You are here

Article Text

Article menu
Original research
Determinants of translating routine health information system data into action in Mozambique: a qualitative study
  1. Nami Kawakyu1,2,
  2. Celso Inguane1,
  3. Quinhas Fernandes1,3,
  4. Artur Gremu4,
  5. Florencia Floriano5,
  6. Nelia Manaca5,
  7. Isaías Ramiro5,
  8. Priscilla Felimone6,
  9. Jeremias Armindo Azevedo Alfandega7,
  10. Xavier Alcides Isidor8,
  11. Santana Mário Missage9,
  12. Bradley H Wagenaar1,10,
  13. Kenneth Sherr1,10,11,
  14. Sarah Gimbel1,2
  1. 1Department of Global Health, University of Washington, Seattle, Washington, USA
  2. 2Department of Child, Family, & Population Health Nursing, University of Washington, Seattle, Washington, USA
  3. 3National Directorate of Public Health, Ministry of Health, Maputo City, Mozambique
  4. 4Comité para Saúde de Moçambique, Chimoio, Mozambique
  5. 5Comité para Saúde de Moçambique, Beira, Mozambique
  6. 6Serviços Provincias de Saúde, Sofala, Mozambique
  7. 7Serviços Distritais de Saúde, Mulher e Acção Social, Sofala, Mozambique
  8. 8Serviços Provincias de Saúde, Manica, Mozambique
  9. 9Serviços Distritais de Saúde, Mulher e Acção Social, Manica, Mozambique
  10. 10Department of Epidemiology, University of Washington, Seattle, Washington, USA
  11. 11Department of Industrial & Systems Engineering, University of Washington, Seattle, Washington, USA
  1. Correspondence to Dr Nami Kawakyu; nkawakyu{at}uw.edu

Abstract

Introduction Routine health information systems (RHISs) are an essential source of data to inform decisions and actions around health facility performance, but RHIS data use is often limited in low and middle-income country contexts. Determinants that influence RHIS data-informed decisions and actions are not well understood, and few studies have explored the relationship between RHIS data-informed decisions and actions.

Methods This qualitative thematic analysis study explored the determinants and characteristics of successful RHIS data-informed actions at the health facility level in Mozambique and which determinants were influenced by the Integrated District Evidence to Action (IDEAs) strategy. Two rounds of qualitative data were collected in 2019 and 2020 through 27 in-depth interviews and 7 focus group discussions with provincial, district and health facility-level managers and frontline health workers who participated in the IDEAs enhanced audit and feedback strategy. The Performance of Routine Information System Management-Act framework guided the development of the data collection tools and thematic analysis.

Results Key behavioural determinants of translating RHIS data into action included health worker understanding and awareness of health facility performance indicators coupled with health worker sense of ownership and responsibility to improve health facility performance. Supervision, on-the-job support and availability of financial and human resources were highlighted as essential organisational determinants in the development and implementation of action plans. The forum to regularly meet as a group to review, discuss and monitor health facility performance was emphasised as a critical determinant by study participants.

Conclusion Future data-to-action interventions and research should consider contextually feasible ways to support health facility and district managers to hold regular meetings to review, discuss and monitor health facility performance as a way to promote translation of RHIS data to action.

  • Decision Making
  • Health services research
  • Health systems evaluation
  • Global Health
  • Qualitative study

Data availability statement

Data are available upon reasonable request. The data generated during this study are not publicly available for ethical reasons (to protect participant confidentiality, as stated in the consent form) but are available from corresponding authors on reasonable request.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjgh-2024-014970

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    WHAT IS ALREADY KNOWN ON THIS TOPIC

    • Routine health information system (RHIS) data use is limited in low and middle-income country contexts. Health facilities often send analysed RHIS data to higher administrative levels for reporting purposes, without ever using the data to inform improvements at the facility.

    • Most RHIS data use interventions focus on improving data collection and analysis practices through technology or training health facility staff, without addressing factors that promote RHIS data use at the health facility, nor focusing on the critical step of translating data into action.

    WHAT THIS STUDY ADDS

    • There are distinct factors that promote or hinder health facilities to take action on decisions made based on RHIS data, which differ from factors for other RHIS data activities such as data analysis.

    • Frequency and group membership of health facility meetings to review RHIS data to assess health facility performance was a key determinant in implementation of decisions made based on RHIS data.

    • Increased understanding of health facility performance indicators and awareness of facility performance motivated health workers to change their clinical practice in order to improve facility performance.

    HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

    • The findings imply that RHIS data use is a multistep process, each with varying factors that promote or hinder translation of RHIS data into action.

    • The findings also imply that interventions to support health facilities and district managers to hold regular meetings to review, discuss and monitor health facility performance could increase translation of RHIS data into action.

    Introduction

    All people should have access to the health services they need, when and where they need it, without financial hardship. This is the promise of universal health coverage, which can only be achieved through improved health system performance. At the foundation of a well-functioning health system is a strong health information system that informs decisions and actions regarding health system financing, leadership and governance, health workforce, service delivery and essential medicines.1

    With widespread implementation of health system decentralisation in low and middle-income countries (LMICs) including Mozambique,2–5 decision-making responsibilities such as health service delivery planning and management has shifted to districts and health facilities,2 3 5–7 though these shifts have been slow in some contexts.5 8 9 Routine health information system (RHIS) data are routinely generated at the health facility and are an important source of information regarding health facility performance.10–12 RHIS data-informed decision-making, a process by which health staff review analysed RHIS data to reach a decision about health system performance priorities and action steps,13 is limited in LMICs.7 14 15 Even when decisions are made based on RHIS data, decisions often do not lead to action,16–18 which is what is ultimately needed to improve health system performance and population health.

