19 research outputs found

    Timing of last visit in 12 months after ART start.

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    <p>*Among 2,166 Option B+ clients. **Among 850 Option B+ clients with known pregnancy and HIV testing dates. Rapid ART start = ART start within 7 days of registration in HIV care; No rapid ART start = ART start >7 days after registration in HIV care. ǂp<0.001 for Chi2 test of equality of proportions between Rapid ART start vs. No rapid ART start and between HIV test before vs. after start of pregnancy.</p

    Risk factors for ART attrition among Option B+ patients<sup>*</sup>.

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    <p>Risk factors for ART attrition among Option B+ patients<sup><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173123#t003fn001" target="_blank">*</a></sup>.</p

    ART attrition indicators by gestational age at ART start.

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    <p>**Among 968 Option B+ patients with available data on pregnancy dates. ǂ p<0.001 ǁ p = 0.01 * p = 0.54 for Chi2 test of equality of proportions across gestational age groups.</p

    The impact of routine data quality assessments on electronic medical record data quality in Kenya

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    <div><p>Background</p><p>Routine Data Quality Assessments (RDQAs) were developed to measure and improve facility-level electronic medical record (EMR) data quality. We assessed if RDQAs were associated with improvements in data quality in KenyaEMR, an HIV care and treatment EMR used at 341 facilities in Kenya.</p><p>Methods</p><p>RDQAs assess data quality by comparing information recorded in paper records to KenyaEMR. RDQAs are conducted during a one-day site visit, where approximately 100 records are randomly selected and 24 data elements are reviewed to assess data completeness and concordance. Results are immediately provided to facility staff and action plans are developed for data quality improvement. For facilities that had received more than one RDQA (baseline and follow-up), we used generalized estimating equation models to determine if data completeness or concordance improved from the baseline to the follow-up RDQAs.</p><p>Results</p><p>27 facilities received two RDQAs and were included in the analysis, with 2369 and 2355 records reviewed from baseline and follow-up RDQAs, respectively. The frequency of missing data in KenyaEMR declined from the baseline (31% missing) to the follow-up (13% missing) RDQAs. After adjusting for facility characteristics, records from follow-up RDQAs had 0.43-times the risk (95% CI: 0.32–0.58) of having at least one missing value among nine required data elements compared to records from baseline RDQAs. Using a scale with one point awarded for each of 20 data elements with concordant values in paper records and KenyaEMR, we found that data concordance improved from baseline (11.9/20) to follow-up (13.6/20) RDQAs, with the mean concordance score increasing by 1.79 (95% CI: 0.25–3.33).</p><p>Conclusions</p><p>This manuscript demonstrates that RDQAs can be implemented on a large scale and used to identify EMR data quality problems. RDQAs were associated with meaningful improvements in data quality and could be adapted for implementation in other settings.</p></div
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