Article Text
PDF
Abstract
Objective The study aimed to assess health management information utilisation and associated factors among health professionals working at public health facilities in North Wollo Zone, Northeast Ethiopia.
Setting The study was conducted at public health facilities in the North Wollo Zone, Northeast Ethiopia.
Participants A total of 664 (56.3% male and 43.7% female) health professionals participated in the study. All health professionals permanently working in North Wollo Zone were included in this study. However, health professionals who were not present during the data collection period by any means and who had less than 6 months of experience were not included in this study.
Primary and secondary outcome measures The main outcome measure was health management information utilisation.
Result About 58.4% (n=388) (95% CI: 54.4% to 62.0%) of the study participants use health management information. The multivariable logistic regression model indicated that participants who had managerial positions are more likely to use health management information with an adjusted OR (AOR) of 3.11 and 95% CI 1.84 to 5.24. Similarly, having a good motivation level (AOR=4.42 (95% CI: 2.82 to 6.93)), perceived good culture of health information (AOR=6.17 (95% CI: 3.35 to 11.36)), a standard set of indicators (AOR=4.11 (95% CI: 2.65 to 6.38)), having good governance of health information system (AOR=1.75 (95% CI:1.13 to 2.72)) and health management information system (HMIS) training (AOR=3.10 (95% CI: 1.89 to 5.07)) were the predictors positively associated with higher utilisation of health management information.
Conclusion This study revealed that utilisation of health management information was still inadequate. Enhancing motivation, building a culture of information use, having standardised indicators, strengthening the governance of health information systems and comprehensive HMIS training were measures to be taken to improve utilisation of health management information in this study setting.
- biotechnology & bioinformatics
- health informatics
- information management
- information technology
- telemedicine
- world wide web technology
Data availability statement
Data are available upon reasonable request.
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/.
Statistics from Altmetric.com
- biotechnology & bioinformatics
- health informatics
- information management
- information technology
- telemedicine
- world wide web technology
Strengths and limitations of this study
The study covers all types of health facilities: health posts, health centres and hospitals with a large sample size, which increases its generalisability.
Recall bias might lower health management information utilisation level.
The study was not supported by the qualitative findings.
The mean score of health management information utilisation questions may also be a limitation of this study.
The data collection was based on self-reported information, which might lead to overestimation of participants’ real utilisation practice.
Background
A health information system (HIS) is an integral part of the healthcare system whose operational boundaries include all resources, organisations, and actors that are involved in the regulation, financing, and provision of actions whose primary intent is to protect, promote and improve health.1 2 Health management information system (HMIS) is a subsystem under HIS that is specially designed to assist the management and planning of health programmes as opposed to delivery of care that provides decentralised decision-making and planning.3 4
HMIS is a system that allows for the collection, storage, compilation, transmission, analysis, and usage of health data that assist decision-makers and stakeholders in managing and planning resources at every level of health services.3 5 6 The main purpose of HMIS is to routinely generate quality health information that provides specific information support to the decision-making process at each level of the health system for improving the performance of health services.7
The Ethiopian HMIS has been implemented since 2008 to capture and provide core monitorable indicators used to improve the provision of health services, and ultimately, to improve the health status of the population. The Ethiopian HMIS was revised later in 2017.8 Since then, the health sector showed significant achievements in planning, budgeting, decentralising, reviewing plans and progress, involving partners and utilising information on decision-making.4 9 The current Ethiopia National Health Sector Transformation Plan (2015–2019) notes that HMIS is a major source of information for monitoring and adjusting policy implementation and resource use.9
Moreover, the government of Ethiopia gives due recognition to HMIS as a management support system for improving the health system in Ethiopia by providing continuous information support to the decision-making process at each level of health institutions such as the Federal Ministry of Health, Regional Health Bureau, Zonal Health Department (ZHD), Woreda Health Office, health centres, health posts and hospitals.10
