December 5-6, 2011 Cherylanne Zeumault Jeanette Harris.

Slides:



Advertisements
Similar presentations
Meeting the Challenge of Mandatory HAI Reporting Marcy Maxwell RN, BSN, CIC Dignity Health March 6, 2012.
Advertisements

Washington State Hospital Association Partnership for Patients Safe Table Reducing Hospital Acquired Infections July 31, 2013 Amber Theel, Director Patient.
0 Hospital Quality Incentive Demonstration (HQID) Key Facts Three year demo ( ); extended for three additional years through Oct hospitals.
Using ICD Codes and Birth Records to Prevent Mismatches of Multiple Births in Linked Hospital Readmission Data Alison Fraser 1, MSPH, Zhiwei Liu 2, MS,
ASE Event Slides  Major Trauma  Sepsis  QIPP 114 June 2010.
STANDARDIZED INFECTION RATIO SIMPLIFIED
Findings from the Virginia Department of Health (VDH) 2010 Central Line-Associated Bloodstream Infection (CLABSI) Data Audit Project Andrea Alvarez,
The Redesigned National Hospital Discharge Survey National Center for Health Statistics Division of Health Care Statistics Hospital Care Team Last Updated:
Guide to Finding Your Hospital’s PC-01 EED Rate
SIR 201: Calculating the Measure, Generating Reports, and Presenting the Data Good afternoon and welcome to the SIR 201: Calculating the Measure, Generating.
Eve Giannetta, RN BSN UVAHS Kathy Bailey, RN CIC Centra Health System
1 Sheryl Hurt AFMC Provider Representative Episodes of Care AFMC has partnered with the initiative to provide communication design and printing.
Sharing and explaining the standardized infection ratio (SIR): Does your audience prefer words, colors, and/or δymβφĨs? Dana Burshell, MPH, CPH, CIC HAI.
Lecture 3: Measuring the Occurrence of Disease
SIR 101: Interpretation and public reporting
The Standardized Infection Ratio Steven P Hudson, MBA, MA Statistician Health Care Excel, Inc.
Update SB 288: Health Care Associated Infections Infectious Disease Epidemiology Workgroup Jan. 9, 2009 Austin, Texas Gary Heseltine MD MPH Infectious.
Variation in the Delivery of Medical Care: Is More Better? Todd Gilmer, PhD Professor of Health Policy and Economics Department of Family and Preventive.
Healthcare Associated Infections: Data Summary and Commonly Asked Questions Shannon Millay, MPH Healthcare Associated Infections Epidemiologist October.
One-Factor Experiments Andy Wang CIS 5930 Computer Systems Performance Analysis.
PU515 – Applied Biostatistics Dana Colbert-Wheeler, MHA, MCHES
NHSN Data Submission Requirements 2013 Health Care Excel Cathie Pritchard LPN, RHIT Quality Data Reporting Technologist October 12, 2012.
Welcome to the GHA Infection Prevention Power Hour January 17, 2013 Denise M. Flook, RN, MPH, CIC Georgia Hospital Association
NHSN SSI Clarifications Janet Sullivan, Oregon SW Washington APIC Jeanne Negley, Oregon Health Policy & Research.
TEMPLATE DESIGN © Major surgery in a minor way Sin WT, Woldman S, Attilia B, Gauthaman N, Karpouzis H, Patwardhan M South.
The Standardized Infection Ratio Linda R Greene, RN, MPS,CIC Rochester General Health System Rochester, NY
Leapfrog’s “Survival Predictor”: Composite Measures for Predicting Hospital Surgical Mortality May 7, 2008.
Healthcare Associated Infections in 2014 Acute Care Hospitals Alfred DeMaria, Jr., M.D. Medical Director, State Epidemiologist Bureau of Infectious Disease.
