Content uploaded by Eyassu Mathewos Oridanigo
Author content
All content in this area was uploaded by Eyassu Mathewos Oridanigo on May 19, 2022
Content may be subject to copyright.
Oridanigo et al., Universal Journal of Pharmaceutical Research 2022; 7(2):48-54
48 CODEN (USA): UJPRA3
Available online on 15.05.2022 at http://ujpr.org
Universal Journal of Pharmaceutical Research
An International Peer Reviewed Journal
ISSN: 2831-5235 (Print); 2456-8058 (Electronic)
Open access to Pharmaceutical research
This is an open access article distributed under the terms of the Creative Commons Attribution-Non
Commercial Share Alike 4.0 License which permits unrestricted non commercial use,
provided the original work is properly cited
Volume 7, Issue 2, 2022
RESEARCH ARTICLE
UTILIZATION OF HEALTH MANAGEMENT INFORMATION SYSTEM AND
ASSOCIATED FACTORS IN HEALTH INSTITUTIONS OF KEMBATA
TEMBARO ZONE, SOUTHERN ETHIOPIA
Hailu Kebede Kondoro1, Eyassu Mathewos Oridanigo1, Tessema Abera Osse2,
Teshome Sosengo3
1Kembata Tembaro Zone Health Department, P.O. box 20, Durame, Ethiopia.
1Department of public health, College of Medical and Health sciences, Wachemo University, Durame Campus, Durame, Ethiopia.
2Durame Town Health Office Kembata Tembaro Zone, Durame, Ethiopia.
3School of Pharmacy, Haramaya University, Harar, Ethiopia.
ABSTRACT
Background: Health Management Information System (HMIS) is one of the six building blocks of a health system designed to
provide important data for continuous quality improvement at all levels of health care administration. It is a major source of
information for monitoring and adjusting policy implementation and resources use. Some studies have been conducted in health
data collection and ways to improve data quality, but little is known about utilization of HMIS in health services organization.
Therefore, this study aimed to assess the utilization of HMIS and associated factors in the study area.
Methods: A facility-based cross-sectional study conducted in public health institutions of Kembata Tembaro zone from March 1
to 30 March 2018. The sample size was calculated using single population proportion formula,and a total of 317 heads of
units/departments of district health offices and health facilities were included. Both quantitative and qualitative data were collected
using structured questionnaires, observational check-lists and interview guide by trained data collectors. Multivariable logistic
regressions were performed using Enter method to identify factors independently associated with dependent variable. Statistical
significant variables were declared at P-value less than 0.05 and Odds ratio with 95% confidence interval were used for data
interpretation.
Result: In this study, overall data utilization was 131(41.59%) with 95% CI of 38.9-46.1%.The data utilization was found to be
98(38.73%) and 33(53.23%) in the health facilities and health administrative units respectively. Training for HMIS [AOR (95%
CI)=3.06(2.15-6.75)], availability of procedure manuals [AOR (95% CI)=3.67(1.78-9.01)], and Supportive supervision[AOR (95%
CI)=5.30(3.05-11.53)] were found to be significant with HMIS utilization.
Conclusion: Utilization of HMIS in public health institution was lower compared to previous studies. HMIS training, supportive
supervision and availability of procedure manuals were positively associated with utilization of HMIS. Health facilities and offices
should avail HMIS manuals and capacity building of health workers through training and supportive supervision was
recommended.
Keywords: Ethiopia, HMIS utilization, Kembata Tembaro, Public health institutions.
Article Info: Received 7 March 2022; Revised 11 April; Accepted 27 April, Available online 15 May 2022
Cite this article-
Kondoro HK, Oridanigo EM, Osse TA, Sosengo T. Utilization of health management information system and
associated factors in health institutions of Kembata Tembaro Zone, Southern Ethiopia. Universal Journal of
Pharmaceutical Research 2022; 7(2):48-54.
