Health Management Information System Data Use Practice and Its Determinants at Health Centers and Woreda Health Office in Fafan Zone, Somali Region, Ethiopia.

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Abdi Farah, Kaldir hassen, Abdi Mohamed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1809396/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sound and reliable information is the foundation of decision-making across all health system building blocks and is essential for health system policy development and its implementation. Ethiopian health sector transformation plan has given special attention to health information management, data use intending to promote the quality and culture of health information data use for decision making. Hence, this study aims to assess the practice of routine Health Information data use for decision-making and its determinants in Fafan Zone Somali region. A cross-sectional study was carried out in August 2021 to assess routine Health Information, data use practices, and its determinants in the Fafan zone Somali region. The participants of the study were 359 health workers from different departments of selected health centers and woreda health offices by using cluster-sampling techniques. The study findings showed that the health workers' practice of RHI data use for decision-making is very low. The determinants of Routine health information management data use practice that was identified in the study include work position level, Health worker's educational level, presence of regular Supportive supervision flowed by timely feedback on performance, training status of data users, and availability of all required inputs for the preparation and display information, and data management guidelines. Therefore, enhancing knowledge, skills, data management inputs, supportive monitoring, and access to user training are important to expand the use of routine health information data in health centers and woreda health offices in the Fafan zone. Routine health information practices and Somali regional state 1. Background Sound and reliable information is fundamental to decision-making in all the building blocks of health systems and is essential for the formulation and implementation of health system policies, governance, and regulation, medical research, human resource development, medical education and training, delivery, and funding services [ 1 ]. Health Management Information System is one of the six building blocks of a health system that integrates data collection, processing, reporting, and use. The effectiveness and efficiency of health services need to be improved through better health management information systems at all levels of the health service delivery system. HIMS can be classified as a population-based health information system and a routine health information system "RHIS". Thus, RHIS is a system in which health data has been recorded, stored, retrieved, and processed to improve health decision-making[ 2 , 3 ]. Since the introduction of primary health care as the essential healthcare strategy in 1978, Health information systems come to the attention of health sector leadership and become a discussion agenda throughout the world[ 4 – 7 ]. Countries around the world including developing countries have instigated to implement an extensive reforms to improve and expand health information systems as part of health system reform[ 5 ]. Ethiopia shown a commitment to institutionalize primary health care strategy following to Almeta conference. Government of Ethiopia has made series reforms on health system to improve access, quality and equity health care services and ensure the health status of Ethiopian citizens[ 8 – 10 ]. To translate this commitment into action and institutionalize the health sector reforms up to grass root level, FMOH has developed and implemented twenty years health Sector development plan which was taken four consecutive phases from 1997 to 2015[ 8 ]. Health information system was part of these reforms, nationally it was introduced in 2006. Its implementation was started as a pilot in some selected regions between 2006 to 2007[ 11 ]. Later on, the implementation was scaleup to different level of health sector and all regions in Ethiopia. The development of HMIS reforms passed through different stages starting from paper-based reporting to digitalized web-based reporting system which currently under implementation[ 8 , 11 , 12 ]. Reforms have taken important steps to address common health information management problems that limit the quality of health care systems, planning and management, and decision-making of managers in Ethiopia[ 13 ]. As a result, the Health Sector Transformation Plan (HSTP) identified the need for an information revolution as one of four transformation programs related to the advancement of methods from data collection to information use culture[ 8 , 13 ]. The main idea of implementing the information revolution agenda is to make a radical transformation in the process of data generation, analysis, and promotion of culture and attitude toward data use in order to optimize health care at all levels by increasing the availability, usability, quality, and use of health information for decision-making processes through the right use of information and communication technology[ 8 , 13 ]. Since the introduction of the information revolution agenda in the Somali region, RHB with the support of the FMOH and partners has made a lot of investments to translate the agenda idea into action and improve data use culture at lower levels of the health system in the Somali region. Some of the supports include organizing different platforms and providing capacity-building opportunities to woreda and health facilities leadership and staff, and distribution of different supplies, and equipment required for strengthening lower-level HMIS implementations[ 14 , 15 ]. Despite all of these efforts, there is no evidence suggesting the progress of the of data use[ 15 , 16 ]. So, it is critical to assess the status and generates evidence to use for further development of the implementation of the agenda. Therefore, this study assesses the practice of routine health information data use for decision making and its determinants in health centers and woreda health offices of Fafan zone, Somali region. 2. Methods A cross-sectional study was carried out from May to June 2021 to assess the practice of routine health information data use for decision making and its determinants at selected health offices and health centers in the Fafan zone Somali region, Ethiopia. Fafan zone is located in the north part of the Somali region which is located 630 KM away from the east of the capital city of Ethiopia. It has a total population of the Fafan area is estimated at 1,314,718 (CSA, 2007). Of this total, 44.1% and 56.3% were women and men, respectively. Fafan area has 14 Woreda, 32 medical centres, and 275 medical stations, including 49 private clinics. According to the 2012 EFY zonal health office report, the potential health service coverage of the Fafan zone is 87%. A total of 5349 different level Health professionals and 1312 Administrative workers are currently providing service to the community in the government health facilities. According to the zonal health office report, the zone has 7 GP, 62 HO,_168 Nurses,_196 Midwives, 79 Lab technicians, and 47 HIT staff[ 14 ]. Fafan zone has a zonal health Team, 11 Rural woreda health offices (WoHo) teams, 3 city administration health office teams, and 36 Health center teams. Due to resource and time constraints, it was not visible to reach all woreda and Health centers in the Fafan zone. So, 50% of each team was selected randomly by using the lottery method, and a total of 6 Rural Woreda health office teams, 2 city administration health office teams, and 18 Health centre teams were selected by using the Cluster-sampling technique. 317 study health workers selected from different departments of health centres and woreda health offices were included in the study. Data was collected using a pre-tested questionnaire that was developed based on the PRISM assessment tool. Data were collected using a pre-tested questionnaire developed using the findings of the different kinds of literature reviewed. In addition to this, the PRISM assessment tool contains behavioural, Technical, and organizational factors affecting routine health information system use [ 5 , 17 ]. Questionnaire pretesting was made immediately after finalizing the questionnaire development process. Then, it was converted to the Kobo tool to make data collection and save data entry time. Descriptive analysis was made to summarize data and frequencies, and percentages and descriptive statics were computed and presented using graphs, and tables. Binary logistic regression was used to determine the factor influencing the data use practice of study participants. Bivariate analysis was made to see variables that have associations and crude odds ratio and confidence interval was computed to measure the association between the utilization of health information and exposure variables. In Bivariate analysis all variables with P-value < 0.2 was considered significant and selected as a candidate variable for multi-variables analysis. Finally, all variables that become significant in the bivariate analysis were selected and looked at in multivariate analysis to see the effect of different variables on information use practice. In Multivariate analysis, all variables with P-value < 0.05 was considered significant. Both Crude odd ratio and Adjusted OR with a 95% confidence interval were calculated to describe the association. Ethical clearance was obtained by institutional review board (IRB) college of business and economics, Further Permission was obtained from Graduate Coordinator of the Department and submitted to the Somali Regional state health bureau at Jigjiga, Fafan zone health office, woreda health Office, and health Center studied. During the interview, each individual was informed about the aim of the study and the possible benefit of the study Informed consent was obtained from each respondent, and they were told to have the right to give up the interview at any time she/he wishes. 3. Results 3.1. Description of Socio-Demographic characteristics of the respondents A total of 359 respondents were included in the study, representing a response rate of 96.8%. Of these, 255 (71%) respondents were male and 103 (29%) were female. Regarding the age of the study subjects, 232 (65%) were under 30 years old, and the remaining 126 (35%) were over 30 years old. The mean age of the respondents was 29.51 (SD ± 4.20 years) while the mean age of the respondents was 29 years old. (Table 1 ) Table 1 ; Study participants' socio-demography, in selected woreda health and Health centers in Fafan Zone. Variables (N = 359) Frequency in N (%) Sex Male 255(71) Female 103(29) Age Above30 126(35) Below30 232(65) Education level Degree 183(51) Master and Above 4(1) Diploma 169(47) Certificate 2(1) Type of profession Nurse 136(39) Health officer 69(20) Midwifery 53(15) Laboratory 32(9) Pharmacy 36(10) Information Technologist 10(3) General Practitioner (Dr) 1(0) Others, 16(5) Position Service provider 223(62) Institution or department head 122(34) Others 13(4) Experience/ Service years 10–14 30(8) 0–4 253(71) 5–9 75(21) Regarding the education level, 169(47%) of the respondent were diplomas, 183(51%) were degree holders and the remaining 4 (1%) were master level degree holders. Of the study participants, 223 (62%) were in a medical institution, and the remaining 122 (34%) were in management positions. The majority of the participants 253(71%) had work experience or service years below 4 years. 8% of the study participants had more than 10 years' work experience while 21% had five up to 10 years of experience. The overall mean of participants' work experience was 4.5 years (SD: ±3.23 years). Regarding profession type of study participants,39% were nurses, 20% were health officers, 15% were midwifery and the remaining study participants were other health professionals. 3.2. HMIS implementation Regarding the availability of inputs required for the implementation HMIS, all most all participants, or 90(59) heard about HMIS while 261(73) knew the importance of HIMS, and 57% of the study participants trained on HMIS. (Table 2 ) Table 2 Availability of required inputs for RHIMS Implementation Variables Response Yes No HMIS Trained 205(57) 153(43) Ever heard HMIS 90(59) 63(41) Know HMIS importance 261(73) 97(27) Register 328(92) 30(8) Tally sheet 296(83) 62(17) Monthly reporting formats 320(89) 38(11) HMIS procedure manual 158(44) 200(56) HMIS user guideline 121(34) 237(66) Required stationeries for recording 242(68) 116(32) data collection standards including case definitions 162(45) 196(55) Availability of key HIMS input, most of the respondents 328(92) had standard service registration books, 296(83) had Standard tally, 320(89) had Standard reporting formats and 121(34) of them had a new HMIS procedure manual. 