Automated approach for quality assessment of RDF resources.

INTRODUCTION: The Semantic Web community provides a common Resource Description Framework (RDF) that allows representation of resources such that they can be linked. To maximize the potential of linked data - machine-actionable interlinked resources on the Web - a certain level of quality of RDF resources should be established, particularly in the biomedical domain in which concepts are complex and high-quality biomedical ontologies are in high demand. However, it is unclear which quality metrics for RDF resources exist that can be automated, which is required given the multitude of RDF resources. Therefore, we aim to determine these metrics and demonstrate an automated approach to assess such metrics of RDF resources. METHODS: An initial set of metrics are identified through literature, standards, and existing tooling. Of these, metrics are selected that fulfil these criteria: (1) objective; (2) automatable; and (3) foundational. Selected metrics are represented in RDF and semantically aligned to existing standards. These metrics are then implemented in an open-source tool. To demonstrate the tool, eight commonly used RDF resources were assessed, including data models in the healthcare domain (HL7 RIM, HL7 FHIR, CDISC CDASH), ontologies (DCT, SIO, FOAF, ORDO), and a metadata profile (GRDDL). RESULTS: Six objective metrics are identified in 3 categories: Resolvability (1), Parsability (1), and Consistency (4), and represented in RDF. The tool demonstrates that these metrics can be automated, and application in the healthcare domain shows non-resolvable URIs (ranging from 0.3% to 97%) among all eight resources and undefined URIs in HL7 RIM, and FHIR. In the tested resources no errors were found for parsability and the other three consistency metrics for correct usage of classes and properties. CONCLUSION: We extracted six objective and automatable metrics from literature, as the foundational quality requirements of RDF resources to maximize the potential of linked data. Automated tooling to assess resources has shown to be effective to identify quality issues that must be avoided. This approach can be expanded to incorporate more automatable metrics so as to reflect additional quality dimensions with the assessment tool implementing more metrics.

Internalizing problems before and during the COVID-19 pandemic in independent samples of Dutch children and adolescents with and without pre-existing mental health problems.

The aim of the study was to assess internalizing problems before and during the pandemic with data from Dutch consortium Child and adolescent mental health and wellbeing in times of the COVID-19 pandemic, consisting of two Dutch general population samples (GS) and two clinical samples (CS) referred to youth/psychiatric care. Measures of internalizing problems were obtained from ongoing data collections pre-pandemic (NGS = 35,357; NCS = 4487) and twice during the pandemic, in Apr-May 2020 (NGS = 3938; clinical: NCS = 1008) and in Nov-Dec 2020 (NGS = 1489; NCS = 1536), in children and adolescents (8-18 years) with parent (Brief Problem Monitor) and/or child reports (Patient-Reported Outcomes Measurement Information System®). Results show that, in the general population, internalizing problems were higher during the first peak of the pandemic compared to pre-pandemic based on both child and parent reports. Yet, over the course of the pandemic, on both child and parent reports, similar or lower levels of internalizing problems were observed. Children in the clinical population reported more internalizing symptoms over the course of the pandemic while parents did not report differences in internalizing symptoms from pre-pandemic to the first peak of the pandemic nor over the course of the pandemic. Overall, the findings indicate that children and adolescents of both the general and clinical population were affected negatively by the pandemic in terms of their internalizing problems. Attention is therefore warranted to investigate long-term effects and to monitor if internalizing problems return to pre-pandemic levels or if they remain elevated post-pandemic.

Diagnosis clarification by generalization to patient-friendly terms and definitions: Validation study.

BACKGROUND: Now that patients increasingly get access to their healthcare records, its contents require clarification. The use of patient-friendly terms and definitions can help patients and their significant others understand their medical data. However, it is costly to make patient-friendly descriptions for the myriad of terms used in the medical domain. Furthermore, a description in more general terms, leaving out some of the details, might already be sufficient for a layperson. We developed an algorithm that employs the SNOMED CT hierarchy to generalize diagnoses to a limited set of concepts with patient-friendly terms for this purpose. However, generalization essentially implies loss of detail and might result in errors, hence these generalizations remain to be validated by clinicians. We aim to assess the medical validity of diagnosis clarification by generalization to concepts with patient-friendly terms and definitions in SNOMED CT. Furthermore, we aim to identify the characteristics that render clarifications invalid. RESULTS: Two raters identified errors in 12.7% (95% confidence interval - CI: 10.7-14.6%) of a random sample of 1,131 clarifications and they considered 14.3% (CI: 12.3-16.4%) of clarifications to be unacceptable to show to a patient. The intraclass correlation coefficient of the interrater reliability was 0.34 for correctness and 0.43 for acceptability. Errors were mostly related to the patient-friendly terms and definitions used in the clarifications themselves, but also to terminology mappings, terminology modelling, and the clarification algorithm. Clarifications considered to be most unacceptable were those that provide wrong information and might cause unnecessary worry. CONCLUSIONS: We have identified problems in generalizing diagnoses to concepts with patient-friendly terms. Diagnosis generalization can be used to create a large amount of correct and acceptable clarifications, reusing patient-friendly terms and definitions across many medical concepts. However, the correctness and acceptability have a strong dependency on terminology mappings and modelling quality, as well as the quality of the terms and definitions themselves. Therefore, validation and quality improvement are required to prevent incorrect and unacceptable clarifications, before using the generalizations in practice.