Advancing Excellence in Health Care 2.0 ETA December 2010 Carol Sniegoski AHRQ Annual Conference September 27, 2010 Preview:
Kentucky AHA/HRET Hospital Engagement Network Charisse Coulombe, MS, MBA, CPHQ; Senior Director, HEN Hospital Engagement Network Health Research & Educational.
Assuring Data Quality Dept. of Healthcare-Associated Infection & Antimicrobial Resistance, Health Protection Agency Jennie Wilson Programme Leader – SSI.
Place picture here Potentially Preventable Readmissions RARE Mental Health Collab. Mark Sonneborn February 2014.
PU 515 Midterm Review Dana Colbert-Wheeler, MHA, MCHES Instructor, Kaplan Health Sciences.
2013 IRF-PAI Updates June 19, 2012 Lisa Werner and Melissa Berkoff.
Cohort Coaching Call “Cohort 9” October 15, 2014 Coaches: Tracy Rutland Jean Allred Jan Ratterree Lynne Hall.
ASC Quality Measure Reporting Ann Shimek, MSN, RN, CASC Senior Vice President Clinical Operations United Surgical Partners International.
Review Design of experiments, histograms, average and standard deviation, normal approximation, measurement error, and probability.
Alfred Junior, MPH Lindsey Weiner, MPH Scott Fridkin, MD Division of Healthcare Quality Promotion CDC November 18, 2015 Notes From the Field: Antibiotic.
Date of download: 6/26/2016 From: Variations in Mortality and Length of Stay in Intensive Care Units Ann Intern Med. 1993;118(10): doi: /
MEASURE Evaluation Data Quality Assurance Workshop Session 3 Introduction to Routine Data Quality Assessment.
Facility ID:Procedure #: *Patient ID:Social Security #: Secondary ID: Patient Name, Last: First: Middle: *Gender: F M*Date of Birth: Ethnicity (specify):Race.
Change Presentation MARY CECCO. Surgical Site Infections We own them!
Characteristics of Health Activation Solutions
NHSN Reporting for Critical Access Hospitals
Quality Measurement A Changing Landscape
Florida Hospital Association
Hospital Engagement Network
Harm Across the Board (HAB): Monthly Update
MHA Keystone Center MICAH QN Meeting - May 19, 2017.
MHA Keystone Center Update
Use of BCBSRI Primary Care Provider Profile to Improve Performance
Impact of State Reporting Laws on Central Line– Associated Bloodstream Infection Rates in U.S. Adult Intensive Care Units Hangsheng Liu, Carolyn T. A.
American Joint Replacement Registry
Measuring Efficiency HSCRC Performance Measurement Workgroup
State HAI Program Changes and Updates
Normal Distribution.
Warm Up If there are 2000 students total in the school, what percentage of the students are in each region?
Warm Up If there are 2000 students total in the school, what percentage of the students are in each region?
Therefore, the Age variable is a categorical variable.
Background and Context
Quality….. The True Sustainable Strategy To Ensure Viability
Who, where, why, and the data behind it.
Queries Training Module.
Dolores Hagan, RN BSN K-HEN Education and Data Manager August 2012
Measuring Efficiency HSCRC Performance Measurement Workgroup
APIC Chapter 123 August 26, 2016.
Maryland HCW Influenza Vaccination Survey Highlights
Texas Healthcare Associated Infection (HAI) Reporting and Validation
Normal Distribution.
CIC Practice Questions
Presentation transcript:

December 5-6, 2011 Cherylanne Zeumault Jeanette Harris

 SSI & CAUTI  CAUTI…..not much is new – but if you have questions…we can help  SSI…..LOTS new in 2012  CMS: Colon surgery  CMS: Abdominal Hysterectomy  Along with all the Washington Mandatory  Reportable surgeries CARD,CBGB,CBGC,HPRO,HYST,KPRO,VHYS full=true# full=true#

 The new NHSN Patient Safety Component Manual  New this month

 Add to your monthly reporting plan – a MUST  Data Verification  800 hospitals  Data Quality Output Options – check yourself  Go to Output Options – Advanced – Data Quality, CDC defined Output:

 NEW REPORTING STEPS………….  Click on Event – Incomplete  Click on Missing PA Events tab  Check report NO EVENTS next to SSI then “save”

 It’s YOUR data  It’s more meaningful, actionable  EXTERNAL SCRUTINY  Plus it helps everyone else for better benchmarking  Identified  Mis-mapped facility locations – leads to incorrect benchmarking  Incomplete denominators  Misidentified lines  Misconceptions of definitions  Missed/Overcalled cases

 Number of beds?  Location mapping?  New reporters? Are they all up do date?  Manual Counting  Electronic Counting Do spot checks  SSI Procedures Are they complete? Look for a secondary source for validation How to find procedures NOT PRIMARILY CLOSED? Check procedure duration and ASA score for all CBGB and CBGC IT can change things and you wouldn’t know it

 Non-autologous transplants –  No longer needed  Estimated Blood loss for C-Sections –  No longer needed  Implants: Temp or permanent  Porcine or synthetic valves  Mechanical heart  Metal rods, screws, sternal wires, cements, internal staples, hemoclips, other

 5 procedures that have additional risk  CSEC, Fusion/Refusion  HPRO  KPRO  Height in ft and inches or meters  Weight in pounds or Kg  C-Sections: Hours of labor in the hospital  Length of time beginning of active labor as an inpatient to delivery

 FUSN/RFUSN  Diabetic Y/N  Spinal Level  Approach  HPRO  Which type - TP, PP, TR, PR  KPRO  Which type – Primary, Revision (total or partial)

 Infection?  Determine which procedure could be associated  If it’s not clear, use the Principal Operative Procedure Selection Lists (Table 3 in the manual)

 SSI “Detected” Field  No more “P” (post-discharge)  Instead, “Detected” will have 2 values RO: if SSI identified due to patient admission to a facility other than where the op was performed RF: if SSI was identified due to patient readmission to the facility where the op was performed  Secondary BSI is required if there was a +BC  The organisms MUST be the same  Linking

 The SIR is an indirect standardized method for summarizing HAI across any number of stratified groups of data.  The SIR is the number of observed infections divided by the predicted (or statistically expected) number of infections.  The expected number is based on the national NHSN average, the number of procedures performed by a hospital and the historical data for those procedures.

 A SIR of 1.0 means the observed number of infections is equal to the number of expected infections.  A SIR above 1.0 means that the infection rate is higher than that found in the "standard population." For HAI reports, the standard population comes from data reported by the hundreds of U.S. hospitals that use the NHSN system. The difference above 1.0 is the percentage by which the infection rate exceeds that of the standard population.  A SIR below 1.0 means that the infection rate is lower than that of the standard population. The difference below 1.0 is the percentage by which the infection rate is lower than that experienced by the standard population.

 IPist notices that “Hospital X” has a higher number of KPRO infections than normal (more than one surgeon). IPist….PREPARES FOR BATTLE  During discussing with the Surgery Committee…Comments from surgeons  “We have harder cases than hospital “Y”  “We do more cases than hospital “Y”  “We don’t like being compared to hospital “Y”!!  IPist notes:  This is your SIR. It is 1.8  That means that you are 80% higher than other similar hospitals – NATIONWIDE  FYI….Hospital “Y” is not in your group (neener, neener)  You are compared to other similar hospitals with similar beds, risk factors, med school affiliation, etc.

 Surgery rebuttle:  “What’s our rate compared to the National Rate? What’s the benchmark?  Ipist: There is no more “benchmark”  There is only Standardized Infection Ratio  This means that you are compared to other surgeons/hospitals with patients with similar risk factors that include more than just ASA score and wound class  This is a BETTER and MORE ACURATE method of comparison  You’re SIR of 1.8 means that you have 80% more infections than similar hospitals across the nation  Surgery: So we really DO have more infections?  Ipist: YES  Surgery Committee Chair: I suggest we get a team together to see what’s going on

 RCA discovered that there were variations in practices that contributed to these infections  Surgery Committee Report:  More help during surgery  Control the number of staff in surgery suite  Positioning  Draping  Dressings  Staff training  Outcome: no infection since (6 months)