234(65) of the respondents were registering their activities and 170(47) of the participants filled out complete registration books. All most all of the participants reported having data transmission, processing, and reporting rules. 171(48) study participants aggregate or compile services from the tally sheet correctly according to the guideline. (Table 3 ) Table 3 RHMIS implementation status at woreda health offices and health centers, Fafan zone. Variables Response Yes No Register all your activities 234(65) 124(35) Register filled completely 170(47) 188(53) Aggregate or compile services from tally sheet correctly 171(48) 187(52) Report submitted complete timely and accurate 184(51) 174(49) Conducted data accuracy taste 130(36) 228(64) Received supervision for the last three months 174(50) 175(50) Get Feedback from the top-level organization 156(47) 176(53) Data collection standards including case definitions 162(45) 196(55) Having data transmission processing and reporting rules 185(52) 173(48) Conducted Self-Assessment on your performance 169(47) 189(53) Regarding the reporting of activities, all woreda health offices and health centers had a uniform reporting schedule set. At the health center level, all deportments were expected to compile data from the tally sheet and send their report to the HMIS focal person of the Health center. Thus 184(51) of the study participants submitted complete, timely, and accurate reports. 28(88%) practices and conducted a Self-Assessment of performance. 174(50) participants reported having supervision from top-level organizations and 156(47) got feedback. 3.3. Health information data use practice About 192(54) study participants had practiced changing the data into information on a monthly basis while around 177(49) of studied subjects also practiced using data for Action planning purposes. Around 211(59) of the study participants used to practice adapting the national target to the local situation by using the woreda Health sector-based national target. In addition to these, 145(41) study subjects also reported having an HMIS multi-disciplinary committee While 144(40) of them also reported having a health information steering committee to set the long-term goals for HIS. (Table 4 ) Table 4 Status of RHIMS utilization indicators in selected woreda health and Health centers in Fafan Zone. Variables Response Yes No Change the data into information every month 192(54) 166(46) Use your data to prepare a plan of action 177(49) 181(51) The adaption national target to the local situation 211(59) 147(41) Has key indicators with charts, tables 172(48) 186(52) Maintain worksheets and charts for monitoring performance 164(46) 194(54) to identify problems in performance, discuss and analyze with unit staff and present possible reasons/causes to review in a team meeting 160(45) 198(55) Present HMIS reports and discusses at the performance monitoring team 165(46) 193(54) In your unit team meetings, was the achievement of targets included 164(46) 194(54) Having HIS/HMIS multi-disciplinary committee 145(41) 213(59) Has a Health information steering committee to set the long-term goals 144(40) 214(60) Monitors key indicators and prepares woreda profile 173(48) 185(52) Supervises health information system activities at facilities 158(44) 200(56) Dept- compare facility performance against plan target 170(47) 188(53) Dept-compare facility performance against target Population 167(47) 191(53) Presence of any display related to your department's activity 170(47) 188(53) The average HMIS utilization practices (Utilization status) 166(46) 192(54) About 158(44) of the study participants reported their department used KPI analysis reports for catchment area profile preparation. Around 200 (56) of the participants stated that their department does not oversee the activities related to the health information system at the facilities. As shown in Table 4 , Fifteen performance measurement indicators were used to collect practice-related information to determine the level of practicing data use for decision making in the departments of the healthcare facilities participated in the study. The average score for these indicators was calculated for each department to be classified the status. Healthcare departments that achieved average score 10 and above were classified as having data use practice, and departments scored below 10 were classified as not having data use practice. The overall average of HMIS usage practices (usage status) in the study area was 46%. 3.4. Bi-variable analysis results of health information data use practice To evaluate the possible associations between the outcome variable and Exposure (predictor) variables bivariate logistics regression analysis was employed. Crude odds ratio and confidence interval was computed to measure the association between the health information data use practice and exposure variables. In bivariate analysis all variables with P-value < 0.25 was considered significant and selected as a candidate variable for multi-variables analysis. According to the bivariate analysis results, Gender, educational background, Age of the respondents, position of work, and years of working experience become statistically significant. (Table 5 ). Table 5 Comparison of socio-demographic characteristics of study subjects with the utilization of RHIMS, Fafan zone Variable Responses Utilization status (%) OR at 95.0% C.I) Value No Yes Sex Female 35(34) 68(66) 0.76(0.45–1.27) 0.23 Male 77(30) 178(70) Position Manager 31(29) 77(71) 3.17(0.7–3.93) 0.15 Service provider 81(32) 169(68) 1 Educational Level /background Degree 53(31) 116(69) 2.38(1.91–3.17) 0.19 Diploma 59(32) 128(68) 1 Age of the respondents 20–29 Years 69(30) 163(70) 1.33(0.78–2.29) 0.22 Above-30 Years 43(34) 83(66) Experience 0–4 78(31) 175(69) 1.06(0.61–1.86) 0.183 10–14 9(30) 21(70) 1.27(0.48–3.34) 0.163 5–9 25(33) 50(67) 0 Similar to a socio-demographic variable, bivariate analysis was also used for other exposure variables related to the level of health information data use practice and factors influencing. According to the analysis result, the health information data use practices-related variables that were found to be significantly associated with outcome variables in bivariate analysis were knowing HMIS importance, HMIS use guidelines and HMIS procedure manual, receiving supervision for the last 3 months, registering all your activities, Aggregate or compile data from tally sheet correctly, Report submitted complete, timely, and accurate and conducted data accuracy checking. (Table 6 ) Table 6 Comparison of factors affecting the level of RHIM practices with the utilization of RHIMS, Fafan zone. Variable Responses Utilization status (%) OR at 95.0% C.I) P-Value No Yes HMIS Trained No 37(24) 116(76) 1 Yes 75(37) 130(63) 2.08(1.57–2.07) 0.08 * know HMIS importance No 25(26) 72(74) 1 Yes 87(33) 174(67) 1.54(0.73–3.26) 0.26 * HMIS use guideline No 50(21) 187(79) 1 Yes 62(51) 59(49) 3.8(2.87–4.95) 0.1 * Register all your activities No 21(17) 103(83) Yes 91(39) 143(61) 0.56(0.29–1.09) 0.09 * Register filled completely No 32(17) 156(83) Yes 80(47) 90(53) 0.7(0.33–1.51) 0.37 Aggregate or compile data from tally sheet correctly No 28(15) 159(85) 1 Yes 84(49) 87(51) 3.33(2.7–3.7) 0.004 * Report submitted complete, timely, and accurate No 27(16) 147(84) 1 Yes 85(46) 99(54) 0.36(0.17–0.76) 0.01 * Received supervision for the last 3 months No 39(22) 136(78) 1 Yes 71(41) 103(59) 3.3(3.04–4.78) 0.17 * having data transmission, processing, and reporting rules No 44(25) 129(75) 1 Yes 68(37) 117(63) 0.91(0.41–2.03) 0.82 HMIS procedure manual No 42(21) 158(79) 0.96(0.4–2.28) 0.92 Yes 70(44) 88(56) 1 know who utilizes HIS No 18(31) 41(69) 1 Yes 52(36) 93(64) 3.64(3.34–4.2) 0.17 * Conduct data accuracy No 51(22) 177(78) 1 Yes 61(47) 69(53) 2.79(2.62–3.1) 0.08 * Self-assessment No 45(24) 144(76) 1 Yes 67(40) 102(60) 0.96(0.4–2.28) 0.92 Get feedback from top-level No 6(23) 20(77) 1 Yes 46(26) 130(74) 1.54(0.6–3.93) 0.37 3.5. Multivariable analysis results of Routine health information data use practice In this study, multivariable logistic regression analysis was carried out to control possible confounders and identify factors independently associated with Routine information utilization. Finally, variables with a p-value less than 0.05 in multivariable logistic regression analysis are considered as independently significant association with Routine information practice. To determine the magnitude of association between the dependent and independent variables odds ratio was used. In our analysis, health information utilization practice was compared with socio-demographic variables such as age, year of services; sex, experience, position of work, and educational status of study participants were analyzed. Educational level and position were significant before adjusting confounders and still show significant association yet in multiple logistic regressions analysis. The remaining socio-demographic variable still did not show statistically significant associations even after adjusted multiple logistic regression. According to our study findings, a managerial level position has a higher likelihood of practicing health information utilization when compared with a health care provider level position at a p-value of 0.035, (AOR = 2.09, (95% C.I, 1.5–2.91). Similarly, the educational level of the respondent had significant associations with HMIS utilization practices after adjustment at a p-value of 0.023 [AOR = 2.09, (95% CI, 1.38–2.61)]. The results of this study also showed that those who were trained were approximately 2.3 times more likely to practice routine health information than those who were not trained in routine health information [AOR = 2, 3; 95% CI: (0.67–2.55)].(Table 7 ) Table 7 Variables evaluated, for a possible association, health information use practice among Health workers working in Fafan zone health institutions. Variable Responses Utilization status (%) COR at 95% C.I) AOR at 95% C.I) P-value No Yes Sex Female 35(34) 68(66) 0.76(0.45–1.27) 0.98(0.49–1.93) .943 Male 77(30) 178(70) 1 1 Position Manager 31(29) 77(71) 3.17(0.7–3.93) 1.97(1.5–2.91) .035 * Service provider 81(32) 169(68) 1 1 Educational Level Degree 53(31) 116(69) 2.38(1.91–3.17) 2.09(1.38–2.61) .023 * Diploma 59(32) 128(68) 1 1 Age of the respondents 20–29 Years 69(30) 163(70) 1.33(0.78–2.29) 0.63(0.09–4.32) .638 Above-30 Years 43(34) 83(66) 1 1 Experience 0–4 78(31) 175(69) 1.06(0.61–1.86) 1.92(0.8–4.62) .146 10–14 9(30) 21(70) 1.27(0.48–3.34) 0.68(0.18–2.51) .563 5–9 25(33) 50(67) 1 1 HMIS Trained No 37(24) 116(76) 1 1 Yes 75(37) 130(63) 2.08(1.57–2.07) 2.3 (0.67–2.55) .031 * know HMIS importance No 25(26) 72(74) 1 1 Yes 87(33) 174(67) 1.54(0.73–3.26) 0(0–0) .000 HMIS user guideline No 50(21) 187(79) 1 1 Yes 62(51) 59(49) 3.8(2.87–4.95) 2.34(1.17–2.68) .002 * Register all your activities No 21(17) 103(83) 1 1 Yes 91(39) 143(61) 0.56(0.29–1.09) 0.71(0.35–1.44) .342 Register filled completely No 32(17) 156(83) 1 1 Yes 80(47) 90(53) 0.7(0.33–1.51) 0(0–0) .000 Aggregate or compile data from tally sheet correctly No 28(15) 159(85) 1 1 Yes 84(49) 87(51) 3.33(2.7–3.7) 2.5(2.67–2.95) .015 * Report submitted complete, timely, and accurate No 27(16) 147(84) 1 1 Yes 85(46) 99(54) 0.36(0.17–0.76) 0.28(0.12–0.63) .002 * Received supervision for the last 3 months No 39(22) 136(78) 1 1 Yes 71(41) 103(59) 3.3(3.04–4.78) 2.2(2.9–3.81) .019 * having data transmission, processing, and reporting rules No 44(25) 129(75) 1 1 Yes 68(37) 117(63) 0.91(0.41–2.03) 0(0–0) .000 HMIS procedure manual No 42(21) 158(79) 0.96(0.4–2.28) 0(0–0) .000 Yes 70(44) 88(56) 0(0–0) 0(0–0) .000 know who utilizes HIS No 18(31) 41(69) 1 1 Yes 52(36) 93(64) 3.64(3.34–4.2) 3.41(3.19–3.89) .024 * Conduct data accuracy test No 51(22) 177(78) 1 1 Yes 61(47) 69(53) 2.79(2.62–3.1) 2.41(1.18–2.92) .031 * At the p-value of 0.024, participants who knew who used the HMIS report had odds of practicing health information data use that were about three times higher than those of their counterparts [AOR = 3.41; 95 percent CI: (3.19–3.89). The odds of routine health information use practice were about 2.4 times more among individuals who conducted data accuracy tests in the last three months when compared with individuals who did not conduct data accuracy at a p-value of .031 [AOR = 2.41; 95% CI: (1.18–2.92)]. This study also found that participants who received supportive supervision over the previous three months had an approximately two-fold higher likelihood of using routine health information usage practices than those who had not received any supervision from a higher level (AOR = 2.2; 95 percent CI: (2.9–3.81)) at p-value 0.019. In addition to this, the study also reported that participants who had an HMIS user guide had an approximately two-fold higher likelihood of using routine health information data usage practices than those who don't have this guideline [AOR = 2.34 95% CI (1.17–2.68)] at p-value 0.002. 