Exploring supportive care and information needs through a proposed eHealth application among melanoma patients undergoing systemic therapy: a qualitative study.

BACKGROUND: During and after systemic therapy, patients with high risk and advanced melanoma experience challenges regarding cancer-related symptoms, treatment-related adverse events, and an impact of these symptoms on their physical and psychosocial well-being. Few studies have investigated the specific needs of these patients and the potential role of eHealth applications in meeting those needs. OBJECTIVE: To explore the supportive care and information needs of high risk and advanced melanoma patients, and how these needs can be supported by eHealth applications. METHODS: In this qualitative study, semi-structured interviews with high risk and advanced melanoma patients during or after systemic treatment were conducted to understand their needs and requirements as possible end-users of mobile eHealth applications. Interview transcripts were independently coded and thematically analyzed. RESULTS: Thirteen participants consented to be interviewed, aged 31 to 71 years. Nearly all patients (n = 12, 92%) experienced unmet information and supportive care needs during and after active treatment. Patients expected to value eHealth applications that facilitate information gathering, wellbeing interventions, and symptom management. The majority of patients (n = 10, 77%) anticipated various advantages from using an eHealth application, including increased autonomy, higher quality of life, and improved disease self-management. DISCUSSION: High risk and advanced melanoma patients have unmet supportive care and information needs during and after systemic treatment. The use of eHealth applications might be an effective way to meet these unmet needs. Patients anticipate a variety of advantages from using these applications, including deriving various benefits from the use of these applications, such as enhanced autonomy.

Cancer as a death sentence: developing an initial program theory for an IVR intervention.

To address current trends in poor health-seeking behaviour and late cancer diagnosis in many low- and middle-income countries, like Uganda, it is important to explore innovative awareness building interventions. One possible intervention is a common digital format, an interactive voice response (IVR) system, which is suitable for individuals with low technological and reading literacy. It is increasingly acknowledged that developing digital interventions requires co-creation with relevant stakeholders and explication of program developers' assumptions, to make them effective, sustainable, and scalable. To this end, we sought to develop an initial program theory for a co-created IVR system for cancer awareness in Uganda. Utilising principles of the realist approach, a qualitative exploratory study was conducted through seven focus group discussions (FGDs) with people living with cancer (PLWC), health workers, and policy makers. Thematic analysis of the transcripts resulted in the emergence of four major themes. Through all themes the most consistent finding was that myths, misconceptions, and misinformation about cancer were related to every aspect of the cancer journey and influenced the experiences and lives of PLWC and their caregivers. Participants were positive about the potential of an IVR system but also had reservations about the design and reach of the system. The resulting initial program theory proposes that a context-specific IVR system has the potential to improve awareness on cancer, provided attention is given to aspects such as language, message framing, and accuracy.

De-novo FAIRification via an Electronic Data Capture system by automated transformation of filled electronic Case Report Forms into machine-readable data.

