The Clinical Documentation Improvement (CDI) platform used across Ontario
Poor data quality affects hospital funding. DQA focuses your efforts to provide an immediate impact on your data quality initiatives.
The Data Quality Assist (DQA) CDI platform
Over the past decade, Clinical Documentation Improvement (CDI) has become increasingly important as Ontario has moved into data-driven funding mechanisms. With the new focus on financially incentivizing performance across the health system, data quality will continue to be a critical factor as it has been in similar models in other countries.
Data Quality Assist (DQA) is a software tool that is installed on-site and immediately identifies the potential problems in coding and clinical documentation that directly affect funding. Reviewing and correcting cases so the coding reflects the reality of the care provided helps ensure that patient acuity is accurately represented, leading to improved financial performance.
Critical success factors for CDI software implementation
When choosing software to support your CDI initiatives, it’s important to know that different types of software accomplish different goals. Some software such as NLP coding assistance tools help with initial coding efficiency and can even help reduce coder headcount. However, the DQA CDI platform was built to ensure that data quality errors that matter most to hospitals are corrected in addition to focusing on coder efficiency.
|DQA CDI PlATFORM||DQA DataHUB MODULE|
|Streamlined initial coding with improved baseline accuracy|
|Short implementation time and low risk|
|Ability to easily integrate new data sources|
|Automated abstract review with 100% case coverage|
|Prioritization of DQ errors by CMI impact|
|Flexible data quality engine for adding new rules|
|Case-level detailed tracking of errors and corrections|
|Re-audits cases when documentation is added or modified|
|Analytics including funding impact and DQ root cause analysis|
|Provincial methodologies (HIG, QBPs) and rules built-in|
Components of the Data Quality Assist platform
The operational challenges of CDI that DQA addresses can be broken down into two main categories:
- Comprehensive collection and compilation of data
- Accurate interpretation of data
Without a data compilation strategy, it is simply not possible for clinical coders and automated systems to be fully effective.
Any robust CDI solution needs to include a frictionless way of introducing new data into the process without requiring substantial technical integration effort or customization from vendors. We have found that a wide mix of data sources, from automated HIS data feeds and CDI data capture applications to uploaded lists of manually compiled data, have their place in this process.
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.
Coders often perform their jobs by accessing a wide variety of screens across many different HIS systems when coding an encounter. Sometimes it’s not even possible to gather all necessary information from relevant data sources and case review becomes a labor-intensive investigative exercise.
To address this issue, we’ve introduced the DataHub module to assist with initial coding. 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.
Accurate initial interpretation of data
These important sub-groups of encounter data need to be considered:
CMI and volume funding criteria are based on properly documented interventions. While larger procedures that drive case mix grouping are unlikely to be overlooked, other interventions such as vascular access devices and feeding tubes can be missed. DQA accomplishes better data quality by cross-reference rules against various hospital data sources to capture those missing interventions when they cannot be discerned from text data supplied at the time of coding.
Proper diagnosis attribution is important as it affects CMI and other key calculations. There are many systems that employ a variety of Natural Language Parsing (NLP) techniques to parse data and recommend (or even pre-code) ICD-10 codes, which reduces initial coding effort. But like all machine learning algorithms, there will always be an error rate and they are only as good as the data they are fed (see the “data compilation” section above).
Key methodology factors
Certain data points (e.g. post-discharge home care, birth weight, patient services) have specific implications in Ontario. A physical stay in the ICU affects case weight and is, therefore, a critical factor in documentation, but is not driven from ICD-10 or CCI codes. Similar to interventions, cross-reference data quality checks within DQA yield excellent results.
Regional standards and policies
While identifying the existence of the factors listed above is important, it is also critical to account for factors pertaining to Canadian coding standards and provincial policies that affect hospital funding and accountability agreements.
As an example, the case weight attributed to an encounter is highly dependent on choosing the proper most responsible diagnosis (MRDx) which can sometimes be difficult to determine for complex cases where the admitting diagnoses ultimately doesn’t represent the underlying care and resource utilization associated with the encounter. Other types of minor coding mistakes can directly affect the eligibility for volume funded cases, directly affecting hospital revenue.
These Ontario-specific rules and calculations are deeply integrated within DQA to ensure that proper attention is paid to the factors that have substantive impact on the hospital. Coding teams have limited resources and they need to focus on what matters most.
Automated case review
Despite best efforts, mistakes can be made during the documentation and coding process. Sometimes a physician doesn’t properly record a key factor, something is misinterpreted, or key information isn’t available at the time of coding.
Automated case auditing has been a staple of the DQA CDI platform since its first version. With the help of many hospitals over several years, we’ve designed case profiling logic that uses a variety of statistics and Ontario/Canadian methodology calculations to determine whether a coded abstract is abnormal. We combine all factors to produce an aggregate outlier score that has yielded very positive results for documentation problems that are difficult to detect.
It is also possible that a hospital chronically under-documents patient care in certain areas. It is particularly difficult to identify a widespread absence of documented information. Our approach has been to analyze peer data (e.g. provincial case costing data) to generate statistics on occurrence rates of CMI factors for similar cases at peer hospitals. If there is large discrepancy in these occurrence rates, it generally points to variation in care practices or undercoding on those factors.
Process efficiency and analytics
DQA includes a suite of supplementary tools to support the case review process in order to minimize the input costs of CDI. This includes:
- Root cause analysis through the use of analytical reporting to identify repeated systematic issues that can be remedied by changes in process or physician education.
- Collaboration tools that support the case review process including centralized case notes and workflow assignment to team members involved in the review process.
- Effectiveness monitoring and rule tuning. In addition to enabling rule tuning to reduce false positives in automated case reviews, detailed statistics on case corrections allow hospitals to directly calculate the impact of their CDI investment. With this level of detailed auditing, attribution of benefit to the system cannot be confounded by unrelated, coincidental factors.
Beginning in 2019, we introduced a new level of analytics for our clients to help explain year-over-year variations in inpatient weighted volume and CMI for volume-funded cases. Finance teams need to understand the underlying root causes for CMI change beyond data quality and this can include many factors including occurrence rates of long stay discharges, variations in conservable days, and changes to patient case mix.
Extensible data quality rules
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 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.
In addition to incorporating novel data quality rules, this flexibility is absolutely required to adapt to changing governmental methodologies. This can be a problem with generic CDI solutions adapted to the Canadian environment, because it is difficult for international vendors to evolve their products at a pace that matches change in local policy.