Patient Generated Health Data (PGHD) are being considered for integration with health facilities, however little is known about how such data can be made machine-actionable in a way that meets FAIR guidelines. This article proposes a 5-stage framework that can be used to achieve this

Hederman, Lucy

Kawu, Abdullahi Abubakar

O\u27Sullivan, Dympna

van Reisen, Mirjam

English

Arrow@TUDublin

Technological University Dublin ARROW@TU Dublin Articles 2023-02-03 FAIR4PGHD: A framework for FAIR implementation over PGHD Abdullahi Abubakar Kawu Technological University Dublin, d21125967@mytudublin.ie Dympna O'Sullivan Technological University Dublin, dympna.osullivan@tudublin.ie Lucy Hederman Trinity College Dublin, Ireland See next page for additional authors Follow this and additional works at: https://arrow.tudublin.ie/creaart  Part of the Computer Sciences Commons, and the Engineering Commons Recommended Citation Kawu, A.A., O’Sullivan, D., Hederman, L. and Van Reisen, M., 2023. FAIR4PGHD: A framework for FAIR implementation over PGHD. FAIR Connect, 1(1), pp.35-40. DOI: 10.3233/FC-230500 This Article is brought to you for free and open access by ARROW@TU Dublin. It has been accepted for inclusion in Articles by an authorized administrator of ARROW@TU Dublin. For more information, please contact arrow.admin@tudublin.ie, aisling.coyne@tudublin.ie, gerard.connolly@tudublin.ie. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License Funder: Science Foundation Ireland Authors Abdullahi Abubakar Kawu, Dympna O'Sullivan, Lucy Hederman, and Mirjam van Reisen This article is available at ARROW@TU Dublin: https://arrow.tudublin.ie/creaart/153 FAIR Connect 1 (2023) 35–40 35DOI 10.3233/FC-230500IOS PressFAIR Supporting Resource (FSR) Description ArticlesFAIR4PGHD: A framework for FAIRimplementation over PGHDAbdullahi Abubakar Kawu a,*, Dympna O’Sullivan b, Lucy Hederman c and Mirjam Van Reisen da Technological University, Dublin, IrelandORCID: https://orcid.org/0000-0003-2531-9539b Technological University, Dublin, IrelandORCID: https://orcid.org/0000-0003-2841-9738c Trinity College, Dublin, IrelandORCID: https://orcid.org/0000-0001-6073-4063d Leiden University Medical Centre & Tilburg University School of Humanities and Digital Sciences, NetherlandsORCID: https://orcid.org/0000-0003-0627-8014Editor: Barbara Magagna (https://orcid.org/0000-0003-2195-3997)Solicited reviews: Wilbert van Panhuis (https://orcid.org/0000-0002-7278-9982); Erik Flikkenschild (https://orcid.org/0000-0002-7285-1651)Received 18 January 2023Accepted 21 January 2023Abstract. Patient Generated Health Data (PGHD) are being considered for integration with health facilities, however little isknown about how such data can be made machine-actionable in a way that meets FAIR guidelines. This article proposes a5-stage framework that can be used to achieve this.Keywords: Patient Generated Health Data, FAIRification, CEDAR, VODAN, patient health record1. IntroductionElectronic health records are a type of scientific data, and like most scientific data, implementing the guiding prin-ciples of FAIR (Findable, Accessible, Interoperable, Reusable) [18] for data management and stewardship becomesnecessary because it enhances the public and appropriate use of this data (Fig. 1). Studies on these exist [14,15]and have also brought forth opportunities to explore other dimensions and needs of this data type. So, FAIRifyingpatient health records is investigated [15,16].*Corresponding author. E-mail: abdullahikawu@ibbu.edu.ng; Tel.: +2348032346198.2949-799X © 2023 – The authors. Published by IOS Press. This is an Open Access article distributed under the terms of theCreative Commons Attribution License (CC BY 4.0).36 A.A. Kawu et al. / FAIR4PGHDFig. 1. The FAIR guiding principles for scientific data management and stewardship [18].However, this does not usually include patient generated health data (PGHD). The implication is that key context –like onset of chronic diseases, disease progression, ailment deterioration or recovery or improvement or pandemic-onset are not immediately seen until a clinic visit or when patients get (terribly) sick. Patient generated health data(PGHD) is health data that is collected by a patient (or their authorized representative or family) outside the clinicsetting and can be used by them or clinicians for their health management [12].PGHD are collected using many mediums including but not limited to mobile