    Despite this, determinants that influence RHIS data-informed decisions and actions are not well understood, and few studies have explored the relationship between RHIS data-informed decisions and actions.13 19–24 Relatedly, most RHIS data use interventions focus on technological enhancements and health workforce training to increase data availability and improve data analysis capacity, but do not address this known gap to move beyond data analysis to increase data-informed decisions and actions.10 16 19 20

    The Integrated District Evidence to Action (IDEAs) strategy aimed to improve RHIS and health system performance by supporting district and health facility staff to make data-informed decisions and implement actions that are low-cost and contextually appropriate. IDEAs united provincial, district and health facility managers to regularly analyse, synthesise and discuss health facility performance using RHIS data. Discussions included identification of priority issues and barriers to adhering to evidence-based guidelines as well as the development and monitoring of action plans.

    The aim of this study was to explore the determinants of successful data-informed actions at the health facility level in Mozambique and how the IDEAs strategy influenced those determinants. Of particular interest was to understand the characteristics of data-informed decisions that successfully led to actions. This information is critical in expanding the evidence base of strategies that support translation of RHIS data into action, which is vital in improving health system performance to achieve universal health coverage.

    Methods

    Study design

    This qualitative thematic analysis study, embedded in a quasi-experimental trial evaluating the impact of the IDEAs strategy, explored the determinants and characteristics of data-informed decisions that successfully led to action, and which of these determinants the IDEAs strategy influenced. Two rounds of data collection through in-depth interviews (IDIs) and focus group discussions (FGDs) conducted in 2019 and 2020 were coded and analysed for themes guided by the Performance of Routine Information System Management (PRISM)-Act framework.13 The Consolidated Criteria for Reporting Qualitative Research25 and Standards for Reporting Qualitative Research26 guided the development of this manuscript. A detailed reflexivity statement can be found in online supplemental material 1.

    Supplemental material

    Definition and conceptual framework

    RHIS data use was defined as ‘the process by which health staff review and discuss analysed RHIS data and collectively identify, address, and monitor health system performance gaps and priorities’.13 The PRISM-Act framework (figure 1)13 served as the conceptual framework for this study, as it illustrates that RHIS data use at the health facility and district, or equivalent administrative level, is a multistep process in which data-informed decisions precede data-informed actions. The framework also illustrates that data-informed actions are the critical last step in the RHIS data use process that contributes to improvements in health system performance.

    Figure 1
    Figure 1

    Performance of Routine Information System Management (PRISM)-Act framework. RHIS, routine health information system.

    RHIS data use activities are influenced by technical, organisational, and behavioural determinants, as defined by the original PRISM framework.27 Technical determinants are factors that relate to the RHIS design itself, such as the indicators collected, data collection forms and technology used to store and exchange the data. Organisational determinants include all factors that are related to the organisational context, such as the organisational hierarchy, resources and its policies and procedures. The behavioural determinants, which are influenced by technical and organisational determinants, include all factors related to the individual, such as their level of knowledge, skills and motivation to conduct RHIS activities.

    Intervention

    The IDEAs strategy is an iterative three-step process of (1) a health system readiness assessment, (2) semiannual, 5-day district performance review and enhancement meetings and (3) targeted facility support. The review meetings were held for each intervention district, uniting facility staff with district and provincial managers to analyse, visualise and present RHIS data comparing achieved versus desired health facility performance. Presentation of data was followed by group discussion and use of audit and feedback tools to identify service gaps and support the development or updating of health facility action plans to improve service delivery. Action plans included microinterventions to be implemented at the facility, ranging from changes in patient flow to reinforcing clinical standards and protocols through on-the-job training in emergency obstetric care.28 The highest performing and two lowest performing facilities, determined based on maternal, neonatal and child health indicators, were then selected to receive targeted support, which included site visits by provincial and district managers along with the IDEAs team. Additional details about the IDEAs strategy have been published previously.28–30

    Setting

    IDEAs was implemented in all 151 primary health facilities of 12 selected districts in Manica and Sofala provinces in central Mozambique. Intervention districts were selected based on large population size, a robust health facility network and geographic accessibility.

    Mozambique’s national healthcare system includes three interconnected and interdependent subsystems: the public, community and private subsystems.31 The public subsystem corresponds to the National Health Service which is organised in four levels32 :

    1. The primary level, delivering the primary healthcare package.

    2. The district level, which oversees primary care facilities, including allocation of human resources and ensuring the routine and consistent flow of health information system data.

    3. The provincial level, which allocates funds and essential supplies to districts.

    4. The quaternary level, which delivers specialised service.

    At the national level, the Ministry of Health is responsible for health policy development and programme coordination, including drug forecasting, acquisition and distribution and oversight of the health information system.5 6 Two province-level entities, the Provincial Health Service and the Provincial Health Directorate, are responsible for carrying out the policies of the Ministry of Health.