4. Discussions The present study tried to assess the practice of health information data uses for decision-making in the studied health institutions Fafan zone. According to the study result, the overall health information utilization practice of the study area was founded to be 46%, which indicates low coverage when we compare with a study conducted in south Korean health facilities which showed over 80% of the use of regular health information was rated highly by the total respondents working in health facilities [ 18 , 19 ]. The difference in utilization rate was because Korean primary health care facilities were better structured and equipped than the Ethiopian health tier system. The study reported that the use of health data for decision-making in Fafan Zone healthcare facilities was less practiced than the study conducted in Addis Ababa, which reported 78% data utilization, and other studies conducted in health facilities in the southern and eastern parts of Ethiopia where, also reported the practice of data use of 54.4% and 53.1%, respectively[ 20 , 21 ]. On the contrary, the use of data for decision-making is more practices/better in our studied health facilities when we compared to the results obtained in the studies conducted in the health facilities of the Jimma, Arsi, and Gonder areas[ 22 – 24 ]. The reason for this variation could explain the difference in the period studied, the type of structure, and other technological developments and advances at HMIS. The results of this study showed that trained individuals were about 31 times more likely to practice routine health information than those who were not trained in routine health information [AOR = 1.31; 95% CI: (0.67–2.55)]. The finding of this study supported other studies conducted at primary healthcare facilities in Western Amhara which reported a significant association between the training of staff on HMIS user guide and data to use for decision making [AOR = 2.85; 95% CI: (0.67–2.55)][ 25 ]. According to this study, people who have received supportive supervision in the previous three months are about twice as likely to use routine health information utilization practices compared to people who have not received supportive supervision [AOR = 2.2; 95 percent CI: (2.9–3.81)] [P-value = 0.019]. This is proved by studies conducted in the Gojam Amhara region in northwestern Ethiopia, which reported supportive supervision as an important determinant for the practice data use culture [(95% CI = [1.71, 5.28] [ 26 ]. In addition to this, the study also reported that participants who had an HMIS user guide had an approximately two-fold higher likelihood of using routine health information data usage practices than those who don't have this guideline [AOR = 2.34 95% CI (1.17–2.68)] at p-value 0.002. This result was confirmed in a study in East Gojam, northwestern Ethiopia, in which participants with data management guides were approximately three times more likely to use daily health information than those participants don’t have [OR = 3; 95 percent CI: (1.27, 8.32)][ 27 ]. 4.1. Strength of the study Use of PRISM tool, which is a standard tool, designed to capture key information on the study subject. Probably this is the first study of its type in the Somali region and will helps other future studies. This study can provide a snapshot of RHIM use/ practice in the study area. It will help or guide the development of some interventions for improving the program implementation. 4.2. Limitation of the study The study may not represent the general population of the study (to the whole region) since it involves only a sample of health facilities in the Fafan zone. It was not also included health posts. So, we cannot generalize all level health facilities. The design of the study (cross-sectional) design and cannot provide detail all the required information's for improving the RHIM in the study area. The use of professional data collectors could also be one of the limitations of this study, as professionals tried to redirect and use the respondents in their own way. The study lacks a qualitative part, which helps us to get more about a topic. 5. Conclusion And Recommendations In conclusion, the findings of this study showed that the level of health workers' practice of RHI data use for decision-making is still very low in Fafan zone health institutions compared to national health sector transformation plan and information revolution road maps expectation or target. The finding of this study also identified the major factors that determine the practice of data use, which include work position level, Health worker's educational level, presence of regular Supportive supervision flowed by timely feedback on performance, training status of data users, and availability of all required inputs for the preparation and display information, and data management guidelines. Thus, to strengthen the data use practice in the studied health facilities, it's critical to focus on improving users' knowledge and skills, availing all necessary inputs and manuals for HMIS implementation. In addition to these, it is also important to implement regular supportive supervision and feedback mechanisms to facilitate the promotion and reinforcement of data use culture in health centers and woreda health offices in Fafan. Declarations Ethics approval and consent to participate Ethical clearance was obtained by institutional review board (IRB) college of business and economics, Jigjiga University with IRB protocol number JJU/0082/14. Further Permission was obtained from Graduate Coordinator of the Department and submitted to the Somali Regional state health bureau at Jigjiga, Fafan zone health office, woreda health Office, and health Center studied. A written informed consent was obtained from all subjects, and this study is done in accordance with declaration of Helsinki procedures. Consent for publication This study doesn’t involve details, images, videos related to an individual’s persons. So, getting consents for publication is not applicable. Availability of data and materials All data generated or analyzed during this study are available at corresponding author but is not publicly available. This is because the raw data collected by the interviewed health facilities contains detailed, and sensitive information about the facilities. The Somali Regional Health bureau (government), owned by the institutions studied, does not allow the sharing of this raw data or information’s directly with third parties or publicly. Competing interests The authors declare that they have no competing interests Funding Somali regional state health bureau has provided some financial support to authors to cover the transportation costs during data collection only. The Authors finalized the remaining research works without getting any other additional supports/ assistance. Authors' contributions AF developed the study design, collected data, and did the analysis, interpretation, and manuscript write-up. KH, AM contributed to the conception of the research idea, participate in the conceptualization of the idea, and assisted draft finalizing. All authors read and approved the final manuscript. Acknowledgements The authors would like to express their deeply gratitude to Jigjiga University, college of Business and economics for support the accomplishment of this study. Authors are thankful for the cooperation and support of Somali regional health bureau, Fafan zone health office, woreda health Office, and all its health Centers. We would also like to special thank supervisors and data collectors for taking for their precious time to collect data. We are glad to thank the respondents who participated in this study and took their time to provide information. References WHO, H., Framework and standards for country health information systems/Health Metrics Network . World Health Organization—, 2008. WHO, Ethiopia Health Data Quality Review: System Assessment and Data Verification for Selected Indicators. , 2016. Manyazewal, T., Using the World Health Organization health system building blocks through survey of healthcare professionals to determine the performance of public healthcare facilities . Archives of Public Health, 2017. 75 (1): p. 1–8. Lippeveld, T., et al., Design and implementation of health information systems . 2000: World Health Organization. Fraser, H.S. and J. Blaya. Implementing medical information systems in developing countries, what works and what doesn’t . in AMIA Annual Symposium Proceedings . 2010. American Medical Informatics Association. Organization, W.H., Everybody's business–strengthening health systems to improve health outcomes: WHO's framework for action. 2007. Organization, W.H., Operational framework for primary health care: transforming vision into action. 2020. ministér, E.Y.e.n.t.e., Health Sector Transformation Plan: 2015/16–2019/20 (2008–2012 EFY) . August 2015 ed. 2015, Addis Ababa: Federal Democratic Republic of Ethiopia Ministry of Health. Mohan, P., Ethiopia health sector development program. 2007. Hartwig, K., et al., Hospital management in the context of health sector reform: a planning model in Ethiopia . The International journal of health planning and management, 2008. 23 (3): p. 203–218. Belay, H., T. Azim, and H. Kassahun, Assessment of health management information system (HMIS) performance in SNNPR, Ethiopia . Measure Evaluation, 2013. FMOH, Health Sector Development program III. , ed. P.a.P. Department. 2005, Addis Ababa: FMOH. Health, M.o., Information Revolution Roadmap . 2018. RHB, S., Annual performance report P. directorate, Editor. 2020. Bureau, S.r.H., Data Quality Review (DQR) in Somali region . 2020. Institute, E.P.H., Ethiopia Health Data Quality Review: System Assessment and Data Verification for Selected Indicators. . 2018, Ethiopia: Ethiopian Public Health Institute: Addis Ababa, Ethiopia. Aqil, A., T. Lippeveld, and D. Hozumi, PRISM framework: a paradigm shift for designing, strengthening, and evaluating RHMIS . Health planning, and policy, 2009. Tierney, W.M., et al., Assessing the impact of a primary care electronic medical record system in three Kenyan rural health centers . Journal of the American Medical Informatics Association, 2016. Seo, K., H.-N. Kim, and H. Kim, Current Status of the Adoption, Utilization and Helpfulness of Health Information Systems in Korea . International journal of environmental research and public health, 2019. 16 (12): p. 2122. !!! INVALID CITATION !!! [Abera, 2016 #44;Teklegiorgis, 2014 #63;Abera, 2011 #64;Abera, 2016 #44]. Adane, T., T. Tadesse, and G. Endazenaw, Assessment on Utilization of Health Management Information System at Public Health Centers Addis Ababa City Administrative, Ethiopia . Internet Things Cloud Comput, 2017. Abera, M., Health policy and the extent of health information use in Woreda health care system of Arsi zone, Oromia Region, Ethiopia. Unpublished thesis Work). Addis Ababa University, Addis Ababa, 2011. Abajebel, S., C. Jira, and W. Beyene, Utilization of health information system at district level in Jimma zone Oromia regional state, South West Ethiopia . Ethiopian journal of health sciences, 2011. Gashaw, A., Assessment of utilization of Health Information System at district level with particular emphasis to HIV/AIDS program in North Gonder . Addis Ababa University, 2006. Asemahagn, M.A., Determinants of routine health information utilization at primary healthcare facilities in Western Amhara, Ethiopia . Cogent Medicine, 2017. Abera, E., et al., Utilization of health management information system and associated factors in Hadiya zone health centers, Southern Ethiopia . Res Heal Sci, 2016. 1 (2): p. e98. Shiferaw, A.M., et al., Routine health information system utilization and factors associated thereof among health workers at government health institutions in East Gojjam Zone, Northwest Ethiopia . BMC medical informatics and decision making, 2017. