Clinical documentation improvement (CDI) is a process used by healthcare providers to collect, track, and analyze patient data and provide valuable feedback to physicians. Although Electronic Health Records (EHR) systems have been around forever, they have only been partially successful in handling complex data sets. There is a growing demand for an improved CDI technology and AI (Artificial Intelligence) tools are the solution. CDI programs backed by AI tools have the power to drastically transform healthcare organizations.
Significance of Accurate CDI in Healthcare
With the growing need for better data quality, accurate reimbursement and improved patient outcomes, the significance of a strong clinical documentation improvement (CDI) has increased. Healthcare systems require accurate CDI to bridge the gaps in coding and maintain quality care.
Along with the patient history and population, CDIs must also reflect the level of care needed for each patient. Inaccurate or non-specific documentation will have a negative impact on quality ratings, reimbursements, denials, and patient well-being.
High-quality documentation that is precise, exact, and error-free will accurately reflect the complexity of patient condition and care provided. It drives accuracy and specificity of diagnosis to ensure that all vital patient data is captured to give clarity to physicians and help coders generate quality data. It will facilitate an effective way of collecting data elements for better analysis and timely reporting of hospital data.
A robust CDI improves focus on accountability, quality of patient outcomes, and mortality rates.
How AI Solutions can Benefit CDI Programs
Handle Complex Workflows
Artificial Intelligence and related technologies are designed to support CDI teams in improving operational efficiency. For instance, consider a CDIS (Clinical Documentation Improvement Specialist) who is managing 100 patient charts in his workflow. Out of these, typically 30% would require clarifications or follow up. Without technology, he would need to manually review all the charts to identify those that need enhancement. AI can help the CDIS automatically capture these complex cases and spend minimum time on them.
AI in Front End CDI
AI can help drastically improve the front end of the CDI process by adding specificity where needed. For instance, a medical virtual assistant is powered by AI that can augment the teams’ knowledge by using natural language processing (a subset of AI) for chart searches, simple EHR navigation, and intelligent data entry. Once the documentation is complete and comprehensive in the front-end, the back-end automatically becomes effective granting more time to physicians and coders to perform other crucial tasks.
AI in Back End CDI
AI in the backend CDI is needed to ensure that there is 10% case coverage for all patients and the documentation is an accurate representation of the care given. Through the AI tools, patient scope can be widened to include emergency operations, same day surgeries and physician office visitors. AI reviews patient charts to spot clinical indicators, procedures, diagnostic details and suggest opportunities through enhanced workflow.
CDIS and coders can rely on AI tools that can capture specificity in CDIs and mitigate the risk of denials beforehand. AI-suggested appeal templates help CDI teams address denials effectively as and when they arise.
Artificial Intelligence can increase coding accuracy to a great extent. It can enhance coder productivity by offering code suggestions, streamlining workflow, and reducing the coding time. It allows traceability of codes to make audits easy.
Real-time data Access
The analytics and dashboard of the AI and NLP tools allow CDI professionals to access critical data in real-time. Physicians will be able to positively influence patient care through key performance indicators or KPIs offering a competitive edge to the organization and help them stay relevant.
HCCs (Hierarchical condition categories) identify complete in-patient and out-patient diagnosis and risks in all sorts of settings where the patient is treated. The accuracy of HCC is important as it represents an entire patient population. Coding and documentation errors can distort the entire healthcare. AI tools can help proactive management of HCCs and help the organization stay compliant. It can help recognize patients with HCC gaps and make lists of patients who did and did not visit in a year.
AI identifies query opportunities linked to the HCCs and alerts the CDI staff to raise a query by attaching the relevant query note for easy reference. Query automation is done effectively without the CDI staff intervention.
Advanced Intelligence for Exceptional Results
Evidently, Artificial Intelligence and related technologies help improve operational efficiency of healthcare organizations by bringing order and efficiency to CDI systems. It can help healthcare organizations to meet demands for authentic documentation, coding, and reimbursement. The automated and continuous review feature of the AI technology enhances documentation concurrency and achieves point-of-care accuracy.