Even if more attention has been given to strengthening evidence-based decisions through good governance, transparency and accountability, HMIS utilisation is poorly practised in developing countries.11–16 Evidence in Africa showed that HMIS utilisation remains very weak and data are sitting in reports, shelves, cabinets and databases, which are left unanalysed to be sufficiently used for policy and programme improvements.12 17–20 A study done in Ghana revealed only 26% of the facilities used health information data for health service decision-making.21 Similarly, a study conducted in Kenya showed that 22% of health professionals used district HIS (DHIS) data.22
Another study in Botswana showed the utilisation of HIS data for evidence-based decision-making was 11%.23 A study finding from Cote d’Ivoire and South Africa indicated that the overall percentages of HMIS information use were 38%24 and 53%,25 respectively. In Ethiopian context, the use of routine health information varied in different areas of the country and it is also considered as partial, not uniform, which is ranging from 32.9% to 78.5%.4 11 26–41
Factors for not using health management information include workload,11 HMIS knowledge, staff motivation,27 attitude toward HMIS,28 competency,37 information use culture,42 data analysis skill,37 friendly format,28 standardised indicator,31 37 43 supervision,28 30 36 37 feedback,28 36 37 44 governance,37 management support and receiving senior directives,30 training,11 28 37 44 resource shortage,11 decisions based on superior directives,27 computer access,11 reference material,11 reporting format,11 performance monitoring by health professionals,27 30 45 completeness of data format,31 consistency of data,31 46 using HMIS data for target setting,30 work location,30 47 catchment population profile charts presentation and quarter plan performance monitoring.26
Previous studies in Ethiopia did not widely address organisational factors and we argue that the relevance of those untouched factors was undeniable, which poses challenges to using health management information. Moreover, studies assessing the utilisation of health management information in Northeast Ethiopia were limited. Our review showed inconsistent findings from previous studies in different parts of Ethiopia. It indicated that there is not a uniform level of utilisation in this country. This means there is low generalisability of those results to the current study setting.
Accordingly, addressing those problems will have a practical benefit for improving coverage and quality of healthcare services. Therefore, this study proposes assessing health management information utilisation and its associated factors among health professionals working at public health facilities in Northeast Ethiopia. The study has valuable contributions to the researchers, programmers, planners, policymakers, academic communities, service providers, healthcare professionals and non-governmental organisations working in this area.
Methods
Study design and setting
An institutional-based cross-sectional study design was conducted to assess utilisation of health management information and determinant factors among health professionals working at public health facilities in North Wollo Zone, Northeast Ethiopia from May to June 2020. North Wollo, which is 1 of 11 zones of the Amhara Region, consists of 15 districts (4 urban and 11 rural districts). The city of North Wollo is Woldia, which is placed 521 km driving distance northeast from the capital city of Ethiopia, Addis Ababa. About 2132 health professionals work within 6 hospitals (1 referral hospital, 1 general hospital and 4 primary hospitals) and 64 health centres. Additionally, a total of 736 health extension workers within 280 health posts are found in this zone.
Study participants, sample size and sampling procedure
The sample size was calculated using single population proportion formula, considering the following assumptions: a 95% level of confidence, a 5% margin of error and a design effect of 2. Additionally, we used p=78.5% from a study conducted in Northwest Ethiopia which has a similar population and study setting as the current study.37 A 5% of non-response rate was considered. Finally, a minimum sample size of 570 was obtained.
There are 6 hospitals, 64 health centres and 280 health posts in the North Wollo Zone. Five districts (small administrative unit next to zone) were selected randomly and all public health facilities within those clusters of districts were included. Accordingly, a total of 2 hospitals and 28 health centres and health posts were included using the cluster sampling technique. We got and approached a total number of 721 healthcare professionals within the selected districts.
Data collection tool and procedure
Data were collected using a pretested self-administered questionnaire and an observation checklist that was adopted from the Performance of Routine Information System Management (PRISM) tools48 49 and related studies.4 11 26 30 31 37 50 A pretested self-administered questionnaire was filled out by health professionals. The questionnaire consists of six main parts. Part 1 includes sociodemographic factors (measured with 9 items), part 2 is related to behavioural factors (measured with 31 items), part 3 assessed technical factors (measured with 4 items), part 4 was about organisational factors (measured with 35 items), and part 5 was about the health management information use (measured with 13 items). The final section of the questionnaire consisted of eight items of an observational checklist.
