Hospital readmissions can impact the health outcomes for patients and result in costly readmission penalties from CMS. Learn how the data analytics teams at Westchester Medical Center Health Network and network member Bon Secours Charity Health System utilized its analytics platform, in coordination with a machine learning algorithm, to build a knowledgeable and accurate readmission risk model that better reflected its patient population.
The Centers for Medicare and Medicaid Services (CMS) readmission penalties are a significant concern for healthcare organizations, with over 2,500 hospitals being penalized each year, resulting in CMS withholding more than $500 million in payments.
For Westchester Medical Center Health Network (WMCHealth), hospital readmissions carried more than financial consequences. Care managers had to use multiple systems and time-consuming, manual processes to identify recently discharged patients at risk for readmission. These processes limited the effectiveness of the care management team, as care managers lost valuable time searching patient records for data needed to prioritize their workload and choose the right interventions.
To address this problem, the data analytics teams at WMCHealth and network member Bon Secours Charity Health System (BSCHS) leveraged artificial intelligence and machine learning to develop a more accurate readmission risk prediction model that would enable care managers to use their time coordinating and engaging with patients more effectively.
Nationally, more than 2,500 hospitals face readmission penalties from CMS each year, with CMS withholding more than $500 million in payments.1 While CMS’ Hospital Readmission Reduction Program decreased CMS’ spending on readmissions by $9 billion by 2014, the rate of hospital readmissions only dropped 0.1 percent on average between 2013 and 2016—hospital readmissions remain a challenge.2
Many organizations and EMRs use the LACE index (length of stay, acuity of admission, Charlson Comorbidity Index, and the number of emergency department visits in the preceding six months) to predict which patients are at the highest risk for readmission. The index was developed using data from 4,812 patients admitted to 11 hospitals in Canada between October 2002 and July 2006.3 While the LACE index is widely used, it was developed using data from middle-aged Canadian patients who did not have serious comorbidities. Therefore, critics have questioned the validity of the LACE index in its applicability to broader patient populations.4,5
BSCHS is a three-hospital health system in the lower Hudson Valley of New York and a member of WMCHealth. WMCHealth has evolved since 2014 from a single tertiary care hospital and trauma facility into a 1,700-bed healthcare system with ten hospitals on eight campuses spanning 6,200 square miles of the Hudson Valley. Given the financial implications of readmissions and the concerns about the accuracy of the LACE index for predicting their occurrences within its patient population, WMCHealth examined its processes to see where they could be improved.
To deal with the challenges associated with effectively managing a growing population of patients with chronic diseases, care managers at BSCHS would attempt to identify which of its recently discharged patients were at highest risk for readmission, engaging with the patients to coordinate care.
Efforts to identify risk were complicated by the multiple systems that care managers had to use, and the time-consuming, manual processes required to identify recently discharged patients at risk for readmission. As a result, care managers were less effective, spending time searching patient records for the data they needed to prioritize their workload and choose the right interventions for their patients.
Seeking a more efficient process, BSCHS attempted to automate patient identification, generating a daily report to work from; however, this report did not provide complete information on discharged patients. There was a risk-scoring feature, but it was more reflective of mortality (with a focus on comorbidities) rather than the risk of readmission, which BSCHS was attempting to pinpoint. Furthermore, the generic algorithm failed to take into consideration the characteristics of the BSCHS patient population.
BSCHS needed a different mechanism for identifying patients at risk for readmission, as its labor-intensive process was inefficient and negatively impacted care managers’ patient engagement opportunities.
BSCHS and WMCHealth data analytics team partnered with Health Catalyst, using the Health Catalyst® Data Operating System (DOS™) Platform, catalyst.ai™, and healthcare.ai™ to develop a more accurate readmission risk prediction model that would enable its care managers to optimize their time spent coordinating care and engaging with the right patients.
This new solution incorporated the historical data on 54,000 patient discharges and 6,000 inpatient readmissions within 30-days of inpatient discharge to train the machine learning platform and to identify specific variables significant to the BSCHS patient population.
The team used an open source machine learning package to compare several algorithms tuned over many hyperparameters to arrive at the final predictive model, which was developed using the random forest algorithm.
After running the hospital’s historical data through the machine learning algorithm, the team used the analytics platform to leverage billing and clinical data to perform trials to validate the model’s predictions against various patient cohorts, which also ensured utility across sub-groups. In the final readmission risk prediction model, 24 variables were included.
When the team was confident that the risk predictions were as accurate as possible, risk scores were supplied to the care managers as part of the Health Catalyst Community Care Advanced Application.
The analytics application enables BSCHS care managers to fill critical gaps in chronic and preventative care through the creation of a new readmission reduction module, making valuable information available to care managers within an application and workflow they routinely use to complete their work. Care managers can easily access lists of recently discharged patients, and their readmission risk score.
To make the patient lists and risks scores more actionable, BSCHS paired them with EMR data that also informs care managers about interventions. Care managers are now able to review the discharge list, risk scores, and supporting data elements in a single visualization.
By utilizing its analytics platform, in coordination with a machine learning algorithm, BSCHS has built a knowledgeable and accurate model that best reflects its patient population. This has resulted in:
“Predictive analytics allows our team to work smarter, and more efficiently and leads to better targeted outreaches and outcomes for our patients.”
– Mary P. Leahy, MD
CEO, Bon Secours Charity Health System
WMCHealth plans to expand the use of the risk prediction information to additional locations and to improve the machine learning model further. The team also intends to integrate social determinants of health data into the predictive model, continuing to improve population health management and patient outcomes.
