Dan Golder, principal at Naperville, Ill.-based healthcare consulting firm Impact Advisors, agrees that the data integration piece could be the toughest. Golder says there are three levels when looking at value-based purchasing: claims data, clinical data, and eligibility data, and they happen to live in three siloes. Most groups, from what Golder has seen, have been working with claims data since it’s most available and “although it’s not easy to integrate it, you can,” he says. But the other two siloes are extremely problematic, he adds. “Linking claims data to other claims data from other payers, as well as clinical data and eligibility data to get it to be actionable at the point of care is an issue. Third-party tools are doing this right now, meaning providers are accessing a second application and leaving the EHR if they want to look at aggregate data and population health data,” Golder says.
Meanwhile, on the readmissions front, SCIO Health Analytics’ Higgins says that the organizations that have tackled this challenge head on are predicting where the outliers are, how to identify those earlier, and then work with those providers and patients to reduce these trends around readmissions. “The penalties are real and meaningful, so the approach for most organizations we are talking to is figuring out how to look at the providers who aren’t as strong in the primary care practice relationships that need to be in place with these patients to make sure there is a good plan of care prior to and after admission,” Higgins says.
AxisPoint Health's Moore adds that payers are becoming increasingly frustrated since they have put programs in place that intuitively, and in an academic research setting, prove that they have value in terms of outcomes for lowering readmission rates, but when these programs are moved into a community-based setting, the community doesn’t act like an academic setting. “So many clients are frustrated that they have put programs in place that don’t work,” Moore says. We need more community-based studies and interventions that can be leveraged across the U.S.,” he says. Moore further points to the “LACE” index—a tool that identifies patients that are at risk for readmission or death—which is used by many hospitals and academic centers. “But unless you have those data points, like the ADT data, you can’t really do anything of significance,” he notes.
One area around readmissions that Cinque has seen an increased utilization of analytics for is psychosocial factors and financial barriers to care. “These readmission rates are higher in lower income areas than elsewhere,” he says. “Organizations are beginning to incorporate either a low-fi method, so collecting information while patients are in the hospital, or through more progressive methods, such as data mining to try to flag patients for readmissions. The integration of those data types in trying to become more predictive about risk factors for readmissions is an emerging area,” Cinque says.
Nonetheless, for some patient care organizations, notably smaller provider groups, incorporating this level of analytics can prove too expensive and overwhelming. To this end, Scott Pillittere, vice president of Impact Advisors, says that these smaller groups will be willing to “take the hit” from CMS regarding penalties for these policy mandates, since they won’t be able to pay for the data analytics that are needed. “We are seeing more consolidation in the marketplace, and this will be another factor that will push standalone or small physician practices into a much larger organization so they have the financing to pay for the data analytics groups that can help them with this part of their care,” Pillittere predicts. “There are not a whole lot of doctors who want to play in this business side of healthcare, so they are looking for help,” he says.
Golder adds that when he reads the tea leaves of Medicare’s new rules with how much payment they want tied to value in the coming years, much of it is budget neutral, meaning for someone to earn an incentive payment, someone else will have to pay a penalty. This represents a difference from the Meaningful Use program in which everyone could earn incentives. “So the inability to pay for systems and the lack of capability to run analytics to do better, will likely shift small practices into larger groups that can be successful in the world of population health and accountable care,” Golder says.
Nevertheless, the shifting healthcare landscape isn’t stopping senior leaders at SCL Health from getting in front of the analytics game. The patient care organization, a nine-hospital health system with three safety-net clinics, one children’s mental health center, and approximately 200 ambulatory sites in three states—Colorado, Kansas, and Montana —last year selected Fort Collins, Col.-based Total Benchmark Solution (TBS) as its vendor for benchmark data and advanced analytics. The platform enabled the health system to quickly and easily compare performance using historical trends, and/or performance targets, and peer group data. It was then able to identify areas of undesirable variation to target for improvement, its officials say.
“The platform allows us to filter and adjust an analysis based on various criteria, such as a certain type of patient or a particular payer,” says Chris Bliersbach, senior director of clinical outcomes at SCL Health. We can integrate data sources, such as our ADT feed, Epic, and Press Ganey to see the whole picture through volume, cost, charges, supplies, quality, patient experience, and many other metrics.”
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