=Paper=
{{Paper
|id=Vol-3155/paper3
|storemode=property
|title=Evaluating Clinical-Care Metadata Share and its FAIRification using the REA Ontology
|pdfUrl=https://ceur-ws.org/Vol-3155/paper3.pdf
|volume=Vol-3155
|authors=Syeda A. Sohail,Faiza A. Bukhsh,Maurice van Keulen,Johannes G. Krabbe,Pavel Hruby
|dblpUrl=https://dblp.org/rec/conf/vmbo/SohailBKKH22
}}
==Evaluating Clinical-Care Metadata Share and its FAIRification using the REA Ontology==
https://ceur-ws.org/Vol-3155/paper3.pdf
Evaluating Clinical-Care Metadata Share and its
FAIRification using the REA Ontology⋆
Syeda A. Sohail1 , Faiza A. Bukhsh1 , Maurice van Keulen1 , Johannes G. Krabbe2 and
Pavel Hruby3
1
University of Twente, Drienerlolaan 5, Enschede, The Netherlands
2
Medische Spectrum Twente, Medlon BV, 7500 KA Enschede, The Netherlands
3
REA Technology, Copenhagen, Denmark
Abstract
The FAIRification of data facilitates a fast-paced, global, FAIR metadata availability across domains for
the sustainable growth of public/private organizations. Likewise, in healthcare, the clinical labs aim
to achieve FAIR (biosample) metadata by keeping patient-specific infectious disease records. However,
the responsible evaluation of the FAIRification process of Dutch clinical lab metadata is lacking at
the local and global levels. From a responsible data science perspective, we normatively (in-principle)
and empirically (in-practice) evaluate the Dutch clinical lab metadata share against FACT principles.
The normative evaluation involved content analysis of FAIR concerning peer-reviewed publications
and official websites. The empirical evaluation comprised a documentation review of standardized
(public/confidential) documents regarding the metadata share of Dutch clinical labs. The evaluations
assisted us in formulating two REA models based on REA ontology. The first REA model depicts the
clinical lab metadata production run at a local/national level. The second REA model specifies the
work (flow) breakdown structure of global FAIRification for FHIR Netherland using linkage relationship
against FACT principles. In-field (IT and REA ontology) experts further evaluated the REA models for
functional and structural veracity. Furthermore, our evaluations verified the presence of an underlying
privacy-utility tradeoff in FAIRification of clinical lab metadata where data utility is prioritized over data
protection.
Keywords
FAIRification, REA ontology, FACT principles, Dutch clinical-care, Responsible Data Science, Data
protection, Data Utility
Proceedings of the 16th International Workshop on Value Modelling and Business Ontologies (VMBO 2022), held in
conjunction with the 34th International Conference on Advanced Information Systems Engineering (CAiSE 2022), June
06–10, 2022, Leuven, Belgium
$ s.a.sohail@utwente.nl (S. A. Sohail); f.a.bukhsh@utwente.nl (F. A. Bukhsh); m.vankeulen@utwente.nl (M. van
Keulen); j.krabbe@mst.nl (J. G. Krabbe); phruby@acm.org (P. Hruby)
https://utwente.nl/en/eemcs/dmb/ (S. A. Sohail); https://utwente.nl/en/eemcs/dmb/ (F. A. Bukhsh);
https://utwente.nl/en/eemcs/dmb/ (M. van Keulen); https://mst.nl (J. G. Krabbe); http://reatechnology.com
(P. Hruby)
0000-0001-8078-0411 (S. A. Sohail); 0000-0001-5978-2754 (F. A. Bukhsh); 0000-0003-2436-1372 (M. van Keulen);
0000-0003-1585-9304 (J. G. Krabbe); 0000-0002-5502-0880 (P. Hruby)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
1. Introduction
The recent intensified FAIRification efforts stem from including global FAIR data repositories,
as an ultimate goal, by pan-European and national public authorities [1, 2, 3]. FAIRification is
the process of making metadata FAIR, i.e., Findable, Accessible, Interoperable, and Reusable
[4, 5, 3]. A similar FAIR data drive seeks an all-rounded, concerted technological, organizational,
and strategic effort in the healthcare domain [6]. In this wake, GDPR (recital 53) obligates
the member states for harmonized conditions to ensure sustainable national/central and
cross-border healthcare services and systems [7]. Furthermore, for optimal data utility, GDPR
(recital 5) ordains uninterrupted personal data share amongst public and private actors (i.e.,
both natural persons and organizations) [8]. The measures mentioned above are an extension
of the GDPR objective of achieving free movement of personal data within the EU [9] and
align with the FAIRification process [1, 2]. Therefore, an ethical evaluation of the FAIRification
process of Dutch healthcare metadata share is required from a responsible data science (RDS)
perspective [10, 11]. In this wake, an evaluation against FACT principles (of RDS) aims for fair,
accurate, confidential, and transparent data handling from healthcare providers’ viewpoint
[11] and seek both: data utility and data protection [12]. Thus, the FACT principles seem
applicable to the healthcare domain in effectively dealing with the dilemma of Privacy Utility
Tradeoff (PUT), where the precision of data analytics (i.e., data utility) is as crucial as the
privacy-preservation of data subjects (i.e., data protection) [13, 14, 15, 16].
