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Parkland Hospital Saves $17M With AI-Powered Predictive Model to Prevent In-Hospital Adverse Drug Events

by Fred Pennic 10/09/2019 Leave a Comment

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Parkland Hospital Saves $17M With AI-Powered Predictive Model to Prevent In-Hospital Adverse Drug Events

– Parkland Hospital delivers over $17M in savings preventing in-hospital adverse drug events using an AI-powered predictive model developed by the Park Center for Clinical Innovation (PCCI).

– The program, Patients at Risk for Adverse Drug Events (PARADE) demonstrated positive results combating ADEs, a problem that impacts more than 450,000 patients nationwide and increases the risk for re-admissions lengthen the stay of patients by 2 to 3 days and adds almost $4B in extra hospital costs annually.

– PARADE screens all adult patients at the point of hospitalization and flags high-risk individuals who can benefit from pharmacist intervention.

Parkland Hospital, a Dallas, TX-based hospital and health system has saved over $17 million dollars by reducing their hospital re-admissions and eliminating adverse drug events using a customized artificial intelligence (AI)-driven predictive model. Developed in partnership with affiliate Park Center for Clinical Innovation (PCCI) over the past 2 years, Parkland has been able to prevent more than 2,000 adverse drug events (ADEs) for hospitalized patients. PCCI combines extensive clinical expertise with advanced analytics and artificial intelligence to enable the delivery of patient-centric precision medicine at the point of care.

Patients at Risk for Adverse Drug Events (PARADE) Program Overview

The program, Patients at Risk for Adverse Drug Events (PARADE), is a partnership between PCCI and Parkland Health & Hospital System. The PARADE implementation at Parkland was led by Kristin Alvarez, PharmD, BCPS, Associate Director Clinical Advancement/Best Practices for Parkland and Brett Moran, MD, Chief Medical Informatics Officer for Parkland

How PARADE Prevents ADEs

PARADE screens all adult patients at the point of hospitalization and flags high-risk individuals who can benefit from pharmacist intervention. To score a patient’s level of risk, PARADE captures a patient’s medical history, including medications and disease complexity, prior healthcare utilization, demographics and social determinants of health. It then provides results in real-time, with seamless integration into a patient’s electronic health record (EHR).

PARADE Outcomes for Patients

During the two years of implementation, PARADE has demonstrated positive results combating ADEs, a problem that impacts more than 450,000 patients nationwide and increases the risk for re-admissions, lengthens the stay of patients by two to three days and adds almost $4 billion in extra hospital costs annually. The most common drug classes associated with ADEs include anticoagulants, diabetes medications, and opioids.

To date, the PARADE program has screened more than 87,000 patients, with 8,731 high-risk patients identified. Of the high-risk patients, 16 percent received timely pharmacy intervention and more than 2,000 ADEs were prevented. For high-risk patients receiving a consult, the 30-day readmission rate was cut by 23.5 percent. Due to the model’s high accuracy and real-time user-friendly information, Parkland has adopted PARADE as a primary tool for pharmacist daily workflow for consult identification with demonstrated impact on preventing potential ADEs.

“Close collaboration with Parkland’s front-line pharmacy team from idea to implementation has been critical for the success of PARADE,” said Manjula Julka, MD, PCCI’s Vice President, Clinical Innovation. “PARADE has proven to improve quality of care by helping the pharmacy team to identify and intervene with high risk patients within 24 to 48 hours of admission. Upwards of 50 percent of ADEs are detectable and preventable and PARADE gives us a potent tool to help hospitals stay ahead of a difficult problem that causes longer stays and drives significant costs for hospitals.”

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Tagged With: adverse drug events (ADEs), AI, Artificial Intelligence, diabetes, Electronic Health Record, MD, model, Pharmacy, point of care, Precision Medicine, Predictive Analytics, risk, Social Determinants of Health

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