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Background: Digital traces are rapidly used for health monitoring purposes in recent years. This approach is growing as the consequence of increased use of mobile phone, Internet, and machine learning. Many studies reported the use of Google Trends data as a potential data source to assist traditional surveillance systems. The rise of Internet penetration (54.7%) and the huge utilization of Google (98%) indicate the potential use of Google Trends in Indonesia. No study was performed to measure the correlation between country wide official dengue reports and Google Trends data in Indonesia. Objective: This study aims to measure the correlation between Google Trends data on dengue fever and the Indonesian national surveillance report. Methods: This research was a quantitative study using time series data (2012-2016). Two sets of data were analyzed using Moving Average analysis in Microsoft Excel. Pearson and Time lag correlations were also used to measure the correlation between those data. Results: Moving Average analysis showed that Google Trends data have a linear time series pattern with official dengue report. Pearson correlation indicated high correlation for three defined search terms with R-value range from 0.921 to 0.937 (p ≤ 0.05, overall period) which showed increasing trend in epidemic periods (2015-2016). Time lag correlation also indicated that Google Trends data can potentially be used for an early warning system and novel tool to monitor public reaction before the increase of dengue cases and during the outbreak. Conclusions: Google Trends data have a linear time series pattern and statistically correlated with annual official dengue reports. Identification of information-seeking behavior is needed to support the use of Google Trends for disease surveillance in Indonesia.

The Covid-19 pandemic has had a tremendous impact on the Indonesian health system. At the peak of the second wave, health care crises occurred in various regions, causing difficulties in accessing essential and emergency health services, accompanied by increased deaths in hospitals and self-isolation. Various efforts have been made to overcome the crisis, from providing field hospitals, adding facilities and infrastructure in hospitals, empowering health workers and volunteers, logistics, and implementing a massive digital system. In addition, strengthening in the upstream aspects starting from tracing, testing to health protocols is also continuously encouraged, along with the acceleration and expansion of vaccinations. The pandemic has encouraged massive utilization of digital systems in various aspects accompanied by an extraordinary increase in the volume of digital data in various types and quality. The critical question then is, what are the opportunities for big data to support the recovery of the health system affected by the pandemic? Likewise, what challenges must be overcome to make optimal use of big data so that the Indonesian health system can quickly recover, grow and become resilient again? To answer the questions above, we reviewed bibliographic databases, gray literature, media, and webinar recordings available online. Next, we group their potential based on the building blocks of the health system. Using the e-health framework from WHO-ITU, we categorize the main challenges of using big data into seven major groups: governance, strategy, data standards, interoperability, application, regulation, and human resources. We provide special notes on each aspect of these challenges along with priority follow-up steps. In the end, the Covid-19 pandemic provides essential lessons for the Indonesian health system to take advantage of digitalization, especially big data intelligently and creatively, to encourage the immediate recovery of the health system. However, several significant challenges need to be overcome so that big data can be utilized optimally to overcome this big global problem.

Background: Dengue fever is a mosquito-borne viral disease with high incidence in over 128 countries. WHO estimates 500,000 people with severe dengue are hospitalized annually and 2.5% of those affected die. Indonesia is a hyperendemic country for dengue with an increasing number of cases in the last decade. Unfortunately, the trends of Indonesian dengue research are relatively unknown. Objective: This research aimed to depict bibliographic trends and knowledge structure of dengue publications in Indonesia relative to that of South-east Asia (SEA) from 2007 to 2016. Methods: Bibliographic data were collected from PubMed filtered by Indonesia country affiliation. The annual growth rate of publication was measured and compared with neighborhood countries in the SEA region. Network analysis was used to visualize emerging research issues. Results: About 1,625 dengue-related documents originated from SEA region, of which Indonesia contributed 5.90%. The publication growth rate in Indonesia, however, is the highest in ASEAN region (28.87%). Total citations for documents published from Indonesia was 980, with an average of 14 citations per publication and h-index of 16. Within the first five years, the main research topics were related to insect vector and diagnostic method. While insect vector remained dominant in the last five years, other topics such as disease outbreak, dengue virus, and dengue vaccine started emerging. Conclusion: In the last 10 years, dengue publications' growth from Indonesia in international journals improved significantly, despite less number of publications compared to other SEA countries. Efforts should be made to improve the quantity and quality of publications from Indonesia. The research topics related to dengue in Indonesia are in line with studies in SEA. Stakeholders and policy makers are encouraged to develop a roadmap for dengue research in the future.

