements such as diagnostic and procedural codes to reduce lookup time.

Despite best efforts, mistakes are often made during the documentation and coding process. Given the impact to funding, research, and other initiatives that rely on clean coded data, hospitals must try to be as accurate as possible in their medical abstracts.

Automated case auditing has been a staple of the DQA platform since its first release in 2015. With the help of over 50 hospitals, we’ve designed case profiling logic that uses a variety of statistical and machine learning techniques to automatically detect potential errors within your coded abstracts.

The automated audit engine takes full advantage of all integrated data, Artificial Intelligence, and data quality rules within our platform. The data quality reports that are generated also prioritize case review that direct medical coders to the highest probability, highest value data quality errors. We know that coding resources are both valuable and limited so we’ve designed DQA to maximize the value of the audit review and correction process.

It is critical to account for factors pertaining to Canadian coding standards and provincial policies that affect hospital funding, quality improvement initiatives, and accountability agreements. In addition to national standards such as CIHI CMG+, there are policies that depend on provincial methodologies such as HIG and Quality Based Procedures (QBPs).

Certain data points within coded medical records have specific implications in terms of funding and performance indicators of key interest for hospitals. These regional specific rules and calculations are deeply integrated within DQA to ensure that proper attention is paid to the factors that have the most impact on your hospital.

DQA includes a growing set of additional features within the platform:

DQA has been successfully used by leading Ontario hospitals to improve their data quality, using the best ideas from clinical coders across the province.

For more information about implementing the DQA platform, please contact us.