The Importance of Accurate Documentation in Clinical Documentation Integrity and Its Impact on Quality
Healthcare institutions dedicate significant resources to providing safe, efficient, and equitable care. The quality of this care is often assessed based on the information in medical records, which is then shared publicly through various quality rating organizations. These ratings help healthcare consumers make informed choices and highlight areas where hospitals can improve.
However, the quality of care can sometimes be misrepresented due to unclear or inaccurate documentation. When medical records lack detail, it can negatively affect a hospital’s performance metrics and quality scores, making it seem like the care provided was subpar. Factors like budget constraints, logistical challenges, human errors, and especially documentation inaccuracies contribute to these flawed assessments.
Iodine’s AwareCDI tool helps address these challenges by improving Clinical Documentation Improvement (CDI) programs. It identifies cases where there’s a gap between clinical evidence and documentation, helping CDI specialists enhance the accuracy of medical records. This leads to better quality metrics and more appropriate reimbursement for healthcare providers.
Iodine’s Approach to CDI and Quality
At Iodine, we believe that CDI programs should focus on documentation integrity rather than just improvement. Our goal is for hospitals to receive accurate payments and quality metrics that truly reflect patient care. We recommend CDI programs:
- Correct all significant documentation issues, regardless of their impact on metrics or reimbursement.
- Select cases for review based on the need for documentation correction, not external factors.
- Issue queries for clinical validation when documentation isn’t supported by clinical evidence.
- Follow compliant query processes aligned with industry standards.
While financial metrics are important, achieving complete documentation integrity should be the priority. This philosophy guides both our best practices for reviewing cases and our machine learning models, which prioritize cases based on documentation discrepancies rather than just financial impact.
How CognitiveML Enhances Documentation
Iodine’s AwareCDI is powered by CognitiveML, our AI and machine learning engine. CognitiveML analyzes clinical data in real-time to identify gaps between evidence and documentation. It helps CDI specialists focus on cases likely to have errors, improving documentation accuracy and coding.
Many of the conditions CognitiveML prioritizes are chronic or comorbid conditions that significantly impact quality scores and rankings. For example, conditions like obesity, COPD, and congestive heart failure are critical for quality risk adjustment. By accurately documenting these conditions, hospitals can improve their quality metrics.
Impact of AwareCDI on Quality Scores
We studied the effect of AwareCDI on quality scores across 245 facilities. Across the 245 facilities in this study, 76% experienced an overall increase in capture of the six quality-impacting comorbidities that were the focus of this study. On average, there was a 23% increase in the capture of key comorbidities that impact quality scores.
By guiding CDI teams to cases with the highest likelihood of documentation discrepancies, AwareCDI improves coding accuracy. This leads to more accurate quality measurements and reimbursement, supporting better overall patient care.
Conclusion
Accurate documentation is essential for reflecting the true quality of care in healthcare institutions. Iodine’s AwareCDI tool powered by our CognitiveML engine helps achieve this by improving documentation integrity. Hospitals using AwareCDI can expect better quality scores and a more accurate representation of the care they provide.
Read more about our Impact on Quality here.