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1809396","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":120022664,"identity":"fe773b3a-003f-4d84-9665-ffda306a5fba","order_by":0,"name":"Abdi Farah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACCSDmYZADkYwPJCqANDNzAzFajEEks4HFGZAWRuK1sElUtoGECGjhn92d+OBNjYG8efvZAxI359VG87cDtfyo2IbbkjtnNxvOOWZgOOdMXoLhzG3Hc2ccZmxg7DlzG7c1N3K3SfOw/WGcwZBjkCy57VhuA1ALM2Mbbi3yN3K3/+b5Z2A/g/+NweG/c47lziekxQBoCzNvm0HiDIkcwwbJhprcDYS0GN7I3Sw5t88geYbEG2MGiWMHcjcCtRzE5xe5G7kbP7z5ZmA7gz/H/IdETV3uvPOHDz74UYHH+2jgMJg8QLR6IKgjRfEoGAWjYBSMEAAApKde4VJUmdYAAAAASUVORK5CYII=","orcid":"","institution":"Jigjiga University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Abdi","middleName":"","lastName":"Farah","suffix":""},{"id":120022666,"identity":"d93f8cb8-a2ee-4438-a647-0d334deada3f","order_by":1,"name":"Kaldir hassen","email":"","orcid":"","institution":"Jigjiga University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kaldir","middleName":"","lastName":"hassen","suffix":""},{"id":120022669,"identity":"83ae4dd4-7dbc-43cb-a993-607bdab12108","order_by":2,"name":"Abdi Mohamed","email":"","orcid":"","institution":"Jigjiga University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Abdi","middleName":"","lastName":"Mohamed","suffix":""}],"badges":[],"createdAt":"2022-06-29 23:14:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1809396/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1809396/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":25591585,"identity":"2b99fce7-4a72-43eb-bfb1-80eae7517828","added_by":"auto","created_at":"2022-08-24 09:44:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":422281,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1809396/v1/28cf634d-e105-4b45-a865-20f9586eb5d6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eHealth Management Information System Data Use Practice and Its Determinants at Health Centers and Woreda Health Office in Fafan Zone, Somali Region, Ethiopia.\u003c/p\u003e","fulltext":[{"header":"1. Background","content":"\u003cp\u003eSound and reliable information is fundamental to decision-making in all the building blocks of health systems and is essential for the formulation and implementation of health system policies, governance, and regulation, medical research, human resource development, medical education and training, delivery, and funding services [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHealth Management Information System is one of the six building blocks of a health system that integrates data collection, processing, reporting, and use. The effectiveness and efficiency of health services need to be improved through better health management information systems at all levels of the health service delivery system. HIMS can be classified as a population-based health information system and a routine health information system \"RHIS\". Thus, RHIS is a system in which health data has been recorded, stored, retrieved, and processed to improve health decision-making[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the introduction of primary health care as the essential healthcare strategy in 1978, Health information systems come to the attention of health sector leadership and become a discussion agenda throughout the world[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Countries around the world including developing countries have instigated to implement an extensive reforms to improve and expand health information systems as part of health system reform[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Ethiopia shown a commitment to institutionalize primary health care strategy following to Almeta conference. Government of Ethiopia has made series reforms on health system to improve access, quality and equity health care services and ensure the health status of Ethiopian citizens[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. To translate this commitment into action and institutionalize the health sector reforms up to grass root level, FMOH has developed and implemented twenty years health Sector development plan which was taken four consecutive phases from 1997 to 2015[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Health information system was part of these reforms, nationally it was introduced in 2006. Its implementation was started as a pilot in some selected regions between 2006 to 2007[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Later on, the implementation was scaleup to different level of health sector and all regions in Ethiopia. The development of HMIS reforms passed through different stages starting from paper-based reporting to digitalized web-based reporting system which currently under implementation[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Reforms have taken important steps to address common health information management problems that limit the quality of health care systems, planning and management, and decision-making of managers in Ethiopia[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As a result, the Health Sector Transformation Plan (HSTP) identified the need for an information revolution as one of four transformation programs related to the advancement of methods from data collection to information use culture[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The main idea of implementing the information revolution agenda is to make a radical transformation in the process of data generation, analysis, and promotion of culture and attitude toward data use in order to optimize health care at all levels by increasing the availability, usability, quality, and use of health information for decision-making processes through the right use of information and communication technology[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the introduction of the information revolution agenda in the Somali region, RHB with the support of the FMOH and partners has made a lot of investments to translate the agenda idea into action and improve data use culture at lower levels of the health system in the Somali region. Some of the supports include organizing different platforms and providing capacity-building opportunities to woreda and health facilities leadership and staff, and distribution of different supplies, and equipment required for strengthening lower-level HMIS implementations[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Despite all of these efforts, there is no evidence suggesting the progress of the of data use[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. So, it is critical to assess the status and generates evidence to use for further development of the implementation of the agenda. Therefore, this study assesses the practice of routine health information data use for decision making and its determinants in health centers and woreda health offices of Fafan zone, Somali region.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eA cross-sectional study was carried out from May to June 2021 to assess the practice of routine health information data use for decision making and its determinants at selected health offices and health centers in the Fafan zone Somali region, Ethiopia.\u003c/p\u003e \u003cp\u003eFafan zone is located in the north part of the Somali region which is located 630 KM away from the east of the capital city of Ethiopia. It has a total population of the Fafan area is estimated at 1,314,718 (CSA, 2007). Of this total, 44.1% and 56.3% were women and men, respectively. Fafan area has 14 Woreda, 32 medical centres, and 275 medical stations, including 49 private clinics. According to the 2012 EFY zonal health office report, the potential health service coverage of the Fafan zone is 87%. A total of 5349 different level Health professionals and 1312 Administrative workers are currently providing service to the community in the government health facilities. According to the zonal health office report, the zone has 7 GP, 62 HO,_168 Nurses,_196 Midwives, 79 Lab technicians, and 47 HIT staff[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Fafan zone has a zonal health Team, 11 Rural woreda health offices (WoHo) teams, 3 city administration health office teams, and 36 Health center teams. Due to resource and time constraints, it was not visible to reach all woreda and Health centers in the Fafan zone. So, 50% of each team was selected randomly by using the lottery method, and a total of 6 Rural Woreda health office teams, 2 city administration health office teams, and 18 Health centre teams were selected by using the Cluster-sampling technique. 317 study health workers selected from different departments of health centres and woreda health offices were included in the study. Data was collected using a pre-tested questionnaire that was developed based on the PRISM assessment tool. Data were collected using a pre-tested questionnaire developed using the findings of the different kinds of literature reviewed. In addition to this, the PRISM assessment tool contains behavioural, Technical, and organizational factors affecting routine health information system use [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Questionnaire pretesting was made immediately after finalizing the questionnaire development process. Then, it was converted to the Kobo tool to make data collection and save data entry time.\u003c/p\u003e \u003cp\u003eDescriptive analysis was made to summarize data and frequencies, and percentages and descriptive statics were computed and presented using graphs, and tables. Binary logistic regression was used to determine the factor influencing the data use practice of study participants. Bivariate analysis was made to see variables that have associations and crude odds ratio and confidence interval was computed to measure the association between the utilization of health information and exposure variables. In Bivariate analysis all variables with P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.2 was considered significant and selected as a candidate variable for multi-variables analysis. Finally, all variables that become significant in the bivariate analysis were selected and looked at in multivariate analysis to see the effect of different variables on information use practice. In Multivariate analysis, all variables with P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. Both Crude odd ratio and Adjusted OR with a 95% confidence interval were calculated to describe the association.\u003c/p\u003e \u003cp\u003e Ethical clearance was obtained by institutional review board (IRB) college of business and economics, Further Permission was obtained from Graduate Coordinator of the Department and submitted to the Somali Regional state health bureau at Jigjiga, Fafan zone health office, woreda health Office, and health Center studied. During the interview, each individual was informed about the aim of the study and the possible benefit of the study Informed consent was obtained from each respondent, and they were told to have the right to give up the interview at any time she/he wishes.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Description of Socio-Demographic characteristics of the respondents\u003c/h2\u003e\n\u003cp\u003eA total of 359 respondents were included in the study, representing a response rate of 96.8%. Of these, 255 (71%) respondents were male and 103 (29%) were female. Regarding the age of the study subjects, 232 (65%) were under 30 years old, and the remaining 126 (35%) were over 30 years old. The mean age of the respondents was 29.51 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;4.20 years) while the mean age of the respondents was 29 years old. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e; Study participants' socio-demography, in selected woreda health and Health centers in Fafan Zone.