Contribution; Using Value Modeling and the REA Ontology: Why Value Modeling? During the
last three decades, the pan-European and local regulations [17, 18, 19] gradually standardized
the care services (events), the performing agents, and (metadata) resource requirements.
Standardized metadata trace of patients’ value care relies upon the uninterrupted (data-utility
prone) care-metadata share amongst Dutch healthcare givers [15]. Data utility is the precision
of (meta) data analytics, whereas data protection aims at privacy preservation of data subjects
[13]. The former is an integral part of the FAIRification process [6], whereas FACT principles of
RDS encompass both [11, 10]. Why the REA Ontology? Previously, we conceptualized the Dutch
care metadata share landscape from a patient’s perspective using REA ontology to highlight:
the vital economic agents, their prime interactions, and collective economic value gain or loss
[15, 14]. Here, the REA ontology identified the underlying financial factors and signified: who,
how, and what leads to the FAIRification of Dutch clinical lab metadata at a local production
run level to the global work (flow) breakdown structure level of FHIR Netherlands against
FACT principles of RDS [10, 11]. The objectives of the research work are twofold. The first is
to evaluate the (dual-level) FAIRification of Dutch clinical lab metadata against FACT principles
of RDS. Secondly, to specify the findings by formulating the REA (Resource, Event, Agent)
models and evaluating their functional and structural authenticity using expert opinion. To
attain the objectives, we followed the approach mentioned under the data analytical approach
in Section 3. The effectiveness of the selected approach relied upon following three factors.
Firstly, upon the successful mapping of FACT principles on labs’ (dual-level) metadata share
(see FACT-mapping determinants in Section 4). Secondly, upon the simplified specification of
(otherwise) complex, Dutch healthcare metadata share landscape using REA ontology. Thirdly,
upon the structural and functional evaluation of REA models using expert opinion.
2. Related Work
Clinical FAIR data was reportedly first required for the timely treatment of rare diseases (5 out
of 10,000) [20]. Later FAIRfication became a challenge across (research) domains to collectively
overcome the hurdles [21] to reap countless socio-economic benefits [22, 4]. FACT principles
were first highlighted as a concerted effort by Dutch scientists across Dutch universities for
responsible data science [11, 10]. The interlink between FAIR and FACT principles was first
reported in the field of crystallography [12]. Since 2018, with the implementation of GDPR
[9, 7, 8], academia and industry have shown a bent toward the FAIRification of metadata across
domains. For example, the Dutch healthcare enterprises [23, 24], governments bodies [25, 2],
research and funding organizations [26] officially set the FAIRification process as a business
goal. Data (pipeline) provenance amongst healthcare providers is maintained to attain data
utility. For example, locally, the hub and spoke model [27, 28] and globally, the FAIRification
of care metadata [2, 23] help assure data precision and transparency. On the other hand, data
protection is achievable with fair and confidential data handling [29, 30] secured by: design,
policy, and patients’ informed consent [16]. The terms ’fair and confidential’ have multifaceted
social connotations and seek multi-dimensional means for their implications. Fair literally
means ’equal treatment,’ but the term has ethical implications in the healthcare domain and
seeks just, benevolent and non-maleficent measures at each step of patient’s value care and
implies service-dominant logic [31, 32]. Confidentiality entails doing research while keeping the
secrets of data subjects. Confidentiality partially covers privacy-preservation [33] and is partly
ensured with technical and organizational measures for data protection [34]. Privacy seeks
protection of an individual’s autonomy and direct/indirect control over personal information
share [35]. Both Fair and Confidential data predominantly depend upon privacy preservation,
and patients’ well-informed consent [36].
