Disparities of Health Program Information Systems in Indonesia

[Full title: Disparities of Health Program Information Systems in Indonesia: A Cross-Sectional Indonesian Health Facility Research 2019]

Citation: Idaiani, S.; Hendarwan, H.; Herawati, M.H. Disparities of Health Program Information Systems in Indonesia: A Cross-Sectional Indonesian Health Facility Research 2019 Int. J. Environ. Res. Public Health 2023 , 20 , 4384. https://doi.org/ 10.3390/ijerph 20054384 Academic Editors: Paul B Tchounwou and George Crooks Received: 4 January 2023 Revised: 9 February 2023 Accepted: 21 February 2023 Published: 1 March 2023 Copyright: © 2023 by the authors Licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/) International Journal of Environmental Research and Public Health Article Disparities of Health Program Information Systems in Indonesia: A Cross-Sectional Indonesian Health Facility Research 2019 Sri Idaiani 1, * , Harimat Hendarwan 1 and Maria Holly Herawati 2 1 Research Centre for Preclinical and Clinical Medicine, National Research and Innovation Agency, Cibinong Science Center, Jalan Raya Jakarta-Bogor Km. 46, Kec. Cibinong, Kabupaten Bogor 16915, West Java, Indonesia 2 Research Centre for Public Health and Nutrition, National Research and Innovation Agency, Cibinong Science Center, Jalan Raya Jakarta-Bogor Km. 46, Kec. Cibinong, Kabupaten Bogor 16915, West Java, Indonesia * Correspondence: sri.idaiani@brin.go.id or sriidaiani@gmail.com Abstract: Although a recording and reporting format for health centers already exists for Indonesia’s standard information system, numerous health applications still need to meet the needs of each program. Therefore, this study aimed to demonstrate the potential disparities in information systems in the application and data collection of health programs among Indonesian community health centers (CHCs) based on provinces and regions. This cross-sectional research used data from 9831 CHCs from the Health Facilities Research 2019 (RIFASKES). Significance was assessed using a chi-square test and analysis of variance (ANOVA). The number of applications was depicted on a map using the spmap command with STATA version 14. It showed that region 2, which represented Java and Bali, was the best, followed by regions 1, which comprised Sumatra Island and its surroundings, and 3, Nusa Tenggara. The highest mean, equaling that of Java, was discovered in three provinces of region 1, namely, Jambi, Lampung, and Bangka Belitung. Furthermore, Papua and West Papua had less than 60% for all types of data-storage programs. Hence, there is a disparity in the health information system in Indonesia by province and region. The results of this analysis recommend future improvement of the CHCs’ information systems Keywords: health information system; community health center; disparity; program; application 1. Introduction The established health system needs a robust health information system. One measure of its success is equitable distribution. In the current era of digitalization, there are demands for data to be brought together for both individuals and health facilities [ 1 – 3 ]. The availability of data and good monitoring enhance their utilization. Hence, they can replace population-based surveys [ 4 ]. Data in health facilities of developed countries are inputted electronically, either online or offline. However, in low-and-middle-income countries (LMICs), entry of health data, especially for primary services, is not adequate [ 3 , 5 ]. Furthermore, other problems include the use of various applications, varied program data requirements, and poor data quality This is due to lack of monitoring and evaluation of the information system used [ 6 ]. Previous research on Indonesian health information systems has covered hospital information systems, electronic medical records, information systems, mHealth, e-health, telemedicine, and the primary healthcare information system (SIMPUS) [ 7 ]. Conversely, in developed countries, research on health information systems has included software, artificial intelligence, clinical decision support systems (clinical DSSs), epidemiological monitoring, data mining, patient safety and outcomes, meaningful use, quality improvement, and social media [ 8 ]. Int. J. Environ. Res. Public Health 2023 , 20 , 4384. https://doi.org/10.3390/ijerph 20054384 https://www.mdpi.com/journal/ijerph

Int. J. Environ. Res. Public Health 2023 , 20 , 4384 2 of 12 As one of the LMICs, Indonesia has a diverse primary care information system. Despite a standard information system already being in place—namely, the existence of records and reports known as the Puskesmas (community health centers) Management Information System (SIMPUS), and all its versions—many health programs, such as maternal, dengue, and other ones, have unique applications [ 6 , 9 ]. Finally, health centers do not work on only one type of system or application that contains various management, facility, and individual data. They have an additional burden of running more than 10 required programs The expected outcome of the current vision for data includes efficient and high-quality information. Furthermore, the ability of data entry in CHCs varies depending on the number of employees, the ability of the human resources, the electronic devices, and organizational support, such as the available Internet network [ 10 ]. These conditions lead to disparities between regions and provinces, which ultimately affect the health information system. This disparity is likely to occur in Indonesia because this country consists of many islands with unequal resources. By examining the existing information systems in primary health services in Indonesia, it is possible to capture the disparities that occur. Demonstrating the disparities is needed to improve health systems [ 11 ]. Therefore, this study was conducted to demonstrate the potential disparities of the health information systems in Indonesia, namely, the application and data collection of programs at the CHCs by province and region 2. Materials and Methods 2.1. Design This was an analysis of the Indonesian Health Facility Research (RIFASKES) data for 2019 [ 12 ]. The RIFASKES design is a survey. The original survey collected data on health facilities, including the characteristics