Given the ongoing COVID-19 pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19's disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to increase their accuracy. However, it remains unclear whether incorporating online search volumes into models leads to better predictive performances for long-term prediction. The aim of this study was to analyze whether search engine query data are important variables that should be included in the models predicting new daily COVID-19 cases and deaths in short- and long-term periods. We used country-level case-related data, NAVER search volumes, and mobility data obtained from Google and Apple for the period of January 20, 2020, to July 31, 2021, in South Korea. Data were aggregated into four subsets: 3, 6, 12, and 18 months after the first case was reported. The first 80% of the data in all subsets were used as the training set, and the remaining data served as the test set. Generalized linear models (GLMs) with normal, Poisson, and negative binomial distribution were developed, along with linear regression (LR) models with lasso, adaptive lasso, and elastic net regularization. Root mean square error values were defined as a loss function and were used to assess the performance of the models. All analyses and visualizations were conducted in SAS Studio, which is part of the SAS OnDemand for Academics. GLMs with different types of distribution functions may have been beneficial in predicting new daily COVID-19 cases and deaths in the early stages of the outbreak. Over longer periods, as the distribution of cases and deaths became more normally distributed, LR models with regularization may have outperformed the GLMs. This study also found that models performed better when predicting new daily deaths compared to new daily cases. In addition, an evaluation of feature effects in the models showed that NAVER search volumes were useful variables in predicting new daily COVID-19 cases, particularly in the first 6 months of the outbreak. Searches related to logistical needs, particularly for \"thermometer\" and \"mask strap,\" showed higher feature effects in that period. For longer prediction periods, NAVER search volumes were still found to constitute an important variable, although with a lower feature effect. This finding suggests that search term use should be considered to maintain the predictive performance of models. NAVER search volumes were important variables in short- and long-term prediction, with higher feature effects for predicting new daily COVID-19 cases in the first 6 months of the outbreak. Similar results were also found for death predictions.

Introduction The incidence of cancer and its prevalence are increasing in Indonesia. It is crucial to ensure national cancer policies are evidence-based and promote research. While cancer research is being conducted across Indonesia, the extent and focus of research activities are not known, with no existing synthesis of the cancer research landscape. We seek to address this gap by characterising trends in the extent and types of cancer research conducted in Indonesia. Methods Scientometric study using descriptive analyses to determine annual growth patterns in publications across all cancer research literature from Indonesia. We developed a classification system for both research type and study design which was applied to all included publications. A visualisation software tool (VOSviewer) was used to explore the geographical distribution of research activity. The Wilcoxon rank-sum test was used to determine the influence of international collaboration on the impact factor of journals in which articles were published. Results We retrieved 1773 cancer-related articles published by Indonesia-affiliated authors from 1961 to 2020, with notable year-on-year increases in the annual total number of published articles since 2015. Most articles (84.0%) were published by authors affiliated with institutions on Java Island. The most commonly published article type was basic research and discovery science (28.8%), using a one-group analytical study design (28.8%). International collaboration was significantly correlated with a higher h-index of the journal in which research was published (P < .0001, r = .317). Conclusion An increase in the number and range of topics explored in cancer-related publications over time was identified. The summary of the current corpus of cancer-related research for Indonesia can be used to direct the development of the national cancer control plan alongside informing the national cancer research strategy. Our novel and feasible scientometric approach can be used to direct future national and regional mapping of cancer research.

In response to the COVID-19 pandemic, mobile-phone data on population movement became publicly available, including Google Community Mobility Reports (CMR). This study explored the utilization of mobility data to predict COVID-19 dynamics in Jakarta, Indonesia. We acquired aggregated and anonymized mobility data sets from 15 February to 31 December 2020. Three statistical models were explored: Poisson Regression Generalized Linear Model (GLM), Negative Binomial Regression GLM, and Multiple Linear Regression (MLR). Due to multicollinearity, three categories were reduced into one single index using Principal Component Analysis (PCA). Multiple Linear Regression with variable adjustments using PCA was the best-fit model, explaining 52% of COVID-19 cases in Jakarta (R-Square: 0.52; p < 0.05). This study found that different types of mobility were significant predictors for COVID-19 cases and have different levels of impact on COVID-19 dynamics in Jakarta, with the highest observed in “grocery and pharmacy” (4.12%). This study demonstrates the practicality of using CMR data to help policymakers in decision making and policy formulation, especially when there are limited data available, and can be used to improve health system readiness by anticipating case surge, such as in the places with a high potential for transmission risk and during seasonal events.

Regulatory sandboxes offer an alternative solution to address regulatory challenges in adopting disruptive technologies. Although regulatory sandboxes have been widely implemented in the financial sector across more than 50 countries, their application to the health sector remains limited. This study aims to explore stakeholders' perspectives on introducing a regulatory sandbox into the Indonesian health system using e-malaria as a use case. Using a participatory action research approach, this study conducted qualitative research, including desk reviews, focus group discussions, and in-depth interviews with stakeholders. This study sought to understand stakeholders' concerns and interests regarding the regulatory sandbox and to collaboratively develop a regulatory sandbox model to support the malaria program. The study revealed that most stakeholders had limited awareness of the regulatory sandbox concept. Concerns have been raised regarding the time required to establish regulations, knowledge gaps among stakeholders, data protection issues, and limited digital infrastructure in malaria endemic areas. Existing regulations have been found to be inadequate to accommodate disruptive healthtech for malaria. Nevertheless, through a collaborative process, stakeholders successfully developed a regulatory sandbox model specifically for e-malaria, with the crucial support of the Ministry of Health. The regulatory sandbox holds the potential for adoption in the Indonesian health system to address the limited legal framework and to facilitate the rapid and safe adoption of disruptive healthtech in support of the malaria elimination program. Through stakeholder involvement, guidelines for implementing the regulatory sandbox were developed and innovators were successfully invited to participate in the first-ever trial of a health regulatory sandbox for e-malaria in Indonesia. Future studies should provide further insights into the challenges encountered during the e-malaria regulatory sandbox pilot study, offering a detailed account of the implementation process.

