TY - JOUR
T1 - Effectiveness of remote risk-based monitoring and potential benefits for combination with direct data capture
AU - Yamada, Osamu
AU - Chiu, Shih Wei
AU - Nakazawa, Toru
AU - Tsuda, Satoru
AU - Yoshida, Mitsuhide
AU - Asano, Toshifumi
AU - Kokubun, Taiki
AU - Hashimoto, Kazuki
AU - Takata, Munenori
AU - Ikeda, Suzuka
AU - Kawabe, Yosuke
AU - Tamura, Yuko
AU - Yamaguchi, Takuhiro
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - Background: In recent years, alternative monitoring approaches, such as risk-based and remote monitoring techniques, have been recommended instead of traditional on-site monitoring to achieve more efficient monitoring. Remote risk-based monitoring (R2BM) is a monitoring technique that combines risk-based and remote monitoring and focuses on the detection of critical data and process errors. Direct data capture (DDC), which directly collects electronic source data, can facilitate R2BM by minimizing the extent of source documents that must be reviewed and reducing the additional workload on R2BM. In this study, we evaluated the effectiveness of R2BM and the synergistic effect of combining R2BM with DDC. Methods: R2BM was prospectively conducted with eight participants in a randomized clinical trial using a remote monitoring system that uploaded photographs of source documents to a cloud location. Critical data and processes were verified by R2BM, and later, all were confirmed by on-site monitoring to evaluate the ability of R2BM to detect critical data and process errors and the workload of uploading photographs for clinical trial staff. In addition, the reduction of the number of uploaded photographs was evaluated by assuming that the DDC was introduced for data collection. Results: Of the 4645 data points, 20.9% (n = 973, 95% confidence interval = 19.8–22.2) were identified as critical. All critical data errors corresponding to 5.4% (n = 53/973, 95% confidence interval = 4.1–7.1) of the critical data and critical process errors were detectable by R2BM. The mean number of uploaded photographs and the mean time to upload them per visit per participant were 34.4 ± 11.9 and 26.5 ± 11.8 min (mean ± standard deviation), respectively. When assuming that DDC was introduced for data collection, 45.0% (95% confidence interval = 42.2–47.9) of uploaded photographs for R2BM were reduced. Conclusions: R2BM can detect 100% of the critical data and process errors without on-site monitoring. Combining R2BM with DDC reduces the workload of R2BM and further improves its efficiency.
AB - Background: In recent years, alternative monitoring approaches, such as risk-based and remote monitoring techniques, have been recommended instead of traditional on-site monitoring to achieve more efficient monitoring. Remote risk-based monitoring (R2BM) is a monitoring technique that combines risk-based and remote monitoring and focuses on the detection of critical data and process errors. Direct data capture (DDC), which directly collects electronic source data, can facilitate R2BM by minimizing the extent of source documents that must be reviewed and reducing the additional workload on R2BM. In this study, we evaluated the effectiveness of R2BM and the synergistic effect of combining R2BM with DDC. Methods: R2BM was prospectively conducted with eight participants in a randomized clinical trial using a remote monitoring system that uploaded photographs of source documents to a cloud location. Critical data and processes were verified by R2BM, and later, all were confirmed by on-site monitoring to evaluate the ability of R2BM to detect critical data and process errors and the workload of uploading photographs for clinical trial staff. In addition, the reduction of the number of uploaded photographs was evaluated by assuming that the DDC was introduced for data collection. Results: Of the 4645 data points, 20.9% (n = 973, 95% confidence interval = 19.8–22.2) were identified as critical. All critical data errors corresponding to 5.4% (n = 53/973, 95% confidence interval = 4.1–7.1) of the critical data and critical process errors were detectable by R2BM. The mean number of uploaded photographs and the mean time to upload them per visit per participant were 34.4 ± 11.9 and 26.5 ± 11.8 min (mean ± standard deviation), respectively. When assuming that DDC was introduced for data collection, 45.0% (95% confidence interval = 42.2–47.9) of uploaded photographs for R2BM were reduced. Conclusions: R2BM can detect 100% of the critical data and process errors without on-site monitoring. Combining R2BM with DDC reduces the workload of R2BM and further improves its efficiency.
KW - Clinical trial monitoring
KW - Direct data capture
KW - Prospective study
KW - Remote monitoring
KW - Risk-based monitoring
KW - eSource
UR - http://www.scopus.com/inward/record.url?scp=85196024978&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85196024978&partnerID=8YFLogxK
U2 - 10.1186/s13063-024-08242-2
DO - 10.1186/s13063-024-08242-2
M3 - Article
C2 - 38877566
AN - SCOPUS:85196024978
SN - 1745-6215
VL - 25
JO - Trials
JF - Trials
IS - 1
M1 - 384
ER -