The de novo FAIRification process of a registry for vascular anomalies

Abstract Background Patient data registries that are FAIR—Findable, Accessible, Interoperable, and Reusable for humans and computers—facilitate research across multiple resources. This is particularly relevant to rare diseases, where data often are scarce and scattered. Specific research questions can be asked across FAIR rare disease registries and other FAIR resources without physically combining the data. Further, FAIR implies well-defined, transparent access conditions, which supports making sensitive data as open as possible and as closed as necessary. Results We successfully developed and implemented a process of making a rare disease registry for vascular anomalies FAIR from its conception—de novo. Here, we describe the five phases of this process in detail: (i) pre-FAIRification, (ii) facilitating FAIRification, (iii) data collection, (iv) generating FAIR data in real-time, and (v) using FAIR data. This includes the creation of an electronic case report form and a semantic data model of the elements to be collected (in this case: the “Set of Common Data Elements for Rare Disease Registration” released by the European Commission), and the technical implementation of automatic, real-time data FAIRification in an Electronic Data Capture system. Further, we describe how we contribute to the four facets of FAIR, and how our FAIRification process can be reused by other registries. Conclusions In conclusion, a detailed de novo FAIRification process of a registry for vascular anomalies is described. To a large extent, the process may be reused by other rare disease registries, and we envision this work to be a substantial contribution to an ecosystem of FAIR rare disease resources.

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The de novo FAIRification process of a registry for vascular anomalies

10.1101/2020.12.12.20245951 ◽

2020 ◽

Author(s):

Karlijn H.J. Groenen ◽

Annika Jacobsen ◽

Martijn G. Kersloot ◽

Bruna Vieira ◽

Esther van Enckevort ◽

...

Keyword(s):

Rare Disease ◽

Real Time ◽

De Novo ◽

Vascular Anomalies ◽

Substantial Contribution ◽

Case Report Form ◽

Common Data Elements ◽

Automatic Data ◽

Semantic Data ◽

Disease Registries

AbstractBackgroundPatient data registries that are FAIR - Findable, Accessible, Interoperable, and Reusable for humans and computers - facilitate research across multiple resources. This is particularly relevant to rare diseases, where data often are scarce and scattered. Specific research questions can be asked across FAIR rare disease registries and other FAIR resources without physically combining the data.ResultsWe successfully developed and implemented a process of making a rare disease registry for vascular anomalies FAIR from its conception - de novo. Here, we describe the five phases of this process in detail: i) pre-FAIRification, ii) facilitating FAIRification, iii) data collection, iv) generating FAIR data in real-time, and v) using FAIR data. This includes the creation of an electronic case report form and a semantic data model of the “Set of Common Data Elements for Rare Disease Registration” released by the European Commission, and the technical implementation in the Electronic Data Capture (EDC) system for real-time, automatic data FAIRification. Further, we describe how we contribute to the four facets of FAIR, and how our FAIRification process can be reused by other registries.ConclusionsIn conclusion, a detailed de novo FAIRification process of a registry for vascular anomalies is described. To a large extent, the process may be reused by other rare disease registries, and we envision this work to be a substantial contribution to an ecosystem of FAIR rare disease resources.

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Semantic modelling of Common Data Elements for Rare Disease registries, and a prototype workflow for their deployment over registry data

10.1101/2021.07.27.21261169 ◽

2021 ◽

Author(s):

Rajaram Kaliyaperumal ◽

Mark D Wilkinson ◽

Pablo Alarcon Moreno ◽

Nirupama Benis ◽

Ronald Cornet ◽

...

Keyword(s):

Rare Disease ◽

Data Repositories ◽

Common Data Elements ◽

Human Phenotype ◽

Domain Experts ◽

Semantic Modelling ◽

Disease Registries ◽

Data Elements ◽

The Eu ◽

Core Framework

Background: The European Platform on Rare Disease Registration (EU RD Platform) aims to address the fragmentation of European rare disease (RD) patient data, scattered among hundreds of independent and non-coordinating registries, by establishing standards for integration and interoperability. The first practical output of this effort was a set of 16 Common Data Elements (CDEs) that should be implemented by all RD registries. Interoperability, however, requires decisions beyond data elements - including data models, formats, and semantics. Within the European Joint Programme on Rare Disease (EJP RD), we aim to further the goals of the EU RD Platform by generating reusable RD semantic model templates that follow the FAIR Data Principles. Results: Through a team-based iterative approach, we created semantically grounded models to represent each of the CDEs, using the SemanticScience Integrated Ontology as the core framework for representing the entities and their relationships. Within that framework, we mapped the concepts represented in the CDEs, and their possible values, into domain ontologies such as the Orphanet Rare Disease Ontology, Human Phenotype Ontology and National Cancer Institute Thesaurus. Finally, we created an exemplar, reusable ETL pipeline that we will be deploying over these non-coordinating data repositories to assist them in creating model-compliant FAIR data without requiring site-specific coding nor expertise in Linked Data or FAIR. Conclusions: Within the EJP RD project, we determined that creating reusable, expert-designed templates reduced or eliminated the requirement for our participating biomedical domain experts and rare disease data hosts to understand OWL semantics. This enabled them to publish highly expressive FAIR data using tools and approaches that were already familiar to them.