DOI: https://doi.org/10.22270/ujpr.v7i2.752
Address for Correspondence:
Eyassu Mathewos Oridanigo, Department of Nursing, College of Medical and Health Sciences Wachemo University, Durame
Campus, Durame, Ethiopia.Tel- +251911598052; E-mail: eyumathi@gmail.com
INTRODUCTION
Health management information system (HMIS) is
defined as collective effort to collect, process,
reportand use health information and knowledge to
influence policy making, programme action and
research1. The purpose of HMIS is to routinely
generate quality health information that provides
specific information support tothe decision-making
process at each level of the health system for
improving the health system performance, to respond
to emergent threats, and to improve health2. Utilization
of data from HMIS is the practice of maintenance and
care of health records by traditional (paper-based) and
electronic means in hospitals, health administrative
office, health departments, health insurance companies,
Oridanigo et al., Universal Journal of Pharmaceutical Research 2022; 7(2):48-54
49 CODEN (USA): UJPRA3
and other facilities to generate quality health
information and use that information for management
decisions to improve the performance of health
services delivery3. Utilization of data from HMIS at all
level of health services organizations is used to
improve health services effectiveness and efficiency3.
Despite the credible use of data from HMIS for
evidence based decision making, countries with the
greatest burden of ill health andthe most urgent needs
for good data have the weakest utilization of health
data in the vast majority of world’s low income
countries4. Although high effort to improve the
efficiency of data utilization in the past few years, low
and ineffective data utilization practicing from HMIS,
poor utilization of data at the local leveland inadequate
knowledge and interest of health service providers in
HMIS was seen in health system5.
Poor/absence of data utilization will result in
occurrence of inadequate transparency between health
administrative units and health care providing centers,
which encounter unfair allocation of resources
according to their need and interrupt supplies within
the organization. As a result, it can frustrate the health
staffs in health facilities compromising the attention
paid to successful application of the system6.
Government of Ethiopia gives due recognition to
HMIS as a management supportsystem for improving
the health system in Ethiopia by providing
continuousinformation support todecision-making
processat each decision-making7. Federal Ministry of
Health (FMOH) emphasized HMIS as a key to a
successful implementation of the Health Sectors
Transformation Plan (HSTP) and used information
revolution for transformation agenda9. HSTP
underlined that routine data generated at district health
facilities should beconsidered as the entrance to
utilizing health information and a primary source of
information for continuous monitoring of health
services in the country, and that data should be utilized
at theplace where it was generated8.
Even though the FMOH has made tremendous efforts
on initiative of HMIS and reform changes,
data/information utilization remains weak, particularly
at district health offices and primary health care
facilities, which have primary responsibility for
operational management and decision making10.
According to study conducted in public HCs of Addis
Ababa, Ethiopia, level of HMIS utilization was
41.7%4. According to HMIS performance base line
survey conducted in Southern Nations Nationalities
and People Republic (SNNPR) of Ethiopia, the
utilization of information was found to be limited in the
assessed zones/special woreda. Absence of guidelines
and limited information feedback to health facilities
were the contributing factors for the observed
minimum use of HMIS11. Therefore, this study was
designed to greatly signal the current status of HMIS
utilization in the study area, which can strengthen the
communication channel for timely delivery of services.
MATERIALS AND METHODS
Study area and period
A facility based cross-sectional study design using both
quantitative and qualitative study was used in public
health institutions of Kembata Tembaro zone from
March 1 to 30, 2018. The Zone is located in Southern
Nations, Nationalities and People Republic (S/N/N/P
/R) of Ethiopia and its capital town, Durame, which is
located 293 kilometers (KM) from Addis Ababa and
118 KM from Hawassa, capital town of S/N/N/P/R
government of Ethiopia. In this zone, there are 8
woreda health offices and 4 health administrative
health units, 1 general and 4 primary hospitals,33
governmental and 3 non-governmental health centers,
136 health posts and 1170 different types of health
professionals.
Source and study population
The source population were all health
units/departments of Zonal health department, district
health offices and Health facilities (HF) while study
population were randomly selected units/departments
of Zonal health department, district health offices and
HF in the zone.
Sample size determination and sampling technique
The sample size was calculated using single population
proportion formula, assuming 5% precision, 95%
confidence interval and 32.9% proportion of overall
utilization of HMIS in Jimma zone at district level12.