    Study participants

    Purposive sampling, based on provincial representativeness and geographic accessibility, was used to select districts from which key informants were invited for IDIs and FGDs. Critical case sampling was used to invite participants for IDIs and FGDs; a case was identified as critical based on their role as data collector, data user and/or decision-maker at the district or health facility level. Cases were also invited based on their length of participation in IDEAs and attendance of at least one performance review meeting. Two provincial-level managers from Sofala were invited to participate in IDIs; provincial managers in Manica were not invited because they were new to their positions and had limited exposure to the IDEAs strategy. At the district level, two district health managers from each of the four selected intervention districts were invited to participate in IDIs. At the facility level, 10 frontline nurses from each of the selected intervention districts were invited to participate in the FGDs conducted in 2019. All frontline nurses who attended the November 2020 performance review meetings were invited to participate in the FGDs conducted in 2020. Sample size targets for data saturation were determined based on researchers’ previous experience30 33 and pragmatic feasibility of time and cost.

    Data collection

    Round 1 data collection occurred in November and December 2019; round 2 data collection occurred in October and November 2020. The second round was built on learnings from the first round to investigate the decision-making process more deeply and how the decision-making process itself may impact successful implementation of decisions. Of 30-to-60-min IDIs and FGDs were conducted in Portuguese by an experienced data collector using a semistructured guide (online supplemental material 2), assisted by a note taker. Key informants were asked to describe the decision-making process in developing facility-level action plans, how RHIS data were used to inform decisions, and what factors supported and hindered implementation of action plans.

    Supplemental material

    Data analysis

    An a priori codebook guided by the PRISM-Act framework was applied to the qualitative IDI and FGD data using ATLAS.ti (V.8). Coding was an iterative process, with initial coding informing the development of a refined codebook and further refined until a final codebook was developed (online supplemental material 3). Codes were reviewed by the research team and any changes or disagreements were discussed until resolved. Once coding was finalised, codes were grouped into themes guided by the PRISM-Act framework. Exemplary quotations were identified to represent the themes. Themes and quotations were then stratified by whether the quotation source worked at a district or health facility with high, mid or low action plan implementation rates to assess if there were differences in themes based on rate of successfully implementing planned actions. Implementation rates were calculated based on available programme data on the number of planned actions implemented divided by the number of actions planned. Facilities implementing 50% or fewer planned actions were categorised as ‘low’, 51%–75% ‘mid’ and ‘high’, 76%–100%.

    Supplemental material

    Results

    Participant characteristics

    A total of 97 participants, comprising 2 provincial-level managers, 15 district-level managers (district directors, chief medical officers, programme supervisors) and 80 facility-level staff (frontline nurses and nurse managers) participated across 27 interviews and 7 FGDs in 2019 and 2020 (table 1).

    View this table:
    Table 1

    Study participants by health system level, role and province

    Overview

    Many intervention participants expressed that the forum to regularly meet as a group to review, discuss, and monitor health facility performance was crucial in translating data into action. Supervision and on-the-job support as well as availability of financial resources were noted as essential elements to developing and implementing action plans. Participants did not mention technical determinants. Table 2 summarises the key facilitators and challenges participants experienced in translating RHIS data into action. Figure 2 illustrates the relationship between the identified determinants and data-informed decisions and actions.

    View this table:
    Table 2

    Facilitators and challenges to translating RHIS data into action at the facility level, Mozambique

    Figure 2
    Figure 2

    RHIS data use framework with determinants of data-informed decisions and actions. RHIS, routine health information system.

    Behavioural determinants

    Indicator and performance awareness

    Several intervention participants, primarily from facilities and districts with high action implementation rates, explained that the IDEAs strategy supported participants to grasp which indicators are collected and how these indicators link directly to clinical practice. One participant explained:

    IDEAs helps us to know the indicators and its purpose. The intervention opened up our eyes. Before the intervention, we thought, ‘What is the purpose of that? What is the purpose of prenatal consultation?’ But with the intervention, I understand the main indicators, what I have to notice, and where we are failing. We know the indicators better and implement them fully.

    —District manager, high action district

    This quotation underscores the dual advantage of the IDEAs strategy in building both health workers’ and managers’ understanding of the benefits of collecting RHIS data and implementing evidence-based clinical practice guidelines. Before IDEAs, participants often viewed data collection and evidence-based practice as burdens to an already full workload, but IDEAs highlighted their crucial role in informing improvements to service delivery.

    Relatedly, the intervention improved health worker skills in monitoring and evaluating health facility indicators, leading to an increased awareness of their own health facility’s performance. This increased awareness of health facility performance and the recognition that their own practice directly affected these indicators were catalysts for health workers to change their practice. This change was noted by participants across all action implementation rates (low, mid and high):

    Reviewing the data and having a person have hyper awareness of certain indicators helps to improve performance, just by the awareness alone.

    —District manager, low action district

    It helped us a lot… evaluating to see that, here I have to do something to improve my data. Not here, something is failing, I have to rectify it.

    —Nurse, high action facility

    Sense of ownership and responsibility

    Ownership and a sense of responsibility for decision-making, action planning and implementation were critical in motivating health workers to implement their action plans. One nurse explained their role in the action planning process:

    Who makes the decision, is us. It’s us. We sat down and reached consensus that this is what we have to do, this we don’t. How we design the plan, how it should be, how we should work this.

    —Nurse, high action facility

    This responsibility to develop, implement and monitor action plans increased nurse motivation to improve health facility performance:

    The health facility is responsible for analyzing the data and decision making, which increases likelihood of implementation.

    —Nurse, mid action facility

    The action plan drawn up in the meetings are the responsibility of the nurse and not the district as it was before. This makes the nurse work harder to do better.

    —Nurse, mid action facility

    Organisational determinants

    Supportive supervision

    District supervisory visits contributed to creating a culture of accountability, a strong motivator for health workers to implement and monitor health facility action plans. District managers described using both the performance review meetings and supervisory visits to create this culture of accountability, while health workers described feeling motivated by the supervisory visits:

    Some colleagues are more dedicated and responsible, and others do not care about the results. But we are fighting to make everyone take responsibility for what they produce.

