FAIR Equivalency in Indonesia’s Digital Health Framework

The objective of this study was to assess the regulatory framework for health data in Indonesia in order to understand the policy context and explore the possibility of expanding the adoption and implementation of the FAIR Guidelines, which state that data should be Findable, Accessible, Interoperable and Reusable (FAIR), in Indonesia. Although the FAIR Guidelines were not explicitly mentioned in any of the policy documents relevant to the Indonesian digital health sector, six out of the eight documents analysed contained FAIR Equivalent principles. In particular, Indonesia’s Population Identification Number (NIK) has the potential, as a unique identifier, to support the integration and interoperability (findability) of data, which is crucial to all other aspects of the FAIR Guidelines. There is also a plan to build standards and protocols into the implementation of information systems in each ministry and government agency to improve data accessibility (accessibility), the integration of the various information systems (interoperability), and the need for a standardised arrangement for health information systems related to health data following the community standard is recognised (reusability). The documents at the core of Indonesia’s digital health/eHealth policy have the highest FAIR Equivalency Score (FE -Score), showing some degree of alignment between the Indonesian digital health implementation vision and the FAIR Guidelines. This indicates that Indonesia's digital health sector is open to using the FAIR Guidelines.

Against this backdrop, Indonesia has an excellent opportunity to answer health challenges by using digital health. The latest Indonesian Internet Service Providers Association survey in 2019 reported that Internet penetration among the Indonesian population had risen by 10.12 percentage points to 64.8% (or 171.17 million people) in 2018 [5]. There has also been an increase in the number of information technology systems implemented in Indonesia. A survey by Sanjaya et al. in 2013 about information technology implementation found that 63.38% of 71 hospitals in the Province of Yogyakarta, one of the biggest cities in Indonesia, used hospital management information systems [6].
However, a severe weakness with these systems is their capability to share patient information between hospitals. Difficulties with interoperability are not only experienced between hospitals, but within hospitals as well.
The FAIR Guidelines -an acronym for 'Findable', 'Accessible' (under well-defined conditions), 'Interoperable', and 'Reusable' -were first discussed in 2014 at the Lorentz workshop 'Jointly Designing a Data Fairport' and published in 2016 [7]. The 15 FAIR facets (sub-principles guiding data management systems) require data to be easy to locate and open, as well as interoperable, transparent, exchangeable and reusable [8]. The FAIR Guidelines identify distinct criteria that promote manual and automated deposition, discovery, sharing, and reuse in contemporary data publishing environments [7]. However, it is important to note that FAIR is not equivalent to 'open data'; the letter 'A' in FAIR stands for 'Accessible' under well-defined conditions, which means that data should be free, but, in some situations, should be protected for a legitimate reason, such as personal privacy, national security, or competitiveness. The FAIR Guidelines are primarily applied in European geographies (67%) and, to a lesser extent in American geographies (14%), together comprising 81% of implementation activities [9].
Data Intelligence Just Accepted MS. https://doi.org/10.1162/dint_a_00171 The FAIR Guidelines are suitable for adoption in Indonesia, as they have the potential to solve the interoperability problem and have been successfully applied in many different contexts. However, it is not known if the FAIR Guidelines are aligned with the governance framework for digital health/eHealth policy in Indonesia and whether or not a policy window is open for adopting and using the FAIR Guidelines. Hence, to determine if the policy environment is ripe for their adoption/implementation, we need to look at how aligned the regulatory framework for digital health/eHealth in Indonesia is with the FAIR Guidelines.

Study design
This research was conducted as a qualitative cross-sectional study involving the examination of

Objective
The general objective of the analysis was to check the regulatory framework for health data in Indonesia in order to understand the policy context and explore the possibility of expanding the adoption and implementation of the FAIR Guidelines. Therefore, the relevant research questions were: Are the FAIR Data Guidelines mentioned in the policy documents in Indonesia's ICT and health/eHealth sectors? What is the level of equivalency of these policy documents with the FAIR Guidelines? FAIR Equivalency measures the degree to which policy documents refer to the relevant aspects of all facets of the FAIR Guidelines [10,11]. The methodological steps are described in detail Data Intelligence Just Accepted MS. https://doi.org/10.1162/dint_a_00171 by Basajja et al. [10] and this research followed the same procedure. Figure 1 shows a flow chart of the methodology used to ascertain FAIR Equivalency in Kenya [12].

