TY - JOUR
T1 - Evaluation of patient safety culture using a random forest algorithm
AU - Simsekler, Mecit Can Emre
AU - Qazi, Abroon
AU - Alalami, Mohammad Amjad
AU - Ellahham, Samer
AU - Ozonoff, Al
N1 - Funding Information:
Data used in this analysis were from the AHRQ Hospital Survey on PSC Comparative Database. The database is funded by AHRQ and managed by Westat under contract #HHSA 290201300003C. This publication is based upon work supported by the Khalifa University of Science and Technology under Award No. RCII-2019-002, Center for Digital Supply Chain and Operations Management. Dr. Ozonoff receives research funding from the Agency for Healthcare Research and Quality (AHRQ) grant 1R01HS026246-01A1.
Funding Information:
Data used in this analysis were from the AHRQ Hospital Survey on PSC Comparative Database. The database is funded by AHRQ and managed by Westat under contract #HHSA 290201300003C. This publication is based upon work supported by the Khalifa University of Science and Technology under Award No. RCII-2019-002 , Center for Digital Supply Chain and Operations Management. Dr. Ozonoff receives research funding from the Agency for Healthcare Research and Quality (AHRQ) grant 1R01HS026246-01A1 .
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/12
Y1 - 2020/12
N2 - Safety culture is a multidimensional concept that may be associated with medical errors and patient safety events in healthcare delivery systems. However, limited evidence is available regarding which safety culture dimensions drive overall patient safety. Moreover, the use of advanced statistical analysis has been limited in past studies of safety culture data. To address these issues, we use hospital-level aggregate survey data from U.S. hospitals to analyze the relationship between the defined safety culture dimensions and the patient safety grade. We use a tree-based machine learning algorithm, random forests, to estimate accurate and stable associations. The results of our analysis show that safety perception, management support, and supervisor/manager expectations are the leading drivers of patient safety grade. More specifically, safety problems in the work unit and work climate provided by hospital management are specific drivers of patient safety outcomes. The random forest model sheds new light on the most important cultural features relevant to patient safety.
AB - Safety culture is a multidimensional concept that may be associated with medical errors and patient safety events in healthcare delivery systems. However, limited evidence is available regarding which safety culture dimensions drive overall patient safety. Moreover, the use of advanced statistical analysis has been limited in past studies of safety culture data. To address these issues, we use hospital-level aggregate survey data from U.S. hospitals to analyze the relationship between the defined safety culture dimensions and the patient safety grade. We use a tree-based machine learning algorithm, random forests, to estimate accurate and stable associations. The results of our analysis show that safety perception, management support, and supervisor/manager expectations are the leading drivers of patient safety grade. More specifically, safety problems in the work unit and work climate provided by hospital management are specific drivers of patient safety outcomes. The random forest model sheds new light on the most important cultural features relevant to patient safety.
KW - Healthcare operations
KW - Machine learning
KW - Patient safety
KW - Random forest algorithm
KW - Safety culture
UR - http://www.scopus.com/inward/record.url?scp=85089705338&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2020.107186
DO - 10.1016/j.ress.2020.107186
M3 - Article
AN - SCOPUS:85089705338
SN - 0951-8320
VL - 204
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
M1 - 107186
ER -