    —District manager, mixed action district

    From the supervisory visits, we have learned a lot. There are even some moments that we ended up relaxing more, right? So when they see you, they encourage you a lot.

    —Nurse, high action facility

    District supervisory visits were also important in providing in-person, on-the-job support to health facilities. One nurse at a facility with high action implementation rates described the hands-on, collaborative process that took place during supervisory visits:

    We work together. Not that they just come to explain to us, but they come and work with us.

    —Nurse, high action facility

    A district director added that this in-person support led to improved health facility performance:

    When the supervision team arrives and is in the health facility to do more on-the-job training, then the results are better… so these inputs, I would call them facilitators, make it more possible [for the health facility] to achieve the objectives.

    —Provincial manager

    Availability of human, financial and material resources

    Another common theme was that the availability of financial resources was necessary components to successful action plan implementation. Many participants commented on how fundamental it was that the IDEAs strategy supported the purchase of materials and equipment to enable implementation of action plans, ultimately leading to improved services and health facility performance. Conversely, some participants expressed that the lack of available materials was a barrier to implementing action plans and improving health services. Participants also commented on the shortage of nurses and the need to prioritise patient care as barriers to dedicating time to data collection, data review and data-informed decisions and actions:

    A colleague at the health facility that is working alone, who has many activities to do and cannot manage everything they should be doing… The (time for) data review will be little because of the overload of their work.

    —District manager, mid action district

    Characteristic of data review process

    Forum to regularly meet, review, discuss, monitor and adjust as a group

    Participants, especially those from facilities with high action implementation rates, emphasised the critical importance of having the forum to regularly sit as a group to review and discuss RHIS data in monitoring health facility performance and in developing, implementing and adjusting action plans. Participants explained that while data collection, analysis and report development were necessary preceding steps to make data-informed decisions, the process to sit, review and discuss the analysed data was the essential element for decision-making and problem-solving:

    For decision making, we first sit down with the top managers. We (the frontline nurses) show the data to see how things are going. Based on this analysis, we make a decision. For decision making, you must first sit down to study the case.

    —Nurse, high action facility

    I don’t work alone, it’s a team effort, right? There is help from other colleagues, the leaders also contribute a lot. I’m talking about my facility director, my intervention team, they support a lot… so we sit down and talk and are able to evaluate the information.

    —Nurse, high action facility

    In addition to the importance of creating the time and space to review and discuss analysed data, it was especially crucial to have this process occur as a group, with the involvement of facility leaders, to ensure decisions led to action:

    When we make a decision, we have to take into account the logistics, the material, the availability of transportation… I can make a decision, but without support, without the teams, adequate equipment, I can hardly do anything. So there has to be a support team there to reach a final decision.

    —Nurse, high action facility

    Interviewer: What factors help to successfully implement the action plan?

    Interviewee: It is a matter of involving the leaders.

    —Nurse, high action facility

    Meeting frequency was also noted to be an important part of prompting action plan implementation and monitoring as well as in adjusting action plans to be more effective in improving service delivery:

    We review the data and action plan monthly. And if we have a very low finding in the monthly summary and we have not done the action plan, we work to achieve it because we have to have the action plan fulfilled.

    —Nurse, high action facility

    We talk about weekly goals. If we work on the week, we no longer have problems at the end of the month.

    —District manager, mixed action district

    Forum to compare and learn from other health facilities

    While the previous theme highlighted the importance of intrahealth facility performance review and discussion, participants also noted the importance of interhealth facility dialogue for problem-solving and promoting action plan implementation. Participants explained that the performance review meetings with other health facilities enabled them to learn from the experience of other facilities, especially in borrowing action plan ideas that had already been tested elsewhere and been proven successful:

    We gain experience from other health facilities, because in the performance review meetings we ask, ‘colleague, how did you manage to reach that goal?’ So from there, we are trying to take advantage of the ideas of other health facilities and there it helps us to change. The project itself creates conditions for us to change because of these meetings.

    —Nurse, high action facility

    When we bring all the health facilities to the same table, each one presenting its performance, there is an exchange of impressions, there is an exchange of experiences. There is an interconnection between the health facilities themselves, the one that has the same problem that the other managed to overcome… and in the next period, we can see the other has already improved.

    —Provincial manager

    Additionally, the interhealth facility exchanges motivated health facility staff to implement planned actions to improve facility performance:

    There is this effort of not to be left behind… so if one day a health facility presented its weaknesses, it is even embarrassing for the health facility itself if it presents that weakness again, and they end up feeling moved to act.

    —Provincial manager

    Discussion

    The aim of this study was to explore the determinants of RHIS data-informed actions at the health facility level, with special attention to the relationship between data-informed decisions and actions, and which determinants were influenced by the IDEAs strategy. Participants discussed behavioural and organisational determinants of data-informed decisions and actions; characteristics of performance review meetings were also noted to be a crucial determinant of translating data into action. Technical determinants, which relate to the RHIS design itself such as data collection forms and the technology used to transmit data (part of ‘RHIS activities’ in the PRISM-Act), were not mentioned by participants, likely because the focus of IDEAs was on data-informed decisions and actions (‘RHIS outputs’).