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Electronic patient-reported data capture as the foundation of a learning health care system

Journal of Clinical Oncology ◽

10.1200/jco.2009.27.15_suppl.6522 ◽

2009 ◽

Vol 27 (15_suppl) ◽

pp. 6522-6522 ◽

Cited By ~ 2

Author(s):

A. P. Abernethy ◽

S. Y. Zafar ◽

R. Coeytaux ◽

K. Rowe ◽

J. L. Wheeler ◽

...

Keyword(s):

Real Time ◽

Point Of Care ◽

Data Capture ◽

Future Research ◽

Accurate Information ◽

Time Data ◽

Data Governance ◽

Sexual Distress ◽

Electronic Data Capture System ◽

Patient Reported

6522 Background: In a “learning healthcare system” clinical decisions are supported by accurate information delivered at point of care; information gathered today iteratively informs future care and research. Methods: Customized software on wireless tablet personal computers presented a review of systems (ROS) instrument, validated research surveys (e.g., quality of life [QOL]), and a satisfaction survey, tailored by user. The system was piloted in the Duke Cancer Clinics and affiliated hospitals. We previously demonstrated equivalence of electronic and paper survey data. We conducted a series of studies using similar procedures to evaluate feasibility, acceptability, and utility. Results: First, we assessed the ability to collect ROS data at point of care to inform the clinic visit for participating breast (n = 65), gastrointestinal (n = 113), and lung (n = 97) cancer patients. Duke physicians reported that the system's clinical reports informed care and increased dictation efficiency. Second, we assessed patient satisfaction in the breast cancer cohort. Participants found the computers easy to read (94%), navigate (99%), and use (98%); the system helped 74% remember forgotten concerns to report to their clinician. Third, we assessed whether these data could contribute to current research. If the patient was on another clinical trial, PRO data (e.g., pain, QOL) were delivered to the investigator for research purposes in real time; data governance rules provided assurance to investigators. Fourth, we identified whether the PRO data could inform future research directions. Symptoms monitored longitudinally in aggregate uncovered unmet needs. Sexual distress was an underserved concern; intervention studies were initiated. Warehoused PRO data were integrated with clinical trials, genomic, biomarker, radiology, and administrative datasets for analyses. The approach has been scaled to 4 clinics and 3 hospitals. Conclusions: An integrated, real-time, electronic data capture system that interdigitates PROs with clinical and other data allows creation of a learning oncology environment that continuously improves care and research. Advantages include: patient-centeredness, description of the PRO phenotype, interoperability, and interface with caBIG infrastructure. No significant financial relationships to disclose.

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Ambulatory Assessment

European Psychologist ◽

10.1027/1016-9040.14.2.109 ◽

2009 ◽

Vol 14 (2) ◽

pp. 109-119 ◽

Cited By ~ 66

Author(s):

Ulrich W. Ebner-Priemer ◽

Timothy J. Trull

Keyword(s):

Real Time ◽

Clinical Psychology ◽

Real Life ◽

Ambulatory Assessment ◽

Computer Assisted ◽

Affective Valence ◽

Time Data ◽

Physiological Processes ◽

Context Specific ◽

Questionnaire Studies

Convergent experimental data, autobiographical studies, and investigations on daily life have all demonstrated that gathering information retrospectively is a highly dubious methodology. Retrospection is subject to multiple systematic distortions (i.e., affective valence effect, mood congruent memory effect, duration neglect; peak end rule) as it is based on (often biased) storage and recollection of memories of the original experience or the behavior that are of interest. The method of choice to circumvent these biases is the use of electronic diaries to collect self-reported symptoms, behaviors, or physiological processes in real time. Different terms have been used for this kind of methodology: ambulatory assessment, ecological momentary assessment, experience sampling method, and real-time data capture. Even though the terms differ, they have in common the use of computer-assisted methodology to assess self-reported symptoms, behaviors, or physiological processes, while the participant undergoes normal daily activities. In this review we discuss the main features and advantages of ambulatory assessment regarding clinical psychology and psychiatry: (a) the use of realtime assessment to circumvent biased recollection, (b) assessment in real life to enhance generalizability, (c) repeated assessment to investigate within person processes, (d) multimodal assessment, including psychological, physiological and behavioral data, (e) the opportunity to assess and investigate context-specific relationships, and (f) the possibility of giving feedback in real time. Using prototypic examples from the literature of clinical psychology and psychiatry, we demonstrate that ambulatory assessment can answer specific research questions better than laboratory or questionnaire studies.

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