The false reporter will get a praise and the one who reported truth will be discouraged’: a qualitative study on intentional data falsification by frontline maternal and newborn healthcare workers in two regions in Ethiopia

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Study Justification:
– Health Management Information Systems (HMIS) are crucial for accountability and decision-making in healthcare.
– Data falsification is a significant issue in maternal and newborn health (MNH) data in Ethiopia.
– Understanding the reasons behind intentional data falsification by healthcare providers is essential for addressing this issue.
– The study aimed to explore the motivations and incentives for data falsification in two regions of Ethiopia.
Study Highlights:
– Participants reported that data falsification is common in healthcare facilities.
– Falsification mainly involves inflating the number of services provided and reclassifying neonatal deaths.
– The health system’s focus on the quantity of services provided creates incentives for falsification and disincentives for accurate reporting.
– To reduce data falsification, policymakers should consider separating rewards and punishments from performance reports based on routine HMIS data.
Study Recommendations:
– Disentangle rewards and punishments from performance reports based on routine HMIS data to reduce facility-level data falsification.
– Conduct further studies to examine the high-level drivers of data falsification at regional, national, and global levels.
– Develop effective interventions to address the drivers of data falsification in healthcare.
Key Role Players:
– Ministry of Health
– Health facility managers
– Quality improvement (QI) focal persons
– Health information technicians
– MNH care providers (midwives, nurses, health officers)
– Health Extension Workers (HEWs)
– QI mentors
Cost Items for Planning Recommendations:
– Training and capacity building for health facility managers, QI focal persons, and health information technicians.
– Development and implementation of new performance reporting systems.
– Monitoring and evaluation of data accuracy and integrity.
– Research and studies to examine high-level drivers of data falsification.
– Development and implementation of interventions to address data falsification.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it provides a clear description of the study objectives, methods, and findings. However, to improve the evidence, the abstract could include more specific details about the qualitative study design, such as the sampling strategy and data analysis process. Additionally, it would be helpful to mention any limitations or potential biases in the study.

Introduction Health Management Information Systems (HMIS) are vital to ensure accountability and for making decisions including for tracking the Sustainable Development Goals. The Ethiopia Health Sector Transformation Plan II includes preventing data falsification as a major strategic initiative and our study aimed to explore the reasons why healthcare providers intentionally falsify maternal and newborn health (MNH) data in two regions of Ethiopia. Methods We conducted a qualitative study in two hospitals, four health centres and their associated health posts in Oromia and Amhara regions. We conducted 45 in-depth interviews with health facility managers, quality improvement (QI) focal persons, health information technicians, MNH care providers, Health Extension Workers and QI mentors. Data were collected in local languages and transcribed in English. During analysis we repeatedly read the transcripts, coded them inductively using NVivo V.12, and categorised the codes into themes. Results Participants were hesitant to report personal data falsification but many reported that falsification is common and that they had experienced it in other facilities or had been told about it by other health workers. Falsification was mostly inflating the number of services provided (such as deliveries). Decreasing the number of deaths or reclassifying neonatal death into stillbirths was also reported. An overarching theme was that the health system focuses on, and rewards, the number of services provided over any other metric. This focus led to both system and individual level incentives for falsification and disincentives for accurate reporting. Conclusion Our finding suggests that to reduce facility level data falsification policy makers might consider disentangling reward and punishments from the performance reports based on the routine HMIS data. Further studies examining the high-level drivers of falsification at regional, national and global levels and effective interventions to address the drivers of data falsification are needed.