INTRODUCTION: Existing methods to make data Findable, Accessible, Interoperable, and Reusable (FAIR) are usually carried out in a post hoc manner: after the research project is conducted and data are collected. De-novo FAIRification, on the other hand, incorporates the FAIRification steps in the process of a research project. In medical research, data is often collected and stored via electronic Case Report Forms (eCRFs) in Electronic Data Capture (EDC) systems. By implementing a de novo FAIRification process in such a system, the reusability and, thus, scalability of FAIRification across research projects can be greatly improved. In this study, we developed and implemented a novel method for de novo FAIRification via an EDC system. We evaluated our method by applying it to the Registry of Vascular Anomalies (VASCA). METHODS: Our EDC and research project independent method ensures that eCRF data entered into an EDC system can be transformed into machine-readable, FAIR data using a semantic data model (a canonical representation of the data, based on ontology concepts and semantic web standards) and mappings from the model to questions on the eCRF. The FAIRified data are stored in a triple store and can, together with associated metadata, be accessed and queried through a FAIR Data Point. The method was implemented in Castor EDC, an EDC system, through a data transformation application. The FAIRness of the output of the method, the FAIRified data and metadata, was evaluated using the FAIR Evaluation Services. RESULTS: We successfully applied our FAIRification method to the VASCA registry. Data entered on eCRFs is automatically transformed into machine-readable data and can be accessed and queried using SPARQL queries in the FAIR Data Point. Twenty-one FAIR Evaluator tests pass and one test regarding the metadata persistence policy fails, since this policy is not in place yet. CONCLUSION: In this study, we developed a novel method for de novo FAIRification via an EDC system. Its application in the VASCA registry and the automated FAIR evaluation show that the method can be used to make clinical research data FAIR when they are entered in an eCRF without any intervention from data management and data entry personnel. Due to the generic approach and developed tooling, we believe that our method can be used in other registries and clinical trials as well.

Coding practice in national and regional kidney biopsy registries.

BACKGROUND: Kidney biopsy registries all over the world benefit research, teaching and health policy. Comparison, aggregation and exchange of data is however greatly dependent on how registration and coding of kidney biopsy diagnoses are performed. This paper gives an overview over kidney biopsy registries, explores how these registries code kidney disease and identifies needs for improvement of coding practice. METHODS: A literature search was undertaken to identify biopsy registries for medical kidney diseases. These data were supplemented with information from personal contacts and from registry websites. A questionnaire was sent to all identified registries, investigating age of registries, scope, method of coding, possible mapping to international terminologies as well as self-reported problems and suggestions for improvement. RESULTS: Sixteen regional or national kidney biopsy registries were identified, of which 11 were older than 10 years. Most registries were located either in Europe (10/16) or in Asia (4/16). Registries most often use a proprietary coding system (12/16). Only a few of these coding systems were mapped to SNOMED CT (1), older SNOMED versions (2) or ERA-EDTA PRD (3). Lack of maintenance and updates of the coding system was the most commonly reported problem. CONCLUSIONS: There were large gaps in the global coverage of kidney biopsy registries. Limited use of international coding systems among existing registries hampers interoperability and exchange of data. The study underlines that the use of a common and uniform coding system is necessary to fully realize the potential of kidney biopsy registries.

Contextual property detection in Dutch diagnosis descriptions for uncertainty, laterality and temporality.

BACKGROUND: Accurate, coded problem lists are valuable for data reuse, including clinical decision support and research. However, healthcare providers frequently modify coded diagnoses by including or removing common contextual properties in free-text diagnosis descriptions: uncertainty (suspected glaucoma), laterality (left glaucoma) and temporality (glaucoma 2002). These contextual properties could cause a difference in meaning between underlying diagnosis codes and modified descriptions, inhibiting data reuse. We therefore aimed to develop and evaluate an algorithm to identify these contextual properties. METHODS: A rule-based algorithm called UnLaTem (Uncertainty, Laterality, Temporality) was developed using a single-center dataset, including 288,935 diagnosis descriptions, of which 73,280 (25.4%) were modified by healthcare providers. Internal validation of the algorithm was conducted with an independent sample of 980 unique records. A second validation of the algorithm was conducted with 996 records from a Dutch multicenter dataset including 175,210 modified descriptions of five hospitals. Two researchers independently annotated the two validation samples. Performance of the algorithm was determined using means of the recall and precision of the validation samples. The algorithm was applied to the multicenter dataset to determine the actual prevalence of the contextual properties within the modified descriptions per specialty. RESULTS: For the single-center dataset recall (and precision) for removal of uncertainty, uncertainty, laterality and temporality respectively were 100 (60.0), 99.1 (89.9), 100 (97.3) and 97.6 (97.6). For the multicenter dataset for removal of uncertainty, uncertainty, laterality and temporality it was 57.1 (88.9), 86.3 (88.9), 99.7 (93.5) and 96.8 (90.1). Within the modified descriptions of the multicenter dataset, 1.3% contained removal of uncertainty, 9.9% uncertainty, 31.4% laterality and 9.8% temporality. CONCLUSIONS: We successfully developed a rule-based algorithm named UnLaTem to identify contextual properties in Dutch modified diagnosis descriptions. UnLaTem could be extended with more trigger terms, new rules and the recognition of term order to increase the performance even further. The algorithm's rules are available as additional file 2. Implementing UnLaTem in Dutch hospital systems can improve precision of information retrieval and extraction from diagnosis descriptions, which can be used for data reuse purposes such as decision support and research.