of facilities, resources, management, organization, planning, supporting facilities, and information systems. Systems consisted of CHCs, hospitals, and other health facilities. Furthermore, the data of this study were related to research on CHCs. The cross-sectional design of the RIFASKES determined the total sample, namely, all the CHCs in Indonesia. The inclusion criteria were registered with the Ministry of Health data in July 2018 and verified by the local District Health Office (DHO). Furthermore, the data of this study focus on CHCs 2.2. Time, Place, and Data Collection Data were collected from April to May 2019 by teams of two members from five CHCs per team. Each team visited the CHC health center for four days to obtain all the survey information. The enumerator criteria were a minimum college education (Diploma III at the Department of Health), <45 years old, a non-civil servant, were currently not studying, resided in the province of the research location, applied to become an enumerator, and participated in the selection and technical research training Data were acquired through interviews with the program manager for the health information system at the CHC, usually the director. Enumerators performed the interviews using a standardized questionnaire. The assessed disease data application is an application installed on the CHC computer. If they had it, we asked if it was used. Additionally, the enumerators observed and searched the documents and applications. This survey did not assess the process and quality of the data 2.3. Questionnaire The focus of this analysis was on the CHC record system. Three main parts were analyzed: the Ministry of Health application, an application to support health insurance, and an application for health and disease programs. Questions included the availability of 13 types of records, namely: SKDR (Sistim Kewaspadaan Dini dan Respon), or Early Warning of Epidemic Diseases; ASPAK (Aplikasi Sarana Prasarana dan Alat Kesehatan), or Health Facilities and Supplies Application; PISPK (Program Indonesia Sehat Pendekatan Keluarga), or the Healthy Family Program; P Care (Primary care), a health insurance ap-

Int. J. Environ. Res. Public Health 2023 , 20 , 4384 3 of 12 plication; HFIS (Health Financing Information System), for financing of health insurance; SITT (Sistim Informasi Tuberculosis Terpadu), for tuberculosis information; SIHA (Sistim Informasi HIV AIDS), for HIV AIDS information; SIHEPI (Sistim Informasi Hepatisis), or Hepatitis Information; SIPTM (Sistim Informasi Penyakit tidak Menular), or Non Communicable Disease Information; SIPD 3 I (Sistim Informasi Penyakit yang Dapat Dicegah dan Diobati dengan Imunisasi), or Diseases Prevented and Cured by Immunization; ESISMAL (Electronic System oh Malaria), or Malaria Information; SISTBM (Sistim Informasi Sanitasi Total Berbasis Masyarakat), or Health Sanitation Population-Based; EPPGBM (Electronic Pencataan dan Pelaporan Gizi Berbasis Masyarakat), or Nutrition Information The information system owned by the CHCs included medical records and SIMPUS This analysis excluded health program applications that were relatively new and had not been widely used, such as the dengue and mental health applications According to a decree issued by the regional government, criteria for CHCs were established based on urban, rural, remote, and very remote areas. Furthermore, the outpatient and inpatient criteria, accreditation status of the CHC, and financial management were determined based on the available document. Division of the territory into seven regions was performed according to the classification of Indonesia’s development areas, as written in the Medium-Term Development Plan [ 13 ]. Regions 1, 2, 3, 4, 5, 6, and 7 were provinces located on the island of Sumatra and its surroundings, Java and Bali, Nusa Tenggara, Kalimantan, Sulawesi, Maluku, and Papua, respectively 2.4. Data Management and Analysis The data processing of RIFASKES 2019 consisted of two stages. The first was conducted in regencies/cities and consisted of data collection, receiving–batching (acceptance– bookkeeping), editing (data quality control), data entry, and sending electronic data. The second was performed in the National Institute of Health Research and Development (NIHRD). It consisted of receiving and merging data for all regencies/cities, cleaning data, merging provincial and national data, cleaning national data, imputation, weighting, and storing electronic data The team submitted a data request to the Repository of NIHRD, namely, the datarelated health information system at a CHC. Data were tabulated, and the significance was assessed using a chi-square test. Furthermore, the number of applications was assessed for the mean, and analysis of variance (ANOVA) determined the significance. Next, data were mapped using the spmap command with STATA version 14. The categorization was based on the default STATA, which automatically divided the data into four quartiles 3. Results The CHCs originated from 34 provinces, which included all Indonesian districts and cities. The total CHSs was 9909. We coordinated with local DHOs before the study was implemented. As a result, 14 health centers were eliminated, since they were declared unavailable. There were 9885 CHCs visited; furthermore, 54 changed their functions during data collection, or their buildings were no longer available. Therefore, there were 9831 CHCs analyzed, and the response rate was 99.2% Table 1 shows the number of health centers per province. Based on that table, the majority of the CHCs, which included those with inpatient status, were located in rural areas. In Indonesia, at least 2262 health centers were not accredited. Being an unaccredited CHC implied that they had not accomplished accreditation when this research was conducted. Regarding financial management, most were non-public services (non-BLUD), which indicated that the operational funds were entirely from the local government, whereas BLUD refers to CHCs that had satisfied the requirements for some degree of financial autonomy The highest numbers of health centers were in West and East Java, and the smallest numbers were in Bangka Belitung and North Kalimantan. Table 2 shows the number of health-center applications based on regional criteria.

