Dr Nic WoodsPhysician Executive, Cerner ANZ

Using the EMR inearly recognition and

management of sepsis

© Cerner Corporation. All rights reserved. This document contains Cerner confidential and/or proprietary information belonging to Cerner Corporation and/or its related affiliates which may not be reproduced or transmitted in any form or by any means without the express written consent .

200+ associates Partnered with 1st client in 1991

3 OfficesSydney

BrisbaneMelbourne

Solutions licensed at over

190 hospitals across the country

100,000+Unique users

Cerner Australia

Australia’s 1st

fully digital hospital and HIMSS Stage 6 in

Hervey Bay

Topics

• The challenge• Some tools• Some outcomes• Key points • Questions

Topics

• The challenge• Some tools• Some outcomes• Key points • Questions

Sepsis – what is it?

Sepsis – what is it?

The challenge – Global burden

From World Sepsis Day Organisation;. www.world-sepsis-day.org

The challenge – Severe Sepsis, ANZ ICU’s

Kaukonen, KM et al. Mortality Related to Severe Sepsis and Septic Shock Among Critically Ill Patients in Australia and New Zealand, 2000-2012. JAMA. doi:10.1001/jama.2014.2637

The challenge – Health system burden (USA)

Torio CM, Andrews RM: National Inpatient Hospital Costs: The Most Expensive Conditions by Payer, 2011: Statistical Brief #160. In: Healthcare Cost and Utilization Project (HCUP) Statistical Briefs [Internet] Rockville (MD): Agency for Health Care Policy and Research (US) 2006-2013 http://hcup-us.ahrq.gov/reports/statbriefs/sb160.jsp

#1 in 2011 for USA hospital costs

>$20 billion (5.2% of national costs)

The challenge – It affects many of us

Topics

• The challenge• Some tools

• Sepsis predictive model• EHR clinical decision support & workflow

• Some outcomes• Key points • Questions

Some tools – Sepsis predictive model

Some tools – Sepsis predictive model

Some tools – Sepsis predictive model

Some tools – Sepsis predictive model

Some tools – Sepsis predictive model

Some tools – Sepsis predictive model

Some tools – Sepsis predictive model

Some tools – Sepsis predictive model

Some tools – Sepsis predictive model

• Predicted Operating Characteristics for the St. John’s Sepsis Alert (n = 68,962 encounters)

Scenario Encounters with Sepsis

Encounters without Sepsis

TruePositives

False

Negatives

False

Positives

True Negatives

Sensitivit

y

Specificit

y

PositivePredictiv

eValu

e

Negativ

ePredictiv

e Valu

eAll Cases 2,457 910 15,142 50,453 0.73 0.77 0.14 0.98

Some tools – Sepsis predictive model

Some tools – Sepsis predictive model

Operating Characteristics• Sensitivity 68-91%• Specificity 91-97.6%• PPV up to 68%

Alert fires when system finds3 signs of systemic inflammatory response syndrome, OR2 signs of SIRS + one sign of organ dysfunction

Topics

• The challenge• Some tools

• Sepsis predictive model• EHR clinical decision support & workflow

• Some outcomes• Key points • Questions

Some tools – EHR CDS and workflow

Some tools – EHR CDS and workflow

Some tools – EHR CDS and workflow

Some tools – EHR CDS and workflow

Topics

• The challenge• Some tools

• Sepsis predictive model• EHR clinical decision support & workflow

• Some outcomes• Key points • Questions

Some outcomes - Implementation

Monitoring>1 million lives> 490 facilities31,250 alerts received per hour748,250 alerts received per day

Some outcomes – Published evaluations

• Multicenter retrospective study, 6,200 patient records • Evaluation by CDS in silent mode with chart evaluation • 195 patients activated alert before sepsis suspected vs 417 patients activated

alert after provider recorded suspected sepsis • Median time from arrival to CDS activation was 3.5 hours, and system

activation to diagnostic collect was another 8.6 hoursAmland, RC, Hahn-Cover, KE. Clinical Decision Support for Early Recognition of Sepsis. American Journal of Medical Quality, November 2014. Available at http://ajm.sagepub.com/content/early/2014/12/10/1062860614557636.full.pdf+html

Some outcomes – Health service evaluations

Since 2010, 1980 lives saved (↓ mortality rate by 4.2%)

In FY2014, 156 lives saved (↓ mortality rate by 1.42%)

1 patient every 2 days

Mortality ↓ 17%. LOS ↓ from 16.5 to 13.6 days

Some outcomes – Health service evaluations

Some outcomes – Health service evaluations

Topics

• The challenge• Some tools

• Sepsis predictive model• EHR clinical decision support & workflow

• Some outcomes• Key points • Questions

Key points

• Sepsis – it’s nasty & not going away• Predictive models implemented back into the clinical workflow

can impact clinical outcome• We do well, but more to be done

Key points

Key points

Key points

Topics

• The challenge• Some tools

• Sepsis predictive model• EHR clinical decision support & workflow

• Some outcomes• Key points • Questions

Health care is too important to stay the same.

TM