The present study involved all the 15 health facilities found in Wogera and Armacheho districts to examine the level of data quality and use. In addition, the entire 95 departments in the two districts were involved to assess their experience and perception in the generation or use of quality information. On the other hand, 16 health workers from Wogera district were involved to explore the factors that influence the generation and use of quality health data.

Quantitative findings

Socio-demographic characteristics of participants

Of the 95 participants from the respective department heads involved in the study, 63 (66%) were males, 56 (59%) were 27 years or above in age, 64 (67%) were diploma holders in educational level, 20 (21%) were clinical nurse diploma by profession, 65 (68%) had 5 or less years of experience, and 49 (52%) were rural residents (Table 1).

Table 1 Socio-demographic characteristics of participants from Wogera and Tach-Armacheho districts, 2020

Facility and departments

Among 15 health facilities included in the quantitative study, 9 were from Wogera, and 6 were from Armacheho districts. Of the 9 health facilities found in Wogera district, 8 were health centers and 1 was a primary hospital. Similarly, 5 of the health facilities in the Tach-Armacheho district are health centers and the sixth one was a primary hospital. Of all departments involved in the study, maternal and child health (MCH) and pharmacy were each 9 (17.6%), out-patient department (OPD) 8 (15.6%), and emergency was OPD 5 (9.8%) (Table 2).

Table 2 Frequency of health facility characteristics of in Wogera and Armacheho districts, 2020

Use of routine health information

The average levels of information use for Wogera and Tach-Armacheho districts were 29 and 35.9, respectively. In addition, the magnitudes of departments with information use above the average score were 39.2% and 45.5% for Wogera and Tach_Armacheho districts, respectively. The proportions of departments that gave feedbacks were 82.4% for Wogera and 68.2% for Tach-Armacheho districts. Decisions were made in 80.4% of Wogera and 72.7% of Tach-Armacheho departments; health coverage was calculated in 76.5% of Wogera and 70.5% Tach-Armacheho departments (Table 3).

Table 3 Routine health information use by health facilities in Wogera and Tach-Armacheho districts, 2020

Data quality

Level of accuracy

The overall average level of accuracy using recounted data from the registered and reported data in the HCs was 0.95 for Wogera and 0.86 for Tach Armacheho districts. Specifically, it was shown that for ANC1, the verification factor was 1.0 for month 1, 1.13 for month 2, and 1.01 for month 3 in Wogera district. In the same district, the three months average verification factor for ANC1 was 1.05. For ANC1, the accuracy level was 0.98, 1.1, and 0.83 for months 1, 2, and 3, respectively for Tach-Armacheho district (Table 4).

Table 4 Level of data accuracy in the health facilities of Wogera and Tach-armacheho districts, 2020

Completeness of data

Regarding the completeness of data, the discrepancy was 98 (3.6%) in Wogera district and 125 (5.6%) in Tach-Armacheho district.

Data quality assurance

Assessment of the quality assurance activities performed by health facilities showed that out of 15 health facilities, 12 performed self-assessment and 11 conducted LQAS. Out of 11 health facilities that conducted LQAS, 9 of them provided it for services reported in month-1, 9 for services report in month 2, and 10 conducted for services report in month 3 (Table 5).

Table 5 Quality assurance conducted by health centers in Wogera and Tach-Armacheho ditricts, 2020

Timelines of reports

Of the 9 health facilities in Wogera district, the service report was submitted on time according to the national reporting guideline (20th to 26th of the month) for month 1 by 8 health facilities, for month 2 by 8, and for month 3 by 7 health facilities. In Tach-Armacheho district, the service report was submitted on time for month 1 by 6 health facilities, for month 2 by 5, and for month 3 by 4 health facilities out of a total of 6 health centers (Table 6).

Table 6 Timeliness of service and disease reports by Tach-Armachiho and Wogera districts, 2020

Qualitative findings

Of the 16 participants involved in the in-depth interview, 15 were recruited from the health centers and 1 was from Wogera district health office. Among all interviewees, 15 of them were males, and their ages ranged from 24 to 38 years. Of the total participants involved in the study, 5 had the position of head of a health center, 6 HIS, and 4 were MCH, and 1 was head of Woreda health office (Table 7).

Table 7 Socio-demographic and work related characteristics of participants in Wogera district, 2020

Interviewees in the current study explained the methods of ensuring data quality by mentioning a set of criteria for determining the quality of a piece of information. Hence the criteria include accuracy, timeliness, completeness, and tangibility. However, in order to ensure the accuracy of the work done, they described that it is necessary to go down to the groundwork and check it. For example, cross-checking the data to ensure whether a certain mother has actually given birth in a health center is one approach mentioned by participants. And, cross-checking to state the quality of information and the accuracy of work done is another technique mentioned.