Int. J. Environ. Res. Public Health 2023 , 20 , 4384 4 of 12 Table 2 shows disparities in the numbers and percentages of available health applications. In general, urban and rural areas had more health applications, except for E SISMAL, which was more widely available in rural and remotes areas—71.16% and 75.39%, respectively. This pattern was different from urban areas, which did not have many of these applications. Table 3 shows the regional division in Indonesia, which consists of seven regions Table 3 shows that many health centers in Indonesia have applications for these programs—more than 50%. Furthermore, ASPAK, PISPK, and P.care were owned by approximately 90% of health centers. As a result, there were some regions, 4, 5, 6, and 7, that had health applications. On average, the health centers had 10 information system programs (mean = 10.37 and median of 10.80). The results have been displayed in the form of a map by region In Figure 1 , the categories are divided into four quartiles, namely, areas with means of 5.5 to <10.5, 10.5 to <10.7, 10.7 to <10.8, and 10.8 to 10.9. The best areas were region 2, followed by 1 (Sumatra), and then 3 (Nusa Tenggara). Meanwhile, 4, 5, 6, and 7 were in one category, as they had means of 5.5 to <10.5 Int. J. Environ. Res. Public Health 2023 , 20 , x FOR PEER REVIEW 5 of 13 - Not available 280 11.44 539 12.99 537 27.60 SIPD 3 I - Available 1952 79.77 3346 80.63 1322 67.93 0.001 - Not available 495 20.23 804 19.37 624 32.07 ESISMAL - Available 1587 64.85 2953 71.16 1467 75.39 0.001 - Not available 860 35.15 1197 28.84 479 18.9 SISTBM - Available 2080 85.00 3594 86.60 1416 72.76 0.001 - Not available 367 15.00 556 13.40 530 27.24 E PPGBM - Available 2175 88.92 3744 90.22 1510 77.60 0.001 - Not available 271 11.08 406 9.78 436 22.40 Table 2 shows disparities in the numbers and percentages of available health applications. In general, urban and rural areas had more health applications, except for E SISMAL, which was more widely available in rural and remotes areas — 71.16% and 75.39%, respectively. This pattern was different from urban areas, which did not have many of these applications. Table 3 shows the regional division in Indonesia, which consists of seven regions. Table 3 shows that many health centers in Indonesia have applications for these programs — more than 50%. Furthermore, ASPAK, PISPK, and P.care were owned by approximately 90% of health centers. As a result, there were some regions, 4, 5, 6, and 7, that had health applications. On average, the health centers had 10 information system programs (mean = 10.37 and median of 10.80). The results have been displayed in the form of a map by region. In Figure 1, the categories are divided into four quartiles, namely, areas with means of 5.5 to <10.5, 10.5 to <10.7, 10.7 to <10.8, and 10.8 to 10.9. The best areas were region 2, followed by 1 (Sumatra), and then 3 (Nusa Tenggara). Meanwhile, 4, 5, 6, and 7 were in one category, as they had means of 5.5 to <10.5. Figure 1. Mean presence of an information system by region. Figure 1. Mean presence of an information system by region Table 1. Characteristics of the CHCs Characteristics n % Province N CHCs Province N CHCs 1 Location of CHC 1 Aceh 347 18 West Kalimantan 241 - Urban 2447 24.9 2 North Sumatera 571 19 Central Kalimantan 197 - Rural 4150 42.2 3 West Sumatera 271 20 South Kalimantan 232 - Remote/Very remote 1946 19.8 4 Riau 216 21 East Kalimantan 178 - Uncategorized 1288 13.1 5 Jambi 193 22 North Kalimantan 55 2 Service category 6 Bengkulu 151 23 West Nusa Tenggara 161 - Outpatient 