We conducted a qualitative study between July and August 2018 in Amhara and Oromia regions of Ethiopia. Oromia and Amhara regions are the most populous in Ethiopia with an estimated 2020 population of 38 and 22 million, respectively. Together the two regions constitute around 60% of the total population of Ethiopia.19 They contribute the largest number of maternal and neonatal deaths of Ethiopia’s 11 regions and rank 3rd and 4th in terms of their neonatal mortality rate. Their neonatal mortality rate is 46 and 39 deaths per 1000 live births for Oromia and Amhara, respectively,20 and their estimated maternal mortality ratio 520 and 369 deaths per 100 000 live births.21 This study was part of a larger qualitative study to explore the functioning of the prototype phase of an MNH quality improvement (QI) initiatives being implemented by the Ministry of Health supported by the Institute of Healthcare Improvement.22 The QI intervention included the formation of learning collaboratives at woreda (district) level, the formation of facility MNH QI teams to plan, implement and monitor QI projects and change ideas and collaborative level learning sessions to share experiences, build clinical skill and develop and review change ideas. QI teams were supported through visits by QI mentors who, among providing other support, validated HMIS data and worked with the team to improve accuracy through trust building, training and feedback.23 From each region we selected one woreda, within which we selected the hospital, a less accessible health centre located at a remote village with access by a rough road and a more accessible health centre located near a main road. Both health centres were ‘typical’ in that they had no unusual additional interventions or staff in place and were in a typical geographic area for the woreda. The two hospitals were better equipped and staffed than the health centres, and two of the health centres had intermittent electricity and one had no running water. One was an upgraded health post and had poor building quality and lacked equipment. Within each facility we conducted in-depth interviews (IDI) with 5–8 participants including the health centre/department manager, the MCH focal person, the health information technicians (HITs), MNH clinical care providers (midwives, nurses and health officer) and HEWs. To be eligible for interviews participants needed to have some experience of the QI project either through attending learning sessions or being part of the facility QI team. We also interviewed three QI mentors. In the majority of cases, participants were identified by the data collectors as facilities were small. When needed, the facility head and the QI mentors helped identify the MCH focal person, the HIT and other who were involved in QI, but they were not directly involved in approaching or recruiting participants. Identified participants were approached at the facility by one of the qualitative interviewers who checked that they were not busy with clinical work and found a private place to take consent and conduct the interview. IDIs were conducted in Amharic or Afaan Oromo languages, and digitally recorded. In total, we interviewed 45 participants. The majority of the participants were females who had worked in the health facilities for 1–4 years. Table 1 summarises the study participants. Background characteristics of the study participants (n=45) *Three missed data. †Excludes woreda/Institute of Healthcare Improvement mentors. ‡Two missed data. QI, quality improvement. Six trained and experienced qualitative interviewers conducted the IDIs using pretested semi-structured interview guides. The guides collected data on the QI intervention but also contained questions related to data quality with participants asked explicitly about falsification: ‘There can be many reasons why health facilities exaggerate their performance. Can you share any experiences of this?’. During the interviews the data collectors took field notes. Daily debriefing meetings with study investigators were organised to discuss the adequacy of the topic guides, review and give feedback on transcripts, increase reflexivity and assess saturation. Analysis began during the daily de-briefs where the importance of data falsification first emerged and was discussed within data collection team. Using the grounded theory approach,24 the research team developed hypotheses as we reviewed all transcripts and extracted all data related to data falsification. Indeed, extracts centred on particular incidents or behaviours were coded inductively through in depth and repeated reading to identify a first set of themes and codes followed by a thorough coding using NVivo software V.12. The research team met regularly during the analysis to discuss the emerging codes, whether any codes should be merged and to discuss patterns, links and contradictions in the data. This study was conducted to assess the perspectives of health workers involved in the planning and implementation maternal and newborn healthcare services and did not involve patients or the public in the design, conduct, reporting, or dissemination of the study findings.

Based on the provided information, it is difficult to determine specific innovations for improving access to maternal health. However, based on the study’s findings, here are some potential recommendations for addressing the issue of intentional data falsification and improving access to maternal health:

1. Implement transparent and accountable reporting systems: Develop systems that promote accurate reporting of maternal and newborn health data, ensuring that health workers are not incentivized or rewarded solely based on the number of services provided. This could involve disentangling rewards and punishments from performance reports based on routine Health Management Information Systems (HMIS) data.

2. Strengthen training and supervision: Provide comprehensive training and regular supervision to health workers on data collection, reporting, and the importance of accurate data. This can help ensure that health workers understand the significance of accurate reporting and are equipped with the necessary skills to collect and report data correctly.

3. Foster a culture of honesty and integrity: Promote a culture within the health system that values honesty and integrity in data reporting. This can be achieved through awareness campaigns, workshops, and ongoing communication emphasizing the importance of accurate reporting and the negative consequences of data falsification.

