In back-to-back months this summer, announcements around new mandatory bundled payment programs from the Department of Health and Human Services (HHS) as well as the latest updates regarding Centers for Medicare & Medicaid Services (CMS) penalties on hospitals for failing to lower their rehospitalization rates, collectively signaled to healthcare leaders that payment reform is here to stay.
The July 25 announcement of the mandatory bundled payment program for heart attack care and for cardiac bypass surgery stated, “The hospital in which a Medicare patient is admitted for care for a heart attack or bypass surgery would be accountable for the cost and quality of care provided to Medicare fee-for-service beneficiaries during the inpatient stay and for 90 days after discharge. The proposed cardiac care policies would be phased in over a period of five years, but would begin July 1, 2017 for hospitals located in the 98 metro areas participating in the model (about one-quarter of all metro areas in the nation).” These new bundled payment models for cardiac care, in addition to the extension of the existing bundled payment model for hip replacements and other hip surgeries, are yet another major step in forcing reimbursement forward into value-based purchasing.
Meanwhile, on the hospital readmissions front, although the news didn’t come out of CMS directly, an August 2 Kaiser Health News report revealed that the federal government’s penalties on hospitals for failing to lower their rehospitalization rates will hit a new high as Medicare will withhold approximately $528 million—about $108 million more than last year. CMS will penalize more than half of the nation’s hospitals—a total of 2,597—for having more patients than expected return within a month, as mandated by the government’s Hospital Readmissions Reduction Program, which adjusts payments for hospitals with higher than expected 30-day readmission rates for six targeted clinical conditions.
These revelations point to a realization beyond payment reform that patient care leaders likely already knew, but is now confirmed: U.S. hospitals are under more pressure than ever before to produce optimal clinical and cost outcomes. Key to this transformation will be leveraging robust data analytics and information technology to help drive continuous performance improvement.
Payers and Providers Converge
A critical element to providers planning for a value-based care future is aligning their needs and goals with those of payers. While this hasn’t always been easy to accomplish, most of the sources interviewed for this story agree that real strides are being made. Tim Moore, M.D., executive vice president of health affairs and chief medical officer of technology provider AxisPoint Health, a Westminster, Colo.-based spinoff of McKesson, which works primarily with payers, says there are plenty of new opportunities emerging around getting payers and providers on the same side of the table to sort out risk-based contracting challenges.
“With better integration and better relationships between payers and providers, through value-based reimbursement, there should be much better use of clinical data that is more timely and can provide interventions that are more appropriate to drive opportunities for savings,” Moore, previously chief medical officer at WebMD Health Services, says. Historically, he notes, payers would be straddled with only 60-day or 90-day-old claims-based administrative data, and by the time they did something with that, 30 more days would pass. “So there was a limit from a time perspective and also an accuracy perspective,” Moore says. He adds, “Providers have more timely claims be it through the electronic health record [EHR] or through the hospital with admission/discharge/transfer [ADT] information. If you have that timely information and you can leverage it, you can much better leverage algorithms and analytics to help predict who needs better support and guidance, from their own real data rather than administrative claims data that’s 90 days old.”
Tim Moore, M.D.
The thing that payers can bring to the table that providers sometimes cannot, continues Moore, is a higher level view of the population that the providers are delivering service to. “Providers sometimes don’t get a good view of the whole population they are serving, as they are only serving one patient at a time. But payers see a longitudinal view of patients over the past year or two,” he says.
Moore gives an example of how some hospitals throughout the country leverage health information exchanges (HIEs) that have good ADT data that hasn’t been shared or used by industry players such as the payer market. “With this ADT data, you can pull out other information including how many ER visits someone has had in the past six months, his or her diagnosis, and when he or she was in the hospital, so you have timely information that says here is a patient that has been in the hospital and because of this condition they have a higher risk of a readmission,” he says.