4094 41.6 7 South Sumatera 328 24 East Nusa Tenggara 374 - Inpatient 5737 58.4 8 Lampung 289 25 North Sulawesi 193 3 Accreditation status 9 Bangka Belitung 55 26 Central Sulawesi 196 - Basic 2434 24.8 10 Riau Islands 80 27 South Sulawesi 452 - Madya (Intermediate) 4247 43.2 11 Special Region of Jakarta 313 28 Southeast Sulawesi 281 - Utama (Advanced) 826 8.4 12 West Java 1069 29 Gorontalo 93 - Paripurna (Very advanced) 62 0.6 13 Central Java 876 30 West Sulawesi 94 - No accreditation 2262 23 14 Special Region of Jogjakarta 121 31 Maluku 199 4 Finance management status 15 East Java 964 32 North Maluku 129 - BLUD 3239 32.9 16 Banten 233 33 West Papua 157 - Non BLUD 6592 67.1 17 Bali 98 34 Papua 356 - Total 9831 9831

Int. J. Environ. Res. Public Health 2023 , 20 , 4384 5 of 12 Table 2. Number of applications or programs in urban, rural, and remote areas Urban Rural Remote and Very Remote p n % n % n % Ministry of Health App ASPAK - Available 2364 96.61 3982 95.95 1622 83.35 0.001 - Not available 83 3.39 168 4.05 324 16.65 PIS-PK - Available 2325 95.09 3925 94.58 1621 20.6 0.001 - Not available 120 4.91 225 5.42 325 48.5 SKDR - Available 1651 67.47 2719 65.52 1041 53.49 0.001 - Not available 796 32.53 1431 34.48 905 46.51 Health Insurance P Care - Available 2414 98.65 4058 97.78 1513 77.75 0.001 - Not available 33 1.35 92 2.22 433 22.25 HFIS - Available 1955 79.89 3113 75.01 844 43.37 0.001 - Not available 492 20.11 1037 24.99 1102 56.63 Health Program App SITT - Available 2236 91.38 3650 87.95 1384 71.12 0.001 - Not available 211 8.62 500 39.3 562 28.88 SIHA - Available 2243 91.70 3626 87.37 1290 66.29 0.001 - Not available 203 8.30 524 12.63 656 33.71 SIHEPI - Available 1637 66.93 2814 67.81 1039 53.39 0.001 - Not available 809 26.5 1336 43.8 907 29.7 SIPTM - Available 2167 88.56 3611 87.01 1409 72.40 0.001 - Not available 280 11.44 539 12.99 537 27.60 SIPD 3 I - Available 1952 79.77 3346 80.63 1322 67.93 0.001 - Not available 495 20.23 804 19.37 624 32.07 ESISMAL - Available 1587 64.85 2953 71.16 1467 75.39 0.001 - Not available 860 35.15 1197 28.84 479 18.9 SISTBM - Available 2080 85.00 3594 86.60 1416 72.76 0.001 - Not available 367 15.00 556 13.40 530 27.24 E PPGBM - Available 2175 88.92 3744 90.22 1510 77.60 0.001 - Not available 271 11.08 406 9.78 436 22.40 In Figure 2 , the area is divided based on quartiles, namely, 5.13 to <9.96, 9.96 to <10.78, 10.78 to <11.27, and 11.27 to 11.87. It is shown that in the best region (region 2), not all provinces had a high mean of 11.27–11.87. Those with the highest mean were observed in the Banten and East Java provinces. The highest mean, that of region 1 (Sumatra Island), was discovered in the Jambi, Lampung, and Bangka Belitung provinces. Apart from regions 1 and 2, only Gorontalo Province a mean in the highest quartile. Meanwhile, only South Sulawesi Province had a high mean of 10.78 to <11.27 Table 4 shows the proportion of CHCs with Information Application or Program by Province. It shows that Papua and West Papua had percentages of less than 60% for all types of information application. Furthermore, Jakarta had less than 50% ownership for several programs, namely, SIHA, SIHEPI, and ESISMAL. Programs with less than 60% ownership in several provinces included SKDR, SIHA, SIHEPI, SIPD 3 I, and ESISMAL. However, there was no SKDR ownership of more than 90%. Many provinces had below 70% ownership of HFIS, even though this was mandatory for the national health insurance provider. The same was true for SIHA and SIHEPI, many of which still had SIPD 3 I percentages below 70%.