4. Enhance data validation processes: Strengthen the validation processes for HMIS data by involving external validators or auditors who can independently verify the accuracy of reported data. This can help identify and address instances of data falsification.

5. Conduct further research: Conduct additional studies to explore the underlying drivers of data falsification at regional, national, and global levels. This research can help identify effective interventions and strategies to address the root causes of data falsification and improve access to maternal health.

It is important to note that these recommendations are based on the specific issue of intentional data falsification highlighted in the study. Other innovations and interventions may also be necessary to address broader access to maternal health challenges.
AI Innovations Description
Based on the provided description, the recommendation to improve access to maternal health would be to address the issue of intentional data falsification by frontline healthcare workers. This can be done through the following steps:

1. Raise awareness: Policy makers and healthcare providers need to be made aware of the prevalence and consequences of data falsification in maternal health. This can be achieved through training programs, workshops, and awareness campaigns.

2. Strengthen accountability: Implement measures to hold healthcare providers accountable for accurate reporting. This can include regular audits, independent verification of data, and consequences for those found to be falsifying data.

3. Incentivize accurate reporting: Create a system that rewards healthcare providers for accurate reporting of maternal health data. This can be done through performance-based incentives, recognition programs, and career advancement opportunities.

4. Improve data collection and management systems: Invest in improving Health Management Information Systems (HMIS) to ensure accurate and reliable data collection. This can include providing training and resources for healthcare providers, improving data entry processes, and implementing quality control measures.

5. Foster a culture of transparency and trust: Create an environment where healthcare providers feel comfortable reporting accurate data without fear of negative consequences. This can be achieved through open communication, feedback mechanisms, and addressing any underlying issues that contribute to data falsification.

By implementing these recommendations, it is possible to develop innovative solutions that improve access to maternal health by ensuring accurate and reliable data, which in turn can inform decision-making and resource allocation for maternal health services.
AI Innovations Methodology
Based on the provided description, here are some potential recommendations to improve access to maternal health:

1. Strengthen Health Management Information Systems (HMIS): Enhance the accuracy and reliability of data collection and reporting systems to prevent intentional data falsification. This can be achieved through improved training, supervision, and accountability mechanisms for healthcare providers.

2. Implement Performance-Based Financing (PBF): Introduce financial incentives tied to the quality and quantity of maternal health services provided. This can motivate healthcare providers to prioritize accurate reporting and improve access to maternal health services.

3. Enhance Quality Improvement (QI) Initiatives: Expand and strengthen QI programs that focus on maternal and newborn healthcare. These initiatives can help identify and address the underlying causes of data falsification, improve service delivery, and promote accurate reporting.

4. Increase Community Engagement: Involve local communities in the planning, implementation, and monitoring of maternal health services. This can help build trust, improve transparency, and ensure that accurate data is reported.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define the indicators: Identify key indicators that reflect access to maternal health, such as the number of antenatal care visits, facility-based deliveries, postnatal care visits, and maternal mortality rates.

2. Collect baseline data: Gather existing data on the selected indicators from the target regions or facilities. This will serve as a baseline for comparison.

3. Introduce the recommendations: Implement the recommended interventions, such as strengthening HMIS, implementing PBF, enhancing QI initiatives, and increasing community engagement.

4. Monitor and collect data: Continuously collect data on the selected indicators after implementing the recommendations. This can be done through routine data collection systems, surveys, or other data collection methods.

5. Analyze the data: Compare the post-intervention data with the baseline data to assess the impact of the recommendations on improving access to maternal health. Analyze trends, changes in indicators, and any correlations between the interventions and the outcomes.

6. Evaluate the findings: Interpret the results of the analysis to determine the effectiveness of the recommendations in improving access to maternal health. Identify strengths, weaknesses, and areas for further improvement.

7. Adjust and refine: Based on the evaluation findings, make adjustments and refinements to the interventions as necessary. This iterative process can help optimize the impact of the recommendations on improving access to maternal health.

It is important to note that the specific methodology for simulating the impact may vary depending on the available resources, data availability, and local context.

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