Case Study: Using Predictive Analytics to Reduce Sepsis Mortality

Transcription

1 Case Study: Using Predictive Analytics to Reduce Sepsis Mortality 1

2 Learning Objectives 1. Understand how an automated, real time IT intervention can help care teams recognize and intervene on critical, resulting in improved outcomes. 2. Understand how to leverage existing data in electronic health records to create interventions to support patient care. 3. Determine scalability to other organizations. 2

3 ABOUT PENN MEDICINE The University of Pennsylvania Health System was created in 1993 and consists of four hospitals (Hospital of the University of Pennsylvania, Presbyterian Medical Center, Pennsylvania Hospital, Chester County Hospital), a faculty practice plan, a primary care provider network, two multi-specialty satellite facilities, home care, hospice, and a nursing home. Licensed Beds 1,637 Total Employees 21,864 IT Employees 419 IT Operating Budget $112, 396, 932 Operating Revenue $4.3 Billion Adult Admissions 75,588 Outpatient Visits 2,080,269+ Physicians 2,251* 3

4 From Best Of Breed To Single Vendor McKesson RxS McKesson RxS Visicu ICU IDX Practice Mgmt. McKesson HSM Emtrac ED Siemens Hosp. Billing Visicu ICU IDX Practice Mgmt. McKesson HSM Emtrac ED Siemens Hosp. Billing Visicu ICU IDX Practice Mgmt. McKesson HSM Emtrac ED Siemens Hosp. Billing McKesson HSM Emtrac ED Siemens Hosp. Billing Emtrac ED Siemens Hosp. Billing Emtrac ED Siemens Hosp. Billing Siemens Hosp. Billing Siemens Hosp. Billing Siemens Hosp. Billing Epic EMR Allscripts CPOE Cerner Lab GE Radiology Cardiology Vendor(s) Epic EMR Allscripts CPOE Allscripts SRX Cerner Lab GE Radiology Cardiology Vendor(s) Epic EMR Epic Practice Mgmt. Allscripts CPOE Allscripts SRX Allscripts KBC Cerner Lab GE Radiology Cardiology Vendor(s) All clinicians on Epic Ambulatory Epic EMR Epic Practice Mgmt. Epic Optime Allscripts CPOE Allscripts SRX Allscripts KBC Cerner Lab GE Radiology Cardiology Vendor(s) Epic EMR Epic Practice Mgmt. Epic OpTime Allscripts CPOE Allscripts SRX Allscripts KBC Allscripts epd Cerner Lab GE Radiology Cardiology Vendor(s) Epic EMR Epic Practice Mgmt. Epic OpTime Epic ASAP ED Allscripts CPOE Allscripts SRX Allscripts KBC Allscripts epd Cerner Lab GE Radiology Cardiology Vendor(s) Epic EMR Epic Practice Mgmt. Epic OpTime Epic ASAP ED Allscripts CPOE Allscripts SRX Allscripts KBC Allscripts epd Cerner Lab Epic Radiant Cardiology Vendor(s) Epic EMR Epic Practice Mgmt. Epic OpTime Epic ASAP ED Allscripts CPOE Allscripts SRX Allscripts KBC Allscripts epd Cerner Lab Epic Radiant Cardiology Vendor(s) Epic Hosp. Billing & ADT Epic EMR Epic Practice Mgmt. Epic OpTime Epic ASAP ED Epic CPOE Epic RxS Epic ClinDoc Epic epd Cerner Lab Epic Radiant Cardiology Vendor(s) 4

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7 Sepsis Sepsis is the 11 th leading cause of inpatient death in U.S. and the incidence of sepsis is increasing. Early recognition and timely intervention reduces mortality Goal: Lower the Sepsis Mortality Index (SMI) at the University of Pennsylvania as determined by University HealthSystem Consortium (UHC) UHC SMI data for FY11 SMI: 1.50 (UPHS) UHC Median:

8 Penn Medicine Center for Evidence-based Practice To support the quality, safety and value of patient care at Penn through evidencebased practice. EVIDENCE SUMMARY 1. All published studies of these early warning systems are of pre-post design, where differences in patient populations and other changes in patient care can affect the results. 2. All studies found made use of the Modified Early Warning Score or one of its variations. Scoring systems usually differed from each other even when they had the same name. 3. Results of these studies were inconsistent with respect to patient mortality, transfer to ICU, and length of hospital stay. None of the studies reported outcomes specific to sepsis. 4. We found no published clinical evidence on commercial patient monitoring systems such as the Rothman Index 8

9 The SIRS Criteria 2 or more of the following criteria: Temperature > F or < 96.8 HR > 90 RR > 20 or PaCO2 < 32 WBC > < 4000, or > 10% immature (band) forms 9

10 Penn Medicine Criteria for Presumed Sepsis Variable Point Temperature <36 C or >38 C 1 Heart Rate >90 beats per minute 1 RR >20 breaths/min; or PACO2 <32 mmhg 1 WBC count <4000 or >12,000 or >10% bands 1 Lactate >2.2 1 Systolic blood pressure <100 1 RR: respiratory Rate; WBC: white blood cell Criterion for presumed sepsis: >4 points 10