Identification of relevant documents
The first step to answer the research question was to collect key documents related to digital health in Indonesia and then analyse the documents for mention of the FAIR Guidelines or FAIR Equivalent principles. A total of 14 policy documents were identified: 4 as directly relating to digital health and 10 as the basis for making policies on digital health. However, not all of the documents were relevant to ICT/health. Table 1 shows the relevance and availability of policy documents and legislation on digital health. Only documents relevant to digital health and which were available were included in the research (marked 'yes' in the two-last columns of Table 1). Therefore, six documents were excluded and eight documents were selected.

Coding and labelling
The selected policy documents were carefully analysed using a code-labelling method [21] to determine whether or not they mentioned the FAIR Guidelines or FAIR-like (FAIR Equivalent) principles. The number '1' was assigned if the document mentioned either the FAIR Guidelines (FAIR Mention) or FAIR Equivalent principles, whereas '0' was assigned if they were not mentioned.

Mention of the FAIR Guidelines in policy documents
The analysis found that although none (0%) of the documents refer explicitly to aspects of the FAIR  Table 3).

FAIR Equivalency in the policy documents
To answer the second question, on the level of FAIR Equivalency in the policy documents analysed, the policy documents were examined to see if they referred to the equivalent of the 15 sub-criteria of the FAIR Guidelines (also known as the 'FAIR facets') [7]. These consist of the following: 'Findability' in each policy document was carried out using a coding-labelling method [21]. The documents were organised into rows using a Microsoft Excel spreadsheet with the FAIR elements arranged in columns.
In each policy document. The mention of a FAIR Equivalent facet was labelled '1,' while the lack of mention was labelled '0' in the corresponding Microsoft Excel spreadsheet data cell; these scores were aggregated to give a FAIR Equivalency Score (FE-Score) for each document (see Table 4).

Findability facets
Findability contains four facets: F1, F2, F3, and F4. Three of these facets -F1, F2, and F4 -were found in the policy documents analysed, while facet F3 was not mentioned. Perhaps the most important among the FAIR Guidelines is 'Findability', especially facet F1, which requires that in order to be 'Findable', data and metadata should be assigned a globally unique and persistent identifier [7].
Without a unique identity, it is not easy for humans or machines to identify a digital object, let alone decide whether or not it is reusable in a particular context. The remaining three principles of FAIR (Accessibility, Interoperability, and Reusability) are partially, or even wholly, related to Findability.
Facet F1 was only found in Minister of Health Regulation Number 46 of 2017 concerning the National E-Health Strategy. This document sets out standards and interoperability requirements, which entails using the NIK and has the potential to support integration and interoperability between existing health care systems.
Facet F2 states that data are described with rich metadata, allowing a computer to automatically accomplish routine and tedious sorting and to prioritise tasks that currently demand much attention from researchers. The rationale behind this principle is that someone should be able to find data based on the information provided by their metadata, even without the data's identifier [22]. This facet is found only in Government Regulation Number 46 of 2014 concerning Health Information Systems, which states that health data must follow a standard. Although it states that health data should have a type, nature, format, database, codification, and metadata that can be easily integrated, the policy statement is different from F2 facet.
The last facet of Findability found in the policy documents is F4, which states that (meta)data are registered or indexed in a searchable resource. Identifiers and rich metadata descriptions alone will not ensure 'findability' on the Internet. Perfectly good data resources may go unused simply because no one knows they exist. There are many ways that digital resources can be made discoverable, including indexing [22]. Article 21, section 8 of the Data and Information Storage of Government Regulation Number 82 of 2012 requires health data and information to be stored in a 'database' in a safe place and not to be damaged or lost by using electronic and/or non-electronic storage media. A database is a place/container for various data collected regularly, according to informatics principles that users can access at any time to produce the necessary information by using the concept of the data warehouse. The purpose of the statement in this article is consistent with facet F4, which require meta(data) to be placed in a searchable resource.

