A central area of expertise at 3terra is helping hospitals improve data quality which can lead to improved funding, as outlined in this article. This year, we expanded our focus and started to analyze the effect of poor data quality on outpatient cases, including Emergency Department (ED) visits. This article provides an overview of those findings.
The case weight (a measure of patient complexity that is used to determine hospital funding) for an ED visit is straightforward compared to inpatient stays. Generally speaking, patients are in the ED for a limited amount of time, so fewer interventions and procedures take place. If the patient requires more complex care, they are usually admitted or transferred from the ED.
The case weight for an ED visit is based on the patient group (Comprehensive Ambulatory Classification System or CACS) they fall under, determined by the patient’s age, the main problem (diagnosis) they present with and what procedures (interventions) are performed in the ED itself. The patient’s CACS group is analogous to the HIG group for inpatient care. Once the patient grouping is specified, additional weight is added based on what diagnostic interventions (x-rays, CT scans, MRIs, ultrasounds, etc.) were performed during the visit. This is where we began our analysis.
THE VALUE PROPOSITION
To justify creating a process of tracking down missing diagnostic procedures, we had to answer two main questions:
- How much approximate funding allocation is lost by missing diagnostics for a single ED visit?
- How many ED visits are actually missing diagnostic procedures?
The funding impact per case is low relative to an inpatient correction. Upwards of several hundred ED visits would need corrections to warrant the effort to find and correct missing procedures. However, given that an ED can see tens of thousands of patients each year, we concluded that a hospital only need miss a small percentage of procedures for the exercise to be valuable. This approach is equally applicable to day surgery cases, so looking there was also of interest.
OUR APPROACH AND THE RESULTS
Working with our partner hospitals across Ontario, we cross-referenced the outpatient abstract (NACRS) records against diagnostic information systems to look for discrepancies. This took a bit of effort since the diagnostic systems do not use the same procedure codes (CCI) used within the abstract records. However, after a mapping exercise we found that there was a significant amount of missing data in the abstracts.
After validation with the hospitals, we concluded that it’s worth the time for a hospital to analyze the data quality of their diagnostic procedures for coded outpatient abstracts. As a direct result of this exercise, we created a software module within our data quality software (DQA) to automatically perform these checks to guide data quality improvement efforts.
In preparation for the next version of Data Quality Assist (DQA), we are investigating the data quality of other factors that directly affect the funding of outpatient visits. There are many such factors and we believe that they are much easier to miss than diagnostic procedures, which is certainly an issue for some hospitals.
There is still time left before the May 31st submission deadline to analyze and correct errors in patient abstracts. DQA optimizes your HBAM/QBP performance by ensuring that your coding reflects the reality of care provided, while reducing case review time by up to 70%. For more information about how we help hospitals improve their provincial funding, please feel free to visit our website or contact us if you have any questions.