Back in 2012, the Affordable Care Act (ACA) established the Hospital Readmissions Reduction Program (HRRP), which requires the Center for Medicare and Medicaid Services (CMS) to reduce payments to Inpatient Prospective Payment System (IPPS) hospitals with excess readmissions for certain medical conditions.
The HRRP was designed to make hospitals pay closer attention to what happens to their patients after they get discharged, but yearly data has shown that this has not been an easy task for patient care organizations. In 2016, the government penalized more than half of the nation’s hospitals—a total of 2,597—for having more patients than expected return within a month. Last year, the news didn’t get a whole lot better. According to Kaiser Health News, almost the same number of hospitals—this time 2,573—will be punished by Medicare for failing to lower their rehospitalization rates. On the financial side, according to KHN, Medicare will withhold $564 million in payments over the next year; the maximum reduction for any hospital is 3 percent.
To this point, hospitals and health systems have no shortage of motivation to keep patients from being readmitted unnecessarily. As officials at UnityPoint Health (UPH), an integrated health system with 29 hospitals, headquartered in Des Moines, Iowa, note, over the last few years, several of UPH’s hospitals did not meet their thresholds and were penalized to varying degrees.
Benjamin Cleveland, a data scientist at UPH, says, “Though readmissions often are influenced by factors largely outside of a healthcare system’s control, most systems conclude that discharge education, care coordination, and post-discharge intervention strategies offer the best chance at reducing their readmission rate.”
Cleveland adds that many readmission strategies are driven by three foundational components: which patients to focus on, what type of intervention should occur, and when the intervention should occur. “Which patients to focus on and when to target interventions lend themselves to the predictive modeling domain, while matching specific interventions with appropriate patients are best studied using controlled experiments,” he says. Rhiannon Harms, executive director of analytics at UPH, adds that analytics could answer the “who” and the “when” questions, which would then lead to care teams and providers being able to answer the “what” question around which type of intervention should occur.
Capturing the Comprehensive Picture
To this end, an enterprise-wide data analytics and artificial intelligence project—centered around preventing readmissions through patient-level “heat maps”—emerged at UPH, and was so innovative that it received first-place status in Healthcare Informatics’ 2018 Innovator Awards Program, Providers Division. Specifically, UPH Strategic Analytics sought to create a readmissions risk model that would be superior at identifying high-risk patients than other existing models—such as the commonly-used LACE [Length of stay, Acuity, Comorbidity and ED use] index and HOSPITAL [Hemoglobin, discharge from an Oncology service, Sodium level, Procedure performed, Index Type of admission (urgent vs. elective), number of Admissions in the last year, and Length of stay] score—as well as provide machine learning technologies to identify when along the 30-day spectrum a patient is most likely to be readmitted to guide care coordination efforts and ensure successful transitions.
These risk profiles would then be computed for every patient in the UPH system, and individualized to their own unique clinical and social scenarios to ensure a common “source of truth” for all parties involved across the care continuum, to coordinate the patient journey out of the hospital, UPH officials explain.
In the development of this project, Cleveland says that UPH partnered heavily with its local care teams to get feedback from everyone involved in the entire process—inclusive of primary care physicians, hospitalists, inpatient case managers, outpatient care coordinators, home health nurses, and more. “What do they see that affects readmissions? What do they think is tied to different patient risks that we should try to incorporate? Who do we talk to from an informatics perspective on how to best pull that data in the EHR [electronic health record]? It’s really a cross continuum, multi-disciplinary effort across our system,” he says.
The result of all of those discussions, organization-wide, says Cleveland, was that a core goal emerged: to capture a patient’s entire scenario comprehensively. “You want to capture the sociodemographic variables; all of our patients have different backgrounds,” he says. “And then you want to capture the severity of the condition they are being seen for today—inclusive of visits, medications, lab tests, and procedures that indicate severity. You also want to look at their past medical history to see what the [true] level of morbidity burden that they are carrying is, along with whatever the problem of the day is. And finally,” explains Cleveland, “You want to capture how they interact with their healthcare. Are there appointment no-shows, are they always late, are they going to their follow-ups? How many inpatient and ED visits have they had over the last few years? That’s how you get a comprehensive picture of the patient,” he attests.
In all, the team pulled in some 300,000 inpatient encounters and all of the associated variables with those encounters at each point in time, and then used machine learning algorithms to appropriately weight each of the variables. Overall, says Cleveland, morbidity burden and clinical severity within that visit were the factors that were most influential, followed by healthcare utilization and social demographics.
A Predictive Model Like No Other
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