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

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

A national standards-based assessment on functionality of electronic medical records systems used in Kenyan public-Sector health facilities.

BACKGROUND: Variations in the functionality, content and form of electronic medical record systems (EMRs) challenge national roll-out of these systems as part of a national strategy to monitor HIV response. To enforce the EMRs minimum requirements for delivery of quality HIV services, the Kenya Ministry of Health (MoH) developed EMRs standards and guidelines. The standards guided the recommendation of EMRs that met a preset threshold for national roll-out. METHODS: Using a standards-based checklist, six review teams formed by the MoH EMRs Technical Working Group rated a total of 17 unique EMRs in 28 heath facilities selected by individual owners for their optimal EMR implementation. EMRs with an aggregate score of ≥60% against checklist criteria were identified by the MoH as suitable for upgrading and rollout to Kenyan public health facilities. RESULTS: In Kenya, existing EMRs scored highly in health information and reporting (mean score=71.8%), followed by security, system features, core clinical information, and order entry criteria (mean score=58.1%-55.9%), and lowest against clinical decision support (mean score=17.6%) and interoperability criteria (mean score=14.3%). Four EMRs met the 60.0% threshold: OpenMRS, IQ-Care, C-PAD and Funsoft. On the basis of the review, the MoH provided EMRs upgrade plans to owners of all the 17 systems reviewed. CONCLUSION: The standards-based review in Kenya represents an effort to determine level of conformance to the EMRs standards and prioritize EMRs for enhancement and rollout. The results support concentrated use of resources towards development of the four recommended EMRs. Further review should be conducted to determine the effect of the EMR-specific upgrade plans on the other 13 EMRs that participated in the review exercise.

Automating indicator data reporting from health facility EMR to a national aggregate data system in Kenya: An Interoperability field-test using OpenMRS and DHIS2.

Introduction: Developing countries are increasingly strengthening national health information systems (HIS) for evidence-based decision-making. However, the inability to report indicator data automatically from electronic medical record systems (EMR) hinders this process. Data are often printed and manually re-entered into aggregate reporting systems. This affects data completeness, accuracy, reporting timeliness, and burdens staff who support routine indicator reporting from patient-level data. Method: After conducting a feasibility test to exchange indicator data from Open Medical Records System (OpenMRS) to District Health Information System version 2 (DHIS2), we conducted a field test at a health facility in Kenya. We configured a field-test DHIS2 instance, similar to the Kenya Ministry of Health (MOH) DHIS2, to receive HIV care and treatment indicator data and the KenyaEMR, a customized version of OpenMRS, to generate and transmit the data from a health facility. After training facility staff how to send data using DHIS2 reporting module, we compared completeness, accuracy and timeliness of automated indicator reporting with facility monthly reports manually entered into MOH DHIS2. Results: All 45 data values in the automated reporting process were 100% complete and accurate while in manual entry process, data completeness ranged from 66.7% to 100% and accuracy ranged from 33.3% to 95.6% for seven months (July 2013-January 2014). Manual tally and entry process required at least one person to perform each of the five reporting activities, generating data from EMR and manual entry required at least one person to perform each of the three reporting activities, while automated reporting process had one activity performed by one person. Manual tally and entry observed in October 2013 took 375 minutes. Average time to generate data and manually enter into DHIS2 was over half an hour (M=32.35 mins, SD=0.29) compared to less than a minute for automated submission (M=0.19 mins, SD=0.15). Discussion and Conclusion: The results indicate that indicator data sent electronically from OpenMRS-based EMR at a health facility to DHIS2 improves data completeness, eliminates transcription errors and delays in reporting, and reduces the reporting burden on human resources. This increases availability of quality indicator data using available resources to facilitate monitoring service delivery and measuring progress towards set goals.

Health and demographic surveillance in rural western Kenya: a platform for evaluating interventions to reduce morbidity and mortality from infectious diseases.

We established a health and demographic surveillance system in a rural area of western Kenya to measure the burden of infectious diseases and evaluate public health interventions. After a baseline census, all 33,990 households were visited every four months. We collected data on educational attainment, socioeconomic status, pediatric outpatient visits, causes of death in children, and malaria transmission. The life expectancy at birth was 38 years, the infant mortality rate was 125 per 1000 live births, and the under-five mortality rate was 227 per 1,000 live births. The increased mortality rate in younger men and women suggests high human immunodeficiency virus/acquired immunodeficiency syndrome-related mortality in the population. Of 5,879 sick child visits, the most frequent diagnosis was malaria (71.5%). Verbal autopsy results for 661 child deaths (1 month to <12 years) implicated malaria (28.9%) and anemia (19.8%) as the most common causes of death in children. These data will provide a basis for generating further research questions, developing targeted interventions, and evaluating their impact.