The OBSOS provided us with a unique opportunity to study how Medicare claims can be used to estimate procedure length, because the study was designed to measure operative time and entailed the merging of chart information with Medicare claims. Procedure length is a fundamental variable associated with quality and outcomes. Many have published on procedure length,1-17
often using chart reviews at single institutions.1-7
If anesthesia claims could be utilized to reliably provide valid information on procedure length, then many questions now relying on single institution studies with relatively small data sets could be answered with much larger and more representative samples. For example, large-scale nationwide studies of anesthesia claim time can be utilized to study a vast assortment of questions involving both clinical and health services research in anesthesiology and surgery. On the clinical side, better measures of anesthesia cumulative exposure may provide methods to study potential toxicities associated with anesthetic agents, and may provide us with a better way to study and develop models that assess postoperative risk due, in part, to deviations from the expected anesthesia time for the actual procedure performed. On the health services side, questions of quality can be studied, with benchmarking across all hospitals that care for Medicare patients. Examples include the study of racial disparities in procedure length inside and between hospitals throughout the United States, again, based on the actual procedures performed.
The results provided in the present report give the potential investigator a higher degree of confidence that anesthesia claims can be utilized to derive anesthesia time. The data presented in this study represents far more observations than those we reported on three years ago. Previously, using data from 1995-1996, we had analyzed 1,931 Medicare patients in 187 hospitals in the state of Pennsylvania. When we compared the chart to the claim, we observed a median absolute error of 5.49 min.29
In the present study, we report on the abstraction of 14,369 Medicare charts in 3 states over 47 hospitals. We find a median absolute difference that was very small, only 5.0 min. In other words, we can be quite certain that for the vast majority of cases, anesthesia claims work well at estimating anesthesia time.
In the present study, like our original study, we did observe occasional errors that were substantial. Therefore, as in the past report, we suggest the use of regression techniques that down-weight outliers when fitting models. Such techniques are ideally suited for problems such as ours, where claims information is usually correct but may occasionally fail to reflect the true procedure length due to mistakes in the algorithm that links claim to procedure, mistakes in the algorithm identifying whether anesthesiologists worked sequentially or concurrently, or mistakes in coding. As it stands, in situations where there is no single member of the anesthesia team that bills for the entire procedure, the claim may underestimate the chart. Furthermore, we may observe situations where the claim overestimates the chart information. These instances may reflect mistaken linkages between the specific procedure for which the claim was made. As anesthesia bills often use a “from-through” date that encompasses multiple procedures, one may mistakenly assign excess time to a single procedure that mistakenly reflects other procedures’ time.
Though this paper has focused on the potential use of anesthesia claim time as a dependent variable (an outcome variable) for many analyses, anesthesia claim time can also be utilized as an independent variable in models designed to predict outcomes. Just as when a claim time is used as the dependent (y) variable in regression it is important to fit these models using a robust method such as m-estimation,44
(because claim times closely reproduce chart times with rare but large errors), when a claim time is used as an independent (x) variable in a model, it is similarly important to fit these models using bounded-influence methods.45,46
While we want investigators to be aware of the potential pitfalls in using claims to determine anesthesia and surgical time, we do not want to overstate these problems. The correlations we report, now in two separate studies spanning over 8 yr of data, and close to 16,000 observations, are high and will be useful for applying the claims estimates to many important questions being studied concerning procedure time.
It is also interesting to note that billing styles were fairly similar across hospitals. We generally found only small differences between hospitals, with the exception of a few that were associated with 10 to 15 min claim-chart time differences. Furthermore, the median difference between the claim time and the chart time was 5 min. This number would not appear to be a coincidence. As one anesthesia time unit equals 15 min, a policy of always rounding up to the higher unit would lead to about a 5 min difference on average (assuming a uniform distribution for the fraction of units remaining before round-up).
In summary, we have demonstrated that the Medicare anesthesia claim can be utilized to construct an excellent measure of procedure time. Future investigators can feel confident that they may utilize our algorithm to better study procedure length through using the Medicare claim, without the need to collect procedure length information directly from the chart.