Reducing Adverse Drug Event Related Readmissions
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Parkland Center for Clinical Innovation developed a real-time adverse event predictive model to identify patients at high risk of readmission. The model screens all hospital admission patients over 18 for risk factors from their demographics, medications, clinical data and consults high-risk patients. Since implementation, the model has reduced readmissions by over 25%, saved over 400 staff hours per year, and is estimated to save $1.3 million annually by preventing adverse drug events and $456,000 through reduced readmissions.
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Reducing Adverse Drug Event Related Readmissions
- 1. Real-Time Adverse Event Predictive Model Reduces Readmissions Dr. Manjula Julka, Vice President Clinical Innovation, PCCI Kristin Alvarez, Associate Pharmacy Director, Parkland Hospital COPYRIGHT 2019 PCCI. All Rights Reserved.
- 2. National Burden – Hospital Readmissions Private Pay: $785M Medicaid: $839M Medicare: $4.3B Readmissions $41.3B COPYRIGHT 2019 PCCI. All Rights Reserved.
- 3. 3rd leading cause of death Increases LOS by 2-3 Days Increases cost by $16K-$24K Increases risk for readmission Any harm or injury that results from medication use ADE INCIDENCE Adverse Drug Events cost $3.5B annually What is an ADE? COPYRIGHT 2019 PCCI. All Rights Reserved.
- 4. COPYRIGHT 2019 PCCI. All Rights Reserved. 58-66% Reduction Med Discrepancies Lower preventable ADEs (1.76 times) Cost less (Pharm Tech/Pharm Student) Reduce ED Visits/Readmissions Identify Errors (4.5 times) Pharmacy-led Interventions are more likely to…
- 5. • 870-bed acute care hospital Level 1 Trauma Center • Ambulatory Care o 120 specialty clinics o 12 community-based clinics o 12 school-based clinics o Mobile units • Dallas County Jail health care • Over 10.5 million prescriptions (FY2017) • 250,000+ ED visits Parkland Hospital COPYRIGHT 2019 PCCI. All Rights Reserved.
- 6. AGILE ⃘ DESIGN THINKING ⃘ INNOVATION ⃘ COLLABORATION ADVANCED DATA ARCHITECTURE & AI PLATFORM MISSION: Reimagine and expand the knowledge base of healthcare through prescriptive analytics and artificial intelligence to deliver precision medicine. Parkland Center for Clinical Innovation COPYRIGHT 2019 PCCI. All Rights Reserved.
- 7. DISCOVER DESIGN DELIVER Human-Centered Design Innovation COPYRIGHT 2019 PCCI. All Rights Reserved.
- 8. EMPATHIZE DEFINE IDEATE PROTOTYPE TEST BUILD MEASURE SUSTAIN DESIGN DELIVERDISCOVER Design Thinking Framework COPYRIGHT 2019 PCCI. All Rights Reserved.
- 9. Scope of Problem 274,198 ED visits - 74,977 Discharges (FY2017) EMPATHIZE 3 FTEs Time and Resources COPYRIGHT 2019 PCCI. All Rights Reserved.
- 10. Inpatient Workflow Pharmacy-led timely interventions Improved Outcomes Patients admitted to hospital Who is at risk? Who is at risk? Discover COPYRIGHT 2019 PCCI. All Rights Reserved.
- 11. Design IDEATEDEFINE COPYRIGHT 2019 PCCI. All Rights Reserved.
- 12. Demographics Medications Clinical Data Social Determinants of Health Real-time Predictive Score Model inputs Dashboard (Director view) Flowsheet (End-User View) Reporting (Executive View) AI-powered variables Design Model outputs TEST PROTO- TYPE COPYRIGHT 2019 PCCI. All Rights Reserved.
- 13. End-User Epic* View Deliver Real-Time ADE predictive Risk Integration ACTIONABLE DATA INSIGHTS Model screens every hospital admission patient ≥18 BUILD COPYRIGHT 2019 PCCI. All Rights Reserved.
