TY  - GEN
AB  - Observational data fill in knowledge gaps when randomized controlled trials (RCTs) are not ethical or feasible. Electronic health records (EHRs) provide a source of observational data from diverse patient samples that reflect real-world settings. However, observational data are susceptible to biases and EHR data contain new forms of bias(es) because they were collected for clinical and not research purposes.
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AU  - Hsu, Frances
AU  - Weiskopf, Nicole
DA  - 2023-08-15
DO  - 10.6083/bpxhc41528
DO  - doi
ID  - 41528
KW  - Electronic Health Records
KW  - Bias
KW  - Systematic Reviews as Topic
KW  - clinical research
KW  - bioinformatics
KW  - randomized controlled trials
KW  - EHR
L1  - https://digitalcollections.ohsu.edu/record/41528/files/ResearchWeek.2023.Hsu.Frances.pdf
L2  - https://digitalcollections.ohsu.edu/record/41528/files/ResearchWeek.2023.Hsu.Frances.pdf
L4  - https://digitalcollections.ohsu.edu/record/41528/files/ResearchWeek.2023.Hsu.Frances.pdf
LA  - eng
LK  - https://digitalcollections.ohsu.edu/record/41528/files/ResearchWeek.2023.Hsu.Frances.pdf
N2  - Observational data fill in knowledge gaps when randomized controlled trials (RCTs) are not ethical or feasible. Electronic health records (EHRs) provide a source of observational data from diverse patient samples that reflect real-world settings. However, observational data are susceptible to biases and EHR data contain new forms of bias(es) because they were collected for clinical and not research purposes.
PB  - Oregon Health and Science University
PY  - 2023-08-15
T1  - Unique biases in electronic health records data for research: a systematic review
TI  - Unique biases in electronic health records data for research: a systematic review
UR  - https://digitalcollections.ohsu.edu/record/41528/files/ResearchWeek.2023.Hsu.Frances.pdf
Y1  - 2023-08-15
ER  -