Utilisation of health management information of the respondents was assessed using 5-point Likert scale questions that ranged from ‘1=strongly disagree’ to ‘5=strongly agree’. The observational checklist was used to collect data on the availability of reference material, health information resources and documents. A total of 3 degree holder health professionals as supervisors and 12 health information technologists as data collectors participated in the data collection process after 2-day training was given before the data collection period. During data collection, participants were informed about the objective and processes of the study and the confidentiality of the information.
Operational definition
Health management information utilisation
Health management information utilisation was defined as the use of such information for treating patients, disease prioritisation, drug procurement, the day-to-day monitoring of health service activities, checking data quality, resource allocation, planning, department performance evaluation, evaluation of staff performance, selection of best experience within the health facility, sharing of health data with other facilities and stakeholders, decision-making, and community mobilisation and discussion.11 28 37
All these components of behavioural factors, the outcome variable, and some of the organisational factors, have a 5-point Likert scale measure, ranging from ‘1=strongly disagree’ to ‘5=strongly agree’. The mean score of health professionals was calculated by summing up the scores of respondents for each item first (rated from 1 to 5) and then divided by total respondents. Health professionals who scored greater than or equal to the mean value of Likert scale questions provided to measure health management information use were labelled as good use of health management information, whereas health professionals who scored less than the mean value of Likert scale questions were labelled as poor use of health management information.
In this study, health professionals are defined as those employees who had at least a diploma certificate in the health profession and who are collecting health data to use health information for the improvement of health status including medical doctors, public health officers, nurses, pharmacists, midwives, laboratory personnel, health extension workers, radiology technicians and so on.
HMIS knowledge
It focuses on the specific knowledge of knowing how to collect, store, compile, check the quality, analyse data, and why or for what to use HMIS data.51 The question for measuring knowledge encompasses know-how for collecting or using aggregated data (in different categories of age, sex, season and disease type), reasons for collecting or using geographical data or residence, and the reasons for using population data. Additionally, the respondents’ know-how about the overall HMIS, that is, consistent, accurate and reliable data was considered to measure their knowledge,
Accordingly, HMIS knowledge was measured by eight items with three response categories (1=True, 2=False and 3=I don’t know). Finally, the responses were dichotomised into two as ‘0’ and ‘1’. If respondents got the answer for the item, it was recoded as ‘1’, otherwise it was recoded as ‘0’. The normality test showed knowledge was not normally distributed, so we computed median and health professionals who responded correctly above the median were labelled as having good knowledge of HMIS, whereas health professionals who scored below the median were considered as having poor knowledge.28 37 51
Attitude toward HMIS
The attitude of the respondents was measured by eight Likert scale questions ranging from ‘1=strongly disagree’ to ‘5=strongly agree’. In this regard, health professionals were asked to rate their opinions regarding HMIS. For instance, they were asked their level of agreement for questions/statements like: ‘Collecting, analysing, reporting and using data are not time-consuming’. Health professionals who scored greater than or equal to the mean were considered as having a good attitude. On the contrary, health professionals who scored below the mean were considered as having a poor attitude toward HMIS.28 31 37
Perceived self-efficacy of HMIS tasks
Perceived self-efficacy is an individual belief in his or her capacity to execute HMIS tasks. The tool used in measuring perceived self-efficacy incorporated three constructs such as perceived self-efficacy in analysing data (two-item questions), perceived self-efficacy in interpreting data (two-item questions) and perceived self-efficacy in using information (four-item questions).48 Overall, eight-item Likert scale questions ranging from ‘1=strongly disagree’ to ‘5=strongly agree’ were used to measure it. Health professionals who scored greater than or equal to 75% were considered as having high competence, according to the recommendation of the PRISM framework.38
Good management support