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariables (N\u0026thinsp;=\u0026thinsp;359)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFrequency in N (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e255(71)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103(29)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbove30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126(35)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBelow30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e232(65)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eEducation level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDegree\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e183(51)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaster and Above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4(1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiploma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e169(47)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCertificate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2(1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"8\" align=\"left\"\u003e\n\u003cp\u003eType of profession\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNurse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e136(39)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHealth officer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69(20)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMidwifery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53(15)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLaboratory\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32(9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePharmacy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36(10)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInformation Technologist\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10(3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGeneral Practitioner (Dr)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers,\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16(5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ePosition\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eService provider\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e223(62)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInstitution or department head\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122(34)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13(4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eExperience/ Service years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026ndash;14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30(8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026ndash;4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e253(71)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026ndash;9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75(21)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eRegarding the education level, 169(47%) of the respondent were diplomas, 183(51%) were degree holders and the remaining 4 (1%) were master level degree holders. Of the study participants, 223 (62%) were in a medical institution, and the remaining 122 (34%) were in management positions. The majority of the participants 253(71%) had work experience or service years below 4 years. 8% of the study participants had more than 10 years' work experience while 21% had five up to 10 years of experience. The overall mean of participants' work experience was 4.5 years (SD: \u0026plusmn;3.23 years). Regarding profession type of study participants,39% were nurses, 20% were health officers, 15% were midwifery and the remaining study participants were other health professionals.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. HMIS implementation\u003c/h2\u003e\n\u003cp\u003eRegarding the availability of inputs required for the implementation HMIS, all most all participants, or 90(59) heard about HMIS while 261(73) knew the importance of HIMS, and 57% of the study participants trained on HMIS. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAvailability of required inputs for RHIMS Implementation\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eResponse\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHMIS Trained\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e205(57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e153(43)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEver heard HMIS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90(59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63(41)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKnow HMIS importance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e261(73)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97(27)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegister\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e328(92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30(8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTally sheet\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e296(83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62(17)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMonthly reporting formats\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e320(89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38(11)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHMIS procedure manual\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e158(44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e200(56)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHMIS user guideline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121(34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e237(66)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRequired stationeries for recording\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e242(68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116(32)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edata collection standards including case definitions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e162(45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e196(55)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of key HIMS input, most of the respondents 328(92) had standard service registration books, 296(83) had Standard tally, 320(89) had Standard reporting formats and 121(34) of them had a new HMIS procedure manual. 234(65) of the respondents were registering their activities and 170(47) of the participants filled out complete registration books. All most all of the participants reported having data transmission, processing, and reporting rules. 171(48) study participants aggregate or compile services from the tally sheet correctly according to the guideline.\u0026nbsp;(Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eRHMIS implementation status at woreda health offices and health centers, Fafan zone.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eResponse\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegister all your activities\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e234(65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e124(35)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegister filled completely\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170(47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e188(53)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAggregate or compile services from tally sheet correctly\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e171(48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e187(52)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReport submitted complete timely and accurate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e184(51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e174(49)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConducted data accuracy taste\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130(36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e228(64)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReceived supervision for the last three months\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e174(50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e175(50)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGet Feedback from the top-level organization\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156(47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e176(53)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eData collection standards including case definitions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e162(45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e196(55)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHaving data transmission processing and reporting rules\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e185(52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e173(48)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConducted Self-Assessment on your performance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e169(47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e189(53)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eRegarding the reporting of activities, all woreda health offices and health centers had a uniform reporting schedule set. At the health center level, all deportments were expected to compile data from the tally sheet and send their report to the HMIS focal person of the Health center. Thus 184(51) of the study participants submitted complete, timely, and accurate reports. 28(88%) practices and conducted a Self-Assessment of performance. 174(50) participants reported having supervision from top-level organizations and 156(47) got feedback.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3. Health information data use practice\u003c/h2\u003e\n\u003cp\u003eAbout 192(54) study participants had practiced changing the data into information on a monthly basis while around 177(49) of studied subjects also practiced using data for Action planning purposes. Around 211(59) of the study participants used to practice adapting the national target to the local situation by using the woreda Health sector-based national target. In addition to these, 145(41) study subjects also reported having an HMIS multi-disciplinary committee While 144(40) of them also reported having a health information steering committee to set the long-term goals for HIS. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eStatus of RHIMS utilization indicators in selected woreda health and Health centers in Fafan Zone.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 88px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eResponse\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003eChange the data into information every month\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e192(54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e166(46)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003eUse your data to prepare a plan of action\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e177(49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e181(51)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003eThe adaption national target to the local situation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e211(59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e147(41)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003eHas key indicators with charts, tables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e172(48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e186(52)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003eMaintain worksheets and charts for monitoring performance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e164(46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e194(54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003eto identify problems in performance, discuss and analyze with unit staff and present possible reasons/causes to review in a team meeting\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e160(45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e198(55)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003ePresent HMIS reports and discusses at the performance monitoring team\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e165(46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e193(54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003eIn your unit team meetings, was the achievement of targets included\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e164(46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e194(54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003eHaving HIS/HMIS multi-disciplinary committee\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e145(41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e213(59)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003eHas a Health information steering committee to set the long-term goals\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e144(40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e214(60)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003eMonitors key indicators and prepares woreda profile\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e173(48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e185(52)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003eSupervises health information system activities at facilities\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e158(44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e200(56)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003eDept- compare