The population correction formula was used since the
source population was less than 10,000(13) and by
assuming 10% non-response rate, the final sample size
was 317. Since all health facilities in the Zone
currently were implementing HMIS, all units/
departments heads from all health facilities and offices
were included in the study. In the study area, there
were 633 units/departments from all health facilities
and health offices. Simple Random Sampling (SRS)
was used to select 64 and 253 study participants from
health administrative units/health offices and health
facilities respectively. For qualitative study, heads of
health offices, hospital and health centres, HMIS focal
persons and case team leaders were selected
purposively for in-depth interview.
Data collection tools and techniques
Data were obtained from heads of units/departments of
health facilities and health offices of the zone. A face-
to-face interview was conducted using self-
administered structured questionnaires that were
developed after reviewing different relevant
literatures4,12,14,15 and observational checklists in the
study units/departments to identify how data and
information is generated like observation of
registration books, monthly and annual reports, and
graph, charts and Maps. Six Bsc nurses and one health
officer were recruited to collect the data and supervise
data collection process respectively.
Data Quality control
The quality of data was assured by proper designing of
the questionnaires and by training the data collectors
and supervisors for two days before the data collection.
Every day after data collection, questionnaires were
reviewed and checked to maintain its accuracy and
completeness by supervisors. The English version
questionnaires were translated into Kambatissa and
Amharic languages (local languages) and again
Oridanigo et al., Universal Journal of Pharmaceutical Research 2022; 7(2):48-54
50 CODEN (USA): UJPRA3
translated back to English version and comparisons
were made on the consistency of these versions. Data
collection tools were pretested at 5% of samplesize in
shone primary hospital and East Badawacho health
office, outside of the study areaprior to its actual use in
data collection.
Data management and statistical analysis
Quantitative data were checked for completeness,
inconsistency then coded and entered into epidata
version 3.1 and exported to SPSS version 21 for
analysis. Descriptive statistics were computed and
tables, graphs and numerical summary presented the
results. Bivariate analysis was carried out to see the
association of each independent variable with
utilization of HMIS. Variables with P-value less than
0.25 in bivariate analysis were considered as
candidates for multivariable logistic regression
analysis. Multivariable logistic regression analysis was
performed using Enter method to identify factors
independently associated with dependent variable.
Statistical significance was declared at P-value less
than 0.05 and the strength of statistical association was
measured by adjusted odds ratios and 95% confidence
intervals. The qualitative data were transcribed and
coded then merged in their thematic areas and a
thematic framework analysis was employed manually.
Based on participants’ explanation, the descriptive
summaries were made, which were used as supplemen-
tation for quantitative data to verify events.
Ethical consideration
The study was conducted after getting permission from
the institutional review board (IRB) of Jimma
university institute of Health (letter No: IRB/205/10
and date: 18/01/2018). Letter of cooperation was
obtained from kembeta Tembero zone health
department and woreda health offices. After clear
discussion about the actual study or explaining of
purpose of the study, verbal informed consent was
obtained from each study subjects.
Operational definition
Utilization of HMIS: Utilization of data from HMIS
was assessed by using matrixes such as information for
decision making to take immediate action, feedback
from respective supervisors, calculation of area
coverage and preparation of maps, presentation of key
indicators with charts or tables and presentation of
achievements of targets. Based on these criteria, the
respondents were considered as utilized data when they
practiced a minimum of three out of five criteria4,12.
Completeness: completeness is measured as filling in
all data elements in the facility report form, and also as
the proportion of facilities reporting in an adminis-
trative area. Completed if > 85 % of them were filled
Consistency: Is correspondence between data reported
and data recorded in registers and patient/client
records, as measured by a Lot Quality Assurance
Sample (LQAS) checked by allunits/department
Consistency >90%.
Table 1: General characteristics of respondents.