11 Results of Retrospective Analysis (6 week period Oct/Nov 2011) 4,700 patients. Criterion for presumed sepsis: >4 points. 193 patients scored 4 or more. A score of 4 or more had a positive predictive value of about 23% for death, transfer to ICU or RRT. On average these events occurred over 48 hours after the score of 4 was first met. We felt there were an additional 20% of patients who had sepsis and would be identified by this trigger and could benefit from early diagnosis and treatment. This early warning system was likely to also identify patients who had new episodes of bleeding, cardiac ischemia, and pulmonary emboli. 11

12 Results Of Retrospective Analysis Data Thresholds Criterion for presumed sepsis: >4 points We tried changing the threshold values in an attempt to increase the positive predictive value but this came at a price in terms of sensitivity: 12

13 Silent Pilot Silent phase: June 6 th to July 6 th, 2012 Goal: Validate the retrospective analysis in a live environment Silent data analysis: Certain services had a higher rate of alarms Oncology had highest rate of silent alarms 235 unique patients alarms across 3 sites Validated retrospective data RRT- 17% ICU Transfer- 14% Death- 10% *Data Excludes ICUs, Maternity, Hospice, and patients <18 years 13

14 Penn Medicine Approach Operational Team Response End User Tool Development Operational response System Wide Team Organized UPHS Predictive Model Designed Evidence/data analytics Data Analyst 14

15 Score of 4+ had an acceptable PPV 23% for hard outcomes Anticipated additional 20-30% for sepsis Operational teams determined they could handle the volume with current staff Exclude ICUs, Maternity, PACU, Hospice, and patients <18 years Fire ONCE per visit to start Decisions Made Limit to patients admitted at go-live or after (9/5/2012) To gradually ramp up and not overwhelm staff 15

16 Results of EWS Alert 9/12/2012-9/12/ EWS Year To Year 9/11/2013 9/11/2014 Total Alarms: Notified Coordinators: 98.87% 97.86% Notified Provider: 80.62% 76.69% Notified Nurse: 88.93% 84.11% RN Task Performed: 95.85% 93.22% Follow up Coordinator Task Performed: 95.85% 93.22% Met at Bedside within 30 minutes: 86.63% 86.39% Team already aware: 48.96% 49.85% Alarm had value: 29.18% 30.77% 16

17 Results of EWS Alert Admit between June 6 & Sept. 4 Control 6/6/12-9/4/12 Intervention: 6/6/13-9/4/13 AND 6/6/14-9/4/14 Discharge between June 6 & Sept days EWS alarm between June 6 and Sept. 10 Chi squared to compare proportions and Wilcoxon Rank Sum to compare medians before and after the alert went live 17

18 PROCESS MEASURES 40% Sepsis care measures before and after implementation of the early warning system (EWS) Pre-EWS (CY12) Post-EWS (CY13) Post-EWS (CY14) % of patients that triggered EWS 30% 20% 10% 15% 29% 26% 23% 23% 22% 17% 13% 10% 11% 20% 18% 5% 10% 9% 0% >500ml fluids within 3hrs of EWS Antibiotic within 3hrs Lactate within 3hrs of Blood culture within of EWS EWS 3hrs of EWS Transfusion within 6hrs of EWS 18

19 15% Proportion of EWS triggering patients who expire in the facility % of patients that triggered EWS 10% 5% 8.7% 7.0% 11.3% 7.1% 7.9% 11.4% 9.1% 6.5% 8.9% 7.4% 8.6% FY12 FY13 FY14 3 month reporting period from 6/6-9/6 1.0% 0% HUP PAH PMC UPHS EWS firings at HUP have decreased by 20% from CY12 to CY14 periods. 19

20 Perceived effectiveness of EWS Survey distributed to all providers and nurses of patients for whom EWS fired in a 6 week period at HUP 10/15/2012 to 11/29/ total alerts during this period Medical floors: 173 alerts Surgical floors: 74 alerts 127/247 providers (51%) and 105/247 nurses (43%) completed survey In 57% of the alerts, surveys were collected from both the provider and the RN for the same patient. 20

21 Key Clinician Perceptions of EWS Effectiveness Survey Question MD RN Alerted to new information 40% 46% Recognized critical illness sooner 16% 25% Patient management changed 44% 55% Appropriately timed 54% 65% Helpful / useful 34% 40% Improved patient care 50% 64% 21

22 UHC Sepsis Mortality Index (SMI) UHC SMI FY11 SMI: 1.50 (UPHS) UHC Median: 1.19 Top 5 Performers: 0.53, 0.65, 0.77, 0.78, 0.78 UHC SMI FY13 SMI: 1.06 (UPHS) UHC Median: Top 5 Performers: 0.49, 0.54, 0.59, 0.75, 0.76 UHC SMI FY14 SMI: 0.92 (UPHS) UHC Median: 1.0 Top 5 Performers: 0.42, 0.61, 0.62, 0.68,

23 Post Live enhancements Post-live Enhancements ER Buffer of 4 hours: June 2 nd, 2014 ICU Buffer of 24 hours: July 3 rd, 2014 Implemented in ED on 12/13/12 Implemented in our LTAC/Rehab Future consideration Improving the predictive model Possible service specific considerations (i.e surgery, onc) Additional populations (OB/NICU) 23

24 Christine VanZanbergen Penn Medicine