3. Data Analytical Approach
The Dutch clinical lab metadata share process is normatively (should-be state/ policy specifi-
cation) and empirically (as-is state/operationalization) evaluated against FACT principles of
RDS at local and global levels see Fig. 1. Normative evaluation includes a content analysis
using peer-reviewed publications and official websites of the FAIRification process by mapping
FACT principles. The empirical evaluation comprised the documentation review of standardized
public/confidential documents for labs’ metadata FAIRification using FACT mapping deter-
minants. The FACT mapping [11] determinants highlighted the metadata share bend either
towards data utility or data protection. Based on our evaluations, we formulated two REA
models [37]. The First REA model identified the production run of patients’ EHR (Electronic
Health Record) by highlighting key economic agents, their increment, and decrement events
using conversion duality for the clinical metadata as an economic resource. The second REA
model identified FHIR, FAIR Healthcare Information Repository, Netherlands work breakdown
structure vis-a-vis FACT (fair, accurate, confidential, transparent) principles. The REA models
were further verified by (in-field) IT and REA ontology experts concerning the functionality
and structural underpinnings of the REA models. For clarity see Fig. 1.
Figure 1: Data Analytical Approach.
4. REA Ontologies and Conceptual Verification using Clinical
Labs as a Use Case
The below given REA models follow the foundational fundamentals by [37].
4.1. REA Model for Clinical Lab (Meta) Data Share at the Local/National Level
This REA model (see Fig. 2) exhibits the lab metadata production run [37]. Here, economic
agents are a mixture of people and machines in locally sharing lab metadata as an economic
resource. The applicant, i.e., the patient or the concerned healthcare giver (GP, a specialist from
the hospital, or another lab), initiates the process with a (bio) sample request message as per
standardized communication protocol as an increment. Then, the lab undergoes decrement
events with the patient’s sample intake. The sampler collects the patient’s (bio) sample with a
separate (unique identifier) UID. The lab transports the sample tubes to the lab analyzer/source.
The analyzer with a UID is (mostly) an automatically functioning machine that conducts the
24/7 tests based on the tubes’ barcodes and authorizes sample results with a UID in the lab
information system. The analyzer utilizes rule-based ML algorithms. Afterward, the executor
does metadata formation of lab results, allows re-identification (i.e., versioning), and attaches
Lab to lab
«Economic Agent»
«Economic «Economic Agent»
«Economic Agent» Govt research
Agent» GPs, specialists
(Clinical) Lab organizations
Patients «provides» (Hospital), pharmacy
(RIVM), TTP
«provides»
«receives» «receives»
«provides» «receives» «provides»
«provides» «receives»
«Economic Resource» «Decrement Event»
Message as per Sample diagnostic «Increment Event» «Economic
«use» request
Communication Lab results Resource»
Protocol reception Digital Patients' Files
«Economic Resource» «produces»
«Decrement Event»
Bio Sample with «consumes»
Unique ID (UID) Sample intake
«grouping»
«conversion
«Economic Resource» duality»
Source/Analyzer UID «use» «Decrement Event» «Group»
with Sample result Sample analysis
Patients' EHR
«Economic Resource» «Decrement Event»
Digital result with «consumes» Data versioning,
indexing and sending
UID of results
«Economic Resource» «Decrement Event»
«consumes» Digital results to «comitted
Patient file with UID applicant provide>>
«comitted «comitted «comitted
provide» «comitted
provide» provide»
provide»
«Economic Agent» «Economic Agent» «Economic Agent» «Economic Agent»
Lab Applicant Lab Sampler Lab Analyzer Lab Executer
Figure 2: REA Model for the Clinical Lab (Meta) Data Production Run at a Local/National Level
results to the patient’s file (i.e., indexing), respectively. The lab executer is also (mostly) an
automatically functioning machine that sends a digital patient file with the lab results to the
applicant’s Electronic Health Record, EHR (i.e., a group [38]). The relationship "produces" can
be further modeled as an REA conversion process, and we omitted the details of this process for
simplicity.
