Data Quality in Health Management Information Systems in Kenyatta National Hospital, Nairobi City County, Kenya
Kenyenga, Benjamin Oreni
MetadataShow full item record
Over the years, data-driven processes have been fundamental in most sectors. However, poor data quality is often encountered in HMIS in resource-poor countries, and this is attributed to multiple factors. In the health sector, the information generated is vital in ensuring that the services provided are of high standards. As the amount of data generated every year continues to grow, the quality of data output also needs to be at the same level. The purpose of this study was to examine the factors influencing data quality in health management information systems (HMIS) at Kenyatta National Hospital (KNH), Nairobi City County, Kenya. Specifically, the study was to determine the personnel, technological and organizational factors influencing data quality in HMIS. The study was conducted at Kenyatta National Hospital and included all health information department staff who gave consent to take part in the study and were present during the study period. As such, a census of all the 193 out of the 195 employees in the department was done. Questionnaires and Key Informant Interviews were used to solicit information from the respondents. Pretesting was conducted at Moi Teaching and Referral Hospital (MTRH) where the validity and reliability of the research instruments were verified. Necessary approvals and consents were sought prior to start of the research. Entry and analysis of the collected data was done using SPSS version 25.0. The results showed that personnel (X2=36.660, P-value = 0.001), technological (X2= 63.341, P-value= 0.001) and organizational factors (X2= 75.473, P-value= 0.001) all play major roles in enhancing data quality. For personnel factors, majority of the study participants (86.5%) could type data into Microsoft Excel spreadsheets. In addition, most respondents (85.5%) indicated that they could comfortably serve patients using the major HMISs in the hospital, with timeliness being a critical component in determining HMIS data quality (77.7%). Age was not considered a critical factor in HMIS data quality management, so long as staff was adequately trained. Results further showed that in terms of technological factors, network connectivity challenges at various working stations (90.2%) posed a huge impact on data quality. For the organizational factors, stakeholder involvement in the IT implementation was considered crucial (82.9%). The culmination of the association analysis between the dependent and independent variables revealed that personnel factors (X2=36.66, P-Value<0.05), technological factors (X2=63.341, P-Value<0.05) and organizational factors (X2=75.473, P-Value<0.05) were all statistically significant. Regression analysis generated a model with predictors being age (X2= 4.717, P-value 0.194), gender (X2 =0.14, P-value =0.514), marital status (X2 = 4.479, P-value=0.214), highest level of education (X2=5.532, P-value 0.021), subsection (X2=2.436, P-value=0.016), personnel factorsX2=36.660, P-value = 0.001), technological (X2= 63.341, P-value= 0.001) and organizational factors (X2= 75.473, P-value= 0.001). The study findings established that there were both positive and negative relationships between the dependent and the independent variables. As such, Personnel factors, Technological factors and Organizational factors are key determinants of data quality in HMIS at Kenyatta National Hospital in Nairobi City County. It was recommended that rigorous training and creating awareness ought to be undertaken by Kenyatta National Hospital management in addition to development and review of policies and guidelines that will essentially strengthen and reduce probability of capturing inaccurate data.