JMIR Publications

ABSTRACT

Background:

Electronic Health Records (EHRs) play an increasingly important role in the delivery of HIV care in low-and-middle-income countries. The data collected is used for direct clinical care, quality improvement, program monitoring, public health interventions and research. Despite widespread EHR use for HIV care in many African countries, challenges remain especially in collecting high quality data.

Objective:

To assess data completeness, quality and timeliness compared to paper-based records and factors influencing data quality in a large scale EHR deployment in Rwanda.

Methods:

We randomly selected 50 health facilities (HF) using the OpenMRS EHR systems for HIV care in Rwanda and carried out a data quality evaluation. All HFs were part of a larger randomized controlled trial with 25 sites receiving an enhanced EHR with clinical decision support systems. Trained data collectors visited 50 HFs to collect 28 variables from both paper charts and the EHR system using the ODK app. We measured data completeness, timeliness, and the degree of matching of paper and EHR data, including use of concordance scores, for 24 required variables. Factors potentially effecting data quality were drawn from a previous survey of users in the 50 sites.

Results:

We randomly selected 3,467 records, reviewing paper and EHR copies of each, (194,152 total data items). Data completeness was above the 85% threshold for all variables except viral load (VL) results, second and third line drug regimens variables. Matching scores for data values were close to or above 85%, but lower for dates particularly for drug pickups and VL. Mean data concordance was 10.2/15 (68%) for 15 variables. Site and user factors (years of EHR use, technology experience, EHR availability/uptime, intervention status) were tested for correlation with data quality measures. EHR system availability/uptime was positively correlated with concordance, and users’ experiences of technology was negatively correlated. The alerts for missing viral load results implemented in 11 intervention sites showed clear evidence of improving timeliness and completeness of initially low matching of VL results in the EHR and paper records (11.9% to 26.7%, P<.001). Similar effects were seen on completeness of recording of medication pickups (18.7% to 32.6%, P<.001).

Conclusions:

The EHR records in the 50 health facilities generally had high levels of completeness except for VL results. Matching results were close to or above 85% threshold for non-date variables. Higher EHR stability and uptime and alerts and reminders for entering VL had a strong effect in improving data quality. Most data was considered fit for purpose, but more regular data quality assessments, training, and technical improvements in EHR forms, data reports and alerts are recommended. Application of quality improvement techniques described here should be beneficial across a wide range of sites and data uses for clinical care, public health and disease surveillance.