Enhancing the routine health information system in rural southern Tanzania: successes, challenges and lessons learned

Abstract

Summaryobjective To describe and evaluate the use of handheld computers for the management of Health Management Information System data.methods Electronic data capture took place in 11 sentinel health centres in rural southern Tanzania.Information from children attending the outpatient department (OPD) and the Expanded Program on Immunization vaccination clinic was captured by trained local school-leavers, supported by monthly supervision visits. Clinical data included malaria blood slides and haemoglobin colour scale results. Quality of captured data was assessed using double data entry. Malaria blood slide results from health centre laboratories were compared to those from the study's quality control laboratory.results The system took 5 months to implement, and few staffings or logistical problems were encountered. Over the following 12 months (April 2006-March 2007, 7056 attendances were recorded in 9880 infants aged 2-11 months, 50% with clinical malaria. Monthly supervision visits highlighted incomplete recording of information between OPD and laboratory records, where on average 40% of laboratory visits were missing the record of their corresponding OPD visit. Quality of microscopy from health facility laboratories was lower overall than that from the quality assurance laboratory.conclusions Electronic capture of HMIS data was rapidly and successfully implemented in this resource-poor setting. Electronic capture alone did not resolve issues of data completeness, accuracy and reliability, which are essential for management, monitoring and evaluation; suggestions to monitor and improve data quality are made.

Mobile Phone Text Messaging: Tool for Malaria Control in Africa

“…Unfortunately, in many African countries, the routine health, logistics, and surveillance systems through which malaria data are reported in an “upstream” direction to managers are weak components of the health systems [15]–[18]. The validity and utility of information is compromised because the poor quality of source information, low reporting rates, delays in data acquisition, and lack of visibility of predefined unit signals and summary indicators preclude prompt responses to threats and emergencies such as malaria outbreaks, stock-outs of antimalarial drugs, presence of suboptimal drugs [19], or occurrences of severe adverse drug reactions and treatment failures.…”

District decision-making for health in low-income settings: a feasibility study of a data-informed platform for health in India, Nigeria and Ethiopia

“…Key challenges include data quality, professional expertise, information-system infrastructure, robustness of technology and a culture of evidence-based decision-making (Nnaji et al 2010; Maokola et al 2011; Qazi and Ali 2011). Data on the contributions of private- and social-sector stakeholders are not readily available, in spite of those stakeholders being major service-providers in some settings (Hanson and Berman 1998; Raban et al 2009).…”

Experiences in running a complex electronic data capture system using mobile phones in a large-scale population trial in southern Nepal

“…We therefore provided chargeable ‘power packs’ that could be charged when electricity was available, alongside phones. Successful solutions used by others include use of solar chargers [4,16,17,25,26], motorcycle batteries [17] and spare batteries [39]. Like others, we found strategies for saving battery life included minimising use of GPS, Bluetooth and WiFi [4,39] and using phones with longer battery life.…”

“…We reviewed literature describing experiences with developing and implementing Electronic Data Capture (EDC) systems in low- and middle-income countries (LMICs) in the past 10 years (search strategy available in Supplemental Appendix 1) and found that mobile devices are widely used in LMICs for healthcare delivery (mHealth) [1–8] including disease surveillance and control [9–15], epidemiological surveys [16–21], other research studies [22–24], remote data collection and monitoring [25–28] and public planning and mapping [29]. …”

“…Smartphones (or handheld computers) were used to collect more complex data [4,14,15]. Mobile phone questionnaire software used included open-source or free-to-use options such as Open Data Kit collect (ODK) [4,15,26,27], Epicollect [15,27], EpiInfo [21] and CommCare [2,3,28], or commercial software such as Pendragon Forms [16,17,25] and Questionnaire Development System [22]. Others programmed custom-made systems [4,11,30,31].…”