Sound and reliable information is the foundation for decision-making across all health system building blocks[1]. Health information systems are important as they are one of the World Health Organisation’s six building blocks essential for health system strengthening. Health Management Information System (HMIS) is a data collection, storage and analysis system specifically designed to support planning, management, and decision making at all levels of the health care delivery system, including the community. Community health management information system (c-HMIS) is a type of health information system that links all community stakeholders, healthcare providers, consumers, providers, purchasers, payers, and researchers in the community health system [2]. A community health systems is defined as “the set of local actors, relationships, and processes engaged in producing, advocating for, and supporting health in communities and households outside of, but existing in relationship to, formal health structures” [3].
Having comprehensive data is essential to informing decision making in community health systems [4]. Information management systems improves the availability, quality and use of the data needed to inform country health sector reviews and planning processes, and to monitor health-system performance [5].
The nature and pattern of c-HMIS has changed over time whereby some low resource settings are migrating from utilizing paper to digitalized information system. The use of mobile personal health records (mPHRs) is another commonly examined c-HMIs application [6]. These are various forms of c-HMIS mobile data collection apps that seeks to replace pen and paper. Though electronic data entry results in less data entry error, it does not solve data fidelity challenges [6]. Despite progress made in adopting c-HMIS in other low and middle income countries, most facilities are still using manual platforms making it difficult to share data easily for evidence-based decision-making [7]. Hence this form of c-HMIS is said to be weak and lacks back up with health information policies, technical personnel and had proliferation of many tools for reporting [7]. Furthermore, it is reported that Community Health Workers (CHWs) use manual notebooks hence challenges related to data completeness and accuracy were not guaranteed at the same time lack of regular feedbacks, enforcement of timeliness and use of standard protocols to guide information process were hindrances to the utility of community information [7].
Collecting routine health information enables timeliness and accessibility of health information [7]. This allows health care workers at different levels to easily make evidence-based decisions. Furthermore, access to accurate and reliable information on the health of communities facilitates provision of appropriate services. The implementation of c-HMIS in a well-coordinated manner across different sectors at community level would increase efficiency in improving health outcomes [7].
Meanwhile studies have shown that implementing c-HMIS is complex process. In some cases, existence of parallel or hierarchical communication structures can limit performance of CBHWs in health systems by affecting the smooth flow of information. In India for example, existence of hierarchical communication structure constrained information flow as well as ability by health systems to address health systems challenges[8]. In Pakistan, having a management information system that is not integrated with overall health system left a vacuum in decision making because the problems and issues from the grass root level were not taken into consideration while making allocations, disbursements, and procurement [8]. In some cases, overall acceptance and use of the c-HMIS has not been optimal. While such challenges exist, there is a dearth of information on regarding the implementation, acceptability and use of c-HMIS in the health systems.
In 2012, the Ministry of Health (MoH) in Zambia, recognized that there was a huge gap in c-HMIS because there was no system in place to collect community level health data [9]. It is for this reason that in 2012, MoH with the support from the Clinton Health Access Initiative (CHAI) and the Department of International Development (DFID developed and implemented the c-HMIS.
In Zambia, the c-HMIS was used by community health assistants (CHAs) and community based volunteers (CBVs),. CBVs are community volunteers that support health facility staff with the provision of basic health services in the community and demand generation for health services provided by the facility [10]. CBVs are coordinated by Neighbourhood Health Committees (NHCs). The Neighbourhood Health Committee (NHC) is a community-based non-partisan health management structure composed of local residents in a defined catchment area and is the link between the community and their nearest Health Centre/Health Post (HC/HP). It is the lowest community structure in the health care system and the NHC is divided into smaller sections called zones [12]. The Community Health Assistant is a cadre that is formally trained to bridge the gap between facility and community by providing basic health services in the community and support facility staff with the provision of basic health services at the facility [11]. The CBVs and CHAs collect, aggregate and enter community level health service delivery data. The CHAs use the Health Information Aggregate forms known as HIA4a while CBVs use forms known as HIA4b. HIA4a covers disease and service delivery data by CHAs for the entire catchment area of a health post (aggregating community data across all zones of the health facility) while HIA4b covers disease and service delivery data by community-based volunteers (CBVs) in their respective zones
Community Health Assistants go door-to-door providing health services and recording the data for the services they provide using household registers and patient care registers [8]. At the end of the month, the data is compiled and entered in the HIA4a form. Hard copies of HIA4a forms are submitted to the District Health Information Officers (DHIO) for data entry into the District Health Information System (DHIS2), an online database that the MoH uses for reporting. The data collected by the CBVs is submitted to the NHC Secretary who compiles and enters the information on the HIA4b booklet. This information is then submitted to the CHA who verifies and conducts data quality audits. The report is then submitted to the facility in-charge who enters it into DHIS2 directly or submits to the district office to be entered into the HMIS (DHIS2).
In 2019, MoH with support from CHAI decided to roll out c-HMIS to Mpongwe district in Copperbet province. To rollout c-HMIS in Mpongwe district, consultative meetings were held with provincial, district and community stakeholders. The meetings focused on developing a c-HMIS, building the capacity of provincial and district health managers to plan, implement and monitor performance of c-HMIS, as well as training health centre/post teams in c-HMIS to coordinate NHCs and implement c-HMIS, training NHC and CBVs in c-HMIS. Additional activities conducted were providing user rights for health workers to enter c-HMIS data in DHIS2 at facility level, holding quarterly health centre committee (HCC) meetings to use c-HMIS data to make decisions including the development and use of community action plans. The HCC is a group elected from among the executive committee members of all the NHCs from all zones under a particular health Centre/health post catchment.
This study aimed to explore the factors that shape the implementation, acceptability and use of c-HMIS in decision making and how it has influenced delivery of health services in Mpongwe district.
Evaluation framework
A conceptual framework on the integration of health innovations into health systems by Atun et al. guided data collection and analysis [13]. This conceptual framework was selected as fostering integration of an intervention, c-HMIS, into a given context is “both relational and complex” due to a plural set of providers, diverse norms, values as well as less formal and horizontal mechanisms which shape coordination, accountability, health practice and health seeking behaviour in communities or different contexts[13].
Atun et al., provides a systematic conceptual framework for analysing the integration of interventions into complex systems such as the health system. This was relevant to the project and is detailed on Fig. 1 below [13]. According to this framework, examining the integration process requires exploring the nature of the problem being addressed (such as lack of comprehensive data, decision making process, data collection), the intervention (i.e. training, c-HMIS), the adoption system (actors at community, internet, human resources, computers, health facility and district levels), the health characteristics (i.e. financing, supplies and commodities such tools), and the broader context (socio-cultural factors).
Figure 1: Conceptual framework for analysing integration process (adopted from Atun et al. 2010).