- 14. Risk Focused Care Model identifies patients at high-risk for ADE at admission Very high risk 6X more likely to be consulted than low risk patients MEASURE 3.2% 14.5% 17.7% 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% Low High Very High %Consulted Top 10%* (6,275) *Real-time predictive score AUC = 0.74 COPYRIGHT 2019 PCCI. All Rights Reserved.
- 15. Deliver MEASURE 2.7% 5.6% 5.9% 0.4% 8.9% 11.8% 0% 2% 4% 6% 8% 10% 12% 14% %Consulted Man vs. Machine Man vs. Machine Man vs. Machine Man vs. Machine LOW (N=55,762) HIGH (N=3,086) VERY HIGH (N=3,189) COPYRIGHT 2019 PCCI. All Rights Reserved.
- 16. Man vs. Machine MEASURE Model Identified Provider Initiated GO-LIVE June 2017 Pre Post COPYRIGHT 2019 PCCI. All Rights Reserved.
- 17. 16% 12% 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% Without Pharm Consult With Pharm Consult 30-DayReadmissionRate Reduced Readmissions P < 0.01 MEASURE N= 4994* N= 954**High/Very High Risk Patients COPYRIGHT 2019 PCCI. All Rights Reserved.
- 18. Model Impact ↑ Patient Safety ↓ 914 minor ADE ↓ 134 major ADE ↓ Readmissions by 25%+ ↓ ED visits REAL-TIME EPIC-INTEGRATED • 62,037 patients screened • 6,275 identified • 2,775 consulted • 6X more likely ‘Very High Risk’ consults • Saved 432 hours/FTE/year • NNT=25 Resource Utilization Clinical Outcomes Operational Outcomes Cost Savings *AHRQ estimates based on cost of ADE prevented and ** HCUP_AHRQ estimates, through reduction in 30-day readmission rate +Relative reduction SUSTAIN $ 1.3 M savings* (ADE) $ 38 K (savings in FTE hours) $ 456 K savings ** (Readmissions) Potential for $5.6 M per year COPYRIGHT 2019 PCCI. All Rights Reserved.
- 19. Take Home Points • End-user engagement • Timely communication • Proactive monitoring • Seamless integration • Scale and Sustain With special thanks to Inpatient Pharmacy and IT team COPYRIGHT 2019 PCCI. All Rights Reserved.
- 20. COPYRIGHT 2019 PCCI. All Rights Reserved.
Notes de l'éditeur
- Sixty-three percent of the 3.3M patients readmitted to hospitals annually are avoidable. Costing $3.5 billion at approximately $5.6 million per hospital, adverse drug events (ADE) significantly contribute to readmissions and are the leading cause of preventable patient harm. PCCI developed and operationalized a real-time predictive algorithm that identifies newly hospitalized patients at high risk for an ADE and facilitates timely pharmacy-led interventions to improve outcomes. In just one hospital, the model, which is integrated with the EHR, screens on an average 65,000 inpatients annually, preventing potential ADEs, decreasing cost of care and demonstrated significant reduction in 30-day readmission rates. The innovation is easily replicable and scalable across settings. This presentation also illustrates use of design thinking framework and end user collaboration from idea to impact.
- Sepsis is one of the leading causes of mortality worldwide. There are more than 750,000 sepsis hospitalizations in the United States annually that cause approximately 200,000 deaths. Early detection and treatment is key to lowering mortality rate since every hour of delay increases the odds of mortality by 20%. But detecting sepsis in an inpatient setting is challenging – the symptoms can be confounded with other conditions and patients can deteriorate rapidly. Traditional risk models such as SIRS criteria to detect sepsis generate a lot of false positives leading to inefficient and ineffective care as well as nursing fatigue. PCCI developed a predictive model to fulfil the criteria identified above. This meant a model designed to predict in real-time the individual risk of a patient becoming septic in the next 12 hours. The PCCI sepsis model is baked into clinical workflows through industry standard APIs. The statistical performance of the model and its integration into workflows differentiates this work from many other sepsis related models and published work.