Variables
Frequency (%)
Age
19-24
18 (5.7)
25-30
138 (43.8)
30-34
82 (26.0)
35-39
60 (19.1)
≥40
17 (5.4)
Sex
Male
197 (62.5)
Female
118 (37.5)
Service year
6m-2yrs
65 (20.5)
2-4 years
131 (41.6)
4-6 years
99 (31.4)
6 years and above
20 (6.3)
Salary in
ETB
< 1249
29 (9.2)
1250-2249
148 (47.0)
>2250
138 (43.8)
Level of
education
Diploma
198 (62.9)
Degree
113 (35.9)
Master
5 (1.6)
Occupation
in the
organization
Health officers
78 (24.8)
Medical Doctors
8 (2.5)
Laboratory technologists/technicians
50 (15.9)
Pharmacists/pharmacy technicians
56 (17.8)
Public health specialists
5 (1.59)
HIT professionals
8 (2.5)
All types of nurses
110 (34.9)
RESULTS
General characteristics of the respondents
In this study, 315 study participants responded to the
questionnaires with a response rate of 99%. Out of total
respondents who responded to the questionnaires, sixty
two were selected from health administrative units
(health offices) while 253 wereselected from hospitals
and health centers. Out of total respondents, majority
of them, 138(43.8) were within the age range of 25-30
with a mean and standard deviation age of 27.24 and
5.4 respectively. The sex distribution of individuals
working in the study units showed that about two third
of them, 197(62.5%) were males.
Oridanigo et al., Universal Journal of Pharmaceutical Research 2022; 7(2):48-54
52 CODEN (USA): UJPRA3
Table 2: Organizational characteristics of the study subjects.
Variables
Frequency (%)
Availability of computers and
computer programs
Yes
99 (31.4)
No
216 (68.6)
Supportive supervision within the
six months
Yes
127 (40.3)
No
188 (59.7)
Receiving of training on HMIS
including in-service training
Yes
168 (53.3)
No
147 (46.7)
Presence of multi-disciplinary
committee
Yes
204 (64.8)
No
111 (35.2)
Frequency of meeting within the
last six months
None
60 (19)
Monthly
106 (51.9)
Quarterly
158 (77.5)
Presence of health information
system steering committee
Yes
168 (53.3)
No
147 (46.7)
Presence of data collection
standards including case definitions
Yes
289 (91.7)
No
26 (8.3)
Adapt national target to local
situation
Yes
296 (94.0)
No
19 (6.0)
No
28 (8.9)
Had monthly and quarterly
reporting formats and tally sheets
Yes
190 (60.3)
No
125 (39.7)
Had standard HMIS registers
Yes
162 (51.4)
No
153 (48.6)
Had HMIS procedure manuals
Yes
191 (60.6)
No
124 (39.4)
About two fifth, 131(41.6%) respondents’service year
was 2-4 years. Regarding educational status of
respondents,198(62.9%) were diploma holders (Table
1).
Organizational characteristics
Among 315 observed units/departments, 99(31.4%) of
them had computers. Based on organizational
classification, 50(15.9%) and 49(15.6%) units/ depart-
ents in health facilities and health offices had
computers respectively. Regarding supervision, 127
(40.3%) units/departments were supervised at least
once by higher bodies to provide and support directions
of health services in the last six months. Among them,
about one quarter, 33(26%) were supervised irregularly
while 42(33%), 32(25%) of them were supervised
once, twice and 3 times respectively.
One of HMIS focal persons from health centre said
that“... supervision was conducted poorly and it was
irregular, and not planned, supported by check list and
well organized. Although, it was conducted as
supportive, was simply traditional type and conducted
during seasonal programs like campaigns.”
About two third of the observed units/departments,
204(64.8%) had HMIS multi-disciplinary committee
for over all design and direction users of data. Among
them, 60(19%) of units/departments didn’t have
schedule for meeting any more.
Figure 1: Utilization of data from HMIS in different health services organizations.
One of key informant from head of health offices said
that“...there were meetings in the departments/units for
reviewing performance. They were conducted not
according to plan and schedule setted but they were
conducted as needed and not problem solving and
some times corrections were not given on the points
that were mentioned and discussed during the
meetings”
Regarding HMIS training and technical support,
168(53.3%) staffs working in the units/departments
received training (Table 2).
Quality dimension of study subjects
In this study, almost all the units/departments prepared
reports to submit next higher officials on weekly,
monthly, quarterly and annual basis. Out of total
units/departments, 301(95.6%) had data transmission,
processing, and reporting rules.
Oridanigo et al., Universal Journal of Pharmaceutical Research 2022; 7(2):48-54
52 CODEN (USA): UJPRA3
Table 3: Quality dimensions of the study subjects in health institutions.