Management support is any support from a superior manager to perform HMIS tasks which was measured by six Likert scale questions ranging from ‘1=strongly disagree’ to ‘5=strongly agree’. In this case, respondents were asked to rate the level of management support at their health facility. For instance, the respondents were asked whether their superior manager encourages gathering data to find the root cause(s) of the problem. In this study, management support was not normally distributed. Therefore, we computed the median score. Study participants who scored greater than or equal to the median of Likert scale questions prepared in this study were considered as having good management support.37
Perceived culture of health information uses the pattern of behaviours and attitudes that express health professionals’ orientation toward information use, which was measured by eight-item Likert scale questions ranging from ‘1=strongly disagree’ to ‘5=strongly agree’. For more clarification, the respondents were asked to rate their level of agreement regarding the culture of information in their working unit. For instance, the respondents were asked to rate their level of agreement for questions or statements like: ‘Health department encourages staff to use data for developing future action plans’. Health professionals who scored greater than or equal to the mean score were labelled as having good perceived culture of health information. On the other hand, health professionals who scored below the mean were considered as having poor perceived culture of health information.48
Motivation toward HMIS
Motivation toward HMIS is the level of desire to use HMIS data, and their level of commitment to perform HMIS tasks such as data collection, compilation, quality checking and reporting. It was measured by seven-item Likert scale questions ranging from ‘1=strongly disagree’ to ‘5=strongly agree’. For instance, respondents were asked to rate their level of commitment to monitoring data collection in their department to make HMIS data better. Health professionals who scored greater than or equal to the mean score were labelled as having a good motivation toward HMIS29 48 (see online supplemental file 1 for more details).
Supplemental material
Data processing and analysis
Data were entered into EpiData V.4.6. Then, it was exported to STATA V.15 software for further analysis. Descriptive statistics mean and the percentage were calculated. Multicollinearity was tested by running a false linear regression iterating the independent variables as a dependent variable. Its result showed all the variance inflation factor values less than 3 and tolerance greater than 0.7, which demonstrated the absence of multicollinearity.52 The data were also checked for outliers by box plot and no outshining outlier effect was observed. Finally, both bivariable and multivariable logistic regression analyses were used to measure associations between the independent variables and the dependent variable (health management information use).
Accordingly, a bivariable analysis was conducted, and predictor variables that were significant at p<0.2 were entered into the multivariable analysis to check for confounding effects on the association from the bivariable analysis. A stepwise forward selection of variables was used to build the multivariable model. The strength of association was described at 95% CI and p value less than 0.05 was considered for multivariable logistic regression analysis.
Patient and public involvement
No patient involved.
Result
Sociodemographic characteristics
Out of 721 distributed questionnaires, 664 responses were received with a response rate of 92.1%. More than half (373; 56.2%) of the respondents were male with the mean age of participants 33.24±8.3 years. In terms of educational level, this study revealed that the majority (387; 58.3%) of the respondents were degree holders. The rest, 243 (36.6%), and 24 (3.6%) had diplomas and master’s degrees, respectively. Regarding their residence, more than half (375; 56.5%) of the study participants lived in rural areas (see table 1 for details).
Sociodemographic and economic characteristics of health professionals in North Wollo Zone, Northeast Ethiopia, April 2020 (n=664)
Behavioural factors
This study implied that health professionals who had good HMIS knowledge were found to be 55.1% (95% CI: 50.4% to 58.7%). Health professionals who had good motivation toward HMIS were found to be 64.3% (95% CI: 59.3% to 68.5%). Perceived culture of health information use of health professionals was 46.7% (95% CI: 42.6% to 49.2%) and routine HIS (RHIS) task self-efficacy was 46.7% (95% CI: 42.6% to 49.2%).
Organisational and technical factors
More than half (372; 56.0%) of health professionals were supervised at least once within 3 months and 309 (46.5%) of them got feedback at least within a year. Likewise, 203 (30.6%) of health professionals had taken training on data management. The study also showed that 351 (52.9%) of respondents faced difficulties in understanding report formats. Among those respondents who faced difficulties, 39.9% faced unspecific terms and 46.6% of them faced inconsistent format, and 13.5% faced abbreviations. On the other hand, more than half (330; 49.7%) of the respondents had difficulties in the reporting systems. Most of the respondents (579; 87.2%) responded that no existence of appropriate technology was used for managing data. Regarding management support, 311 (46.8%) of the respondents agreed that they got good support from their senior managers (see table 2 for details).
