Analytics Model Predicts Heart Failure Readmissions
An analytics model developed by researchers from the University of Texas at Dallas shows that health information technology systems can help predict hospital readmission rates for congestive heart failure patients and to identify those high-risk patients.
An analytics model developed by researchers from the University of Texas at Dallas shows that health information technology systems can help predict hospital readmission rates for congestive heart failure patients and to identify those high-risk patients.
Tracking patient demographic, clinical and administrative data from across 67 hospitals in North Texas during a four-year period, researchers assessed the link between hospital usage of health IT and readmission risk. From 2006 to 2010 in the Dallas-Fort Worth region, nearly 30 percent of congestive heart failure patients were readmitted within 30 days.
Among other findings, the study published in the latest issue of Information Systems Research found that health IT has a beneficial impact on readmissions. Specifically, researchers revealed that hospitals which have implemented cardiology and administrative information systems are more likely to exhibit lower readmission rates compared to hospitals that have not implemented these systems.
Also See: Survey Finds Only 15% of Hospitals Use Advanced Predictive Modeling
Hospitals should consider the use of innovative information technologies, including electronic health records and patient portals, to improve communication between patients and clinicians in order to improve the quality of care delivery to patients with chronic diseases such as congestive heart failure, said Indranil Bardhan, professor and area coordinator of information systems in the Naveen Jindal School of Management at UT Dallas.
According to Zhiqiang Zheng, professor of information systems, building an early warning system that identifies predictors for likely readmissions is crucial. Towards that end, researchers discovered several important determinants of patient readmission risk, including patient demographics, hospital characteristics and payer type (Medicare, Medicaid, self-pay, private insurance, etc.).
For instance, while Medicare patients are more likely to be readmitted, their frequency of future readmissions is lower after their first readmission. And, hospitals are more focused on taking better care of these patients once they are readmitted, said Bardhan.
In addition, repeat care delivery at the same hospital reduces the risk of future readmissions significantly, researchers conclude, indicating that a patient treated at the same hospital across multiple visits tends to receive better quality of care, thereby reducing their risk of being readmitted.
Hospitals can use the approach that we have developed to not only identify and stratify patients based on their readmission risk propensity, but also reduce their frequency of future readmissions by delivering appropriate treatment and providing more efficient post-acute care, added Bardhan.
Tracking patient demographic, clinical and administrative data from across 67 hospitals in North Texas during a four-year period, researchers assessed the link between hospital usage of health IT and readmission risk. From 2006 to 2010 in the Dallas-Fort Worth region, nearly 30 percent of congestive heart failure patients were readmitted within 30 days.
Among other findings, the study published in the latest issue of Information Systems Research found that health IT has a beneficial impact on readmissions. Specifically, researchers revealed that hospitals which have implemented cardiology and administrative information systems are more likely to exhibit lower readmission rates compared to hospitals that have not implemented these systems.
Also See: Survey Finds Only 15% of Hospitals Use Advanced Predictive Modeling
Hospitals should consider the use of innovative information technologies, including electronic health records and patient portals, to improve communication between patients and clinicians in order to improve the quality of care delivery to patients with chronic diseases such as congestive heart failure, said Indranil Bardhan, professor and area coordinator of information systems in the Naveen Jindal School of Management at UT Dallas.
According to Zhiqiang Zheng, professor of information systems, building an early warning system that identifies predictors for likely readmissions is crucial. Towards that end, researchers discovered several important determinants of patient readmission risk, including patient demographics, hospital characteristics and payer type (Medicare, Medicaid, self-pay, private insurance, etc.).
For instance, while Medicare patients are more likely to be readmitted, their frequency of future readmissions is lower after their first readmission. And, hospitals are more focused on taking better care of these patients once they are readmitted, said Bardhan.
In addition, repeat care delivery at the same hospital reduces the risk of future readmissions significantly, researchers conclude, indicating that a patient treated at the same hospital across multiple visits tends to receive better quality of care, thereby reducing their risk of being readmitted.
Hospitals can use the approach that we have developed to not only identify and stratify patients based on their readmission risk propensity, but also reduce their frequency of future readmissions by delivering appropriate treatment and providing more efficient post-acute care, added Bardhan.
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