Routine use of DHIS2 data: a scoping review

Abstract

Background In regard to health service planning and delivery, the use of information at different levels in the health system is vital, ranging from the influencing of policy to the programming of action to the ensuring of evidence-informed practices. However, neither ownership of, nor access to, good quality data guarantees actual use of these data. For information to be used, relevant data need to be collected, processed and analysed in an accessible format. This problem of underused data, and indeed the absence of data use entirely, is widespread and has been evident for decades. The DHIS2 software platform supports routine health management for an estimated 2.4 billion people, in over 70 countries worldwide. It is by far the largest and most widespread software for this purpose and adopts a holistic, socio-technical approach to development and implementation. Given this approach, and the rapid and extensive scaling of DHIS2, we questioned whether or not there has been a parallel increase in the scaling of improved information use. To date, there has been no rigorous review of the documentation on how exactly DHIS2 data is routinely being used for decision-making and subsequent programming of action. This scoping review addresses this review gap. Methods The five-stage approach of Arksey and O’Malley progressed by Levac et al. and Peters was followed. Three databases (PubMed, Web of Science and Embase) were searched, along with relevant conference proceedings and postgraduate theses. In total, over 500 documents were reviewed and data from 19 documents were extracted. Results Overall, DHIS2 data are being used but there are few detailed descriptions of this usage in peer reviewed or grey literature. We find that, commonly, there exists a centralised versus decentralised pattern of use in terms of access to data and the reporting of data ‘up’ in the system. We also find that the different conceptualisations of data use and how data use is conceptualised are not made explicit. Conclusions We conclude with some suggestions for a way forward, namely: i) the need to document in more detail and share how data are being used, ii) the need to investigate how data were created and who uses such data, iii) the need to design systems based on work practices, and in tandem develop and promote forums in which ‘conversations’ around data can take place.

The use of routine health facility data for micro-stratification of malaria risk in mainland Tanzania

“…Complex modelling approaches of parasite prevalence are often challenged by limited national capacity and ownership issues [ 14 , 47 , 48 ]. As NMCPs are gaining more analytic capacity and confidence in using routine DHIS2 data, including the local development of embedded malaria dashboards, quality checks and monthly/quarterly reports, this situation is changing [ 49 – 51 ]. Statistical modelling of routine health data, spatially and temporally, in low-income countries is in its nascent stages and largely driven by partners outside of malaria-endemic countries.…”

Investigating rapid diagnostic testing in Kenya’s health system, 2018–2020: validating non-reporting in routine data using a health facility service assessment survey

“…There is potential to leverage DHIS2 data to improve data reporting, completeness, and timeliness [ 30 , 47 , 49 ]. The DHIS2 platform has inbuilt data quality checks and customizable data analysis features that encourage data use at the point of collection and the lowest level [ 47 ] However, the culture of using DHIS2 information for decision-making still needs improvement [ 47 , 50 ]. For example, a study conducted in Nairobi shows that health facilities were not utilizing performance reports on completeness and timeliness to identify those performing poorly [ 30 , 50 ].…”

“…The DHIS2 platform has inbuilt data quality checks and customizable data analysis features that encourage data use at the point of collection and the lowest level [ 47 ] However, the culture of using DHIS2 information for decision-making still needs improvement [ 47 , 50 ]. For example, a study conducted in Nairobi shows that health facilities were not utilizing performance reports on completeness and timeliness to identify those performing poorly [ 30 , 50 ]. In addition, the people involved in data collection and entry differ from those involved in data analysis and the generation of reports [ 50 ].…”

“…For example, a study conducted in Nairobi shows that health facilities were not utilizing performance reports on completeness and timeliness to identify those performing poorly [ 30 , 50 ]. In addition, the people involved in data collection and entry differ from those involved in data analysis and the generation of reports [ 50 ]. This has influenced the perception that the use of data for decision-making is centralized at the national and sub-national levels and that health facilities are limited to data input.…”

Towards applying the essential public health functions for building health systems resilience: A renewed list and key enablers for operationalization

“…In addition, building the capacity of health information systems to incorporate and collect data from a population perspective and leveraging existing information systems can also enhance monitoring and evaluation of EPHFs. Routine health information systems such as District Health Information Software (67,68) provide rich information on population health needs (which supports the prioritization of public health action), performance of public health system and programmes (which are public health functions themselves), and population health outcomes (which reflects the effectiveness of EPHFs implicitly). Utilizing the results of assessments to build institutional capacities is crucial to support health systems resilience for public health (27, 64).…”