    Behavioural determinants

    The importance of health worker sense of ownership and responsibility in developing, implementing and monitoring action plans as a determinant of RHIS data-informed actions has not been a common theme in the literature. A few studies have noted that the ‘decision space’, whereby local officials may or may not have the authority to make decisions, especially related to mobilisation and allocation of resources, influence RHIS data-informed decisions.7 21 34 Studies have also found that the lack of clarity about who is responsible for RHIS activities can be a barrier to RHIS data use34 35; therefore, clear expectations of RHIS data use responsibilities among IDEAs participants may have contributed to the successful translation of data into action.

    The infrequency of this determinant in the literature may not be reflective of its importance or commonality in affecting data-informed actions, but a reflection of what interventions are implemented and how they are studied. That is, sense of ownership and responsibility may not be as influential of a factor for RHIS data analysis and reporting, which are the focus of much of the RHIS data use literature.13 17 Relatedly, the two behavioural determinants of data-informed actions identified in this study, indicator and performance awareness and sense of ownership and responsibility, are not behavioural determinants in the original PRISM framework, in which ‘RHIS data use’ is a general, broad concept. This suggests that each step of the RHIS data use process may have differing determinants, and interventions and evaluations could benefit from considering these differences.

    Organisational determinants and characteristics of performance review meetings

    The strongest theme across interview and FGD transcripts was how the forum to regularly meet as a group to review, discuss and monitor health facility performance was critical in translating data into action. This is a key finding in that, while the organisational context affects whether meetings are held and who participates, it is the characteristic of the performance review process that influences whether RHIS data-informed decisions are made and successfully implemented. These findings suggest that the characteristics of the performance review process mediate the outcome of translating data into action. Future studies could explore and confirm this relationship through quantitative methods.

    While the district-wide IDEAs performance review meetings occurred every 6 months, nurses working at facilities with high action plan implementation rates emphasised the importance of meeting weekly or monthly as a facility, alongside facility leadership, to monitor and adjust action plans. This regularity of meeting frequency was perceived to encourage implementation of planned actions and support development of effective action plans that improved health facility performance. The importance of meeting frequency in promoting RHIS data use was identified by a 2020 systematic review on data use factors in LMICs,24 though this evidence was found only in the grey literature and not in peer-reviewed publications. The importance of supportive health facility leadership aligns with other studies that have highlighted the importance of actively involved local leaders, such as district and health facility managers, in promoting RHIS activities.24 33 36–42

    Meeting frequency and leadership participation at the health facility level may explain some of the differences in action plan implementation rates across IDEAs intervention facilities. Increasing meeting frequency and facility leadership participation at facilities with lower action plan implementation rates may improve action plan implementation rates. Policy-makers and funders may consider investing in contextually feasible ways to support health facility and district managers to hold regular, effective data review meetings as a way to promote translation of RHIS data to action. Further investigation of factors that influence health facility performance review meeting frequency, optimal meeting frequency and factors health facilities should consider when determining meeting frequency will be informative in designing more targeted and effective RHIS data use interventions in the future.

    The shortages of health workers, particularly nurses, and its barrier to RHIS data use are well established. Staff shortages at health facilities mean high workloads, with little time for tasks beyond the delivery of health services.10 15 34 43–46 A qualitative study from Pakistan described the negative cycle, whereby health workers do not enter data because of lack of time, motivation and perception of value and then because they do not receive feedback for this behaviour, their motivation and perception of value of collecting data diminishes further.34 This indicates that audit and feedback and supportive supervision may help mediate some of the negative effects of the health worker shortage on RHIS data use, but without addressing this widespread system challenge in LMICs, RHIS data use cannot be optimised.

    Strengths and limitations

    This study helps fill a major gap in the RHIS data use literature by examining the determinants of translating RHIS data into action, focusing specifically on the relationship between data-informed decisions and actions. This relationship has rarely been investigated in the literature; Wickremasinghe et al, in their systematic review of district-level decision-making, found an absence of studies that investigated whether decisions actually led to change in practice.7 Similarly, Rendell et al, in their systematic review of factors that influence RHIS data use, noted the paucity of studies that investigate the relationship between data-informed decisions and actions.24 Instead, most RHIS data use studies and interventions focus more broadly on RHIS activities such as data collection, data analysis and data review, without specifically examining and addressing promoters and barriers to data-informed decisions and actions.13 24

    Common determinants identified through this broad examination of ‘RHIS data use’ include accessibility and timeliness of RHIS data14 19 34 42 44 45; in other words, if data are unavailable to analyse and review, it cannot be used to inform decisions and actions. However, while data availability influences whether data are available to analyse, there is no indication from the literature or this study that data availability influences whether a decision, based on available and analysed data, leads to action. Similarly, many RHIS data use interventions focus on improving the data collection and analysis skills of health workers,10 19 which are necessary prerequisites to making decisions and taking action informed by RHIS data, but insufficient to promote data-informed action.11 In contrast, several of the determinants identified through this study, health worker awareness of health facility performance and linkage to their clinical practice, sense of ownership, responsibility and the ‘decision space to improve health facility performance and the forum to meet regularly as a group, were found to be important facilitators to translating data into action but have rarely or never been described in the RHIS data use literature.