The current study mainly focused on assessing what factors influence the production and use of quality health data and how those factors affect both quality data generation and use in the context of Wogera district. The assessment revealed that there are a number of individual, relational, and organizational/community-level characteristics that affect the production and use of quality health data. In addition, effective use of data improves the health service delivery which usually results in improved health. In turn, understanding the significance of quality health data in improving the health of the community, those characteristics of individuals, relationships, and community/organizational would be modified in such a way that the production and use of quality health data will be improved.

The study revealed that the production and use of quality health data was not simply the influence of a few characteristics but the interwoven effect of factors from various domains. Thus, analysis of all characteristics that affect the quality and use of health data using the social-ecological model ended up with three themes. The three themes that emerged were related to individual, relational, and organizational level characteristics under which different factors were identified (Fig. 1).

Individual level characteristics

Valuing data

In the present study, it has been shown that quality information is a primary transformation agenda that needs a high data quality revolution. It is also indicated that an organization is a real institution for the service it is established if all the work done is backed by data. Therefore, it is unlikely to deem that actual work is done if the respective data is not available. A respondent forwarded his opinion as, “I can say work is done in our institution if properly reported to the relevant body, cross-checked and stored” (Interview # 4, man); he also added saying “In our institution, it would be worthless if we claim we have indicators yet without data backing it.”

The significance of quality health data is highly acknowledged by participants. It is also understood that we have a history of poor data management though there are currently improvements or it is getting the attention of most actors. Supporting this idea a participant said, “The case team here understands that information is an important asset and a wealth to the country and for the community” (Interview # 1, man). He also added saying “information is the evidence that reflects what we did”. Considering that quality data helps to improve the health of persons and saves lives, a participant said that, “As to me, data/information is a life” (Interview # 3, man).

As evidence of valuing data, there are case team leaders in each health facility to monitor the quality of the health data at each unit during performance evaluation and they also check the data verification before sending it to the next level. An indication of good attitudes towards data is, quality service delivered to clients/patients and the data elements filled with the attributes of individuals who received the services need to be equal during crosschecking so that this can be an indication for a good attitude towards data. Attempts to recording, tallying, documenting all the data elements from patients and proper handling and reporting them are pieces of evidence for valuing data. In order to ensure those things, the person responsible has to always be in a state of readiness and ought to properly document any given information. A health worker who performs these activities regularly may develop a sense of ownership in the data recording and reporting process which is helpful to improve its quality. Patients or service seekers should also be cooperative for the generation of quality health data. “If a mother seeking health service gives a false age data, it does not only affect the treatment effect but also the data generated as well” (Interview # 1, man).

The availability of a performance management team (PMT) and striving for its responsibility are considered as evidence for valuing data. This can be confirmed from the regular meetings to evaluate the activities they performed in every month by PMT which helped some of the health workers to be role models in generating quality data. A respondent confirmed that “Even though health workers fall short in managing the data, we make sure it’s corrected in the next month. …Since there is a PMT and there is accountability now, I believe they are performing well” (Interview # 5, man). Evaluation of each other among health workers and provision of feedbacks regarding the health data are also mentioned as evidence of valuing data.

On the contrary, they also disclosed the prevailing problem of valuing and giving less attention to data, and it is due to the limitation health workers have in generating, handling, analyzing, and using health data. The poor attitude health workers have towards quality health data may be a factor that negatively affects the production and use of quality data and can be reflected in many ways such as carelessness, negligence, and being hurry to complete or register data properly which all have some link with valuing data.

The poor information culture that the health workforces have is also related to failing to value data. The value given for data is reportedly little and it is not really because of the lack of training but rather it is a failure to implement the training delivered properly. There was a lack of understanding on part of the person who has been given the responsibility. In this regard, a respondent said, “We are in an information age. We even have support from the University of Gondar. Generally it’s our lack of understanding and willingness that has become a barrier” (Interview # 4, man).

Getting training

Believing that training can facilitate the generation of quality data, capacitating staffs specifically on a basic computer or data handling and management was repeatedly proposed by respondents. In connection, lack of utilizing software can be an obstacle to the production and use of quality health data. Data sharing procedures such as sending and receiving information is easy using software, and lack of knowledge on software affects negatively the practice of health data. A respondent confirmed this saying, “… If training and basic infrastructures are put together, I believe we can achieve quality data use…” (Interview # 5, man).

However, study participants disclosed that the output of training including the development of information culture does not be seen soon. In this regard, a respondent said, “Quality data production and use may not be improved immediately yet we will see that in the process (Interview # 5, man). On the other hand, another respondent suggested that waiting on site or offsite training to solve problems related to data production and handling is not good; instead, he recommended saying, “solving any issue by asking others who have a good performance during the morning session discussion which is a common practice in our office” (Interview # 7, man). The study also showed that training cannot necessarily maintain data quality for certain unless it has been implemented soon after its delivery.

