1 / 25

Penn Medicine

Penn Medicine . The Impact of an Early Warning System for Sepsis CHOP Healthcare Informatics April 25 , 2014 Joel Betesh, MD Gordon Tait, BS Asaf Hanish, MPH Benjamin French, PhD Neil Fishman MD Barry Fuchs, MD Christine Vanzandbergen, PA, MPH Craig A Umscheid, MD, MS.

naiara
Download Presentation

Penn Medicine

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Penn Medicine The Impact of an Early Warning System for Sepsis CHOP Healthcare Informatics April 25 , 2014 Joel Betesh, MD Gordon Tait, BS Asaf Hanish, MPH Benjamin French, PhD Neil Fishman MD Barry Fuchs, MD Christine Vanzandbergen, PA, MPH Craig A Umscheid, MD, MS

  2. The University of Pennsylvania Health System • Three acute care hospitals: • Hospital of the University of Pennsylvania • Penn Presbyterian Medical Center • Pennsylvania Hospital • 1,540 acute care beds • 80,020 acute care admissions in 2012 • 73 ACGME accredited training programs

  3. Sepsis Mortality Index (SMI) • Definition of Sepsis Mortality Index includes patients with: • Discharge status of ‘Expired’ • Principal and/or secondary diagnosis/diagnoses related to sepsis • Expected rate based upon MS-DRG-based algorithm

  4. Penn Medicine SMI for FY 2011 • Goal: Lower the high 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), 1.54 (HUP) • UHC Median: 1.19 • Top 5 Performers: 0.53, 0.65, 0.77, 0.78, 0.78 • UHC Rank for Penn: 67/113 • Target (Best Quartile): 0.98

  5. The Opportunity • Since the fall of 2011, we have vital sign, covering provider and covering nurse data in our inpatient EHR, Sunrise Clinical Manager. • Other hospitals have begun to use automated alerts for Sepsis based on data in their EHRs.

  6. The SIRS Criteria • 2 or more of the following criteria: • Temperature > 100.4°F or < 96.8 • HR > 90 • RR > 20 or PaCO2 < 32 • WBC > 12000  < 4000, or > 10% immature (band) forms

  7. Penn Medicine Criteria for Presumed Sepsis • VariablePoint • 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

  8. 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. Results of Retrospective Analysis (4 week period Oct 2011)

  9. EWS Data Gathering and Goals • Decrease total and sepsis related inpatient mortality • Decrease time to antibiotics • Decrease ICU transfers (ICU transfers could also increase) • Decrease RRTs (RRTs could also increase)

  10. Decisions Made Score of 4+ had an acceptable Positive Predictive Value 23% for hard outcomes of death, ICU transfer or RRT Anticipated additional 20-30% for sepsis Exclude ICUs, Maternity, PACU, ER, Hospice, and patients <18 years Fire ONCE per visit to start Alert to bring to bedside within 30 minutes patient’s intern and nurse manager (notified by pager) and patient’s nurse (notified by pop up alert in EHR) Limit to patients admitted at go-live or after (9/5/2012) To gradually ramp up and not overwhelm staff

  11. EWS System Architecture

  12. Alarm Process (I) • 1) The Alarm Process will: • Auto-enter the “Early Warning System Assessment” order • Will generate a primary task for the Covering Nurse (see below): Task Form-based • Will retrieve offending criteria • Will fetch and display: • Last 24 hours of Vital Signs • Last 48 hours of relevant Labs

  13. Alarm Process (II) • 2)Primary task completion will create a follow-up task (aka Survey) • See below:

  14. Primary Process Evaluation • NOTIFICATION • Coordinator page sent - 99% • Small, well defined group with limited devices • Nursing pop-up notification viewed - 72% • Relies on accurate nursing assignment in SCM • Covering provider notification sent - 83% • Relies on accurate assignment in SCM • Large number of devices and carriers • SCM Task completion • Primary RN task - 95% • Follow up coordinator task - 95% Team gathered at bedside within 30 minutes - 90%

  15. Preliminary results of EWS alert Silent/control period: Admitted between Jun 06, 2012 and Sep 04, 2012 Discharged by Oct 04, 2012 15,570 admissions 595 patients triggered the alert

  16. Preliminary results of EWS alert Loud/intervention period: Admitted between Sep 12, 2012 and Dec 11, 2012 Discharged by Jan 10, 2013 16,103 admissions 731 patients triggered the alert. Chi square compared proportions and Wilcoxon Rank Sum to compare medians before and after the alert went live Results adjusted for age, gender, admitting service and Charlson index

  17. EWS Process and Outcome Measures

  18. OUTCOME MEASURES Trend downward but did not achieve significance.

  19. Subset Analysis We looked at subsets of patients who might show more of a benefit for the alert such as: Elective admissions Emergency admissions Medicine admissions Surgery admissions Patients with a discharge diagnosis of sepsis

  20. Subgroup Analysis Patients with a Discharge Diagnosis of Sepsis

  21. Since go live in 2012 • Done: • Alert added in the ER • Added alert in Acute Rehab and LTachUnits • Added second alert after 10 day buffer at Rehab and LTACH • Being Discussed: • Allowing a second trigger during a single hospital stay • Heavy analysis of data with a view to decrease false positives andincreasing sensitivity of alert • Using change in systolic BP rather than absolute value • Machine learning approach to identify new/better parameters for the alert

  22. Penn Medicine Sepsis Mortality Index for FY 2013

  23. Acknowledgments Katherine Clark Jessica Guidi Mark Upton Hilary Faust Denise Feeley Meghan Lane-Fall Mark Mikkelsen William Schweickert Patrick Donnelly Jean Romano Kirsten Smith Barry Fuchs And many others

  24. Contact: Joel.Betesh@uphs.upenn.eduJoanne. Resnic@uphs.upenn.eduGordon.Tait@uphs.upenn.eduCraig.Umscheid@uphs.upenn.edu Questions? Suggestions?

More Related