Revenue cycle management is becoming increasingly important to every EMS agency. This will become evident as reimbursements remain relatively stagnant when costs continue to rise. Obtaining accurate patient demographics, signatures and insurance information is critical to ensuring the financial solvency of an agency; however, they’re often overlooked.
Many management teams may not even be aware that this data can be easily obtained through any electronic patient care reporting (ePCR) system. At Galveston (Texas) EMS, we began utilizing the ePCR data to improve patient outcomes and offset operational costs.
One of the more common practices in EMS, specifically among municipal agencies, is that complete patient insurance information isn’t being collected by the crew. This is based on an assumption that a billing department or provider will obtain it at a later date.
After a quick audit of several reports, it became clear that this was common practice within our own agency as well. Knowing there was room to improve revenue generation, we went to work increasing the amount of funds that could be captured in an effort to reduce our reliance on taxpayer funds. After all, EMS is one of the few public programs that can generate revenue to offset operating expenses.
Using Data Effectively
For years there’s been a push to standardize the data collected by ePCR systems. Although primarily motivated by a desire to improve patient outcomes with standardized metrics, agencies can also operationally benefit from data standardization.
The data we used to identify our issue and subsequently monitor performance comes from standardized National EMS Information System (NEMSIS) fields. Depending on the specific application used by your service, these fields may be labeled differently. However, the data elements can be found in most stock reports. (See Table 1.)
To gain a better understanding of the situation and determine whether it was a systemic issue or attributed to a few factors, the data was extracted and graphed. Furthermore, the data provided reference points and established a baseline for gauging the success of the implemented solutions.
It’s important to start with a broad approach. As you begin to identify a potential problem, you can add additional data elements to drill down further. In this case, it was important to start by confirming that all attempts at collecting patient insurance information were being made prior to ending patient contact.
Key metrics that we identified included:
- Agency averages by payer group;
- Provider-specific rates by payer group;
- Zip code-specific rates by payer group; and
- Payer groups by run type.
Agency Average by Payer Group
Having the agency averages shows not only the progress of the agency, but also allows for the comparison of an individual employee to the overall group.
Because each service is unique, often operating in a different area, it can be a challenge to rely on the financial performance of an agency of similar size and scope. Even a neighboring agency might have a completely different socioeconomic status, funding source or structure.
Provider-Specific Rates by Payer Group
Reviewing provider-specific rates by payer group allows for easy identification of individuals who could provide insight into a more efficient workflow or those who need may need some additional training or prompting. In Figure 1, you can see that the provider has a low no payer rate compared to the agency average.
Figure 1: Provider-specific rates by payer group for FY 2017 (all run types). Images courtesy Nathan Jung/Galveston EMS
Zip Code-Specific Rates by Payer Group
Payer mix can vary widely across a county; there can be fluctuations even within a neighborhood. Zip codes provide the most readily available source for geospatial analysis, however, with geocoded addresses we’re able to drill down with more accurate data.
Understanding the payer mix documented in correlation with data supplied by the United States Census Bureau can help justify funding requests or support financial performance. This can be crucial for a service that bids on a 9-1-1 contract or an agency looking to explore a potential new market entry and is concerned about the patient population outside of a facility.
An area to consider for 9-1-1 providers is the documented no payer rate compared to collection rate and the Census Bureau-reported insured rate. This quick check will allow you to see if further analysis is warranted.
Payer Groups by Run Type
The final measure considered was payer groups by run type. Our primary operations are divided into two distinct divisions, 9-1-1 and non-emergency transports (NET). Within those divisions are unique run types that can have a different type of payer associated. (See Figure 2.)
Figure 2: Payer groups by run type for incidents in FY 2017
As expected, the 9-1-1 division accounts for the majority of the documented self pay encounters, while the NET division almost always has some form of funded payer documented to it. Another area to take note of is the ratio of Medicaid encounters in comparison to Medicare or other commercial insurance, since Medicaid traditionally pays lower.
Specifically, we looked to see if the no payer issue was related to one division over the other. As expected, the number of no payers encountered was higher among 9-1-1 responses. Further analysis revealed that the individual provider had more impact on this than the physical location of the patient.
After reviewing the data, conducting audits of random PCRs and consulting with several of the long-term supervisors, it became clear that the majority of the no payer documentation was a result of conflicting information.
This led to increased costs associated with labor in research, delays in payments and lost revenue for the claims in which no information could be recovered due to an incorrect medical record number or misspelled patient name.
Other risks that could impact a service include lost revenue through missed filing deadlines, increases in billing charges associated with outsourced vendors and the need to hire additional personnel for in-house billing teams.
The most optimal time to obtain this information is prior to the EMS crew leaving the patient.
The first step in overcoming this issue was educating the entire agency on the importance of collecting insurance information. The second step was walking through ePCRs. This allowed us to identify the fields associated with insurance information and explain what they are, how they’re used and why they need to be filled out.
Finally, we shared the agency data with everyone. For those we identified as being above the threshold, we showed their specific data in comparison to the agency averages and worked with them individually, sending weekly updates on their performance.
Training on the importance of collecting automotive policy insurance information, documenting on-the-job injuries (workers’ compensation claims) and developing better ways to ensure revenue is captured will continue. Educating staff and reducing the no payer rate will be an ongoing process.
Figure 3. Payer rates over time
Starting in FY 2015 Q4, we saw measurable progress as our documented no payer rate declined while our documented funding sources increased. We’re continuing to make progress even with our call volume on track to be 2,000 incidents higher than FY 2015.
In the current and previous fiscal years, our agency has been able fund a total of $1.3 million and is proposing another $1 million in capital expenditures with no impact to taxpayers. We were also able to address the increasing call volume with an additional 9-1-1 ambulance at no cost to tax payers.
Although there are many questions regarding what the future holds for health insurances and ambulance reimbursements, there’s one thing that’s certain: The cost to operate an ambulance service will only continue to rise. Through quick analysis of the data that many services already collect, management teams can quickly spot potential problems and maximize their revenue stream. jems
Nathan Jung, EMT-P, is EMS administrator for Galveston County Health District in Galveston, Texas.