Bundled payments are emerging as an increasingly important element of the healthcare industry's shift from volume- to value-based care, as CMS seeks to lower healthcare expenditures and providers aim to achieve the triple aim: providing the best possible care at the lowest price and improving the overall health of a population. However, succeeding under bundles requires robust data analytics to help providers identify the main drivers of spending and opportunities to rein in costs and improve outcomes.
Bundled payments, the earliest iterations of which Medicare introduced in 2009, have evolved through several subsequent models, including the Bundled Payment for Care Improvement bundle of 2013, the Comprehensive Care for Joint Replacement Model of 2016, the Oncology Care Model of 2016 and the recently proposed bundled payment program for heart attacks and bypass surgery.
Under a bundled payment, hospitals aim to spend less than a predetermined target price for all of the episodes of care for a certain diagnosis-related group during a given performance period. If it exceeds the target price, the hospital must repay the insurer the difference. If the hospital's spending is under the target and it meets or exceeds quality standards, it can keep the savings. Because hospitals participating in bundled payments are on the hook financially for a patient's outcomes, the payment model galvanizes providers to redesign many of the workflows and processes that affect patients' experiences in the hospital, as well as their recovery after discharge.
"The idea is if you align incentives behind a way the provider delivers care and how payers pay for care, you can make significant progress in terms of better quality and lower costs," Kelly Price, Vice President and Chief of Healthcare Data Analytics at DataGen, said during a webinar hosted by Becker's Hospital Review.
The CJR model is CMS' first mandatory bundled payment program for hospitals in designated areas. It focuses on hip and knee replacements. For hospitals to succeed, clinical care teams must carefully screen patients for comorbidities that can lead to complications or readmissions; develop effective streams of communication to ensure close coordination between all clinical stakeholders; communicate with patients and their families before discharge to make sure the patient will have support as they recover; and predetermine the best discharge plan given the patient's state of health and risk for issues during recovery. The more reliably hospitals accomplish these tasks, the more likely they are to control spending and reap the savings under the bundle.
With the new rules imposed by Medicare Access and CHIP Reauthorization Act, Ms. Price hypothesizes that CMS will look to open more alternative payment model opportunities to enable physicians to avoid the Merit-based Incentive Payment System, which applies payment adjustments of up to 4 percent based on performance, quality and value of care. Therefore, a robust data analytics infrastructure that can point providers toward opportunities for reducing costs and improving care is integral.
How data analytics supports hospitals participating in bundled payments
John Kalamaras, Healthcare Informatics Analyst at DataGen, demonstrated how DataGen's data analytics platform helps hospitals sort and zero-in on a variety of data points to identify the greatest sources of spend under the CJR model, as well as problem areas.
The platform uses claims data and organizes information by DRG for a given performance period. Users can sort and view the data for a variety of metrics. However, it is important to note that a small sample size warrants caution, Mr. Kalamaras explained. In other words, if a hospital only performs two knee replacement surgeries during a performance period and the readmissions rate is 50 percent, this is not a reliable measure.
The highest volume of patients fit into the DRG 470 category: non-fracture, elective hip and knee replacement. According to Mr. Kalamaras, physicians and the clinical care team should optimize these patients as much as possible ahead of surgery to ensure a safe recovery and prevent costly complications. Optimizing patients, or preparing them for surgery by mitigating comorbidities and creating a comprehensive discharge plan, increases the likelihood that the patient can be discharged to the home instead of a skilled nursing home or inpatient-rehab, which are more expensive and increase the likelihood of a readmission.
"Every patient is unique, and some need to go to SNF care or inpatient rehab, but if these patients are elective they are more likely to be optimized for self-care or home healthcare," he said.
When looking at the total spend, for example, it is easy to see that hospital inpatient cost is relatively stable throughout a performance period, while the greatest variation occurs in the post-acute care setting. A user can then drill into different reports to identify what accounts for this variation. For instance, a report can indicate whether a certain SNF has a longer average length of stay than most, or if it has a higher direct readmissions rate. Using this information, a hospital can recommend patients to SNFs tied to better patient outcomes.
Users can also use the platform to look at how individual surgeons contribute to costs and patient outcomes. A report might indicate that one surgeon has a high rate of patients discharged to the home, while another has a high rate of discharges to SNFs or inpatient rehab facilities. This information can be used to inform meaningful conversations with providers and show them precisely what elements of the discharge process require improvement.
Bundled payments seem simple in theory, but they require careful monitoring of numerous metrics and the use of data analytics to inform decisions. With a comprehensive data analytics platform, hospitals are equipped with an easy-to-use interface that instantly provides them with the information they need to thrive.
View the webinar file here.
View the presentation on YouTube here.