FACT mapping determinants allowed mapping of FACT principles on Dutch clinical lab meta-
data share environment. We located the following determinants for each FACT indicator in
FAIRification concerning public/confidential documents, official websites, and peer-reviewed
scientific publications: for fairness: patient’s right over explicit Informed Consent, data era-
sure/editing, data purpose, storage, and time limitations via authorities. For accuracy: prove-
nance maintaining measures for data pipeline such as UIDs usage for precise data entry. For
confidentiality: patients’ sensitive information-share protection by utilizing technical, orga-
nizational, and data-oriented means. For transparency: measures to ensure re-identification
of accurately published data. In FACT principles, accuracy and transparency ensure the data
utility (precision of data analytics). At the same time, fairness and confidentiality ascertain
patients’ data protection concerning privacy (i.e., patient’s autonomous decision making con-
cerning personal information-share) preservation. FACT principles allowed us to identify the
FAIRification bend towards either data utility or data protection.
Discussion and Evaluation: The below-given insights convey the crux of clinical labs’
(dual-level) metadata share evaluation. The insights highlight the areas where the FAIRification
process is compared against FACT principles of RDS and show a bend towards data utility at
the expense of data protection.
Insight 1, Clinical labs’ data collection and metadata formulation prepare for later FAIRification:
Locally, the FAIRification by design, policy, and training, corresponds with the global level go
build, go change and go train [2] measures that are in place to attain FAIRification as a business
goal [6, 39, 1, 26]. FAIRifcation by design includes standardized IS architecture, (metadata concern-
ing) semantic models, syntax for language consistency using global UIDs for re-identification,
and technical interoperability assistance by ForeCare [40]. FAIRification by policy ascertains
[41, 42? ] an uninterrupted, transparent exchange of care metadata across stakeholders via
local/global standardizing, licensing, and oversight organizations[43]. Moreover, clinical labs
share completely unmasked data with the country-wide (public) care data repository ([25]) for
infectious diseases, renal diseases, antibiotic treatments, and scans. Additionally, via a national
cyber-lab, the lab tests are shared with pharmacies in real-time [44, 45]. FAIRification by training
facilitates healthcare providers’ with FAIRification trainings [46, 26, 47, 48, 49] as per set (global)
standards[42? ].
Insight 2, Fully automated clinical labs, with automated IS and equipment, ensure transparent,
accurate metadata but raise ethical concerns [50]. Specifically, the (presumed) machine-based
decision-making algorithms for the labs’ (executer) functionality in a fully automatic Lab In-
formation System is ethically concerning [51]. Insight 3, Partial fairness and confidentiality
lead to privacy-lapse: The technical data security measures and access governance controls
are fully in place [52]. Still, privacy lapse is evident as patients’ autonomous decision-making
regarding personal information-share, their right to erasure/editing, knowledge for long/short
term storage, and time are disregarded in an opt-out IC intake. Therefore, an explicit, well
Informed Consent (IC) is less fully-practiced nationally[53, 17, 42].
4.2. REA Model for Workflow Breakdown Structure of FHIR Netherlands and
FACT Mapping Derterminants
This REA model (see Fig. 3) is not an extension of the first REA model. Instead, separately em-
phasizes the FAIRification workflow [54, 2] of FHIR (Fast Healthcare Interoperability Resources)
Netherlands [24] vis-à -vis FACT (fair, accuracy, confidentiality, transparency) principles [11].