    This highlights the importance of clearly delineating RHIS activities such as data-informed decisions and actions when investigating the determinants of RHIS activities, as is presented in the PRISM-Act framework.13 This delineation allows precise diagnosis of the facilitators and barriers health facilities face in translating data into action at every step of the RHIS data use process, enabling the development of more targeted, effective interventions to promote RHIS data use. This is critical, given that a persistent challenge in the RHIS data use field is the low rate of data-informed decision making in LMICs and that decisions often do not lead to action.16–18

    Study findings also suggest that the IDEAs strategy was successful in influencing key behavioural and organisational determinants in the translation of data to decision-making and action. This is notable, given the paucity of RHIS data use interventions that influence organisational determinants10 19 or strengthen the link between data analysis, data-informed decisions and data-informed actions.7 13 16 17

    There are limitations to this study. First, care should be taken when generalising these findings to settings beyond the intervention districts and facilities. While purposive sampling was used to select intervention health facilities to capture the experience of participants working in diverse settings, it is not representative of all health facilities and districts in Mozambique. Second, while the study sought to understand the determinants of RHIS data-informed actions, its findings were not comprehensive and may have been limited to determinants influenced by the IDEAs strategy. More studies are needed to build on findings from this novel investigation, to build a more complete understanding of the determinants of each step of the RHIS data use process.

    Conclusion

    This study fills a crucial gap in the RHIS data use literature by investigating the determinants of successful data-informed actions at the health facility level. Findings indicate the determinants of RHIS data use varies across each step of the data use process, emphasising the need to tailor interventions and research to each targeted step. The study also examined the key determinants influenced by the IDEAs strategy. Findings imply that increased investments and research in supporting health system managers to conduct effective data review meetings could increase translation of RHIS data into action, the last and most critical step in the transformation of health systems.

    Data availability statement

    Data are available upon reasonable request. The data generated during this study are not publicly available for ethical reasons (to protect participant confidentiality, as stated in the consent form) but are available from corresponding authors on reasonable request.

    Ethics statements

    Patient consent for publication

    Ethics approval

    This study was approved by the institutional review boards of the Ministry of Health (#IRB00002657) and the University of Washington (#STUDY00003926). Interviews and focus groups were conducted after obtaining written informed consent from participants. Participants gave informed consent to participate in the study before taking part.

    Acknowledgments

    The authors sincerely thank all study participants for sharing their time, knowledge and experiences with the study team. Many thanks to the Ministry of Health of Mozambique for approving this study and the Directorates of Health of Manica and Sofala Provinces for their support.