health application (mHealth) appsand wearables. Research has been done to integrate PGHD with electronic health records [7,8]. Additionally, infor-mation models that simplify and broaden the scope of data exchange are studied [1,10]. Evidence also points to theuse of current health IT standards for documenting and sharing PGHD with clinicians [2]. The creation of wearablemetadata has also been examined in certain research [5], although this is not general and does not take into accountthe variety of PGHD sources.GO FAIR identify seven (7) steps involved in FAIRification as follows: (i) retrieving raw (non-FAIR) data, (ii) an-alyzing the retrieved data, (iii) semantic model definition, (iv) making data linkable, (v) assigning (custom) license,(vi) defining the metadata for the dataset, and (vii) deploying or publishing the FAIR data resource [3]. However, be-cause health data requires some key consideration viz ethical, legal and privacy concerns [13], provides an enhancedFAIRification guideline for health data research (Fig. 2).Expanding on this, van Reisen, et. Al [15], demonstrate a design for electronic health record in facilities, whichlargely also adhere to FAIR guidelines but also complies with health data sensitivities and regulations (NGDPR andGDPR) [9,16]. For facility-based health records, diverse deployment techniques and technologies have been createdto comply with FAIR. Two (2) approaches were demonstrated using CEDAR technology (for individual datasets)and bespoke Excel2RDF format – for bulk datasets [15]. The Center for Expanded Data Annotation and RetrievalA.A. Kawu et al. / FAIR4PGHD 37Fig. 2. An architecture implementing the FAIRification workflow for health data [13].Fig. 3. PGHD-FAIR framework using localised CEDAR.(CEDAR) technology was established in 2014 to create a computational ecosystem for the development, evaluation,use, and refinement of biomedical metadata [4]. It is important to state that FAIR concept is not the same withFHIR – which stands for Fast Health Interoperability Resources (FHIR), one of the recent standards to enhanceinteroperability in health systems employing lightweight web services [11]. However, in applying FAIR concept onhealth data, FHIR standards can be employed (see Fig. 3).2. MethodologyUnderstanding PGHD peculiarities [6], adapting the framework of FAIR for health data [13] and learning fromexperiences of the use of localized CEDAR and Excel2RDF bulk data upload in VODAN [17], we provide a frame-work for making PGHD FAIRified (Fig. 3). The architecture employs a privacy by design approach, such that allPGHD data communicated to the service provider is one way i.e. not accessible outside the health facility. Thedetails of each stage of the framework is given below.2.1. Data sourceData Source refers to the multiple, sometimes combined sources of PGHD generated by patients or their repre-sentatives that are reported to the health facility, either as part of a health program or a patient portal for personaluse. For instance, data from wearables and self-reported data on nutrition to account for data that a diabetic patientmay report towards their diabetes management program.38 A.A. Kawu et al. / FAIR4PGHDFig. 4. CEDAR implementation of PGHD-FAIRification framework.2.2. Data consolidationSome raw PGHD (for example, data from wearables) are sometimes staged and consolidated before being com-municated over mobile applications to a web server before they are accessible via an API from a third party system.As shown in the architecture, this step does not apply to all PGHD sources, as some sources can directly be sent fordata curation without being consolidated, this depends on the source and the needs of clinicians.2.3. Data curationAt this stage, all PGHD sources will be curated using appropriate localised CEDAR templates (see Fig. 4).Depending on the type of PGHD, it is curated using a selected template. Templates are predesigned to be linkableto ontologies (using the VALUES property of CEDAR).2.4. Data validationOn submission or receipt of PGHD, with the right CEDAR templates, data from fields that are based on therequirement of the PGHD source(s) are further validated against known constraints, value ranges and conformanceto privacy requirements such as data de-identification or pseudonymisation. Simultaneously, depending on the typeof field, select fields can be linked/mapped to relevant ontologies and standards like