Evaluating the Accuracy of Sampling Strategies for Estimation of Compliance Rate for Ventilator-Associated Pneumonia Process Measures.
BACKGROUND Measuring processes of care performance rates is an invaluable tool for quality improvement; however, collecting daily process measure data is time-consuming and burdensome. OBJECTIVE To evaluate the accuracy of sampling strategies to estimate monthly compliance rates with ventilator-associated pneumonia prevention measures. SETTING AND PARTICIPANTS A total of 37 intensive care units affiliated with 29 hospitals participating in a 2-state 35-month ventilator-associated pneumonia prevention collaborative. Analysis was limited to 325 unit-months with complete data entry rates. METHODS We calculated unit-month level actual and sample monthly compliance rates for 6 ventilator-associated pneumonia prevention measures, using 4 sampling strategies: sample 1 day per month, sample 1 day per week, sample 7 consecutive days per month, and sample 7 consecutive days per month plus additional consecutive days as necessary to obtain at least 30 ventilator-days for that month whenever possible. We compared sample versus actual rates using paired t test and χ2 test. RESULTS Mean sampling accuracy ranged 84%-97% for 1 day per month, 91%-98% for 1 day per week, 92%-98% for 7 consecutive days per month, and 96%-99% for 7 consecutive days with at least 30 days per month if possible. The most accurate sampling strategy was to sample 7 consecutive days with at least 30 ventilator-days per month if possible. With this strategy, sample rates were within 10% of actual rates in 88%-99% of unit-months and within 5% of actual rates in 74%-97% of unit-months. CONCLUSION Sampling process measures intermittently rather than continually can yield accurate estimates of process measure performance rates. Infect Control Hosp Epidemiol 2016;37:1037-1043.
1Department of Medicine,Johns Hopkins Hospital,Baltimore,Maryland.
Adam
A
Diehl
2Armstrong Institute for Patient Safety and Quality,Johns Hopkins University School of Medicine,Baltimore,Maryland.
Ting
T
Yang
2Armstrong Institute for Patient Safety and Quality,Johns Hopkins University School of Medicine,Baltimore,Maryland.
Kathleen
K
Speck
4Agency for Healthcare Research and Quality (AHRQ),Rockville,Maryland.
James
J
Battles
5Division of Infectious Diseases,Department of Medicine,Johns Hopkins University School of Medicine,Baltimore,Maryland.
Sara E
SE
Cosgrove
2Armstrong Institute for Patient Safety and Quality,Johns Hopkins University School of Medicine,Baltimore,Maryland.
Sean
S
Berenholtz
6Department of Population Medicine,Harvard Medical School & Harvard Pilgrim Health Care Institute,Boston,Massachusetts.
Michael
M
Klompas
D006801:Humans; D017053:Infection Control / Q000379:methods*; D007362:Intensive Care Units / Q000592:standards*; D008137:Longitudinal Studies; D008396:Maryland; D010414:Pennsylvania; D053717:Pneumonia, Ventilator-Associated / Q000453:epidemiology* / Q000517:prevention & control*; D017064:Process Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data*; D058996:Quality Improvement; D015983:Selection Bias