In today’s value-based healthcare landscape, where hospitals and healthcare institutions are compensated based on the quality of care, even the smallest deviation in documentation can leave gaping holes in the revenue stream. Clinical documentation improvement becomes even more crucial as Medicare continues to be the biggest payer for US hospitals. With an expected 72 million people falling under its bracket by 2025, the exponential growth in this sector will only place further burden on patient documentation in the years ahead.
To accurately capture care quality and ensure proper reimbursement, care providers must continuously realign their healthcare revenue cycle management through comprehensive and quantifiable performance metrics or key performance indicators (KPIs). This is especially true since overlooking even a single criterion could lead hospitals and clinics to suffer significant losses in reimbursement. Having a robust set of KPIs can not only help hospitals track performance and prevent such dire circumstances but also provide organization-wide visibility of the overall success of the hospital’s revenue cycle.
However, despite the meticulous adherence to the revenue cycle, healthcare institutions often struggle to optimize their KPIs. This is especially true 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. In fact, research has shown that hospitals lose about 2% to 5% of their net patient revenue in unpaid claims.
Understanding the Various Key Performance Indicators
The ability to accurately visualize and comprehensively utilize 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, everything boils down to the efficiency of the coding and documentation process. These are governed by two key driving factors, which include:
- Coding productivity: As productivity is determined by the number of hours divided by the number of records, optimal efficiency is dependent on the least amount of time for the most amount of coded work. For example, 24 inpatient coding records per eight hour paid workdays can be considered an average benchmark for efficient coding productivity. However, this metric will need to be adjusted based on factors such as additional coder duties, organizational complexity and case mixing, among others.
- Coding accuracy: By comparing the number of errors against the number of codes assigned, accuracy is determined to ensure that coding efficiency occurs with the absence of deviations. Since the organization is assigning coding levels, a clear guideline document or defined metric will need to be established to effectively measure the accuracy of coding levels. Online coding tools and official manuals form the bulk of resources that are used to establish benchmarks to improve coding accuracy and efficiency.
While these factors form a strong basis for medical coding and documentation, there are several specific KPIs that can help track revenue cycle health when it comes to outpatient procedures. These include the following:
- A/R Days:
Days in accounts receivable represents the average length of time it takes for a claim to be paid. While practices wait for payment, cash flow — and opportunities to invest and earn interest — decrease.
- Coding denials:
Denials from coder error occur when an incorrect diagnosis, treatment, or procedure code is filed through a claim.
- 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, identifying issues take up a significant chunk of their time.
- Risk score capturing:
Risk adjustment is a methodology that equates the health status of a person to a risk score, to predict healthcare costs. Inaccurate HCCs and Risk Adjustment Factor (RAF) can lead to imprecise care and documentation, ultimately resulting in denied claims. With revenue being directly linked to HCCs and risk scores in Medicare adjustment, risk adjustment is the single most important factor 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 into the process 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 better decision-making that is geared towards higher revenue generation and improved healthcare quality.
With this in mind, EZDI’s computer-assisted coding solution allows care providers to simplify the coding workflow dramatically. By utilizing, industry-leading clinical NLP technology, the EZDI 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 for outpatient and NLP-based tools are proven to significantly improve coding accuracy and other outpatient KPIs. A prime example of this is the case of Auburn Community Hospital where EZDI’s computer-assisted coding solution optimized the institute’s coding workflow and revenue cycle. This led to a 50% decrease in DNFC days, a 4.59% increase in CMI, and over 50% improvement in coder efficiency. Once in effect, the EZDI computer-assisted coding solution offers marked 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 computer-assisted coding software is NLP-driven and applied effectively it can open up new avenues for medical coding to successfully progress and meet the needs of a value-based healthcare ecosystem.