Getting supportive supervision

The study identified that close and supportive supervision is vital for the generation and use of quality health data. In an environment where staff turnover is high, supportive supervision is mandatory after delivering relevant training. A 29-year participant argued as follows: “Saying do this and don’t do that couldn’t bring a change in the health system; rather coaching health care providers going where they are and observing what they work and showing on the spot is mandatory to bring quality in health data” (Interview # 15, man).

Being patriotic staff

The current study revealed that any person is said to be patriotic if he loves his country and if he loves what he does and serves his country with his profession. For health professions related to information, data must be generated, stored, and analyzed properly. It is worthless to work without properly storing information. In this context, a respondent said, “Someone who loves his country will do things carefully. If a person is patriotic, he needs to generate and store data carefully for future generation.” (Interview # 4, man). Another respondent substantiated the ideas saying, “a person who loves his country feels as if he killed his citizen or community whenever he produces false data” (Interview # 1, man).

An interviewee described how a patriotic person considers the establishment of a good health information system saying, “As a patriot, you know that the information you have at hand is an invaluable resource for your country. Even another interviewee emphasized that, “Just as you hold your mother in the highest regard, you ought to do the same for data because it is crucial for your country… By doing this, you greatly benefit your country” (Interview # 6, man). Another respondent explained what the modeled professionals in relation to data quality as, “By the way, there are people who are naturally inclined to doing good and who are attentive to information in general. Therefore they have taken the initiative to do what they do without expecting a reward” (Interview # 4, man).

Relational/interpersonal level characteristics

Coaching and supportive supervision

Activities including teaching, facilitating, advising, advocating, and mentoring which are major tools of coaching are frequently mentioned as remedies to develop skill and motivation among the health workforce in generating quality health data and using it for planning and decision making. Supportive supervision is important especially after training to understand better and develop skills of quality data generation. A respondent explained his feeling saying, “Supervision and coaching should be followed with constant feedbacks ensuring its continuity.”

Peer-to-peer learning and mentoring

Peer-to-peer learning was raised as one of the preferred options to enhance the skill of producing quality data and its utilization; the justification for the preference is it easily opens peer discussion to reflect and evaluate one’s own practice on data and learn with a trusted professional in data management. It was argued that peer-to-peer discussions allow health workers to have others’ point-of-view on the matter which can help them develop their confidence in their practice. In addition to the ease to get peers both formally and informally, it has a strong social component which is one of its main strengths. A respondent explained his feeling saying, “For me, it is easy to discuss with peers and learn more; it is also easy to get them both in office or outside.” It has been mentioned that in addition to sharing knowledge, effective mentoring builds strong relationships within your organization.

Subordinate-supervisor relation

Working relationships between subordinates and managers both down and up the health facilities and offices make a vital difference in the effectiveness of data producers. The relationship between a health worker and his or her supervisor will have an effect on their level of motivation which in turn influences their performance. When data clerks feel connected to their supervisors, a conducive work environment would be created and thus enjoy their job more; they would also be loyal to their organization which would also reduce employee turnover which otherwise needs training new employees. “Good boss-subordinate relationship helps to develop a better working environment that encourages them to do their job in love; it also retains experienced staff longer which sustains the quality of data recordings.”

Staff turnover

The high turnover of health professionals was also mentioned as a factor influencing the production of quality data and use. In such a situation, unless constant training is given, the quality of data fails. A respondent stressed this saying, “When those who are trained staff goes and the untrained come, the quality of health data will be affected negatively” (Interview # 5, man). They also proposed that as long as a new professional comes, the available senior staff should be responsible to train and coach the health professional.

Establishing accountability

In addition to other measures to improve data quality and use, it is also suggested to establish accountability across staff, departments, and organizations, and possibly in the community. A respondent explained the need for accountability saying, “It’s not just training that can solve this problem. In order for us to achieve high data quality, there needs to be accountability. Principles of accountability must be dispensed and people in that area must be critically held accountable” (Interview # 2, man). A respondent confirmed the reason why some health professionals did not work better and were not accountable for their poor performance saying, “Now the chain of accountability has been greatly weakened. Nowadays the person who does not take full responsibility for his work is not held accountable. Even more, it’s now considered a right to evade work; on the other hand, the one who does his work properly is considered a fool! We have not paid enough attention to developing a culture of accountability” (Interview # 4, man).

Organizational characteristics


It has been discussed that when information is handled in a traditional way, it is not readily available when needed. On the other hand, if the data management system is frequently upgraded and integrated with the latest technology, it would be easy to access data when inquired. By using the latest technology, it is easy to record and access any given information. Research can also be done based on the data at any given time and possible to store in soft than in hard copy and view them safely.

