In today’s value-based healthcare landscape, where hospitals and healthcare institutions are compensated based on the quality of care, even the most minor deviation in documentation can leave gaping holes in the revenue stream. Clinical Documentation Improvement (CDI) becomes even more crucial as Medicare continues to be the biggest payer for U.S. hospitals. With an expected 72 million people falling under its bracket by 2025, the exponential growth in this sector will only place a further burden on patient documentation in the years ahead.
Care providers must continuously realign their healthcare revenue cycle management (RCM) through comprehensive and quantifiable performance metrics or Key Performance Indicators (KPIs) to accurately capture care quality and ensure proper reimbursement. Overlooking even a single criterion could lead hospitals and clinics to suffer significant reimbursement losses. A robust set of KPIs helps hospitals track performance and prevent dire circumstances. It also provides organization-wide visibility of the hospital’s revenue cycle success.
Healthcare organizations struggle to optimize their KPIs despite meticulous adherence to the revenue cycle, especially for outpatient procedures where volumes and scope for errors are high. As a result, they suffer millions of dollars in lost revenue through claim denials and reworks. Research shows hospitals lose about 2% to 5% of their net patient revenue in unpaid claims.
Understanding Healthcare RCM Key Performance Indicators
The ability to effectively use KPIs is the deciding factor in reducing claims denials and maintaining a healthy revenue cycle. When looking at KPIs in a value-based healthcare model, the efficiency of the medical coding and documentation process is most important. Two key factors govern these:
- Coding productivity: The number of hours divided by the number of records. Efficiency depends on the least amount of time for the most coded work. For example, 24 inpatient coding records per eight-hour paid workdays can be an average benchmark for efficient coding productivity. However, this metric will need to be adjusted based on additional coder duties, organizational complexity, case mixing, etc.
- Coding accuracy: Accuracy is determined by comparing the number of errors against the number of codes assigned – the goal being an absence of deviations. Since the organization is assigning coding levels, a guideline document measures the accuracy of coding levels. Online coding tools and official manuals benchmark and improve coding accuracy and efficiency.
While these factors form a solid basis for medical coding and documentation, there are several KPIs that can help track the revenue cycle health of outpatient procedures, including:
- A/R days: The average time it takes for claim payment. While practices wait for payment, cash flow (and opportunities to invest and earn interest) decreases.
- Coding denials: Occur when an incorrect diagnosis, treatment, or procedure code is filed.
- Coding and documentation productivity: Due to the prevalence of paper-based legacy workflows, coding and documentation often take up more time prioritizing worklists and identifying problems. For more than 90% of revenue cycle managers, issue identification takes significant time.
- Risk score capturing: A methodology that assigns a score to a person’s health to predict healthcare costs. Inaccurate Hierarchical Condition Categories (HCCs) and Risk Adjustment Factors (RAF) can lead to imprecise care and documentation, resulting in denied claims. Since revenue links to HCCs and risk scores in Medicare adjustments, risk adjustment is crucial in a value-based care setting.
NLP: Driving Action, Not Just Insights
The introduction of Natural Language Processing-enabled (NLP) computer-assisted coding (CAC) software has bolstered professional medical coding in new and innovative ways. Having been trained for years on longitudinal clinical charts, the ability of AI-powered CAC systems to visualize KPIs in a more organized manner allows for decision-making geared toward higher revenue generation and improved healthcare quality.
With this in mind, AGS Health’s computer-assisted coding allows providers to simplify the coding workflow. By using, industry-leading, clinical NLP technology, AGS’s CAC tool provides:
- Intelligent worklist prioritization
- Accurate diagnosis and charge codes based on documentation
- NLP-driven auto calculation of correct E/M levels
- Visualizing coding trends to make decisions to improve productivity
Computer-assisted coding and NLP-based tools significantly improve coding accuracy and other outpatient KPIs. For example, Auburn Community Hospital used AGS’s CAC to optimize its coding workflow and revenue cycle, which led to :
- 50% decrease in DNFC days
- 4.59% increase in CMI
- 50+% improvement in coder efficiency
Once in effect, the AGS CAC solution offers considerable benefits for medical coding workflows. With the NLP-driven solution in place, hospitals can expect to see the following metrics:
- 20% increase in risk score capture
- 30% reduction in coding denials
- 30% reduction in days in A/R
- 40% increase in coder productivity
Revenue management and integrity are governed by KPIs that effectively identify areas where payments are low but compliance risks are high. When CAC software is NLP-driven and applied effectively, it creates new avenues for medical coding to successfully progress and meet the needs of a value-based healthcare ecosystem.
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