facility performance against plan target\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e170(47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e188(53)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003eDept-compare facility performance against target Population\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e167(47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e191(53)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003ePresence of any display related to your department's activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e170(47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e188(53)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 764px;\" align=\"left\"\u003e\n\u003cp\u003eThe average HMIS utilization practices (Utilization status)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 44.6562px;\" align=\"left\"\u003e\n\u003cp\u003e166(46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 43.3438px;\" align=\"left\"\u003e\n\u003cp\u003e192(54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbout 158(44) of the study participants reported their department used KPI analysis reports for catchment area profile preparation. Around 200 (56) of the participants stated that their department does not oversee the activities related to the health information system at the facilities. As shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Fifteen performance measurement indicators were used to collect practice-related information to determine the level of practicing data use for decision making in the departments of the healthcare facilities participated in the study. The average score for these indicators was calculated for each department to be classified the status. Healthcare departments that achieved average score 10 and above were classified as having data use practice, and departments scored below 10 were classified as not having data use practice. The overall average of HMIS usage practices (usage status) in the study area was 46%.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4. Bi-variable analysis results of health information data use practice\u003c/h2\u003e\n\u003cp\u003eTo evaluate the possible associations between the outcome variable and Exposure (predictor) variables bivariate logistics regression analysis was employed. Crude odds ratio and confidence interval was computed to measure the association between the health information data use practice and exposure variables. In bivariate analysis all variables with P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 was considered significant and selected as a candidate variable for multi-variables analysis. According to the bivariate analysis results, Gender, educational background, Age of the respondents, position of work, and years of working experience become statistically significant. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of socio-demographic characteristics of study subjects with the utilization of RHIMS, Fafan zone\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eResponses\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUtilization status (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOR at 95.0% C.I)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eValue\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35(34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68(66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76(0.45\u0026ndash;1.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77(30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e178(70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePosition\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eManager\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31(29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77(71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.17(0.7\u0026ndash;3.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eService provider\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81(32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e169(68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eEducational Level /background\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDegree\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53(31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116(69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.38(1.91\u0026ndash;3.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiploma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59(32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e128(68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAge of the respondents\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u0026ndash;29 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69(30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e163(70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.33(0.78\u0026ndash;2.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbove-30 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43(34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83(66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eExperience\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026ndash;4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78(31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e175(69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.06(0.61\u0026ndash;1.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.183\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026ndash;14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9(30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21(70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.27(0.48\u0026ndash;3.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.163\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026ndash;9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25(33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50(67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eSimilar to a socio-demographic variable, bivariate analysis was also used for other exposure variables related to the level of health information data use practice and factors influencing.\u003c/p\u003e\n\u003cp\u003eAccording to the analysis result, the health information data use practices-related variables that were found to be significantly associated with outcome variables in bivariate analysis were knowing HMIS importance, HMIS use guidelines and HMIS procedure manual, receiving supervision for the last 3 months, registering all your activities, Aggregate or compile data from tally sheet correctly, Report submitted complete, timely, and accurate and conducted data accuracy checking. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of factors affecting the level of RHIM practices with the utilization of RHIMS, Fafan zone.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eResponses\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUtilization status (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOR at 95.0% C.I)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eP-Value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHMIS Trained\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37(24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116(76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75(37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130(63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.08(1.57\u0026ndash;2.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.08\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eknow HMIS importance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25(26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72(74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87(33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e174(67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.54(0.73\u0026ndash;3.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.26\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHMIS use guideline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50(21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e187(79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62(51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59(49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.8(2.87\u0026ndash;4.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.1\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRegister all your activities\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21(17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103(83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91(39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e143(61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.56(0.29\u0026ndash;1.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.09\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRegister filled completely\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32(17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156(83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80(47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90(53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7(0.33\u0026ndash;1.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.37\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAggregate or compile data from tally sheet correctly\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28(15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e159(85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84(49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87(51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.33(2.7\u0026ndash;3.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.004\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eReport submitted complete, timely, and accurate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27(16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147(84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85(46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99(54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.36(0.17\u0026ndash;0.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.01\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eReceived supervision for the last 3 months\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39(22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e136(78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71(41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103(59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.3(3.04\u0026ndash;4.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.17\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ehaving data transmission, processing, and reporting rules\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44(25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129(75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68(37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117(63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91(0.41\u0026ndash;2.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHMIS procedure manual\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42(21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e158(79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96(0.4\u0026ndash;2.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.92\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70(44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88(56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eknow who utilizes HIS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18(31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41(69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52(36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93(64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.64(3.34\u0026ndash;4.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.17\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eConduct data accuracy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51(22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e177(78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61(47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69(53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.79(2.62\u0026ndash;3.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.08\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSelf-assessment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45(24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e144(76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67(40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102(60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96(0.4\u0026ndash;2.