At the same time, payers can help by looking across different hospitals and pick out which ones are outliers in terms of high readmission rates. “Some hospitals are good at [avoidable readmissions], so you need to put resources towards the ones that are outliers,” Moore says. “Providers don’t have that full view like payers do. I think that leveraging the two sides can open up a whole new way of taking the data, and putting together and focusing the resources on where it will be most impactful,” he says.
To this end, Independence Blue Cross Blue Shield in southeastern Pennsylvania, serving two million members in five counties in and around Philadelphia, uses a predictive tool that calculates an individual’s likely future health state based on associated clinical conditions or diagnoses. The risk matrix, from San Mateo, Calif.-based healthcare analytics company Lumiata, helps the payer identify where members might be at risk for or might have certain conditions, and then helps alert their providers, explains Michael Vennera, senior vice president and CIO at Independence Blue Cross Blue Shield.
Vennera says that with the analytics tool, the payer can go to providers in its market and say that there is a chance patient X has a certain condition, even though it’s not diagnosed on his or her claims. All different types of data goes into that risk engine, says Vennera—medical claims data, prescription drug claims, lab results, and also basic demographics such as age, gender, and location. “Then what we get out of it is a prediction around diseases with different confidence levels for different members. And then you can use that to follow up,” he says.
Being right in the thick of the payer-provider relationship, Vennera notes how there are now lots of opportunities to combine the depth of information that a payer has with the depth of information a provider has, and put analytics on top of it. “What you typically see in most markets is that providers will have deep information, such as services rendered in an EHR,” he says. “So if you go to a hospital, all the information about that stay and the procedures done make for a rich and deep data set. But then the challenge is when people go to multiple providers. We know interoperability is long way off. But payers do have a broad set of data which covers most of your healthcare since most of healthcare flows back to your insurers,” he says.
Rose Higgins, president of the West Hartford, Conn.-based analytics solution company SCIO Health Analytics, additionally notes the challenge of payers and providers being as transparent with the data as possible. Higgins says that while it begins with recognizing that the data has intrinsic value to both sides, there has to be a willingness to be open with respect to the information, and share it, for opportunities to be identified and acted on. “It’s challenging to mix different types of payer data sets. Providers have multiple contracts, so there are different approaches with each payer. This means that a payer may not want to share data with a provider when they know another payer’s data may be mingled with their own,” Higgins explains.
Drilling Down with Policy Implications
When CMS’ Bundled Payments for Care Improvement (BPCI) initiative first got off the ground five years ago, there were high expectations for investments in technology—to track performance on bundles, to make more predictions on performance, and to potentially price commercial bundles, notes Matthew Cinque, executive director, product management at The Advisory Board, a Washington, D.C.-based consulting and technology company. “But at that time, the market did not move as quickly as was expected on the analytics side,” Cinque says. “Folks signed up for the bundled payment programs, so there were lots of conversations around bundles in general, but when push came to shove, there was not a lot of movement.”
Cinque explains that one of the reasons for this was the upside in the CMS program was not big enough to justify freestanding investments in new analytics. “Folks would get by with what they had in Excel. On the commercial side, we saw a lot of interest but there was hesitance on the part of payers to try to adjudicate bundled payments,” he says, adding that with a commercial population under the age of 65, the numbers showed that there was not a lot of volume of any one thing.” Even with the [Comprehensive Care for Joint Replacement Model] announced last year, there has not been “a huge move around analytics investment,” Cinque says, noting that he expects that to “get more serious sooner than later.” He says, “I would say it is an immature market, but one that I expect to have more dedicated focus on bundled payment-specific analytics as CMS rolls out more mandatory programs related to this.”
Cinque adds that “getting more serious” involves an investment in integrating different data sources. “One thing that makes bundled payments so challenging, especially if you look at the cardiac care model, is that so much of what you’re trying to manage happens outside of the four walls of the hospital. You need to be able to get data across inpatient metrics and get visibility into what happens in the physician office and skilled nursing facilities. Those are almost always entirely different data sets,” he says. Thus, data aggregation has to be a big point of investment, be it through a data warehouse or something else, he notes. “It’s about accumulating that data and then manipulating it. The data aggregation component of it is really what makes it cost prohibitive today,” he says.