A clinical decision support system is associated with reduced loss to follow-up among patients receiving HIV treatment in Kenya: a cluster randomized trial.

BACKGROUND: Loss to follow-up (LFTU) among HIV patients remains a major obstacle to achieving treatment goals with the risk of failure to achieve viral suppression and thereby increased HIV transmission. Although use of clinical decision support systems (CDSS) has been shown to improve adherence to HIV clinical guidance, to our knowledge, this is among the first studies conducted to show its effect on LTFU in low-resource settings. METHODS: We analyzed data from a cluster randomized controlled trial in adults and children (aged ≥ 18 months) who were receiving antiretroviral therapy at 20 HIV clinics in western Kenya between Sept 1, 2012 and Jan 31, 2014. Participating clinics were randomly assigned, via block randomization. Clinics in the control arm had electronic health records (EHR) only while the intervention arm had an EHR with CDSS. The study objectives were to assess the effects of a CDSS, implemented as alerts on an EHR system, on: (1) the proportion of patients that were LTFU, (2) LTFU patients traced and successfully linked back to treatment, and (3) time from enrollment on the study to documentation of LTFU. RESULTS: Among 5901 eligible patients receiving ART, 40.6% (n = 2396) were LTFU during the study period. CDSS was associated with lower LTFU among the patients (Adjusted Odds Ratio-aOR 0.70 (95% CI 0.65-0.77)). The proportions of patients linked back to treatment were 25.8% (95% CI 21.5-25.0) and 30.6% (95% CI 27.9-33.4)) in EHR only and EHR with CDSS sites respectively. CDSS was marginally associated with reduced time from enrollment on the study to first documentation of LTFU (adjusted Hazard Ratio-aHR 0.85 (95% CI 0.78-0.92)). CONCLUSION: A CDSS can potentially improve quality of care through reduction and early detection of defaulting and LTFU among HIV patients and their re-engagement in care in a resource-limited country. Future research is needed on how CDSS can best be combined with other interventions to reduce LTFU. Trial registration NCT01634802. Registered at www.clinicaltrials.gov on 12-Jul-2012. Registered prospectively.

Evaluation of lexical clarification by patients reading their clinical notes: a quasi-experimental interview study.

BACKGROUND: Patients benefit from access to their medical records. However, clinical notes and letters are often difficult to comprehend for most lay people. Therefore, functionality was implemented in the patient portal of a Dutch university medical centre (UMC) to clarify medical terms in free-text data. The clarifications consisted of synonyms and definitions from a Dutch medical terminology system. We aimed to evaluate to what extent these lexical clarifications match the information needs of the patients. Secondarily, we evaluated how the clarifications and the functionality could be improved. METHODS: We invited participants from the patient panel of the UMC to read their own clinical notes. They marked terms they found difficult and rated the ease of these terms. After the functionality was activated, participants rated the clarifications provided by the functionality, and the functionality itself regarding ease and usefulness. Ratings were on a scale from 0 (very difficult) to 100 (very easy). We calculated the median number of terms not understood per participant, the number of terms with a clarification, the overlap between these numbers (coverage), and the precision and recall. RESULTS: We included 15 participants from the patient panel. They marked a median of 21 (IQR 19.5-31) terms as difficult in their text files, while only a median of 2 (IQR 1-4) of these terms were clarified by the functionality. The median precision was 6.5% (IQR 2.3-14.25%) and the median recall 8.3% (IQR 4.7-13.5%) per participant. However, participants rated the functionality with median ease of 98 (IQR 93.5-99) and a median usefulness of 79 (IQR 52.5-97). Participants found that many easy terms were unnecessarily clarified, that some clarifications were difficult, and that some clarifications contained mistakes. CONCLUSIONS: Patients found the functionality easy to use and useful. However, in its current form it only helped patients to understand few terms they did not understand, patients found some clarifications to be difficult, and some to be incorrect. This shows that lexical clarification is feasible even when limited terms are available, but needs further development to fully use its potential.

From lexical regularities to axiomatic patterns for the quality assurance of biomedical terminologies and ontologies.