Organisational factors of health professionals in North Wollo Zone, Northeast Ethiopia, April 2020 (n=664)
Health management information utilisation
The finding implied that 353 (53.2%) of the respondents used routine health data for treating patients, 453 (68.2%) for disease prioritisation, 432 (65%) for drug procurement and 379 (57.1%) of them used data for monitoring day-to-day health service activities. Additionally, 454 (68.3%) of those respondents used HMIS data for checking data quality, 351 (52.8%) for resource allocation and 353 (53.2%) for planning. The number of respondents who used HMIS data for department performance evaluation was 420 (63.3%).
About 438 (66%) of the study participants used HMIS data for evaluation of staff performance, and 392 (59%) for selection of best experience within a health facility. On the other hand, 388 (58.4%) of the study participants used HMIS data for sharing health data with other facilities and stakeholders, 320 (48.2%) for decision-making, and 329 (49.6%) of them used for community mobilisation and discussion. Overall, good routine health management information utilisation was noted among 58.4% (n=388) (95% CI: 54.4% to 62.0%) of the health professionals (see table 3 for details).
Health management information utilisation among health professionals working at public health facilities in North Wollo Zone, Northeast Ethiopia, April 2020 (n=664)
Factors associated with health management information utilisation
In the bivariable logistic regression analysis, position, knowledge about HMIS, motivation level, the perceived culture of information use, RHIS self-efficacy, standardised indicator, management support, governance of HIS, availability of reference material, training for HMIS and supervision were factors associated with good routine health information utilisation at a p value of less than 0.2. Consequently, these variables were subjected to the multivariable logistic regression analysis to control potential confounders, and it was noted that position, motivation level, the perceived culture of information use, standardised indicator, training for HMIS and governance of HIS were significantly associated with good data management practice at a p value of less than 0.05.
In this study, higher odds of good RHIS utilisation were noted among health professionals who had a position (adjusted OR (AOR)=3.11; 95% CI: 1.84 to 5.24), good motivation level (AOR=4.42; 95% CI: 2.82 to 6.93), good perceived culture of information (AOR=6.17; 95% CI: 3.35 to 11.36), standardised indicator (AOR=4.11; 95% CI: 2.65 to 6.38), good governance of HIS (AOR=1.75; 95% CI: 1.13 to 2.72), and among those who took HMIS training (AOR=3.10; 95% CI: 1.89 to 5.07) (see table 4 for details).
Factors associated with utilisation of health management information among health professionals at public health facilities in North Wollo Zone, Northeast Ethiopia, April 2021 (n=664)
Discussion
In this paper, we have examined the determinants of health management information utilisation using tools adopted from the PRISM framework. The finding showed that the utilisation of health management information was 58.4% (95% CI: 54.4% to 62.0%). From this finding, we can understand the utilisation of health management information was inadequate, according to WHO standards. In general, the result implied managerial decisions in this study setting were made without health information evidence.
The finding was almost in line with a study finding in Hadiya Zone, Oromia Regional State, and Southwest Ethiopia, where utilisation of HMIS at health facilities was 54.9%,32 57.9%,27 and 57.3%,39 respectively. This finding was also consistent with a previous study report in Ethiopia where the level of good utilisation was 57.42%.40 In addition, the finding in this study was similar to the study conducted in Tanzania, in which 60%43 of health professionals were good at using HMIS data.
The finding in the current study was also higher than another study in Kenya where 22% of health professionals were good at using DHIS.22 The possible explanation for this study could be the structure of the HIS between our study setting and in the study conducted in Kenya. In this regard, Kenya fully deployed the DHIS system, whereas Ethiopia is in the initial stage of deploying the DHIS and used HMIS until the data collection period of this study. On the other hand, the study conducted in Kenya was on three purposively selected district health systems which could be the reason for this discrepancy.
Our finding is also higher than the study reported in Ghana, in which 26% of health professionals had good utilisation of routine health information for health service decision-making.21 The possible justification for this variation could be the study population. The study in Ghana was conducted among district directorates and district hospitals, whereas the current study was conducted at health posts, health centres and hospitals. Additionally, utilisation was assessed based on documented evidence in a study in Ghana, which might be the reason for this variation.