Variables
Frequency (%)
Prepared reports to higher officials
Yes
312 (99.05)
No
2 (0.95)
Keep their reports and registrations
In organized hard copy
201 (63.81)
Both hard and soft copy form.
88 (27.94)
Didn’t organize at all
26 (8.25)
Converted data into information
Yes
225 (87.31)
No
90 (12.69)
Completeness of data
Yes
249 (79.05)
No
66 (20.95)
Consistency of data with register book,
tally sheets and reporting formats.
Yes
198 (62.86)
No
117 (37.14)
Among the total units/departments, 248(78.7%) keep
their reports and registrations in well-organized hard
copy form while 56(17.8%) keep their reports in both
hard and soft copy form. Regarding submission of
reports, 117(37.1%) submit reports within 20-24 days
(Table 3). From the total interviewed respondents in
the units/departments, 186(59.1%), 58.7%, and 46.7%
revealed ambiguity and absence of WHO codes,
redundancy and incompleteness of reporting formats
respectively.
One of the HMIS focal person from the health centers
said that “…routine data was collected from both
individual and working unit level but the tally process
was laid to the HMIS focal person. Therefore, the data
were not tallied in daily basis due to negligence,
shortage of tally sheets and problem of awareness on
reporting formats ….”
Table 4: Multivariable logistic regression analysis showing predictors of data utilization in units/departments
of health sectors.
Variables
Category
Utilization of HMIS
COR
AOR(95% CI)
Utilized
Not utilized
HMIS training
Yes
107(63.7%)
61(36.3%)
2.40
3.06(2.15, 6.75)
No
62(42.2%)
85(57.8%)
1
1
Availability of
procedure manuals
Yes
98(51.0%)
94(49.0%)
2.73
3.67(1.78, 9.01)
No
34(27.6%)
89(72.4%)
1
Supportive
supervision
Yes
101(79.5%)
26(20.5%)
17.60
15.30(13.05, 21.53)
No
34(18.1%)
154(81.9%)
1
Keep their reports
and registrations
In organized hard
copy
106(52.7%)
95(47.3%)
1
1
In organized hard and
soft copy
58(65.9%)
30(34.1%)
1.73
2.03(0.98, 6.78)
Didn’t organize at all
12(46.2%)
14(53.8%)
0.77
0.58(0.49, 2.43)
Availability of
computers and
computer programs
Yes
65(65.7%)
34(34.3%)
2.21
2.64(0.78, 6.67)
No
100(46.3%)
116(53.7%)
1
1
Service years
respondents in the
units/departments
6m-2 years
28(43.1%)
37(56.9%)
1
1
2-4 years
74(56.5%)
57(43.5%)
1.72
3.52(0.64, 2.78)
4-6 years
63(63.6%)
36(36.4%)
2.31
2.12(0.61, 5.43)
6 years and above
12(60%)
8(40%)
2.01
4(0.133, 7.09)
Presence of data
collection standards
including case
definitions
Yes
176(60.9%)
113(39.1%)
1.82
1.42(0.78, 4.32)
No
12(46.2%)
14(53.8%)
1
1
Data utilization
More than half of the units/departments, 182(57.8%)
calculated area coverage. Regarding receiving of
feedback to recommend future action, more than half,
162(51.4%) of the units/departments received
feedback. Most of the units/departments, 287(91.1%)
had key indicators and about half of the
units/departments presented their achievement of the
targets. Based on measurement criteria, the overall
data utilization was 131(41.59%) with 95% CI: (38.9-
46.1%). The data utilization was found to be
98(38.73%) and 33(53.23%) in the health facilities and
health administrative units/health offices respectively
(Figure 1).
One of the head of health centers said that“...the
utilization of data was gearing back ward to traditional
type since there was inappropriate data management
due to inadequate investment and attention given in the
data utilization and management from concerned
bodies. Most of the health workers considered the data
utilization as responsibilities of heads and HMIS focal
persons...”
One of HMIS focal person of woreda health office said
that…. “Most reports were aggregated but not
analyzed and interpreted in work units at health center
level. But this was relatively better worked in Woreda
health offices and zonal health department; the
problem is due to the complexity of reporting formats,
Oridanigo et al., Universal Journal of Pharmaceutical Research 2022; 7(2):48-54
53 CODEN (USA): UJPRA3
miss matching of calculation indicators and
understanding level of health workers.”