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.”
Prior to this technology implementation, two SCL Health care sites and a commercial payer already had been particularly interested in hip and knee surgery improvement within the Comprehensive Care for Joint Replacement bundled payment model. Both care sites had desirable performance with length of stay (LOS) as measured against Medicare and all-payer benchmarks in the TBS database, its officials attest.
But they realized that customized benchmarks would provide a stretch goal appropriate to best-practice hip and knee surgery outcomes at the care sites. To develop the benchmarks, SCL Health and TBS collected and analyzed data from Healthgrades on organizations that had five-star ratings for hip and knee surgery. Importantly, organizations that could offer stretch goals for the care sites also required appearance on the U.S. News and World Report “Best Hospitals” list, and a similar patient volume to the care sites. Indeed, 80 hospitals providing knee surgery and 56 providing hip surgery met the criteria for “best practice” organizations with both low LOS and low complication rates. Data from those organizations were used to establish the tailor-made benchmarks, SCL Health officials say.
Advice for the C-Suite
All of the healthcare leaders interviewed for this story agree with the notion that with all of the initiatives that federal healthcare officials are creating now—around readmissions reduction, value-based purchasing for both hospitals and physicians, bundled payments, and accountable care—the leveraging of data and healthcare IT will be critically important.
So what is the best plan of action for CIOs and CMIOs right now around leveraging robust data analytics to bend the healthcare cost curve? Well, there isn’t one single answer for organizations nationwide, says Impact Advisors’ Golder, who notes several options for health systems such as: aggregating data by building data warehouses; integrating data sources themselves; looking at their existing vendor partners to help them since their doctors don’t want to leave the EHR; and finding third-party vendors for data aggregation. “So, for the provider organization, what’s your appetite for risk?” Golder asks.
Independence Blue Cross Blue Shield’s Vennera says that payers should be talking to the providers they work with in their market, if they aren’t already, about how they can share data, particularly if there is no regional data exchange program or HIE in place. And on the analytics specific side, he adds, “The big thing for CIOs is to strike the right balance between insourcing and outsourcing. With the analytics arms race and the cost of analytics resources, you can’t build everything in-house. But also you can’t bend your way to the answers. You need a combination of vendor solutions and building the in-house talent to interpret results, challenge findings, and think through and develop proprietary analytics.”
Moore agrees with this advice. He says: “Put stakeholders in a room together and have them each bring the data they believe is most important and share it with each other, so they could understand the data that exists rather than create something new.” Moore believes that there is a tendency in healthcare to try to create something new and constantly look at something differently. “Right now, we have many different data points that are not necessarily used as well as they could be and integrated as well as they could be. Start with the data you have and figure out how to best leverage it,” he says.
The Advisory Board’s Cinque further brings up the point that when an organization begins collecting information and data, and synthesizes it across different sites of care, it forces interactions with other EHR systems, or in some cases, places that don’t even have EHRs. “You need to understand the IT landscape of your partners and other providers in your community. That will influence your success on these programs,” he says. “For CIOs and CMIOs, there is a belief in if they are not on our EHR platform, we shouldn’t work with them or it shouldn’t matter. That’s not a tenable strategy with these interconnected programs.”
On the risk-based side, Moore also notes the fact that there is still such a mix of fee-for-service and value-based reimbursement, which ultimately slows things down. He calls it “a schizophrenic way for a provider to try to practice.” He says that provider organizations need to figure out how to segregate, meaning having a fee-for-service group and value-based group that is at least 75 percent reimbursed from one side or the other. “Until the majority of a doctor’s compensation is tied to one side, they won’t behave in that certain way. That’s one of the biggest challenges for us in the next five years,” he says.