On the other hand, our finding is higher than the study finding from Cote d’Ivoire and South Africa, where the overall percentages of HMIS information use were 38%24 and 53%.25 This could be due to the difference in HIS structure between countries and also the motivation of health professionals toward performing HMIS tasks. Moreover, the study conducted in Cote d’Ivoire used an intervention study that has a methodological difference from the current study that could be another possible justification for this variation.
The other possible reason for this variation might be due to the difference in ways of measuring utilisation of HMIS and the study population. Concerning this, in the study in Cote d’Ivoire, RHIS data use was measured based on three indicators such as: whether RHIS information was discussed in staff meetings, whether decisions evolved from these discussions and whether the decisions were referred to upper management for action.24 However, in the current study, we used 13-item questions developed from the PRISM framework to measure information use, which might be the possible justification for this variation.
The finding in Botswana implied only 11%23 of the participants used health information effectively, which has a higher variation than the current study finding. The variation could be due to the very small sample size used in research conducted in Botswana and the study population was district management teams; while in our study, all health professionals at public health facilities were involved. Additionally, the health information structure might be different between Botswana and Ethiopia. In this regard, the DHIS was fully deployed in Botswana. In the study in Botswana, health management information use was measured with a single-item question which could be another reason for the variation of the result compared with our finding.
Our finding is lower than the result reported from the North Gondar Zone and findings in South Ethiopia where the levels of information use were 78.5%,37 69.3%31 and 62.7%.29 This could be due to the variation of the study population and setting for that the RHIS governance could vary within it. The study in the North Shewa Zone reported 71.6%41 of good routine health information utilisation, which was higher than the current finding. The possible justification for this discrepancy was the sampling technique and study setting. In this regard, the study in North Shewa Zone used the purposive sampling technique to select managers of hospitals as study units. Accordingly, the study design and study participants might be the reason for this discrepancy.
Furthermore, this finding is higher than the study reported in different parts of Ethiopia, in which the overall routine health information use at facility level was 31% in Jimma,26 41.7% in Addis Ababa,4 53.1%35 in Eastern Ethiopia and 54.2% in East Wollega.34 This discrepancy could be due to the study period and study population. Those previous studies were conducted before 5 years, which might be a significant change in HIS structure and health professionals’ motivation could be changed. The study populations in those previous studies were health professionals at the primary healthcare unit, whereas health professionals working at primary, secondary, and tertiary health facilities were represented in the current study. The other possible justification could be the difference in sampling technique and data collection method. Those previous studies prefer small sample size, which could lower the generalisability to the current study setting.
Our finding was higher than the report in the Oromia Special Zone, in which the level of utilisation was 52.8%.38 The difference could be due to sample size and study setting. The study in the Oromia Special Zone used purposive sampling, which could be the reason for this variation. It was also higher than the result reported from a study finding at health facilities in Western Amhara in which good utilisation of RHIS was 38.4%.11 This could be due to the fact that the study in Western Amhara was conducted at selected health centres and it used a small sample size.
Our finding was also higher than the report in the Gojam Zone and Amhara Regional State as a whole where the levels of overall RHIS utilisation were 45.8%28 and 46.0%,30 respectively. This difference could be due to the study period and within this gap, the government made immense efforts to enhance the culture of routine health information use including information revolution road map.47 In the current study, 24.2% were management members that could inflate the overall utilisation of health management information28 since managers’ responsibility could enforce them to use health management information more than the staff. In this regard, our finding implied that health professionals who were management staff were more likely to use health management information compared with the staff. This implied that a high gap in not using health management information is found among health professionals who were not management staff. Hence, intervention should focus on those groups to increase the utilisation of health information.
Based on the multivariable logistic regression analysis, motivated health professionals were more likely to use health management information. This was consistent with studies reported elsewhere.27 This could be due to motivated health professionals being more eager and confident to make decisions based on evidence.