Int. J. Environ. Res. Public Health 2023 , 20 , 4384 6 of 12 Table 3. Numbers and percentages of the presence of information programs based on the number of CHCs in each region in Indonesia Region Indonesia 1 2 3 4 5 6 7 p n % n % n % n % n % n % n % n % Ministry of Health App ASPAK - Available 9103 92.59 2447 96.07 3587 97.05 506 94.58 849 94.02 1237 94.50 232 70.73 245 47.76 0.001 - Not available 728 7.41 100 3.93 109 2.95 29 5.42 54 5.98 72 5.50 96 29.27 268 52.24 PIS-PK - Available 8945 91.02 2372 93.17 3518 95.24 501 93.64 811 89.81 1173 89.61 266 81.10 304 59.26 0.001 - Not available 883 8.98 174 6.83 176 4.76 34 6.36 92 10.19 136 10.39 62 18.90 209 40.74 SKDR - Available 6183 62.89 1645 64.59 2530 68.45 338 63.18 545 60.35 784 59.89 154 46.95 187 36.45 0.001 - Not available 3648 37.11 902 35.41 1166 31.55 197 36.82 358 39.65 525 40.11 174 53.05 329 63.55 Health insurance App P care - Available 9134 92.91 2471 97.02 3678 99.51 521 97.38 845 93.58 1244 95.03 218 66.46 157 30.60 0.001 - Not available 697 7.09 76 2.98 18 0.49 14 2.62 58 6.42 65 4.97 110 33.54 356 69.40 HFIS - Available 6784 69.01 1824 71.61 3181 86.07 241 45.05 571 63.23 772 58.98 95 28.96 47 9.16 0.001 - Not available 3047 30.99 723 28.39 515 13.93 294 54.95 332 36.77 537 41.02 233 71.04 466 90.84 Health program App SITT - Available 8333 84.76 2114 83.00 3386 94.75 428 80.00 699 77.41 1081 82.58 233 71.04 276 53.80 0.001 - Not available 1498 15.24 433 17.00 194 5.25 107 20.00 204 22.59 228 17.42 95 28.96 237 46.20 SIHA - Available 8121 82.6 2114 83.03 3365 91.04 413 77.2 678 75.08 1083 82.73 202 61.59 266 51.85 0.001 - Not available 1709 17.39 432 16.97 331 8.96 122 22.80 225 24.92 226 17.27 126 38.41 247 48.15 SIHEPI - Available 6223 63.31 1759 69.09 2332 63.10 346 64.67 557 61.68 898 68.60 172 52.44 159 30.99 0.001 - Not available 3607 36.69 787 30.91 1364 36.90 189 35.33 346 38.42 411 31.40 156 47.56 354 69.01

Int. J. Environ. Res. Public Health 2023 , 20 , 4384 7 of 12 Table 3. Cont Region Indonesia 1 2 3 4 5 6 7 p n % n % n % n % n % n % n % n % SIPTM - Available 8223 83.64 2162 84.88 3267 88.39 463 86.54 744 82.39 1116 85.26 239 72.87 281 54.78 0.001 - Not available 1608 16.36 385 15.12 429 11.61 72 13.46 159 17.61 193 14.74 89 27.13 232 45.22 SIPD 3 I - Available 7535 76.65 2051 80.53 2938 79.49 400 74.77 665 73.64 1016 77.62 215 65.55 250 48.73 0.001 - Not available 2296 23.35 498 19.47 758 20.51 135 25.23 238 26.36 293 22.38 113 34.45 263 51.27 ESISMAL - Available 6807 69.24 2042 80.17 1904 51.52 444 82.99 723 80.07 1107 84.57 243 74.09 344 67.05 0.001 - Not available 3024 30.76 505 19.83 1792 48.48 129 17.01 180 19.93 202 15.43 85 25.91 169 32.95 SISTBM - Available 8090 82.29 2182 85.67 3133 84.77 452 84.49 743 82.28 1135 86.71 219 66.77 226 44.05 0.001 - Not available 1741 17.71 365 14.33 563 15.23 83 15.51 160 17.72 174 13.29 109 33.23 287 55.95 E PPGBM - Available 8491 86.4 2300 90.30 3285 88.90 466 87.10 775 85.83 1154 88.16 242 73.48 269 52.44 0.001 - Not available 1339 13.62 247 9.70 410 11.10 69 12.90 128 14.17 155 11.84 86 26.22 244 47.56 Number of Application Mean 10.37 10.79 10.86 10.57 10.19 10.54 8.32 5.77 Median 11 12 11 11.5 11 12 9.5 6

Int. J. Environ. Res. Public Health 2023 , 20 , 4384 8 of 12 Table 4. Proportion of CHCs with Information Application or Program by Province ASPAK KS SKDR PCare HFIS SITT SIHA SIHEPI SIPTM SIPD 3 I ESISMAL SISTBM EPPGBM Mean n % n %P n % n % n % n % n % n % n % n % n % n % n % 1 2 3 4 5 6 7 8 9 10 11 12 13 1 Aceh 323 93.1 320 92.2 200 57.6 340 98 236 68.0 252 72.6 257 74.1 245 70.6 271 78.1 257 74.1 247 71.6 280 80.7 294 84.7 10.1 2 North Sumatrera 538 94.2 518 90.7 330 67.4 543 95.1 410 71.8 434 76 429 75.1 315 55.2 443 77.6 409 71.6 372 65.1 470 82.3 499 87.4 10.0 3 West Sumatera 264 97.4 253 93.4 168 62 267 98.5 203 74.9 239 88.2 237 87.5 175 64.6 230 84.9 233 86 237 87.5 237 87.5 261 96.3 11.1 4 Riau 214 99.1 201 93.1 173 80.1 211 97.7 133 61.6 188 87.0 180 83.3 143 66.2 191 88.4 175 81.0 187 96.6 185 85.6 191 88.4 10.9 5 Jambi 191 99.0 183 94.8 139 72.0 189 97.9 152 78.8 167 86.5 160 82.9 158 81.9 174 90.2 169 87.6 177 91.7 182 94.3 184 