Using an Electronic Immunization Registry (Aplikasi Sehat IndonesiaKu) in Indonesia: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Electronic immunization registries (EIRs) are being increasingly used in low- and middle-income countries. In 2022, Indonesia's Ministry of Health introduced its first EIR, named Aplikasi Sehat IndonesiaKu (ASIK), as part of a comprehensive nationwide immunization program. This marked a conversion from traditional paper-based immunization reports to digital routine records encompassing a network of 10,000 primary health centers (puskesmas). OBJECTIVE: This paper provides an overview of the use of ASIK as the first EIR in Indonesia. It describes the coverage of the nationwide immunization program (Bulan Imunisasi Anak Nasional) using ASIK data and assesses the implementation challenges associated with the adoption of the EIR in the context of Indonesia. METHODS: Data were collected from primary care health workers' submitted reports using ASIK. The data were reported in real time, analyzed, and presented using a structured dashboard. Data on ASIK use were collected from the ASIK website. A quantitative assessment was conducted through a cross-sectional survey between September 2022 and October 2022. A set of questionnaires was used to collect feedback from ASIK users. RESULTS: A total of 93.5% (9708/10,382) of public health centers, 93.5% (6478/6928) of subdistricts, and 97.5% (501/514) of districts and cities in 34 provinces reported immunization data using ASIK. With >21 million data points recorded, the national coverage for immunization campaigns for measles-rubella; oral polio vaccine; inactivated polio vaccine; and diphtheria, pertussis, tetanus, hepatitis B, and Haemophilus influenzae type B vaccine were 50.1% (18,301,057/36,497,694), 36.2% (938,623/2,595,240), 30.7% (1,276,668/4,158,289), and 40.2% (1,371,104/3,407,900), respectively. The quantitative survey showed that, generally, users had a good understanding of ASIK as the EIR (650/809, 80.3%), 61.7% (489/793) of the users expressed that the user interface and user experience were overall good but could still be improved, 54% (422/781) of users expressed that the ASIK variable fit their needs yet could be improved further, and 59.1% (463/784) of users observed sporadic system interference. Challenges faced during the implementation of ASIK included a heavy workload burden for health workers, inadequate access to the internet at some places, system integration and readiness, and dual reporting using the paper-based format. CONCLUSIONS: The EIR is beneficial and helpful for monitoring vaccination coverage. Implementation and adoption of ASIK as Indonesia's first EIR still faces challenges related to human resources and digital infrastructure as the country transitions from paper-based reports to electronic or digital immunization reports. Continuous improvement, collaboration, and monitoring efforts are crucial to encourage the use of the EIR in Indonesia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".