Health professionals who had good perceived culture of information were more likely to use health management information. This is supported by the study finding elsewhere in the world.29 This could be due to the culture of information improving the socially shared patterns of behaviours, norms and values, and having good culture of information enables one to understand the significance of using health management information to the improvement of accessibility of health services.
The finding of this study also indicates that professionals who had a standard set of indicators were more likely to use HMIS as compared with those who did not have such indicators. This is supported by the previous studies conducted in Northwest Ethiopia37 and Southern Ethiopia.29 This could be as a result of standard indicators helping as a source of information and standard to use health management information.
Moreover, respondents who had good governance of HIS were more likely to use health management information. This is supported by the studies in Northwest Ethiopia.37 The possible explanation for this could be good governance of HIS could enhance the commitment level and improve the accountability of health professionals to make every decision based on evidence. Furthermore, it helps to get the required technical support during processing data into information.
Like other studies conducted elsewhere,11 28 29 37 the odd of health management information utilisation in this study was higher among health professionals who got HIS training than those who did not get this training. A pre/post-interventional study in Northwest Ethiopia also strengthened this finding which stressed training intervention improved health information utilisation.44 This might be the case since HIS training built health professionals’ capacity to analyse, interpret data, and prepare data for utilisation, and it might create skilled human resources that are confident and motivated to use health management information. Additionally, training could capacitate health professionals on how to change data to information and use it.
Conclusion
In summary, this study revealed that utilisation of health management information was inadequate. Enhancing motivation, building a culture of information, having standardised indicators, strengthening the governance of HIS and having comprehensive HMIS training were measures to be taken to improve utilisation of health management information.
Limitation of the study
This study was aimed to assess the major determinant factors of routine health management information use in which the study covers all types of health facilities: health posts, health centres and hospitals with a large sample size that increases its generalisability. However, it was not supported by qualitative findings. Additionally, this study used a cross-sectional study design which leads to not showing temporal relationships that might lead to recall bias. In this study, an observational checklist was used, which might be of help to overcome some of those limitations. Moreover, the data collection was based on self-reported information, which might lead to overestimation of participants’ real utilisation practice. In this regard, we used the mean score of health management information utilisation questions that might also be a limitation of this study.
Measuring health management information utilisation with a Likert scale (strongly disagree to strongly agree) is another limitation of this study. For instance, some respondents who are not willing to answer a particular question might put a check on ‘neutral’. The context of ‘neutral’ for such respondents is an escape and the true essence might not be achieved. Additionally, the frequency distribution for individual items of HMIS utilisation was done with limitation by converting the Likert scale to dichotomy (0=no, 1=yes). This study did not include health officers at the district health office and ZHD. We recommend that future investigators include all health institutions and support it by a qualitative study.
Implication
Policymakers, healthcare providers, and planners should be concerned about the training of all health professionals, as well as improving motivation, the culture of information and RHIS governance. Redesigning standardised indicators is encouraged to improve HMIS utilisation. Finally, we, the authors of this study, recommend that future investigators support our finding by using a qualitative study.
Supplemental material
Data availability statement
Data are available upon reasonable request.
Ethics statements
Patient consent for publication
Ethics approval
This study involves human participants and ethical clearance was obtained from the ethical review board of the Institute of Public Health, the University of Gondar (no. /IPH/837/06/2012). The supporting letter was obtained from Amhara Region Public Health Institute and the North Wollo health department. Informed consent was obtained from each study participant after they were informed of the objective and benefits of the study. To keep the confidentiality of information provided by the study subjects, the data collection procedure was anonymous.
Acknowledgments
The authors are indebted to the University of Gondar Institute of Public Health’s ethical review board for the approval of ethical clearance and the Amhara Region Institute of Public Health, North Wollo Zone health department, and the respective district health offices for giving a supporting letter. The authors would like to extend their heartfelt thanks to facility managers, health professionals, data collectors and supervisors who participated in this study.
References
Supplementary materials
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
Contributors HSN and SGK made significant contributions to conception, design, data collection, supervision, data analysis, interpretation and write-up of the manuscript. MDK and MHA have contributed to developing the proposal, revising the manuscript, preparing figures, analysis and interpretation of data as well. Finally, all authors reviewed and approved the final manuscript.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
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.