95.3 11.5 6 South Sumatera 327 99.7 303 92.4 202 61.6 325 99.1 249 75.9 286 87.2 290 88.4 249 75.9 292 89.0 279 85.1 279 85.1 277 84.5 267 90.5 11.1 7 Bengkulu 130 86.1 138 91.4 95 62.9 147 97.4 90 59.6 119 78.8 130 86.1 106 70.2 130 86.1 124 82.1 135 89.4 128 84.8 131 86.8 10.6 8 Lampung 293 98.0 294 98.3 225 75.3 298 99.7 241 80.6 287 96.0 282 94.3 242 87.6 281 94.0 261 87.3 269 90.0 272 91.0 287 96.0 11.9 9 Babel Island 53 96.4 51 92.7 45 81.4 55.0 100 49 89.1 39 70.9 51 92.7 34 61.8 46 83.6 47 85.5 46 83.6 47 85.5 50 90.9 11.1 10 Riau Island 79 98.8 78 97.5 45 56.3 60.0 75 37 46.3 72 90.0 66 82.5 46 57.5 70 87.5 62 77.5 58 72.5 69 86.3 70 87.5 10.1 11 Special Region of Jakarta 275 87.9 193 61.7 198 63.3 312 99.7 251 80.2 274 87.5 136 43.5 129 41.2 255 81.5 183 58.5 79 25.2 228 72.8 248 79.2 8.8 12 West Java 1039 97.2 1043 97.6 763 71.4 1056 98.8 970 81.4 992 92.8 982 91.9 635 59.4 970 90.7 893 83.5 431 40.3 918 85.9 989 92.5 10.8 13 Central Java 859 98.1 863 98.5 594 67.8 873 99.7 772 88.1 850 97.0 854 97.5 563 64.3 777 88.7 711 81.2 553 63.1 771 88.0 797 91.0 11.2 14 Special Region of Jogjakarta 104 99 104 100 75 71.4 105 100 97 92.4 103 98.1 105 100 55 52.4 98 93.3 95 90.5 37 35.2 93 88.6 104 99.0 13 15 East Java 950 98.5 956 99.2 660 68.5 964 100 889 92.2 939 97.4 943 97.8 736 76.3 868 90 781 81 630 65.4 823 85.4 822 85.3 10.4 16 Banten 225 96.6 226 97 145 62.2 232 99.6 200 85.8 215 92.3 211 90.6 132 56.7 179 76.8 167 71.7 89 38.2 177 76.0 196 84.1 10.3 17 Bali 98 100 98 99 70 71.4 98 100 72 73.5 96 98.0 96 98.0 60 61.2 86 87.8 82 83.7 65 66.3 90 91.8 95 96.9 11.3 18 NTB 161 100 160 99.4 110 68.3 161 100 114 70.8 149 92.5 150 93.2 116 72.0 150 93.2 125 77.6 154 95.7 146 90.7 150 93.2 11.5 19 NTT 345 92.2 341 91.2 228 61 360 96.3 180 48.1 279 74.6 263 70.3 230 61.5 313 83.7 275 73.5 290 77.5 306 81.8 316 84.5 9.9 20 West Kalimantan 222 92.1 216 89.6 132 54.8 228 94.6 144 59.8 175 72.6 179 74.3 133 55.2 193 80.1 169 70.1 178 73.9 186 77.2 195 80.9 9.7 21 Central Kalimantan 186 94.4 169 85.8 140 71.1 181 91.9 111 56.3 154 78.2 131 66.5 113 57.4 169 85.8 150 76.1 172 87.3 167 84.4 208 89.7 10.2 22 South Kalimantan 216 93.1 219 94.4 137 59.1 227 97.8 180 77.6 167 72.0 167 72.0 155 66.8 198 85.3 170 73.3 176 75.9 189 81.5 167 84.8 10.4 23 East Kalimantan 168 97.7 153 89 102 59.3 165 95.9 111 64.5 153 89.0 145 84.3 113 65.7 139 80.8 129 75 148 86.0 151 87.8 159 92.4 10.7 24 North Kalimantan 51 92.7 48 87.3 29 52.7 38 69.1 20 36.4 44 80.0 50 90.9 39 70.9 40 72.7 43 78.2 45 81.8 44 80.0 40 72.7 9.6 25 North Sulawes 185 95.9 158 81.9 130 67.4 165 85.5 134 69.4 156 80.8 136 70.5 135 69.9 170 88.1 162 83.9 169 87.6 157 81.3 168 87.0 10.5 26 Central Sulawesi 184 93.9 180 91.8 122 62.2 186 94.9 124 63.3 153 78.1 165 84.2 136 69.4 161 82.1 154 78.6 159 81.1 169 86.2 172 87.8 10.5 27 South Sulawesi 445 98.5 430 95.1 270 59.7 450 99.6 282 62.4 378 83.6 403 89.2 323 71.5 401 88.7 355 78.5 395 87.4 420 92.9 436 96.5 11.0 28 Southeast Sulawesi 242 86.1 229 81.5 134 47.7 262 93.2 135 48.0 230 81.9 209 74.4 170 60.5 206 73.3 199 70.8 217 77.2 223 79.4 199 70.8 9.4 29 Gorontalo 91 97.8 84 90.3 66 71 92 98.9 53 57 84 90.3 85 91.4 70 75.3 91 97.8 78 83.9 86 92.5 85 91.4 88 94.6 11.3 30 West Sulawesi 90 95.7 92 97.9 62 66 89 94.7 44 46.8 80 85.1 85 90.4 64 68.1 87 92.6 68 72.3 81 86.2 81 86.2 91 96.8 11.0 31 Maluku 124 62.3 159 79.9 78 39.2 107 53.8 36 18.1 131 65.8 109 54.8 91 45.7 130 65.3 121 60.8 127 63.8 121 60.8 136 68.3 7.4 32 Nort Maluku 108 83.7 107 82.9 76 58.9 111 86.0 59 45.7 102 79.1 93 72.1 81 62.8 109 84.5 94 72.9 116 89.9 98 76.0 106 82.2 9.8 33 West Papua 89 56.7 70 44.6 31 19.7 62 39.5 11 7.0 75 47.8 77 49.0 32 20.4 62 39.5 66 42.0 106 67.5 59 37.6 65 41.4 5.1 34 Papua 156 43.8 234 65.7 156 43.8 95 26.7 36 10.1 201 56.5 189 53.1 127 35.7 170 47.8 184 51.7 238 66.9 167 46.9 204 57.3 6.0

