The View Out the Rearview Mirror: The Problem(s) with Claims Data

As part of our ongoing thought leadership webinar series, Esther Moas, Senior Director, Care Continuum at Mount Sinai, and two of her colleagues joined us to discuss the work the team has done to understand and improve the care that patients receive outside of Mount Sinai. In addition to being one of the largest and most well-renowned health systems in New York, Mount Sinai is also an accountable care organization (ACO) so gaining greater visibility into post-acute care was a top priority for Esther’s team. As they started trying to track patients in these settings, however, the team encountered multiple obstacles related to the data that was available to them at that time. Their reliance on claims data, in particular, created three major hurdles in reaching their goals. Download the webinar recording to learn more about how the team came to value real-time data over claims data and began collecting its own data on post-acute care providers.

Problem #1: Claims data is too old

The first question that Esther and her team posed was, “Where are our patients?” Christophe Hunt, the team’s data scientist, found that the answer wasn’t exactly straightforward. The only data that he had on ACO patient discharges occurring outside of Mount Sinai was from claims, which meant that he didn’t receive it until six or sometimes even nine months down the road. Based on his analysis of the data he did have, over half of Mount Sinai’s ACO patients were getting discharged from non Mount Sinai hospitals, so this lag in receiving data was a significant issue. To further complicate things, Chris found that patients were being discharged to almost 100 different skilled nursing facilities. Trying to track the transitions to all these different facilities with claims data was, as Esther put it, like trying to drive your car by looking in your rearview mirror. They never knew where their patients were in the moment, only where they had been in the past. This made it difficult to intervene and influence outcomes.

Problem #2: Claims data doesn’t tell the full story

In addition to claims data being old, Christophe found that it also isn’t comprehensive to make predictions around key metrics. A key function of his job is using data to help guide decisions but relying on claims meant that there were inevitable gaps. His ability to make predictions was severely limited. Furthermore, Christophe felt he couldn’t provide Esther a good sense of where the team is going which limited her ability to operationalize his insights. The gaps in data also made it difficult to follow up with patients, make predictions with little data-driven insight or target facilities with confidence. Christophe felt behind the curve and needed more than claims to support his predictive models.

Problem #3: Claims data doesn’t reflect the work that post-acute partners have (or have not) done

Problems #1 and #2 compound into a third problem that is just as significant with its impact on patient care. Relying on claims data made it hard for Mount Sinai to accurately assess the performance of post-acute providers, and that negatively impacted their relationships with these partners, whose help they needed to improve care for their ACO patients. If, for example, Esther was to approach a post-acute facility about having a high readmission rate, that partner might balk, explaining that they had put significant effort into building processes to reduce readmissions and had successfully reversed the trend over the past three months. The partner would feel discouraged that their efforts were unacknowledged, but so would Esther, because she didn’t have any data to confirm that the provider had actually improved. The opposite scenario was true as well. Esther might decide not to speak with a provider because their readmission rate appeared very low, when in fact over the past several months, it had begun to climb back up. But it would be another nine months before Esther could see that from the claims data. The worst thing, however, was when post-acute partners were proactive and asked if they were on the right track—and Esther struggled to give them an answer because she didn’t have accurate information.

Esther and her team decided that lagged claims data simply was not sufficient to meet their goals of improving care in post-acute settings and accurately evaluating post-acute providers. Instead, they implemented CarePort, a source of real-time data from post-acute settings, and developed processes to aggregate their data and distill it down to a few key metrics that could be used to hold post-acute providers accountable. Download the webinar recording to learn about the simple scorecard they created for post-acute providers to drive meaningful dialogue with these partners.