SNOMED-CT, VODANA-Ontology and/or communication protocols like FHIR, such that they can be made to be interoperable and reusable.However, patient identifiable fields like patient name, patient number and address are not allowed to be exposedoutside clinical settings hence not allowed to be linkable.2.5. FAIR-ready dataWith validated data, after the required mapping, data is made into a machine-actionable form in RDF and JSON-LD format – and can only be accessible based on the regulations in place for the PGHD data.3. DiscussionPGHD can come from multiple sources and formats, and as part of a health program (e.g Diabetes ManagementPlan – where CGM or blood sugar data, nutrition data and fitness data are generated by patient themselves buteventually transmitted for use by clinicians), or as a bulk data from one PGHD source, say in CSV format (e.g inthe case of wearables (Fitbit) that allow export of data as CSV by users) or as a single raw data directly from onesource (through APIs). This framework illustrates and suggests that, in each of the aforementioned cases, with theuse of appropriate technologies, we can achieve FAIR-ready data that are verifiable by clinicians and fit for practicalpurposes.A.A. Kawu et al. / FAIR4PGHD 39As a practical example, we can create a CEDAR template on Blood Pressure Management Programme for Hy-pertensive Patients to cater for PGHD from multiple sources (Fig. 4). This fits our use-case of the framework wherethe PGHD-Programme data scenario is used. In this case, a template that fits the programme is created as PGHD-BP Management and added to existing templates say OPD, ANC, COVID-19 templates that are developed underVODAN-CEDAR technology, to receive PGHD for this program. PGHD related to the program are then receivedand made FAIR-ready (Fig. 4).4. ConclusionWhile working with PGHD, having the right template that makes data FAIR-ready is important. CEDAR toolenables stakeholders to comply with FAIR metadata standards. The FAIRification process for PGHDs differs fromscientific data and a workplan is proposed that is specific to the nature of PGHDs. The value of a FAIRificationprocess of PGHDs is in the possibility of integrating different sources of PGHD to expand the knowledge and un-derstanding that can be derived. The model allows a diversity of PGHD sources to be linked for clinical use, and thisframework portrays how we can integrate these sources using CEDAR or any other annotating tool. We provide four(4) use scenarios to illustrate common use of PGHD in practice – which can appropriately be further modified tomeet users’ needs. With this, PGHD can be made available during encounters (real-time data) or through historicaldata (bulk data). With such frameworks used and templates in place, clinicians can easily reuse the template, modi-fying it to fit their PGHD data source(s) or programmes, create workflows from it, collect data, create metadata, andvalidate results in a FAIR-manner.AcknowledgementsThe authors have no acknowledgments to report.FundingThis work was conducted with the financial support of the Science Foundation Ireland Centre for Research Train-ing in Digitally Enhanced Reality (d-real) under Grant No. 18/CRT/6224 and at the ADAPT SFI Research Centrefor AI-Driven Digital Content Technology under Grant No. 13/RC/2106_P2. For the purpose of Open Access, theauthor has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from thissubmission.Conflict of interestThe authors have no conflict of interest to report.References[1] A. Alamri, Ontology middleware for integration of IoT healthcare information systems in EHR systems, Computers 7(4) (2018), 51. doi:10.3390/computers7040051.[2] S. El-Sappagh, F. Ali, A. Hendawi, J.H. Jang and K.S. Kwak, A mobile health monitoring-and-treatment system based on integration ofthe SSN sensor ontology and the HL7 FHIR standard, BMC Medical Informatics and Decision Making 19(1) (2019), 1–36. doi:10.1186/s12911-018-0723-6.[3] FAIRification process. Available at: https://www.go-fair.-org/fair-principles/fairification-process/. Accessed October 10, 2022.[4] R.S. Gonçalves, M.J. O’Connor, M. Martínez-Romero, A.L. Egyedi, D. Willrett, J. 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FAIR4PGHD: A framework for FAIR implementation over PGHD

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FAIR4PGHD: A framework for FAIR implementation over PGHD

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