Knowledge to Practice; Applying New Skills

Transcription

1 Knowledge to Practice; Applying New Skills Linda R. Greene, RN, BS, MPS,CIC UR Highland Hospital Rochester, NY Kim M. Delahanty, RN, BSN, PHN,MBA/HCM,CIC UCSD Health System 1

2 Review of the Key Issues Converting data to information Data are factual information, especially information organized for analysis or used to make decisions. Discrete data count (e.g., the number of patients on a ventilator, the number of patients with central lines) Continuous data measure (e.g., rate of surgical site infection) 2

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4 Understanding Surveillance Data Surveillance Definitions Identify trends within a population for the purpose of prevention and research Comply to limited, predetermined data components Objective: clinical judgment introduces potential bias Clinical Diagnoses Identify disease in, and treatment needs for, individual patients All diagnostic information is used for decision making Subjective: clinical judgment is valued Adapted from Bridson, KA NHSN Patient Safety Component Manual 2014 HAI Surveillance Overview Essential elements of surveillance Assess the patient population and identify those at greatest risk for the outcome and process measures of interest: outcome = HAI; process = practices aimed at HAI prevention Select the outcome and process measures Determine the surveillance time period Choose the surveillance methodology Monitor the selected measure using standardized definitions Collect denominator data for rate calculation Analyze the data Report the data in a timely manner 8 4

5 HAI Surveillance Overview Surveillance methodology CDC recommends active, prospective, targeted surveillance that yields risk-adjusted incidence rates ACTIVE: trained personnel (IP) vigorously look for HAIs using a variety of data sources PROSPECTIVE: during the patients hospitalization and for SSIs post-discharge for the defined surveillance period TARGETED: surveillance objectives defined with focus on specific events, organisms, procedures RISK-ADJUSTED: variations in the distribution of major risk factors associated with the occurrence of an event are controlled allows for inter-and intra-hospital comparisons 9 HAI Surveillance Overview INCIDENCE: count only new events during the defined time period; PREVALENCE: count all events -new and previously existing Numerator data collection What to collect Demographic: name, gender, admit date, medical record number, medicare beneficiary number: REGISTRATION (ADT) Infection data: onset date, site of infection, location of HAI onset Risk factors: devices, procedures, comorbidities e.g. diabetes, obesity Laboratory and microbiology data Radiology 10 5

6 HAI Surveillance Overview How to collect Screen ADT data for high-risk patient admissions and patients admitted with infection recent discharge? Reviews lab and microbiology data positive cultures, unusual organisms e.g Legionella pneumophila Ward rounds Reviews disparate data of patients suspected to have an HAI physician and nursing notes, radiology, surgery reports, lab Denominator data collection Device days Patient admissions/days Surgical procedures 11 High Quality Surveillance Data Complete Accurate Timely 12 6

7 Why Analyze? Provide feedback to internal stakeholders Analyzing HAI data can help facilitate internal validation activities Reports can help inform prioritization and success of prevention activities. Data entered into NHSN may be used by: CDC, CMS, your state health department*, your corporation*, special study groups*, etc. At the end of the day, these are YOUR data you should know your data better than anyone else. Are these data accurate? Data Analysis Has our HAI experience changed over time? Which measure is used by my state and by CMS? What is a priority for my hospital? Where do I get started??? What is behind the number? 7

8 Metrics and Reports Don t limit yourself! A number of different types of reports are helpful in analyzing your data Line Lists Frequency Tables Charts/graphical reports Rate Tables Standardized Infection Ratios (SIRs) Descriptive statistics (e.g., mean, median, mode, distribution, outliers, etc.) Metrics and Reports Line Lists Allow for a record-level review of data Helpful in pinpointing issues in data validity/quality Can help to inform rates or other summarized measures Can help in the identification of any trends Can be used for SSI postdischarge surveillance efforts 8

9 Metrics and Reports Rate Tables A summary of HAI or process measure data Oftentimes these data are stratified by one or more factors found to be significant Can use these data to make external comparisons (e.g., NHSN) or internal comparisons to determine if there are any trends Standardized Infection Ratios (SIRs) Another way to summarize HAI data in a risk-adjusted manner Measure used by CDC, CMS, and many state health departments Checklist Before you begin analyzing, ask yourself these questions: What data are you analyzing? What is the time period of interest? Why are you analyzing these data? Who is the audience? 9

10 Data are useless Data are unactionable Data are unrevealing Data are stagnant Changes minds Changes behavior Is actionable Is fluid Information 10

11 The key is changing data into information You need to understand What others think What motivates behavior change for your audience Leadership Support Access to an Executive Champion Visibility I ve got your back mentality Lead by example No finger pointing Development Opportunities Mentorship Education 11