Int. J. Environ. Res. Public Health 2023 , 20 , 4384 9 of 12 Int. J. Environ. Res. Public Health 2023 , 20 , x FOR PEER REVIEW 10 of 13 In Figure 2, the area is divided based on quartiles, namely, 5.13 to <9.96, 9.96 to <10.78, 10.78 to <11.27, and 11.27 to 11.87. It is shown that in the best region (region 2), not all provinces had a high mean of 11.27 – 11.87. Those with the highest mean were observed in the Banten and East Java provinces. The highest mean, that of region 1 (Sumatra Island), was discovered in the Jambi, Lampung, and Bangka Belitung provinces. Apart from regions 1 and 2, only Gorontalo Province a mean in the highest quartile. Meanwhile, only South Sulawesi Province had a high mean of 10.78 to <11.27. Figure 2. Mean presence of an information systems by province. Table 4 shows the proportion of CHCs with Information Application or Program by Province. It shows that Papua and West Papua had percentages of less than 60% for all types of information application. Furthermore, Jakarta had less than 50% ownership for several programs, namely, SIHA, SIHEPI, and ESISMAL. Programs with less than 60% ownership in several provinces included SKDR, SIHA, SIHEPI, SIPD 3 I, and ESISMAL. However, there was no SKDR ownership of more than 90%. Many provinces had below 70% ownership of HFIS, even though this was mandatory for the national health insurance provider. The same was true for SIHA and SIHEPI, many of which still had SIPD 3 I percentages below 70%. Figure 2. Mean presence of an information systems by province 4. Discussion The analysis showed disparities between the regions and provinces regarding the number of applications and data collection of health programs. Figure 1 indicates that the eastern part of Indonesia is significantly different from the western part. The central part was almost the same as eastern Indonesia regarding the average number of applications owned. Furthermore, some provinces had a small average of applications or programs compared to others. This is common in LMICs where the health information systems are strongly influenced by human-resource factors, institutional funding, foreign aid, corruption, transparency, and poor priorities [ 5 , 14 ]. The province of Jakarta, where the capital city is located, had an average number of applications or health programs which was not high compared to other provinces on the island of Java. Although this has never been analyzed, it is possible, since the CHCs in Jakarta are different from those in other regions. For example, the CHCs in this province are located almost exclusively in sub-districts (kelurahan). However, they are independent in their work and not branch of the CHCs in some large sub-district [ 15 ]. Therefore, many subdistricts’ CHCs did not have information-system programs, since they shared tasks with sub-district health centers. Another explanation is that Jakarta was not an endemic area for diseases such as malaria; hence, the use of ESISMAL was deficient, at approximately 25%. In contrast to the disparity problem in eastern Indonesia, there are issues pertaining to the availability of PCs, the electricity supply, workforce availability and their skills, Internet access, etc This disparity in one part of the health information system causes various problems The primary issue is interoperability with security and intellectual property [ 1 , 14 , 16 – 18 ]. The poor implementation is caused by the absence of monitoring or evaluation [ 6 , 19 ]. Furthermore, its evaluation is complex in many countries, since there are no tools available to assess them, unlike the information system in the maternal sector, which has the MADE IN/OUT method for evaluation [ 9 ]. A system for recording health and disease programs can replace extensive surveys and provide good-quality data [ 4 ]. They are used in research, even for randomized clinical trials [ 2 , 20 ]. Additionally, some health facilities use information systems capable of diagnosing diseases [ 21 ]. Their implementation is related to the competence of human resources and updating programs or applications [ 22 – 25 ]. However, a lack of human resources and not being consistent with the number of patients will cause the system to not work well and produce poor-quality data [ 23 , 25 , 26 ]. Support from the government is required to improve the implementation of the health information system [ 4 , 27 ]. The disparities in the health sector among regions in Indonesia exist in the utilization of health facilities, not only in the information system alone [ 28 ].

