How Predictive Modeling Cuts Hospital Readmissions
"There's a lot of value in doing this [modeling] because we have an enormous amount of clinical need and a fixed amount of resources, and that's true for all hospitals," Amarasingham says. "Clinical resources are finite, and that's a real problem."
Based on Parkland's preliminary success with this algorithmic approach to preventable readmissions, it received a grant from the Commonwealth Fund to expand the model to all conditions and across several hospitals including its 968-licensed-bed Dallas hospital, two UT Southwestern hospitals, and five Texas Health Resources facilities. The goal is to build the first electronic readmission model that can be applied to any patient in any hospital where EMRs are available and reduce readmission rates. However, Parkland is also interested in ensuring clinical resources are being focused in the right areas.
Amarasingham, who started the predictive modeling project with a team of four and now has 15 people working on the project, says the use of predictive modeling has been well-received by many of the clinicians. However, while many see the value in having this data in the system, not everyone was on board at first or keen to follow the advice of the algorithm.
"It's a culture change, and the clinicians see the value in it. As computers play a larger role in medical decision-making and the delivery of care, I think attitudes change," Amarasingham says. "We need to see how this model changes care and what the human-to-computer interface in clinical decision-making will be, because it's becoming increasingly impossible for clinicians to keep track of the level of detail—both clinical and social—that's needed in order to arrive at a risk-level assessment. Eventually, I believe physicians will demand this type of predictive modeling technology. Ultimately, clinicians and all healthcare professionals will want to adopt practices to get to that level, including the predictive model."
Mount Sinai Medical Center, New York City: Admissions data and readmission rates
For nearly two years, Maria Basso Lipani, LCSW, coordinator of the preventable admissions care team at Mount Sinai Medical Center in New York City, and Jill Kalman, MD, director of the cardiomyopathy program, associate professor of medicine at Mount Sinai's Cardiovascular Institute, and the PACT medical director, have been using admission history data to identify and target for intervention patients at high risk for readmission. With funding from the United Hospital Fund and assistance from the Department of Health Evidence and Policy at Mount Sinai, the team was able to validate that hospitalization history alone is a reasonable proxy for more formal multivariable regression models in predicting 30-day readmission risk.
PACT, which consists of both a social work–led transitional program and an NP-led medical clinic, enrolled patients based upon data culled from Mount Sinai's existing EMR. A physician from the IT department creates a daily list that identifies hospitalized patients who had a least one admission within the past 30 days or two admissions within the past six months, says Basso Lipani.
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