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.92\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGet feedback from top-level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6(23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20(77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46(26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130(74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.54(0.6\u0026ndash;3.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.37\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.5. Multivariable analysis results of Routine health information data use practice\u003c/h2\u003e\n\u003cp\u003eIn this study, multivariable logistic regression analysis was carried out to control possible confounders and identify factors independently associated with Routine information utilization. Finally, variables with a p-value less than 0.05 in multivariable logistic regression analysis are considered as independently significant association with Routine information practice. To determine the magnitude of association between the dependent and independent variables odds ratio was used.\u003c/p\u003e\n\u003cp\u003eIn our analysis, health information utilization practice was compared with socio-demographic variables such as age, year of services; sex, experience, position of work, and educational status of study participants were analyzed. Educational level and position were significant before adjusting confounders and still show significant association yet in multiple logistic regressions analysis. The remaining socio-demographic variable still did not show statistically significant associations even after adjusted multiple logistic regression.\u003c/p\u003e\n\u003cp\u003eAccording to our study findings, a managerial level position has a higher likelihood of practicing health information utilization when compared with a health care provider level position at a p-value of 0.035, (AOR\u0026thinsp;=\u0026thinsp;2.09, (95% C.I, 1.5\u0026ndash;2.91). Similarly, the educational level of the respondent had significant associations with HMIS utilization practices after adjustment at a p-value of 0.023 [AOR\u0026thinsp;=\u0026thinsp;2.09, (95% CI, 1.38\u0026ndash;2.61)]. The results of this study also showed that those who were trained were approximately 2.3 times more likely to practice routine health information than those who were not trained in routine health information [AOR\u0026thinsp;=\u0026thinsp;2, 3; 95% CI: (0.67\u0026ndash;2.55)].(Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eVariables evaluated, for a possible association, health information use practice among Health workers working in Fafan zone health institutions.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eResponses\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUtilization status (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCOR at 95% C.I)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAOR at 95% C.I)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35(34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68(66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76(0.45\u0026ndash;1.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98(0.49\u0026ndash;1.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.943\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77(30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e178(70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePosition\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eManager\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31(29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77(71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.17(0.7\u0026ndash;3.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.97(1.5\u0026ndash;2.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.035\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eService provider\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81(32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e169(68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eEducational Level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDegree\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53(31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116(69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.38(1.91\u0026ndash;3.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.09(1.38\u0026ndash;2.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.023\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiploma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59(32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e128(68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAge of the respondents\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u0026ndash;29 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69(30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e163(70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.33(0.78\u0026ndash;2.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.63(0.09\u0026ndash;4.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.638\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbove-30 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43(34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83(66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eExperience\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026ndash;4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78(31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e175(69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.06(0.61\u0026ndash;1.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.92(0.8\u0026ndash;4.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.146\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026ndash;14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9(30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21(70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.27(0.48\u0026ndash;3.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.68(0.18\u0026ndash;2.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.563\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026ndash;9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25(33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50(67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHMIS Trained\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37(24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116(76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75(37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130(63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.08(1.57\u0026ndash;2.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.3 (0.67\u0026ndash;2.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.031\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eknow HMIS importance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25(26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72(74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87(33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e174(67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.54(0.73\u0026ndash;3.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0(0\u0026ndash;0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHMIS user guideline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50(21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e187(79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62(51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59(49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.8(2.87\u0026ndash;4.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.34(1.17\u0026ndash;2.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.002\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRegister all your activities\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21(17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103(83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91(39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e143(61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.56(0.29\u0026ndash;1.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.71(0.35\u0026ndash;1.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.342\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRegister filled completely\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32(17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156(83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80(47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90(53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7(0.33\u0026ndash;1.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0(0\u0026ndash;0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAggregate or compile data from tally sheet correctly\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28(15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e159(85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84(49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87(51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.33(2.7\u0026ndash;3.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.5(2.67\u0026ndash;2.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.015\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eReport submitted complete, timely, and accurate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27(16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147(84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85(46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99(54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.36(0.17\u0026ndash;0.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.28(0.12\u0026ndash;0.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.002\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eReceived supervision for the last 3 months\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39(22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e136(78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71(41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103(59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.3(3.04\u0026ndash;4.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.2(2.9\u0026ndash;3.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.019\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ehaving data transmission, processing, and reporting rules\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44(25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129(75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68(37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117(63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91(0.41\u0026ndash;2.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0(0\u0026ndash;0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHMIS procedure manual\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42(21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e158(79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96(0.4\u0026ndash;2.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0(0\u0026ndash;0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70(44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88(56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0(0\u0026ndash;0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0(0\u0026ndash;0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eknow who utilizes HIS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18(31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41(69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52(36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93(64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.64(3.34\u0026ndash;4.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.41(3.19\u0026ndash;3.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.024\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eConduct data accuracy test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51(22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e177(78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61(47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69(53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.79(2.62\u0026ndash;3.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.41(1.18\u0026ndash;2.