The FACT principles ensure responsible data science, whereas the FHIR NL comprises the
FAIRification procedural steps for better data utility. FAIRification value chain comprises
data reuse, curation, validation, de- identification, semantic model implication, data linkage,
licensing, versioning, data indexing, publishing, and metadata formation [54] and starts from
reusable stored clinical care data for research/clinical purposes. For clarity and concision, the
FAIRIfication steps [54] vis-a-vis FACT mapping determinants are condensed into 2-3 steps
in the top three boxes using linkage types [37]. In the top three right boxes, the ‘quantity
Figure 3: REA Model for Work Breakdown Structure: FHIR Netherlands vis-a-vis FACT principles.
required’ are the steps required for FHIR Netherlands [41, 43, 42, 24, 55]. The dark color of
these boxes emphasizes their (active) operationalization in practice. Contrarily, the left top
three boxes are light-colored because the FACT principles are (officially) ordained but partially
applied by downplaying fair and confidential data requirements. Finally, the top 3 (grey-colored)
middleboxes specify the operationalized/practiced economic resource types that ultimately
produce the economic resource of FAIR data for FHIR NL in the bottom-most (middle) boxes.
Similarly, in the bottom left box, the ‘quantity used’ is of FACT metadata which is accurate and
transparent but marginally fair and confidential.
Discussion and Evaluation: Insight 4, Marginal fairness and confidentiality: The gradual
compromise on fair and confidential data is perceivable across the left boxes under quantity
required Fig. 3 for FACT principles. Because, in addition to an implicit, marginal IC intake,
patients’ de-identification is substituted with pseudonymization that allows later unmasking
personal information with a key. Moreover, globally, the standardized licensing, oversight and
regulatory authorities for FAIR data do the data publishing, data validating, and data versioning
that further reduces data protection [49]. Insight 5, grey areas concerning data-ownership of giant
healthcare enterprises: If labs are located within a Dutch hospital’s premises, the metadata own-
ership rights rest unclear. The data transfer agreements with hospitals and GPs, ambiguously
bridge the gap. Some out-patient Dutch clinical labs [56] are not FHIR NL members. Still, as
almost all Dutch hospitals are members of FHIR NL, the former are PRESUMABLY sharing labs’
clinical data via the hospital’s EHR. Insight 6, Deliberative decision-making and alignment of
normative pledges and their operationalization: There is a dire need to bridge the strategic gap
between the EU policymakers, lawmakers, over-sight bodies, and the healthcare providers to
align the operationalization of clinical care metadata share with the official public/confidential
documents’ specifications. This is only possible with the inclusion of domain-specific industry
personnel into the policymaking, law-making, and oversight authorities as advisors.
5. Conclusion
We performed the evaluation of the Dutch clinical labs’ (dual-level) metadata share against
FACT principles of RDS. Based on the findings, we formulated two REA models that simplified
the otherwise complex Dutch care metadata share landscape. The (local) production run level
evaluation exhibited that the labs’ metadata formulation, processing, and storing take place
as per legal standards [57, 58, 59, 52] in preparation for the global FAIRification process. The
FACT mapping determinants identified that the provenance of the data pipeline using UIDs
ascertains the precision of data analytics (i.e., data utility) and transparency concerning patients’
value care. However, labs’ metadata as an economic resource is partially fair/just because of
the lack of patients’ explicit Informed Consent’s intake by labs. Patients’ confidentiality is also
partially ensured because the clinical labs share unmasked data with a country-wide (public)
data repository for infectious and renal diseases, antibiotic treatments, and scans. Additionally,
fully automatically interacting clinical labs with automated equipment and ISs raise ethical
concerns (regarding patients’ confidentiality and fairness). The first REA model specified the
economic agents interacting via events to perform the conversion duality of patients’ metadata
as an economic resource. The FACT mapping determinants demonstrate the bend towards data
utility over data protection. The second REA model, specifying the work (flow) breakdown
structure of FHIR NL vis-a-vis FACT mapping determinants, identifies the linkage relationship
for the quantity required for each resource type (i.e., functional step). In addition, the (linkage)
quantity used for the FAIR metadata in the bottom-most boxes specifies that the patients’ (actual)
metadata share contradicts the policy specifications in official public/confidential documents.
The contradiction occurs because, with each workflow step, the FAIR data is kept: accurate and
transparent but marginally fair and confidential to prioritize data utility. The research work
specified and evaluated the presence of Privacy Utility Tradeoff (PUT) in the Dutch clinical labs’
(dual-level) metadata share as an extension of our ongoing research [13, 14, 15, 16]. In future
work, based on a more detailed local production run model, we aim to produce a mimic dataset
for PUT concerning process evaluation, including possible solutions.
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