Factors associated with data utilization
Among sixteen variables in bivariate logistic regression
analysis, seven of them had a P-value less than 0.25;
hence, they were candidates for multivariable logistic
regressions. The candidate variables were again entered
in to multivariable logistic regression model to obtain
variables which were independently associated with
outcome variable, utilization of data. The variables
with P-value less than 0.05 in multivariable logistic
regression analysis were taken as significant predictors
of outcome variable. Supportive supervision,
availability of procedure manuals, and receiving of
HMIS training was found to be significantly associated
with data utilization. Health units/departments, which
had trained staffs were 3.06 times more likely utilizing
routine data as compared to the units/departments
without trained staffs [OR (95% CI)=3.06(2.15, 6.75)].
Health units/departments, which had HMIS procedure
manuals were 3.67 times more likely utilizing data as
compared to units/departments without HMIS
procedure manuals [OR (95% CI)=3.67(1.78, 9.01)]
(Table 4).
DISCUSSION
Sound and reliable information has remarkable
importance on decision-making across all health
system building blocks, and it is essential for health
system policy development and implementation16. The
finding of this study revealed that utilization of HMIS
was 41.6% in all study units/departments. This finding
was comparable with study conducted at public health
centers in Addis Ababacity that reported the data
utilization of 41.7%4. However, it was lower than what
was documented in studies conductedin East Ethiopia,
53.1%17 and East Gojam Zone of Northwest Ethiopia,
45.8%9. This variation might be due to inadequate
provision of training and supervision for healthcare
providers in this study than previous studies.
In this assessment, health units/departments, which
used HMIS manuals as reference and guidelines were
more likely utilizing routine data as compared to
units/departments, which didn’t use HMIS procedure
manuals for data utilization. This finding was
comparable to study conducted in Addis Ababa city
and S/N/N/P/R4,11. This might be due to utilizing HMIS
procedure manual may guide the operation and used as
reference for routine health data generated from daily
health care service in health facility level18. Receiving
of training on HMIS was an important predictor that
was significant with utilization of HMIS. Health
units/departments, which had trained staffs, were more
likely utilizing routine data as compared to
units/departments without trained staffs. This finding
was supported by studies conducted in different regions
of Ethiopia9,17,19. Staff training is the most important
motivator and could improve the potential of health
workers to analyze and make evidence-based
decision20,22,23. It is known that continuous training as a
part of capacity development is important to create
awareness on data utilization and decrease data
misinterpretation due to the lack of the right capacity,
which is experienced in all developing countries21. In
this study, supportive supervision was another
important factor that was significant with utilization of
routine data. This finding was supported with study
conducted in Northwest Ethiopia9. This might be due to
the fact that supervision has a significant role in
identifying the gaps of routine health data use and
provides feedback on identified problems and
improving health workers’ performance. Availing of
manuals for HMIS and capacity building of health
workers through training and supportive supervision
was recommended.
Limitation
Limitation of the study was relatively small sample
size which might reduce the power of the study and
increase margin of error.
CONCLUSION
Utilization of HMIS in public health institution was
lower compared to previous studies for decision
making in health institutions of Kembata Tembaro
Zone. There was poor capacity building of health
workers in HMIS training and inadequate and irregular
provision of supportive supervision to service
units/departments from higher officials. Among many
factors affecting the utilization of HMIS, only
receiving of training for HMIS, availability of
procedure manuals and supportive supervision were
found to be significantly associated.Woreda health
offices should avail the procedure manuals for the
units/departments of both health facilities and heath
offices. SNNPR health bureau should arrange HMIS
training for health workers in the study area. The data
sets used and/analyzed during this study are available
from the corresponding author up on reasonable
request.
ACKNOWLEDGEMENTS
We are grateful to Jimma University for the financial
support of data collection of this work. Our thanks go
to managers of study health facilities for their
permission to conduct the study in their facilities. We
also acknowledge our study participants for providing
the necessary information and the data collectors for
collecting the data carefully.