    References

    1. ↵
      1. World Health Organization
      . Monitoring the building blocks of health systems: a handbook of indicators and their measurement strategies. Geneva: World Health Organization, 2010:92.
    2. ↵
      1. Ndegwa SN
      . Decentralization in Africa: a stockstaking survey, in Africa working paper series. World Bank, 2002.
    3. ↵
      1. Chol C,
      2. Negin J,
      3. Garcia-Basteiro A, et al
      . Health system reforms in five sub-Saharan African countries that experienced major armed conflicts (wars) during 1990-2015: a literature review. Glob Health Action 2018;11:1517931. doi:10.1080/16549716.2018.1517931
    4. ↵
      1. McCannell RS
      . Comparative assessment of decentralization in Africa: final report and summary of findings. 2011.
    5. ↵
      1. Sherr K,
      2. Cuembelo F,
      3. Michel C, et al
      . Strengthening integrated primary health care in Sofala, Mozambique. BMC Health Serv Res 2013;13:S4. doi:10.1186/1472-6963-13-S2-S4
    6. ↵
      1. Ministry of Health
      . Health sector strategic plan 2014-2019. Republic of Mozambique, 2014-2019.
    7. ↵
      1. Wickremasinghe D,
      2. Hashmi IE,
      3. Schellenberg J, et al
      . District decision-making for health in low-income settings: a systematic literature review. Health Policy Plan 2016;31:ii12–24. doi:10.1093/heapol/czv124
      OpenUrlCrossRefPubMed
    8. ↵
      1. Chen J,
      2. Ssennyonjo A,
      3. Wabwire-Mangen F, et al
      . Does decentralization of health systems translate into decentralization of authority? A decision space analysis of Ugandan healthcare facilities. Health Policy Plan 2021;36:1408–17. doi:10.1093/heapol/czab074
      OpenUrlPubMed
    9. ↵
      1. Kesale AM,
      2. Mahonge C,
      3. Muhanga M
      . Effects of decentralization on the functionality of health facility governing committees in lower and middle-income countries: a systematic literature review. Glob Health Action 2022;15:2074662. doi:10.1080/16549716.2022.2074662
    10. ↵
      1. Hoxha K,
      2. Hung YW,
      3. Irwin BR, et al
      . Understanding the challenges associated with the use of data from routine health information systems in low- and middle-income countries: a systematic review. Hlth Inf Manag 2022;51:135–48. doi:10.1177/1833358320928729
      OpenUrl
    11. ↵
      1. Mutale W,
      2. Chintu N,
      3. Amoroso C, et al
      . Improving health information systems for decision making across five sub-Saharan African countries: implementation strategies from the African Health Initiative. BMC Health Serv Res 2013;13:S9. doi:10.1186/1472-6963-13-S2-S9
    12. ↵
      1. Lippeveld T,
      2. Sauerborn R,
      3. Bodart C
      . Design and implementation of health information systems. Geneva: World Health Organization, 2000:xi–270.
    13. ↵
      1. Kawakyu N,
      2. Coe M,
      3. Wagenaar BH, et al
      . Refining the Performance of Routine Information System Management (PRISM) framework for data use at the local level: an integrative review. PLoS ONE 2023;18:e0287635. doi:10.1371/journal.pone.0287635
    14. ↵
      1. Nutley T,
      2. Gnassou L,
      3. Traore M, et al
      . Moving data off the shelf and into action: an intervention to improve data-informed decision making in Côte d’Ivoire. Glob Health Action 2014;7:25035. doi:10.3402/gha.v7.25035
    15. ↵
      1. Ndabarora E,
      2. Chipps JA,
      3. Uys L
      . Systematic review of health data quality management and best practices at community and district levels in LMIC. Inf Dev 2014;30:103–20. doi:10.1177/0266666913477430
      OpenUrl
    16. ↵
      1. Lippeveld T
      . Routine Health Facility and Community Information Systems: creating an Information Use Culture. Glob Health Sci Pract 2017;5:338–40. doi:10.9745/GHSP-D-17-00319
      OpenUrlFREE Full Text
    17. ↵
      1. Osterman AL,
      2. Shearer JC,
      3. Salisbury NA
      . A realist systematic review of evidence from low- and middle-income countries of interventions to improve immunization data use. BMC Health Serv Res 2021;21:672. doi:10.1186/s12913-021-06633-8
    18. ↵
      1. Endriyas M,
      2. Alano A,
      3. Mekonnen E, et al
      . Decentralizing evidence-based decision-making in resource limited setting: a case of SNNP region, Ethiopia. PLoS ONE 2020;15:e0236637. doi:10.1371/journal.pone.0236637
    19. ↵
      1. Lemma S,
      2. Janson A,
      3. Persson LÅ, et al
      . Improving quality and use of routine health information system data in low- and middle-income countries: a scoping review. PLoS ONE 2020;15:e0239683. doi:10.1371/journal.pone.0239683
    20. ↵
      1. Leon N,
      2. Balakrishna Y,
      3. Hohlfeld A, et al
      . Routine Health Information System (RHIS) improvements for strengthened health system management. Cochrane Database Syst Rev 2020;8:CD012012. doi:10.1002/14651858.CD012012.pub2
    21. ↵
      1. Hotchkiss DR,
      2. Diana ML,
      3. Foreit KGF
      . How can routine health information systems improve health systems functioning in low- and middle-income countries? Assessing the evidence base. Adv Health Care Manag 2012;12:25–58. doi:10.1108/s1474-8231(2012)0000012006
      OpenUrlPubMed
    22. ↵
      1. Ivers NM,
      2. Grimshaw JM,
      3. Jamtvedt G, et al
      . Growing literature, stagnant science? Systematic review, meta-regression and cumulative analysis of audit and feedback interventions in health care. J Gen Intern Med 2014;29:1534–41. doi:10.1007/s11606-014-2913-y
      OpenUrlCrossRefPubMed
    23. ↵
      1. Ivers NM,
      2. Sales A,
      3. Colquhoun H, et al
      . No more 'business as usual' with audit and feedback interventions: towards an agenda for a reinvigorated intervention. Impl Sci 2014;9:14. doi:10.1186/1748-5908-9-14
      OpenUrl
    24. ↵
      1. Rendell N,
      2. Lokuge K,
      3. Rosewell A, et al
      . Factors That Influence Data Use to Improve Health Service Delivery in Low- and Middle-Income Countries. Glob Health Sci Pract 2020;8:566–81. doi:10.9745/GHSP-D-19-00388
      OpenUrlAbstract/FREE Full Text
    25. ↵
      1. Tong A,
      2. Sainsbury P,
      3. Craig J
      . Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care 2007;19:349–57. doi:10.1093/intqhc/mzm042
      OpenUrlCrossRefPubMedWeb of Science
    26. ↵
      1. O’Brien BC,
      2. Harris IB,
      3. Beckman TJ, et al
      . Standards for reporting qualitative research: a synthesis of recommendations. Acad Med 2014;89:1245–51. doi:10.1097/ACM.0000000000000388
      OpenUrlCrossRefPubMed
    27. ↵
      1. Aqil A,
      2. Lippeveld T,
      3. Hozumi D
      . PRISM framework: A paradigm shift for designing, strengthening and evaluating routine health information systems. Health Policy Plan 2009;24:217–28. doi:10.1093/heapol/czp010
      OpenUrlCrossRefPubMedWeb of Science
    28. ↵
      1. Dinis A,
