Results of the quantitative data
Characteristics of the study participants and facilities in the Central Zone, Tigray, Northern Ethiopia, 2018
A total of 48 professional and paraprofessional health staff members from seven PHCUs and four Woreda health offices participated in the study. Among these, 29 (60.4%) were males and had a diploma level of education. With respect to workplace, more than half (66.7%) were from health centers, and almost all (85.3%) had health-related occupations (Table 2).
Among the seven PHCUs assessed, 4 (57.1%) had personnel specifically assigned to HMIS duties, and 5 (71.4%) were equipped with the necessary equipment for HMIS operation.
Among the 48 participants, 21 (43.8%) had received training in the last 2 years. Six (85.7%) departments collected daily patient data. Thirty-four (70.8%) of the participants responded that they had received directives from higher levels regarding data quality (Table 3).
Three-month LQAS results in PHCUs in the central zone of tigray, Northern Ethiopia, 2018
Over a three-month period, LQAS was conducted across seven health centers in the Central Zone of Tigray. It is performed by reviewing reports for twelve indicators and comparing them with their source documents, registers and tallies. The reported and recounted consistent results for each health center are shown for the three months. The average number of consistent results across the three months and the percentage coverage are provided according to the LQAS decision table (see Supplementary File 1) and the results show variation in performance across both time and sites. The average coverage of all the PHCUs was between 35% and 60%, which is below the national threshold (90%) (Fig. 2).
Three-month LQAS results (sample size = 12) in the selected PHCU health centers of the central zone, Tigray, Northern Ethiopia, 2018
Results of the qualitative data
The results are presented under 2 headings of data quality and information use with their respective themes. The results are presented with key quotes from the key informants, which are supplemented by their characteristics.
Data quality
Understanding data quality
The nature of the comments concerning data quality was positive. In knowing and understanding the dimensions of data quality, there is variation. The informants associated data quality with the consistency of reports across different tools, such as registers, tally sheets, and the report itself. However, there was confusion with respect to data quality. They had different perspectives; some focused on technical aspects, whereas others focused on their importance.
The training and skill levels of the informants varied substantially. Their ability to fill the formats is poor. Typically, training is given to HIT professionals only. This created a challenge to handle work by other health professionals and, worse, in health facilities that do not have HIT professionals. Failure to understand the importance of data for decision making and the negligence of health professionals are also reported as the main problems. According to the informants:
“I don’t know it; it might be reporting what we have done in our work.” (HEW, Diploma, Female.
“It is consistency of data on registration, tally sheet and report. Data quality is an indicator of the overall quality of health system data.” (HO, BSc, Male).
Challenges in data quality
In describing their own facility data quality, it was found to be insufficient, which was a challenge for maintaining and using it for decision making. Almost all the informants reported that there was a problem with data quality in their facility. They reported problems of timeliness, completeness and accuracy in general. Since there is no checking mechanism for the content of their reports, its reliability, timeliness and double reporting lead to overachievements. The key informants also identified the shortage of staff as a major limitation, with some facilities lacking dedicated personnel to register patient data and report it.
The key informant stated, “Double counting is the main problem that can affect quality. This was due to poor referral linkage. For example, a mother who received first antenatal care (ANC) at a health post could go for the next ANC visit to a health center that would be registered as the first ANC visit. The same problem occurs with report-related family planning (mismatch between family planning by age and family planning by type).” (IT, diploma, male).
The key informants also pointed to broader organizational factors such as the following:
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low salaries
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absence of training
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shortage of health information technician professionals
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lack of transport and budgets to cross-check the accuracy of data
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Work-over load
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poor education quality
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problem of work site rotation
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presence of parallel reports from health development arms
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presence of reporting formats requested by different experts and stockholders
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low proportion of urban HEWs to population ratio
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absence of a clear structure for urban health extension programs
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needs to receive recognition and reward without working/performing well
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absence of follow-up and accountability for those who failed to achieve proper work
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not including HMIS in school curricula at the university level
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not having computerized systems to solve the presence of a high number of reporting formats
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presence of very tinny and unclear registers and tally sheets
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different reporting formats for a single indicator
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Satisfaction of leaders by numbers or figures only
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understanding problems with the English language by some professionals
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being in Tigrigna, the formats for HEW
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Poor focus of managers on data quality or focus only on planning versus achievement
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Poor attitudes of staff toward data quality
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Weakness of governance
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not conduct data accuracy regularly on the basis of the guidelines and
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The demotivation of health workers has compromised data quality. For example, the informants stated the following:
“Need being competent without working, sometimes we know it at the ground; it is not implemented, but we send it to the higher level without checking. We support talk a lot only to good reports but not for the reports with gabs.” (HO, BSc, Male).