Int. J. Environ. Res. Public Health 2023 , 20 , 4384 10 of 12 According to the regulation of Minister of Health Number 97 in 2015, the government emphasized the procurement of data on health facilities, data flow, and access. Hence, the problem of interoperability and data quality was not a significant issue [ 1 ]. Furthermore, many things should be considered in the health information system, such as electronic medical records, and integration or bridging with other data, especially with population identity data, as attempted in Brazil [ 17 , 29 ], including system modifications for certain groups [ 30 ]. A limitation of this research was the absence of an evaluation in the implementation process and the quality of the data output. Furthermore, not all information systems in health centers were assessed, such as medical records, or CHC information systems, such as SIMPUS. In contrast, the inclusion of national data from all CHCs registered in the Health Service and the Ministry of Home Affairs would require a great effort and an enormous cost Based on the disparities shown through the results of this study, it is necessary to strengthen the health system in the form of surveillance of data and more significant support from the provincial and district levels to CHCs to ensure the uploading and operation of the required applications. Another suggestion is that the government should be expected to evaluate the information systems of the CHCs, especially regarding data entry for disease-based health programs Additionally, interoperability capabilities, deployment of human resources, and updating the system according to the current situation should also be monitored continuously Disease data applications involve the digitalization of disease records. The deployment of digital systems requires well-designed applications, supporting and changing management, and strengthening human resources to realize and sustain system health gains [ 31 ]. This process does not just happen, but begins with digitization and digital transformation [ 32 ]. Nowadays, the digitalization of the system has become an important determinant [ 33 ]. 5. Conclusions This analysis showed disparities in the health information system in Indonesia by province and region. For example, although the islands of Java, Bali, and Sumatra had attractive situations when broken down, some provinces had poor information systems in their CHCs regarding availability and the number of applications or data-storage programs used. The results of this analysis recommend future improvement of the CHCs’ information systems. The gap between the western and the eastern parts of Indonesia needs to be overcome by increasing resources, including human resources and supportive management Author Contributions: Conceptualization, S.I. and H.H.; methodology S.I. and H.H.; formal analysis, S.I. and M.H.H.; writing—original draft preparation, S.I.; writing—review and editing, S.I. and M.H.H. All authors have read and agreed to the published version of the manuscript Funding: This study used the National Survey Data funded by the Indonesian government through Ministry of Health budget (APBN) Institutional Review Board Statement: Ethical approval was obtained from the Research Ethics Commission of the National Institute of Health Research and Development (NIHRD) of Ministry of Health, number LB.02.01/2/KE.318/2018, and amendment number: LB.02.01/2/KE.011/2019 Informed Consent Statement: All methods were conducted in accordance with the Helsinki Declaration and all its future amendments. Written informed consent was obtained from all participants prior to the interviews Data Availability Statement: The datasets generated during and/or analyzed during the current study are not publicly available due to ethical reasons but are available upon request from the Director of the NIHRD, which has now been renamed the Health Policy Agency of Ministry of Health Indonesia.

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