      2. Fernandes Q,
      3. Wagenaar BH, et al
      . Implementation outcomes of the integrated district evidence to action (IDEAs) program to reduce neonatal mortality in central Mozambique: an application of the RE-AIM evaluation framework. BMC Health Serv Res 2024;24:164. doi:10.1186/s12913-024-10638-4
    29. ↵
      1. African Health Initiative Partnership Collaborative for Embedded Implementation Research
      . Embedding Research on Implementation of Primary Health Care Systems Strengthening: A Commentary on Collaborative Experiences in Ethiopia, Ghana, and Mozambique. Glob Health Sci Pract 2022;10:e2200061. doi:10.9745/GHSP-D-22-00061
    30. ↵
      1. Inguane C,
      2. Soi C,
      3. Gimbel S, et al
      . Applying the Consolidated Framework for Implementation Research to Identify Implementation Determinants for the Integrated District Evidence-to-Action Program, Mozambique. Glob Health Sci Pract 2022;10:e2100714. doi:10.9745/GHSP-D-21-00714
    31. ↵
      1. Mozambique National Press
      . Resolution no.13/2021: approves the health policy and its implementation strategy. 2021.432–8.
    32. ↵
      1. Mozambique National Press
      . Ministerial diploma no.127/2002: approves the regulation that defines the technical characterization and statement of functions of the institutions of the national health service. 2002.156–73.
    33. ↵
      1. Inguane C,
      2. Sawadogo-Lewis T,
      3. Chaquisse E, et al
      . Challenges and facilitators to evidence-based decision-making for maternal and child health in Mozambique: district, municipal and national case studies. BMC Health Serv Res 2020;20:598. doi:10.1186/s12913-020-05408-x
    34. ↵
      1. Qazi MS,
      2. Ali M
      . Health Management Information System utilization in Pakistan: challenges, pitfalls and the way forward. Biosci Trends 2011;5:245–54. doi:10.5582/bst.2011.v5.6.245
      OpenUrlCrossRefPubMed
    35. ↵
      1. Mucee EM,
      2. Odhiambo-Otieno GW,
      3. Kaburi LW, et al
      . Routine health management information use in the public health sector in Tharaka Nithi County, Kenya. Imp J Interdiscip Res 2016;2.
    36. ↵
      1. Prakash R,
      2. Dehury B,
      3. Yadav C, et al
      . Establishing evidence-based decision-making mechanism in a health eco-system and its linkages with health service coverage in 25 high-priority districts of Uttar Pradesh, India. BMC Health Serv Res 2021;21:196. doi:10.1186/s12913-021-06172-2
    37. ↵
      1. Akerele A,
      2. Obansa R,
      3. Adegoke OJ, et al
      . Determinants of improved data consistency across routine immunization data tools for health facilities in Kano State, Nigeria. Pan Afr Med J 2020;36:148. doi:10.11604/pamj.2020.36.148.20498
    38. ↵
      1. Wude H,
      2. Woldie M,
      3. Melese D, et al
      . Utilization of routine health information and associated factors among health workers in Hadiya Zone, Southern Ethiopia. PLoS ONE 2020;15:e0233092. doi:10.1371/journal.pone.0233092
    39. ↵
      1. Kebede M,
      2. Adeba E,
      3. Chego M
      . Evaluation of quality and use of health management information system in primary health care units of east Wollega zone, Oromia regional state, Ethiopia. BMC Med Inform Decis Mak 2020;20:107. doi:10.1186/s12911-020-01148-4
    40. ↵
      1. Shiferaw AM,
      2. Zegeye DT,
      3. Assefa S, et al
      . Routine health information system utilization and factors associated thereof among health workers at government health institutions in East Gojjam Zone, Northwest Ethiopia. BMC Med Inform Decis Mak 2017;17:116. doi:10.1186/s12911-017-0509-2
    41. ↵
      1. O’Hagan R,
      2. Marx MA,
      3. Finnegan KE, et al
      . National Assessment of Data Quality and Associated Systems-Level Factors in Malawi. Glob Health Sci Pract 2017;5:367–81. doi:10.9745/GHSP-D-17-00177
      OpenUrlAbstract/FREE Full Text
    42. ↵
      1. Wagenaar BH,
      2. Hirschhorn LR,
      3. Henley C, et al
      . Data-driven quality improvement in low-and middle-income country health systems: lessons from seven years of implementation experience across Mozambique, Rwanda, and Zambia. BMC Health Serv Res 2017;17:830. doi:10.1186/s12913-017-2661-x
    43. ↵
      1. Harrison K,
      2. Rahimi N,
      3. Carolina Danovaro-Holliday M
      . Factors limiting data quality in the expanded programme on immunization in low and middle-income countries: a scoping review. Vaccine (Auckl) 2020;38:4652–63. doi:10.1016/j.vaccine.2020.02.091
      OpenUrl
    44. ↵
      1. Nicol E,
      2. Bradshaw D,
      3. Uwimana-Nicol J, et al
      . Perceptions about data-informed decisions: an assessment of information-use in high HIV-prevalence settings in South Africa. BMC Health Serv Res 2017;17:765. doi:10.1186/s12913-017-2641-1
    45. ↵
      1. Begum T,
      2. Khan SM,
      3. Adamou B, et al
      . Perceptions and experiences with district health information system software to collect and utilize health data in Bangladesh: a qualitative exploratory study. BMC Health Serv Res 2020;20:465. doi:10.1186/s12913-020-05322-2
    46. ↵
      1. Nyamtema AS
      . Bridging the gaps in the Health Management Information System in the context of a changing health sector. BMC Med Inform Decis Mak 2010;10:36. doi:10.1186/1472-6947-10-36
    47. ↵
      1. Kawakyu N
      . Determinants of translating routine health information system data into action, in department of global health. Seattle, Washington, USA: University of Washington, 2022:91.

    Supplementary materials

    Footnotes

    • Handling editor Fi Godlee

    • X @kennethsherr

    • Contributors All authors were involved in the initial conceptualisation and methodology of this work, which was part of NK's PhD dissertation, "Determinants of Translating Routine Health Information System Data into Action."47 CI and NK designed the data collection tools with input from AG and SG. CI collected, transcribed and translated the data with support from AG, FF, IR, JAAA, NM, PF, XAI and SMM. NK led the analysis of the data with input from AG, BHW, CI, KS, QF and SG. NK wrote the first draft of the manuscript. All authors were involved in reviewing and editing the manuscript. All authors read and approved the final manuscript. NK is responsible for the overall content as guarantor

    • Funding The research reported in this publication is supported by the Doris Duke Charitable Foundation’s African Health Initiative grant number 2016106 and the Eunice Kennedy Shriver National Institute of Child Health and Development of the National Institutes of Health under award R01HDHD092449. Neither funding entity had any role in the design of the study, collection, analysis, interpretation of the data nor in writing the manuscript.

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer-reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.