Background The demand for quality maternal and child health (MCH) data is critical for tracking progress towards attainment of the Sustainable Development Goal 3. However, MCH cannot be adequately monitored where health data are inaccurate, incomplete, untimely, or inconsistent. Thus, this study assessed the level of MCH data quality. Method A facility-based cross-sectional study design was adopted, including a review of MCH service records. It was a stand-alone study involving 13 healthcare facilities of different levels that provided MCH services in the Cape Coast Metropolis. Data quality was assessed using the dimensions of accuracy, timeliness, completeness, and consistency. Health facilities registers were counted, collated, and compared with data on aggregate monthly forms, and a web-based data collation and reporting system, District Health Information System (DHIS2). The aggregate monthly forms were also compared with data in the DHIS2. Eight MCH variables were selected to assess data accuracy and consistency and two monthly reports were used to assess completeness and timeliness. Percentages and verification factor were estimated in the SPSS version 22 package. Results Data accuracy were recorded between the data sources: Registers and Forms, 102.1% (95% CI = 97.5%—106.7%); Registers and DHIS2, 102.4% (95% CI = 94.4%—110.4%); and Forms and DHIS2, 100.1% (95% CI = 96.4%—103.9%). Across the eight MCH variables, data were 93.2% (95% CI = 82.9%—103.5%) complete in Registers, 91.0% (95% CI = 79.5%—102.5%) in the Forms, and 94.9% (95% CI = 89.9%—99.9%) in DHIS2 database. On the average, 87.2% (95% CI = 80.5%—93.9%) of the facilities submitted their Monthly Midwife’s Returns reports on time, and Monthly Vaccination Report was 94% (95% CI = 89.3%—97.3%). The overall average data consistency was 93% (95% CI = 84%—102%). Conclusion Given the WHO standard for data quality, the level of MCH data quality in the health care facilities at the Cape Coast Metropolis, available through the DHIS2 is complete, reported on timely manner, consistent, and reflect accurately what exist in facility’s source document. Although there is evidence that data quality is good, there is still room for improvement in the quality of the data.

Background Electronic health records (EHRs) are useful tools in healthcare settings but implementation in low and middle-income countries (LMIC) face challenges. Objective To explore post-implementation challenges affecting the deployment of EHRs and their use in selected health facilities in Ghana. Method Using a qualitative research approach, 21 in-depth interviews were conducted with health workers in two hospitals in the study area in Ghana, in February and June 2020. Purposive sampling was used to select participants. All interviews were audio recorded, transcribed, and coded into themes using QSR Nvivo12 software to aid thematic analyses. Results Post-implementation challenges were grouped into lack of technological, logistical and managerial support, and inadequate training. Inadequate equipment was the most reported post-implementation challenge that affected EHR use. Unreliable Internet and network connectivity was a source of frustration, which caused staff to develop negative attitudes towards use of the system. Lack of funding stalled implementation of the system and limited its use to critical care units only. It was also the reason replacement of equipment delayed. Conclusion While EHR post-implementation challenges facing health facilities are surmountable, managerial support, backed with the requisite logistical and technical support is needed. It is not enough to rely on funding; health institutions should prioritise emerging EHR post-implementation challenges in their operating budgets. Implications A national framework is needed to guide effective and sustainable EHR implementation across the country.

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Background Routine Health Information Systems (RHIS) are important for not just sure enough control of malaria, but its elimination as well. If these systems are working, they can extensively provide accurate data on reported malaria cases instead of presenting modelled approximations of malaria burden. Queries are raised on both the quality and use of generated malaria data. Some issues of concern include inaccurate reporting of malaria cases as well as treatment plans, wrongly categorizing malaria cases in registers used to collate data and misplacing data or registers for reporting. This study analyses data quality concerning health staff’s proficiency, timeliness, availability and data accuracy in the Sissala East Municipal Health Directorate (MHD). Methods A cross-sectional design was used to collect data from 15 facilities and 50 health staff members who offered clinical related care for malaria cases in the Sissala East MHD from 24th August 2020 to 17th September 2020. Fifteen health facilities were randomly selected from the 56 health facilities in the municipality that were implementing the malarial control programme, and they were included in the study. Results On the question of when did staff receive any training on malaria-related health information management in the past six months prior to the survey, as minimal as 13 out of 50(26%) claimed to have been trained, whereas the majority 37 out of 50 (74%) had no training. In terms of proficiency in malaria indicators (MI), the majority (68% - 82%) of the respondents could not demonstrate the correct calculations of the indicators. Nevertheless, the MHD recorded monthly average timeliness of the 5th day [range: 4.7–5.7] within the reporting year. However, the MHD had a worse average performance of 5.4th and 5.7th days in July and September respectively. Furthermore, results indicated that 14 out of 15(93.3%) facilities exceeded the target to accomplish report availability (> = 90%) and data completeness (> = 90%). However, the verification factor (VF) of the overall malaria indicator showed that the MHD neither over-reported nor under-reported actual cases, with the corresponding level of data quality as Good (+/-5%). Conclusions The Majority of staff had not received any training on malaria-related RHIS. Some staff members did not know the correct definitions of some of MI used in the malaria programme, while the majority of them could not demonstrate the correct calculations of MI. Timeliness of reporting was below the target, nevertheless, copies of data that were submitted were available and completed. There should be training, supervision and monitoring to enhance staff proficiency and improve the quality of MI.

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