dc.contributor.author |
Kimaiyo Sylvester |
|
dc.contributor.author |
Ribeka Nyoman W |
|
dc.contributor.author |
Were Martin C |
|
dc.contributor.author |
Mohammed-Rajput Nareesa A |
|
dc.date.accessioned |
2019-02-07T13:10:08Z |
|
dc.date.available |
2019-02-07T13:10:08Z |
|
dc.date.issued |
2010-11-13 |
|
dc.identifier.uri |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041308/ |
|
dc.identifier.uri |
http://ir.mu.ac.ke:8080/xmlui/handle/123456789/2723 |
|
dc.description.abstract |
Randomized trials are difficult to perform in resource-limited settings. We developed a Randomization and Enrollment Tool (RET) within a live EHRs which automated enrollment, randomization, and data-collection in support of robust EHRs-based randomized interventions. We describe an observational assessment of RET which we piloted at three Kenyan HIV clinics for a decision support trial. We manually evaluated RET’s adequacy and accuracy in its core functions. RET enrolled 327/6626 patients, 100% meeting criteria based on EHRs data. Human reviews reveal that only 250 patients (76.5%) should have been enrolled as the EHRs contained inaccurate data for the other 77 (23.4%). 23 eligible patients were also missed through sole reliance on EHRs data. 18 (5.5%) RET-enrolled patients never received the intervention because of missed appointments. An automated randomization tool has potential to reduce human and financial costs of conducting EHRs-based randomized trials, but remains vulnerable to data quality and workflow limitations. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
PMC US National Library of Medicine National Institutes of Health |
en_US |
dc.subject |
Robust EHRs |
en_US |
dc.title |
Creating and Evaluating a Dynamic Study Randomization and Enrollment Tool within a Robust EHRs |
en_US |
dc.type |
Article |
en_US |