“…For example, when I graduated from university as a health officer with an undergraduate degree, I had no knowledge of HMIS. However, when I started working in health institutions, I noticed that HMIS was a routine activity.” HO, BSc, Male.
“They don’t focus on quality. For example, if the ANC first visit reaches 100%, the manager will definitely be happy… However, the appropriate questions should be why and how this achievement is attained. This is due to the leadership culture promoting high achievement without considering quality. Although this is not our country’s government initiative, unfortunately, the country has entered into a new bad system. The district-level leaders are forced to lie by the zonal managers, and the zonal managers are in turn forced by the regional-level leaders.” BSc, Female”.
Information use
Importance of health information use for decision making
Generally, the key informants agree on the importance of HMIS data for informed decision making. They recognized that the data are useful for assessing health service coverage, evaluating performance, and identifying trends, such as unusual disease prevalence. Nevertheless, they noted that in actual practice, data are often not utilized, and informed decisions are frequently practiced by pressure to meet targets for only specific programs rather than by utilizing the analysis results of our data. The key informant quotes are as follows:
“HMIS data are very important for sustaining our services at every service delivery point.” HO, BSc, Male.
“No more than report. However, the HMIS data can be used for research, and it has to be used in our facility first.” HIT, Diploma, Male)
Challenges in using data for decision making
Despite the acknowledged importance of data for informed decision making, the culture of using it for decision making is limited. In some instances, it is used to identify poor and good achievements on the basis of plans, especially for sensitive events such as maternal and neonatal deaths. Many facilities lacked clear guidelines for using the data, and there were significant barriers to its proper use, including a lack of training, organizational issues, and the absence of a structured decision-making process, as stated by the key informants.
“Sometimes, we are writing performance review meeting minutes without having actual meetings, but this is not beneficial for us or for the nation.” HIT, Diploma, Female.
In addition to these cultural challenges, the key informants also identified organizational characteristics such as unclear roles and responsibilities, inadequately allocated budgets for HMIS activities, data quality problems and no clear guidelines for the use of HMIS data from higher to lower levels, and not working on schedules and guideline sets are major obstacles to effective information use. Furthermore, the lack of focus on long-term planning and data-driven decision-making further worsened the problem.
Key informant quote:
“There is a schedule and procedure that had been set. However, we are not working as a schedule or guideline set. “Management, BSc, Male.
Motivation and head empowerment in information use
Motivations for using data effectively were identified as low. Many key informants mentioned a lack of commitment and carelessness among professionals, a lack of rewards, a lack of recognition, a lack of incentives, a limited capacity of professionals or frustrations, a lack of commitment at leadership levels, poor head empowerment and a greater degree of training for obtaining money rather than cascading their knowledge to lower/grassroot levels, which were considered bottle necks for information use. Despite the above obstacles, some of the informants were motivated by the presence of training, successful work, and follow-up of the region to use information, the availability of accessible data through electronic systems, and the presence of a government structure (chain of reporting).
They said, “The region evaluates institutions at least every six months. Each institution is then graded as green, yellow or red. Green is given for those that performed better than 75%, whereas 50–75% is graded as yellow. For this fact, we will work hard on documentation.” HO, BSc, Male.
Even though the habit of decision making is limited in our setting, it is practiced on the basis of several factors, such as personal liking, the supervisor’s order, and evidence and health needs.
The views of the informants seeking feedback from the concerned body were similar. Most of them were interested in receiving monthly feedback to correct their work early. They mostly expressed that they are receiving feedback at the program level, especially for TB and HIV. A few managers also used HMIS data for planning, especially for 5-year basic strategic planning for selected indicators; however, many of them did not. As the interviewers mentioned,
“They take the report from me, but I don’t have information about whether they used it for decision making at the cabinet level.” HIT, Diploma, Male.
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