A Method for Attributing Patient-Level Metrics to Rotating Providers in an Inpatient Setting
BACKGROUND: Individual provider performance drives group metrics, and increasingly, individual providers are held accountable for these metrics. However, appropriate attribution can be challenging, particularly when multiple providers care for a single patient.
OBJECTIVE: We sought to develop and operationalize individual provider scorecards that fairly attribute patient-level metrics, such as length of stay and patient satisfaction, to individual hospitalists involved in each patient’s care.
DESIGN: Using patients cared for by hospitalists from July 2010 through June 2014, we linked billing data across each hospitalization to assign “ownership” of patient care based on the type, timing, and number of charges associated with each hospitalization (referred to as “provider day weighted”). These metrics were presented to providers via a dashboard that was updated quarterly with their performance (relative to their peers). For the purposes of this article, we compared the method we used to the traditional method of attribution, in which an entire hospitalization is attributed to 1 provider, based on the attending of record as labeled in the administrative data.
RESULTS: Provider performance in the 2 methods was concordant 56% to 75% of the time for top half versus bottom half performance (which would be expected to occur by chance 50% of the time). While provider percentile differences between the 2 methods were modest for most providers, there were some providers for whom the methods yielded dramatically different results for 1 or more metrics.
CONCLUSION: We found potentially meaningful discrepancies in how well providers scored (relative to their peers) based on the method used for attribution. We demonstrate that it is possible to generate meaningful provider-level metrics from administrative data by using billing data even when multiple providers care for 1 patient over the course of a hospitalization.
© 2017 Society of Hospital Medicine
Presenting Results
Providers were only shown data from the day-weighted approach. For ease of visual interpretation, scores for each metric were scaled ordinally from 1 (worst performance) to 9 (best performance; Table 1). Data were displayed in a dashboard format on a password-protected website for each provider to view his or her own data relative to that of the hospitalist peer group. The dashboard was implemented in this format on July 1, 2011. Data were updated quarterly (Figure 2).
Results were displayed in a polyhedral or spider-web graph (Figure 2). Provider and group metrics were scaled according to predefined benchmarks established for each metric and standardized to a scale ranging from 1 to 9. The scale for each metric was set based on examining historical data and group median performance on the metrics to ensure that there was a range of performance (ie, to avoid having most hospitalists scoring a 1 or 9). Scaling thresholds were periodically adjusted as appropriate to maintain good visual discrimination. Higher scores (creating a larger-volume polygon) are desirable even for metrics such as LOS, for which a low value is desirable. Both a spider-web graph and trends over time were available to the provider (Figure 2). These graphs display a comparison of the individual provider scores for each metric to the hospitalist group average for that metric.
Comparison with the Standard (Attending of Record) Method of Attribution
For the purposes of this report, we sought to determine whether there were meaningful differences between our day-weighted approach versus the standard method of attribution, in which the attending of record is assigned responsibility for each metric that would not have been attributed to the discharging attending under both methods. Our goal was to determine where and whether there was a meaningful difference between the 2 methodologies, recognizing that the degree of difference between these 2 methodologies might vary in other institutions and settings. In our hospital, the attending of record is generally the discharging attending. In order to compare the 2 methodologies, we arbitrarily picked 2015 to retrospectively evaluate the differences between these 2 methods of attribution. We did not display or provide data using the standard methodology to providers at any point; this approach was used only for the purposes of this report. Because these metrics are intended to evaluate relative provider performance, we assigned a percentile to each provider for his or her performance on the given metric using our attribution methodology and then, similarly, assigned a percentile to each provider using the standard methodology. This yielded 2 percentile scores for each provider and each metric. We then compared these percentile ranks for providers in 2 ways: (1) we determined how often providers who scored in the top half of the group for a given metric (above the 50th percentile) also scored in the top half of the group for that metric by using the other calculation method, and (2) we calculated the absolute value of the difference in percentiles between the 2 methods to characterize the impact on a provider’s ranking for that metric that might result from switching to the other method. For instance, if a provider scored at the 20th percentile for the group in patient satisfaction with 1 attribution method and scored at the 40th percentile for the group in patient satisfaction using the other method, the absolute change in percentile would be 20 percentile points. But, this provider would still be below the 50th percentile by both methods (concordant bottom half performance). We did not perform this comparison for metrics assigned to the discharging provider (such as discharge summary turnaround time or readmissions) because the attending of record designation is assigned to the discharging provider at our hospital.
RESULTS
The dashboard was successfully operationalized on July 1, 2011, with displays visible to providers as shown in Figure 2. Consistent with the principles of providing effective performance feedback to providers, the display simultaneously showed providers their individual performance as well as the performance of their peers. Providers were able to view their spider-web plot for prior quarters. Not shown are additional views that allowed providers to see quarterly trends in their data versus their peers across several fiscal years. Also available to providers was their ranking relative to their peers for each metric; specific peers were deidentified in the display.
There was notable discordance between provider rankings between the 2 methodologies, as shown in Table 2. Provider performance above or below the median was concordant 56% to 75% of the time (depending on the particular metric), indicating substantial discordance because top-half or bottom-half concordance would be expected to occur by chance 50% of the time. Although the provider percentile differences between the 2 methods tended to be modest for most providers (the median difference between the methods was 13 to 22 percentile points for the various metrics), there were some providers for whom the method of calculation dramatically impacted their rankings. For 5 of the 6 metrics we examined, at least 1 provider had a 50-percentile or greater change in his or her ranking based on the method used. This indicates that at least some providers would have had markedly different scores relative to their peers had we used the alternative methodology (Table 2). In VTE prophylaxis, for example, at least 1 provider had a 94-percentile change in his or her ranking; similarly, a provider had an 88-perentile change in his or her LOS ranking between the 2 methodologies.