Accessibility facets
Facets A1, A1.1, and A1.2 of Accessibility are mentioned in the policy documents, but none of the documents mentioned a requirement for access to metadata even when the data are no longer available (A2). Facet A1 acknowledges that it may not always be possible to allow fully automated access to data in the case of highly sensitive data. In these situations, presenting contact information such as e-mail address, telephone number, or other information for a person who can request access to the data also satisfies the FAIR Guidelines [22]. Two of the more recent policies that relate to digital is implemented with quality assurance according to international standards.

Interoperability facets
None of the policy documents contained facets I2 and I3, but five out of the eight documents mentioned I1. Facet I1 requires (meta)data to use a formal, accessible, shared and broadly applicable language for knowledge representation. In other words, it is critical to use commonly used controlled vocabularies, ontologies, thesauri, and a good data model to ensure the automatic findability and interoperability of datasets.
Generally, the number of policies that mention interoperability shows that it is an important aspect of the implementation of digital health in Indonesia. All policies state the importance of communication between systems to achieve data exchange. Integration includes both technical systems (systems that can communicate with each other) and content (the same data set). Integrated capable of providing a mechanism for interconnecting information subsystems in various ways as needed. In addition, the particular way to achieve interoperability is mentioned in Government Regulation Number 46 of 2014, which states that there is a need for an electronic-based data standard service utilising existing technology (web services, APIs) so that it can be used by eHealth stakeholders, especially for the development of a health service information system.

Reusability facets
FAIR's ultimate goal is to optimise the reuse of data. Therefore, metadata and data should be well- aims to make the data easier to find and reuse data by attaching many labels to the data. Principle R1 is related to F2, but R1 focuses on the ability of a user (machine or human) to decide if the data is useful in a particular context [22]. Facet R1.3 was found in three of the documents analysed and has the highest score among the Reusability facets.
Minister of Health Regulation Number 97 of 2015 highlights the need for a standardised arrangement of health information systems, carried out through data codification, the preparation of a health data dictionary, and the setting of priority indicators to address the issue of health data integration and exchange. The Minister of Health Regulation Number 46 of 2017 also mentioned standards, stating that standards can be seen from various perspectives, including functional standards of electronic information systems, data standards and health terminology, security and privacy standards, as well as electronic data communication standards (data exchange protocols). Both statements in these policies have the objective of making data meet domain-relevant community standards.

Conclusion and future work
Indonesia has an excellent opportunity to address health challenges using digital health, evidenced by its high level of Internet penetration among the Indonesian population and the fact that most of the hospitals in Yogyakarta, one of the biggest cities in Indonesia, have adopted hospital management information systems. Nevertheless, the main problem experienced is the inability to share patient information between hospitals. The FAIR Data Guidelines -that data be 'Findable', 'Accessible', 'Interoperable', and 'Reusable' -and its 15 facets are guiding principles for data management systems, requiring data to be easy to locate, open, interoperable, transparent, exchangeable, and reusable. The FAIR Guidelines could help the digital health sector in Indonesia to solve the problem of interoperability, as long as these principles are implemented with contextual awareness. This study's general objective was to assess the regulatory framework for health data in Indonesia to understand the context and explore the possibility of the FAIR Guidelines being used to extend Satu Data Indonesia in the eHealth sector. In order to do this, 14 policy documents were identified as essential to the health/eHealth and ICT sectors in Indonesia, of which 8 were examined. A detailed coding-labelling approach was used to examine the documents to determine whether or not they contain FAIR Guidelines or FAIR-like/FAIR Equivalent principles, following Basajja et al. [10]. The documents were then reviewed using the same method to determine whether or not they mention the equivalent of any of the 15 FAIR facets. The analysis found that none (0%) of the 8 policy documents mention the FAIR Guidelines directly, but 6 (75%) mention the equivalent of the FAIR

Author's contribution
Putu Hadi Purnama Jati (putuhadi2808@gmail.com) wrote this article based on his research on comparison between FAIR Guidelines and Satu (One) Data Indonesia.