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Audit
With the approaching deadline for submissions to the Canadian Institute for Healthcare Information (“CIHI”) for NACRS and DAD data holdings, Semantic Health hosted a webinar on February 24, 2022 to provide an overview of current submission preparation processes and the ways that your healthcare organization can be empowered to address current issues using Semantic Health’s technology.
The process of preparing submissions to CIHI can be optimized to address current challenges in meeting timeline requirements and efficiently ensuring that submissions are of high quality.
The current submissions process involves manual work that can result in documentation errors, increased workload for coders & auditors, and reduced data quality within submissions to CIHI. These issues highlight the need for an optimized submission process that can be adopted by any healthcare organization, regardless of digital maturity, to create higher quality and faster submissions.
Semantic Health integrates advances in artificial intelligence and machine learning to optimize clinical and coded data quality to ensure an enhanced submission process at your healthcare facility well in advance of CIHI submission deadlines.
Click here to watch the CIHI Webinar
The current process for preparing submissions to CIHI is labor intensive. Reviewers will spend a large amount of time searching through both structured and unstructured data and in completing reconciliation checks. There is a significant burden on coding and audit staff, especially as we approach submission deadlines, to complete these reviews. With the limited amount of time available and the significant pressures on staff to complete requisite data quality checks, healthcare providers will frequently experience pressure at the end of the year to get things ready in time for submission. These pressures can lead to gaps in data quality opportunities, inaccuracy of coding, and new backlogs of coding.
In many current auditing processes that are labour-intensive, it can be difficult to quickly and easily identify gaps in documentation. Flagging important gaps within documentation can result in time and cost savings by easily identifying missed, underspecified and incomplete documentation. In particular, when diving into the documentation, it can be difficult for a manual reviewer to quickly and easily identify: (1) elements of change within copy-forwarded text; (2) details around underlying cause of diagnosis; (3) unclear summaries.
In a rush to meet deadlines and limit coding backlogs, it can be difficult to ensure that we are accurately capturing all appropriate codes within a particular chart. This is particularly true where there may be opportunities to flag missed interventions, underspecified coding, and potential comorbidities. Each of these factors directly contribute to our understanding of case acuity but can often be overlooked in preference to a principal diagnosis.
AI-powered auditing algorithms, such as those included in the Semantic Auditor, if deployed correctly and with specific regard to an organization’s unique patient population, can streamline and augment existing data quality audit processes in hospitals. These algorithms can complete full, complete coverage reviews of all abstracted data to ensure that the most high-value data quality opportunities are flagged for review in a prioritized and timely manner.
Deploying artificial intelligence in the CIHI submission process can significantly improve auditor efficiency, ensure that staff is focused on the most high-value opportunities, and limit the use of labour-intensive sample audits.
Semantic Health’s AI-assisted auditing software, Semantic Auditor, is a best-in-class AI tool that relies on proprietary deep learning models to complete full, complete coverage reviews of all abstracted and coded data. Our algorithms are trained and tuned on existing and retrospective hospital data, including to your unique patient population, to considerably reduce time spent in audits and improve efficiencies through identification of the issues listed above. The ability to quickly and easily identify all issues makes our tool one of the most efficient ways to optimize your data quality, regardless of your organization’s digital maturity.
With Semantic Auditor, we’ve identified several data quality opportunities present in both documentation and in coding.
Our platform works by automating reviews of coded charts and documentation, flag the most high-value opportunities (along with rationale), and ultimately simplify the CIHI submission process. This is achieved by identifying missed or underspecified codes and pointing the auditor to the exact rationale in the documentation and/or coding that requires further review with explainable AI. These insights help health providers to develop data-driven, high-quality clinical documentation programs and, in turn, resulting in file integrity and better quality data submissions to CIHI.
Key findings involve the clinical fields that are being abstracted within the DAD database. Specifically, this involves analyzing and identifying the most accurate ICD and CCI codes by identifying specific findings such as the most responsible diagnosis, the secondary diagnoses codes, and flagged interventions. This validates the accuracy of the coding, mRDX, principal interventions and, by extension, the CMG+/HIG groupings. These data quality checks have identified many missed opportunities that are not currently found in current data quality checks.
Data quality and team impact are two main factors where a return on investment can be driven with the Semantic Auditor. The Semantic Auditor measures the impact on data quality through the increased findings of weighted cases (according to the CMG+/HIG methodology). Historically, there has been a 25 to 30% increase of audit yield (relative to existing processes and use of other market-leading software). With respect to the impact on the team, we look to identify how long year-end data quality checks historically took and measure against this to identify the increase in auditor efficiency. We have historically found our process to be 3x faster with a reduced need for variable staffing.
Yes, the software has been designed to perform traditional audits and also indicate CDI opportunities by clinical area, provider, and by type of case. These insights can be targeted to improve the current CDI program or build up a new CDI program using fewer resources.
Click here to watch the CIHI Webinar
By implementing an AI medical auditing platform to address manual auditing failures, coding and documentation issues can be resolved proactively, saving time and money. If you are interested in learning more about how AI in medical auditing can improve efficiency and accuracy, and enhance CDI initiatives, feel free to contact our team of experts at contact@semantichealth.ai.
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Semantic Health helps hospitals and health systems unlock the true value of their unstructured clinical data. Our intelligent medical coding and auditing platform uses artificial intelligence and deep learning to streamline medical coding & auditing concurrent with patient admission, improve documentation quality, optimize reimbursements, and enable real-time access to coded data for secondary analysis.
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