A respondent confirmed the significance of infrastructure saying, “if the health facility is not equipped with infrastructure like generator, we cannot use modernized methods or computerized works which affect the data quality” (Interview # 1, man). Another participant added saying, “If we have electric power, then information can reach us easily and quickly … If we have a network, we can communicate by e-mail; it reduces wastage of effort. Infrastructures can help us to shift from manual-based to computer-based works” (Interview #3, man). The establishment of internet connection is also mentioned as a factor to upgrade the knowledge of staff via Google and Facebook and so forth. However, trained personnel are needed to effectively use and capitalize on the infrastructures. On the other hand, another respondent claimed that infrastructure cannot be a concern to establish. In this context, a respondent said, “I don’t think infrastructure is the challenge. … If we decide to set up infrastructure we have plenty of time to do it.”

Support from stakeholders is also mentioned as one reason for the improvement in data quality in health facilities. A respondent expressed how an institution works on data quality saying, “In the current context as the institution is supported by the University of Gondar, we are giving considerable attention for high data quality. I don’t reckon this institution has gaps in this area. We are actually working in keeping up with the current trend” (Interview #5, man). Frequent communications and meetings among HIT, PMT, woreda health office staff, and stallholders such as UoG were identified as major reasons for improvements in data quality.

Organizational culture

Organizational culture is mentioned as a reason for the generation of quality health data. Different structure team-ups in one-in-five, one-in-thirty, and other political structures are contributors to improved data. In this context, a respondent justified that, “One-in-five structures are very helpful to maintain data quality by evaluating their performance during their weekly meeting. One-in-thirty structures also evaluate their performance every two weeks and that helps to maintain data quality.”


The study identified that in a health workforce, recognition can be relevant to establish a competitive environment among the health workforce. Rather than letting professionals become indifferent due to lack of recognition, at least it’s better to offer compensation based on the result they achieved. If possible, it is better to compensate with money or else give a certificate of recognition. Otherwise, health professionals might be discouraged. In this regard, a respondent said, “If we treat all together both hard worker and careless, it may demotivate hard workers and affect data quality” (Interview # 15, man). An interviewee explained the importance of incentives to boost the morale of health workers, saying, “If there is no any incentive, even a person can lose the data itself anywhere” (Interview # 2, man). Participants believed that incentives can motivate not only individuals but also higher levels such as health organizations. As individuals are incentivized, staff turnover can be minimized, may develop a sense of ownership for data quality, can value data, and develop a better organizational culture which all could result in better data quality and utilization.

However, the study identified that incentivizing staff was not a well-cultivated culture that might need more work to emphasize. In this context, a respondent explained his feeling saying, “There is no any experience of motivating professionals in our facility, though we have the plan to do it…” (Interview # 14, man). Of course, it has been argued that health professionals should work on health data effectively regardless of the provision of incentives. The same person expressed his feeling saying, “Everyone should be responsible for his/her duty regardless of the presence of incentive.” In this context, though incentive was not generally given emphasis to motivate staffs, some staff were still outshining in their duty regardless of it.


Respondents argued that the improvements in the health services and health of persons have a great effect on the quality of health data to be produced and its utilization. If the health of the population is significantly improved, people would be motivated by the change and start listening and obeying what the healthcare providers are recommending. Supporting this idea, a respondent forwarded his feeling saying, “…If people see their health improved as a result of efforts by health workforce, then they would be motivated by the change and start cooperating with them including in data production…” (Interview # 2, man). However, the effect of the improvement of health services and health on the quality and use of health data mainly may not be a direct one. Instead, improved health services and health may modify the constituents of the social-ecological model which would intern affect the quality of health data and use. In this context, whenever people see improvements in health, they may start valuing data, be cooperative to give genuine information to healthcare providers and work seriously on data management and use, etc. which would result in quality data production and use.

Therefore, all direct contributors of improved health services and the health of persons are important for improved data quality and use. For instance, there may be feedback of relationships between the use of quality health data and the health of persons that can be worked on to improve each of them. The study identified that a quality health information system is beneficial not only for writing reports but also for the health of the community as a whole. For instance, one might report a case that does not exist which may create a problem to the health of the community. Respondents explained the link between quality health data and health-supporting with an example saying “For instance, for mothers on ANC, the health and growth of their children as well as their own health can be monitored for a better outcome if and only if we have quality data, and this will result in better health outcomes (Interview # 3, man).

Other contributing factors of health services and the health of persons such as regular supervision and monitoring of the routine work could improve service providers’ performance and services so that the linkage between health centers and other health facilities will be improved. The absence of strong linkage among health facilities could lead to inconsistency or fallacy between data report and data register. An interviewee reported that “Good data will bring a good service quality and promote patient satisfaction as well” (Interview # 8, man). It has been reported that based on the registers and tallies filled with clients’ or patients’ information, one can identify weakness or poor performance so that we can improve it for the next service delivery processes.

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