12 Technology Support Think small and BIG Appropriate training to use available tools Tablet for surveillance/data collection Electronic capture of denominators Automate everything possible Data mining tools Electronic surveillance tools Human Support How many people do you really need? What s the standard? 12

13 Get the right kind of human Admin/Office Manager Project Manager Epi Support Infection Preventionist How many IP should there be? SENIC (mid-1980s): 1 IP per 250 acute care beds (ACB) Updated in various venues over the last 15 years Ideal IP staffing ratios ranging from 1 IP per 167 to 100 ACB Several recent ( ) publications report survey results from responding facilities to have actual staffing ratios ranging from 1:151 to 1:83 ACB Limited by small sample size and may not be generalizable across the US hospitals 13

14 ACB enough to consider? Acute Care Bed Equivalents used to Calculate IP Staffing Ratios Variable Acute Care Bed Equivalent Acute care bed 1 Intensive care bed 2 Long term care bed ½ Dialysis facility 50 Ambulatory surgery center 50 Ambulatory clinic 10 Private physician office 5 New York State Department of Health. Hospital-Acquired Infections New York State Available from Putting it all Together Let s look at scenarios to put this all together 14

15 The Case of the Influenza Vaccine I WILL Get the Flu vaccine. I WILL Get the Flu vaccine. I WILL Get the Flu vaccine. I WILL Get the Flu vaccine. I WILL Get the Flu vaccine. I WILL Get the Flu vaccine. I WILL Get the Flu vaccine. I WILL Get the Flu vaccine.. Influenza Vaccination The long term care facility has developed a comprehensive plan for improving healthcare worker vaccination compliance to influenza vaccination. The overall Immunization rate for the season was 72%. For the season the rate rose to 88% Is this improvement significant? 15

16 What data do we need to assess this? Numerator and denominator for each time period. In , we had 200 eligible HCWs employed. 144 received the vaccine. In , there were 230 eligible HCWs, 202 received the vaccine Calculate Fisher s Exact or Chi Square Results: P< 0.01 Improvement is statistically significant

17 What other data is important? Discipline MD/ Mid level Number Eligible Received Vaccine % Nurses % Nurse Tech % Support Services Clerical or administrative % % % Compliance Reasons for refusing vaccine Fear of needles - 0 I never get sick- 10 Impurities in the vaccine-1 Cultural or political issues-2 Personal rights- 6 Questions about vaccine effectiveness-2 It makes me sick -7 17

18 Areas to concentrate next year I Never get sick Personal rights It makes me sick Other areas to analyze: Absenteeism based on vaccination rate The Data Let s look at this scenario: 18

19 How do you interpret the data Which of the following is the best answer? 1.CAUTI mean is higher than comparative data but not by a statistically significant difference 2. CAUTI is statistically higher than the NHSN mean 3. The P value shows a significant difference but the confidence interval does not 4. Both the mean and the confidence level indicate that the CAUTI Rate for this time period is significant The Line List 19

20 What else? Your administration is concerned about this and would like to know what actions are necessary. After you review the literature and look at the data, which of the following seems like a good place to start? 1. Start with aseptic insertion, always start with the basics. 2. Look at getting the catheters out sooner, explore nurse driven protocols, automatic stop orders,etc. 3. Look at urine culture protocols 4. Look at antiseptic urinary catheters The rest of the data What does the SIR mean? How about the confidence interval? 20

21 6 Months later During the second half of the year, the CAUTI rate decreased to 2.1 per 1,000 urinary catheter days. You now have 2 infections in the second half of the year in 1400 urinary catheter days. How would you determine if your improvement was significant? CAUTI Rates 21

22 Wait, we re not done yet It seems that in the next few months we have had another spike in rates. We have had 6 infections What should we look at? 22

23 Another Look at the Data Line list Adherence to Nurse Driven Protocols Maintenance Obtaining cultures Using the EHR 23

24 Data and Information Target information to the Audience Many tools and graphical displays Be sure your information makes sense for the intended audience Device Use Ratio 24

25 Device Use Ratio 1.0 ICU Urinary Catheter Device Utilization Rate YTD 0.9 Device Utilization Rate M01 M02 M03 M04 M05 M06 M07 M08 M09 M10 M11 M12 M01 M02 M03 M04 M05 M06 M07 M08 M09 M10 M11 CathDU CathDU_Mean Which chart would be most useful in your setting? Both 25

26 Data can be simple 120 Number of Days without a CAUTI 30 KEEPING THE MOMENTUM 26

27 Did we make a difference? A community hospital examined 2 year s worth of C difficile data Year Number HO Cases Patient Days Rate per 10,000 Patient days Analysis 27

28 Results Data is powerful Conclusion Data drives improvement Data speaks Use the data tools, identify, analyze, communicate 28

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