CONFLICT OF INTEREST
There is no conflict of interest associated with this
study.
REFERENCES
1. World Health Organization (WHO). Analysing disrupted
health systems in countries in crisis. WHO. 2011.
2. Health Metrics Network/World Health Organization.
Framework and Standards for Country Health Information
Systems. 2012.
3. Belay H, Azim T, Kassahun H. Assessment of Health
Management Information System ( HMIS ) Performance in
SNNPR, Ethiopia. 2014.
Oridanigo et al., Universal Journal of Pharmaceutical Research 2022; 7(2):48-54
54 CODEN (USA): UJPRA3
4. Adane T, Tadesse T, Endazenaw G. Assessment on
Utilization of Health Management Information System at
Public Health Centers Addis Ababa City Administrative,
Ethiopia. Internet Things Cloud Comput. 2017;5(1):7–18.
5. Michelle Li IB and EG. Barriers to use of health data in
Low- and Middle- Income Countries: A review of the
literature. Measure Evaluation and USAID. 2018.
6. Ruiling Guo, Tracy J. Farnsworth PMH. Information
resources for hospital administrator healthcare management
decision making. J Hosp Libr. 2016; 15(3):274–83.
PMID: 27134574
7. Ethiopia M of HFDR of. HMIS Information Use Guide:
Technical standards. Vol. 2. 2013.
8. Ethiopian Federal Ministry of Health (FMOH). Health
Sector Transformation Plan (HST). 2015.
9. Shiferaw AM, Zegeye DT, Assefa S, Yenit MK. Routine
health information system utilization and factors associated
thereof among health workers at government health
institutions in East Gojjam Zone, Northwest Ethiopia. BMC
Med Informatics Decis Mak 2017;17(116):1–9.
PMID: 28784115
10. Federal Ministry of Health Republic of Ethiopia. National
Health Information System Road Map. 2012.
11. Belay H, Azim T, Kassahun H. Assessment of Health
Management Information System (HMIS) Performance in
SNNPR , Ethiopia 2014.
12. Sultan Abajebel, Jira C, Beyene W. Utilization of health
management information system at district level in Jimma
zone Oromia region, South West Ethiopia. Ethiop J Health
Sci. 2011; 21(Augst):65–76. PMID: 22435010
13. Glenn D. Israel. Determining Sample Size. 2003; PEOD6.
14. Abera E, Daniel K, Letta T, Tsegaw D. Utilization of Health
Management Information System and Associated Factors in
Hadiya Zone Health Centers, Southern Ethiopia. J Res
Health Sci 2016;1(2):98–109.
15. Teklegiorgis K, Tadesse K, Mirutse G, Terefe W. Factors
associated with low level of health information utilization in
resources limited setting , Eastern Ethiopia. Int J Intell Inf
Syst. 2014; 3(6):69–75.
16. WHO. Health information systems:Toolkit on monitoring
health systems strengthening. World Health Organization.
2008.
17. Teklegiorgis K, Tadesse K, Mirutse G, et al. Level of data
quality from Health Management Information Systems in a
resources limited setting and its associated factors , eastern
Ethiopia Previous literatures. South African J Inf Manag.
2016;1–8.
18. Measure Evaluation U. Health Management Information
System (HMIS): Facilitator’s Guide for Training of Trainers.
2010.
19. Wude H, Woldie M, Melese D, Lolaso T, Balcha B.
Utilization of routine health information and associated
factors among health workers in. Plose One. 2020; 1–11.
20. World Health Organization (WHO). Monitoring results with
health facility information systems : A technical consultation
Summary report. WHO. 2014.
21. Archangel N. The critical issues affecting the introduction of
health management information systems in developing
countries in Africa. 2007.
22. Zareipour P, Hessam S, Yazdanpanah A. Examining the
impact of access to healthcare information in providing
service in hospital level of Hormozgan province. Universal J
Pharm Res 2018; 3(3): 49-52.
https://doi.org/10.22270/ujpr.v3i3.167
23. Saeed AA. Infection control: standard precaution measures
knowledge, attitude and practice of medical doctors
and nurses in Sudan. Universal J Pharm Res 2021; 6(5):7-13.
https://doi.org/10.22270/ujpr.v6i5.666