Interoperability in health care is a critical aspect for the exchange of health information. The Fast Healthcare Interoperability Resources (FHIR) framework has become widely adopted to provide interoperable data exchange in the health care industry. The COVID-19 pandemic has demonstrated the significance of interoperable data in tracking patients who have contracted the virus and keeping track of the vaccinated population. Indonesia is one of the many countries that have implemented interoperable data systems to track patients with COVID-19, and it has aspirations to expand the system to other use cases, particularly in the primary health care setting. The primary health care providers in Indonesia include Puskesmas (community health centers) and private clinics. To promote interoperable health data exchange in the primary health care sector, the Indonesian government has launched the Satusehat project. The goal of the Satusehat platform is to make health data in Indonesia interoperable and exchangeable between health care organizations, particularly Puskesmas and private clinics. For a successful implementation of the Satusehat platform in Puskesmas and private clinics, it is crucial to understand the challenges that may arise. This study analyzed the pain points of the Satusehat platform based on a content analysis of the Satusehat Social Networking Service Telegram group messages. The study revealed the pain points and suggested existing approaches to address them, which can be used as a proposed design of interoperability for Puskesmas and private clinics, making it easier for these organizations to adopt the Satusehat platform. The pain points identified in this study include issues with the FHIR server, problems with FHIR profile selection, and the mapping of electronic medical record data into standardized data, such as mapping into the Systematized Nomenclature of Medicine Clinical Terminology. The results show that the value of the mapping issue is 37, profile issue 9, and server issue 61. Among the 3 categories, server issues had the highest population, followed by mapping issues and then profile issues. To address these issues, the study proposed practical approaches, including a federated architecture for the FHIR server instead of a centralized architecture, an FHIR writer and FHIR viewer system inspired by the Standardized Structured Medical Record Information eXchange system in Japan, and an FHIR conversion framework that integrates with our FHIR writer and FHIR viewer system. These proposed solutions can help resolve the pain points identified in the study and help the advancement of the Satusehat platform implementation in Puskesmas and private clinics in Indonesia. We believed that the proposed solutions have potential to be adopted to other countries with similar issues when conducting nationwide project in health care interoperability design.

Unified Theory of Acceptance and Use of Technology (UTAUT) is an integrative concept that has been used widely to measure IT adoption. However, a recent study in a developing country concluded that UTAUT is not adequate in predicting IT adoption within the context of health system strengthening (HSS). It has been suggested that context-specific dimensions to modify UTAUT should be considered. The objective of this paper is to propose an extension of the theory, called UTAUT for HSS, as a reference for contextualizing health system variables for health IT adoption studies in the developing countries. We combined the multi-level framework of UTAUT with WHO health system building blocks. Modification of the original multi-level framework was performed on the 3 levels. i.e:  the higher-level contextual factors, middle-level, and individual-level contextual factors. Based on this, we propose a modified multi-level framework of technology acceptance and use for health system strengthening setting (UTAUT for HSS).  Given the complexities of health systems, more thoughts regarding the methodologies will be useful to enrich this initial framework.  Commentaries and discussions are invited for improvement, before implementation to obtain more complete story of health IT adoption in the low resources setting.

Background Comprehensive reports of malaria in Menoreh Hills, Central Java, Indonesia, a unique district cross-boundaries area under three districts and two provinces have been published previously. However, no study was performed to identify the hotspots of malaria in this cross-boundaries area, Kaligesing and Bagelen Subdistricts in Purworejo, Jawa Tengah Province and Kokap Subdistrict in Kulon Progo, Yogyakarta Province, using a longitudinal spatial data. Methods Monthly reports of malaria cases at primary health centres during 2005–2015 were collected and processed with ArcGIS and SaTScan to identify the malaria distribution at the village level. Malaria distribution was analysed using global spatial autocorrelation (Moran index) in ArcGIS. Cluster analysis was conducted using SaTScan purely spatial clustering and purely temporal clustering. Cluster characteristics resulted from three different approach were compared and analysed. Results During the last 11 years, 3812 malaria cases were reported and the number of high case incidence (HCI) villages were increased continuously. Malaria spatial distribution in Menoreh Hills was clustered spatially. Using three different approaches of time period ranges, consistent conclusion was found i.e. most likely clusters always occurred in the Purworejo district while the secondary clusters appeared later in the cross-boundaries districts. Conclusion Spatiotemporal analysis of an 11 years surveillance data showed that hotspots of malaria cases in Menoreh Hills were continuously located in Purworejo district. The success of malaria elimination in the cross boundaries area of Menoreh Hills might be depended on the success in malaria case management and surveillance in this hotspot area.