TY - GEN
T1 - Randomized G-Computation Models in Healthcare Systems
AU - Spera, Emiliano
AU - Gallo, Giovanni
AU - Allegra, Dario
AU - Stanco, Filippo
AU - Maugeri, Andrea
AU - Quattrocchi, Annalisa
AU - Barchitta, Martina
AU - Agodi, Antonella
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/20
Y1 - 2018/7/20
N2 - Healthcare system quality improvements depend both on the availability of innovative technologies and on proper investments to transfer experimental policies into daily practices that could be easily adopted in all hospitals. Unfortunately, funds are generally not enough to cover all the addressable issues and the policy makers are faced with the difficult problem to decide where to allocate the money to produce the most relevant positive outcomes. To support this decision process, data gathering, and analysis play a key role. In this contribution we propose a simplified pipeline that starting from observational data to achieve statistical conclusions as valid as in designed randomized studies. After detailing the proposed analytic method, its soundness is proved using an important case study: the problem of the reduction of Healthcare-Associated Infections, and especially those acquired in Intensive Care Units. In particular, we show how to estimate the preventable proportion of Intubation-Associated Pneumonia in ICUs. In our study, using G-Computation based approach, we found out that the preventable proportion for IAP is of 44%. Interestingly, when bundle compliance is added in the statistical model, the preventable proportion for IAP is of 40%.
AB - Healthcare system quality improvements depend both on the availability of innovative technologies and on proper investments to transfer experimental policies into daily practices that could be easily adopted in all hospitals. Unfortunately, funds are generally not enough to cover all the addressable issues and the policy makers are faced with the difficult problem to decide where to allocate the money to produce the most relevant positive outcomes. To support this decision process, data gathering, and analysis play a key role. In this contribution we propose a simplified pipeline that starting from observational data to achieve statistical conclusions as valid as in designed randomized studies. After detailing the proposed analytic method, its soundness is proved using an important case study: the problem of the reduction of Healthcare-Associated Infections, and especially those acquired in Intensive Care Units. In particular, we show how to estimate the preventable proportion of Intubation-Associated Pneumonia in ICUs. In our study, using G-Computation based approach, we found out that the preventable proportion for IAP is of 44%. Interestingly, when bundle compliance is added in the statistical model, the preventable proportion for IAP is of 40%.
KW - G-Computation
KW - Healthcare
KW - ICU
UR - http://www.scopus.com/inward/record.url?scp=85050964480&partnerID=8YFLogxK
U2 - 10.1109/CBMS.2018.00021
DO - 10.1109/CBMS.2018.00021
M3 - Conference contribution
AN - SCOPUS:85050964480
T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems
SP - 77
EP - 81
BT - Proceedings - 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018
A2 - Kane, Bridget
A2 - Hollmen, Jaakko
A2 - McGregor, Carolyn
A2 - Soda, Paolo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018
Y2 - 18 June 2018 through 21 June 2018
ER -