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.031\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eAt the p-value of 0.024, participants who knew who used the HMIS report had odds of practicing health information data use that were about three times higher than those of their counterparts [AOR\u0026thinsp;=\u0026thinsp;3.41; 95 percent CI: (3.19\u0026ndash;3.89). The odds of routine health information use practice were about 2.4 times more among individuals who conducted data accuracy tests in the last three months when compared with individuals who did not conduct data accuracy at a p-value of .031 [AOR\u0026thinsp;=\u0026thinsp;2.41; 95% CI: (1.18\u0026ndash;2.92)]. This study also found that participants who received supportive supervision over the previous three months had an approximately two-fold higher likelihood of using routine health information usage practices than those who had not received any supervision from a higher level (AOR\u0026thinsp;=\u0026thinsp;2.2; 95 percent CI: (2.9\u0026ndash;3.81)) at p-value 0.019. In addition to this, the study also reported that participants who had an HMIS user guide had an approximately two-fold higher likelihood of using routine health information data usage practices than those who don't have this guideline [AOR\u0026thinsp;=\u0026thinsp;2.34 95% CI (1.17\u0026ndash;2.68)] at p-value 0.002.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cp\u003eThe present study tried to assess the practice of health information data uses for decision-making in the studied health institutions Fafan zone. According to the study result, the overall health information utilization practice of the study area was founded to be 46%, which indicates low coverage when we compare with a study conducted in south Korean health facilities which showed over 80% of the use of regular health information was rated highly by the total respondents working in health facilities [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The difference in utilization rate was because Korean primary health care facilities were better structured and equipped than the Ethiopian health tier system.\u003c/p\u003e \u003cp\u003eThe study reported that the use of health data for decision-making in Fafan Zone healthcare facilities was less practiced than the study conducted in Addis Ababa, which reported 78% data utilization, and other studies conducted in health facilities in the southern and eastern parts of Ethiopia where, also reported the practice of data use of 54.4% and 53.1%, respectively[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the contrary, the use of data for decision-making is more practices/better in our studied health facilities when we compared to the results obtained in the studies conducted in the health facilities of the Jimma, Arsi, and Gonder areas[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The reason for this variation could explain the difference in the period studied, the type of structure, and other technological developments and advances at HMIS.\u003c/p\u003e \u003cp\u003eThe results of this study showed that trained individuals were about 31 times more likely to practice routine health information than those who were not trained in routine health information [AOR\u0026thinsp;=\u0026thinsp;1.31; 95% CI: (0.67\u0026ndash;2.55)]. The finding of this study supported other studies conducted at primary healthcare facilities in Western Amhara which reported a significant association between the training of staff on HMIS user guide and data to use for decision making [AOR\u0026thinsp;=\u0026thinsp;2.85; 95% CI: (0.67\u0026ndash;2.55)][\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to this study, people who have received supportive supervision in the previous three months are about twice as likely to use routine health information utilization practices compared to people who have not received supportive supervision [AOR\u0026thinsp;=\u0026thinsp;2.2; 95 percent CI: (2.9\u0026ndash;3.81)] [P-value\u0026thinsp;=\u0026thinsp;0.019]. This is proved by studies conducted in the Gojam Amhara region in northwestern Ethiopia, which reported supportive supervision as an important determinant for the practice data use culture [(95% CI = [1.71, 5.28] [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In addition to this, the study also reported that participants who had an HMIS user guide had an approximately two-fold higher likelihood of using routine health information data usage practices than those who don't have this guideline [AOR\u0026thinsp;=\u0026thinsp;2.34 95% CI (1.17\u0026ndash;2.68)] at p-value 0.002. This result was confirmed in a study in East Gojam, northwestern Ethiopia, in which participants with data management guides were approximately three times more likely to use daily health information than those participants don\u0026rsquo;t have [OR\u0026thinsp;=\u0026thinsp;3; 95 percent CI: (1.27, 8.32)][\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Strength of the study\u003c/h2\u003e \u003cp\u003eUse of PRISM tool, which is a standard tool, designed to capture key information on the study subject. Probably this is the first study of its type in the Somali region and will helps other future studies. This study can provide a snapshot of RHIM use/ practice in the study area. It will help or guide the development of some interventions for improving the program implementation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Limitation of the study\u003c/h2\u003e \u003cp\u003eThe study may not represent the general population of the study (to the whole region) since it involves only a sample of health facilities in the Fafan zone. It was not also included health posts. So, we cannot generalize all level health facilities. The design of the study (cross-sectional) design and cannot provide detail all the required information's for improving the RHIM in the study area. The use of professional data collectors could also be one of the limitations of this study, as professionals tried to redirect and use the respondents in their own way. The study lacks a qualitative part, which helps us to get more about a topic.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion And Recommendations","content":"\u003cp\u003eIn conclusion, the findings of this study showed that the level of health workers' practice of RHI data use for decision-making is still very low in Fafan zone health institutions compared to national health sector transformation plan and information revolution road maps expectation or target. The finding of this study also identified the major factors that determine the practice of data use, which include work position level, Health worker's educational level, presence of regular Supportive supervision flowed by timely feedback on performance, training status of data users, and availability of all required inputs for the preparation and display information, and data management guidelines. Thus, to strengthen the data use practice in the studied health facilities, it's critical to focus on improving users' knowledge and skills, availing all necessary inputs and manuals for HMIS implementation. In addition to these, it is also important to implement regular supportive supervision and feedback mechanisms to facilitate the promotion and reinforcement of data use culture in health centers and woreda health offices in Fafan.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eEthical clearance was obtained by institutional review board (IRB) college of business and economics, Jigjiga University with IRB protocol number JJU/0082/14. Further Permission was obtained from Graduate Coordinator of the Department and submitted to the Somali Regional state health bureau at Jigjiga, Fafan zone health office, woreda health Office, and health Center studied. A written informed consent was obtained from all subjects, and this study is done in accordance with declaration of Helsinki procedures.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eThis study doesn\u0026rsquo;t involve details, images, videos related to an individual\u0026rsquo;s persons. So, getting consents for publication is not applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eAll data generated or analyzed during this study are available at corresponding author but is not publicly available. This is because the raw data collected by the interviewed health facilities contains detailed, and sensitive information about the facilities. The Somali Regional Health bureau (government), owned by the institutions studied, does not allow the sharing of this raw data or information\u0026rsquo;s directly with third parties or publicly.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eSomali regional state health bureau has provided some financial support to authors to cover the transportation costs during data collection only. The Authors finalized the remaining research works without getting any other additional supports/ assistance.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eAF developed the study design, collected data, and did the analysis, interpretation, and manuscript write-up. KH, AM contributed to the conception of the research idea, participate in the conceptualization of the idea, and assisted draft finalizing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors would like to express their deeply gratitude to Jigjiga University, college of Business and economics for support the accomplishment of this study. Authors are thankful for the cooperation and support of Somali regional health bureau, Fafan zone health office, woreda health Office, and all its health Centers.\u003c/p\u003e\n\u003cp\u003eWe would also like to special thank supervisors and data collectors for taking for their precious time to collect data. We are glad to thank the respondents who participated in this study and took their time to provide information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eWHO, H., \u003cem\u003eFramework and standards for country health information systems/Health Metrics Network\u003c/em\u003e. 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BMC medical informatics and decision making, 2017.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Routine health information practices, and Somali regional state","lastPublishedDoi":"10.21203/rs.3.rs-1809396/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1809396/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSound and reliable information is the foundation of decision-making across all health system building blocks and is essential for health system policy development and its implementation. Ethiopian health sector transformation plan has given special attention to health information management, data use intending to promote the quality and culture of health information data use for decision making. Hence, this study aims to assess the practice of routine Health Information data use for decision-making and its determinants in Fafan Zone Somali region.\u003c/p\u003e\u003cp\u003eA cross-sectional study was carried out in August 2021 to assess routine Health Information, data use practices, and its determinants in the Fafan zone Somali region. The participants of the study were 359 health workers from different departments of selected health centers and woreda health offices by using cluster-sampling techniques.\u0026nbsp;\u003c/p\u003e\u003cp\u003eThe study findings showed that the health workers' practice of RHI data use for decision-making is very low. The determinants of Routine health information management data use practice that was identified in the study include work position level, Health worker's educational level, presence of regular Supportive supervision flowed by timely feedback on performance, training status of data users, and availability of all required inputs for the preparation and display information, and data management guidelines. Therefore, enhancing knowledge, skills, data management inputs, supportive monitoring, and access to user training are important to expand the use of routine health information data in health centers and woreda health offices in the Fafan zone.\u003c/p\u003e","manuscriptTitle":"Health Management Information System Data Use Practice and Its Determinants at Health Centers and Woreda Health Office in Fafan Zone, Somali Region, Ethiopia.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-19 13:56:24","doi":"10.21203/rs.3.rs-1809396/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"437659de-b688-4c4c-a59e-ba27e2a7a3b5","owner":[],"postedDate":"July 19th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-08-24T09:44:28+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-19 13:56:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1809396","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1809396","identity":"rs-1809396","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}