Ontologies and terminologies have been identified as key resources for the achievement of semantic interoperability in biomedical domains. The development of ontologies is performed as a joint work by domain experts and knowledge engineers. The maintenance and auditing of these resources is also the responsibility of such experts, and this is usually a time-consuming, mostly manual task. Manual auditing is impractical and ineffective for most biomedical ontologies, especially for larger ones. An example is SNOMED CT, a key resource in many countries for codifying medical information. SNOMED CT contains more than 300000 concepts. Consequently its auditing requires the support of automatic methods. Many biomedical ontologies contain natural language content for humans and logical axioms for machines. The 'lexically suggest, logically define' principle means that there should be a relation between what is expressed in natural language and as logical axioms, and that such a relation should be useful for auditing and quality assurance. Besides, the meaning of this principle is that the natural language content for humans could be used to generate the logical axioms for the machines. In this work, we propose a method that combines lexical analysis and clustering techniques to (1) identify regularities in the natural language content of ontologies; (2) cluster, by similarity, labels exhibiting a regularity; (3) extract relevant information from those clusters; and (4) propose logical axioms for each cluster with the support of axiom templates. These logical axioms can then be evaluated with the existing axioms in the ontology to check their correctness and completeness, which are two fundamental objectives in auditing and quality assurance. In this paper, we describe the application of the method to two SNOMED CT modules, a 'congenital' module, obtained using concepts exhibiting the attribute Occurrence - Congenital, and a 'chronic' module, using concepts exhibiting the attribute Clinical course - Chronic. We obtained a precision and a recall of respectively 75% and 28% for the 'congenital' module, and 64% and 40% for the 'chronic' one. We consider these results to be promising, so our method can contribute to the support of content editors by using automatic methods for assuring the quality of biomedical ontologies and terminologies.

Development and validation of a model for the adoption of structured and standardised data recording among healthcare professionals.

BACKGROUND: Healthcare professionals provide care to patients and during that process, record large quantities of data in patient records. Data in an Electronic Health Record should ideally be recorded once and be reusable within the care process as well as for secondary purposes. A common approach to realise this is to let healthcare providers record data in a standardised and structured way at the point of care. Currently, it is not clear to what extent this structured and standardised recording has been adopted by healthcare professionals and what barriers to their adoption exist. Therefore, we developed and validated a multivariable model to capture the concepts underlying the adoption of structured and standardised recording among healthcare professionals. METHODS: Based on separate models from the literature we developed a new theoretical model describing the underlying concepts of the adoption of structured and standardised recording. Using a questionnaire built upon this model we gathered data to perform a summative validation of our model. Validation was done through partial least squares structural equation modelling (PLS-SEM). The quality of both levels defined in PLS-SEM analysis, i.e., the measurement model and the structural model, were assessed on performance measures defined in literature. RESULTS: The theoretical model we developed consists of 29 concepts related to information systems as well as organisational factors and personal beliefs. Based on these concepts, 59 statements with a 5 point Likert-scale (fully disagree to fully agree) were specified in the questionnaire. We received 3584 responses. The validation shows our model is supported to a large extent by the questionnaire data. Intention to record in a structured and standardised way emerged as a significant factor of reported behaviour (ß = 0.305, p < 0.001). This intention is influenced most by attitude (ß = 0.512, p < 0.001). CONCLUSIONS: This model can be used to measure the perceived level of adoption of structured and standardised recording among healthcare professionals and further improve knowledge on the barriers and facilitators of this adoption.

Clustering clinical models from local electronic health records based on semantic similarity.

BACKGROUND: Clinical models in electronic health records are typically expressed as templates which support the multiple clinical workflows in which the system is used. The templates are often designed using local rather than standard information models and terminology, which hinders semantic interoperability. Semantic challenges can be solved by harmonizing and standardizing clinical models. However, methods supporting harmonization based on existing clinical models are lacking. One approach is to explore semantic similarity estimation as a basis of an analytical framework. Therefore, the aim of this study is to develop and apply methods for intrinsic similarity-estimation based analysis that can compare and give an overview of multiple clinical models. METHOD: For a similarity estimate to be intrinsic it should be based on an established ontology, for which SNOMED CT was chosen. In this study, Lin similarity estimates and Sokal and Sneath similarity estimates were used together with two aggregation techniques (average and best-match-average respectively) resulting in a total of four methods. The similarity estimations are used to hierarchically cluster templates. The test material consists of templates from Danish and Swedish EHR systems. The test material was used to evaluate how the four different methods perform. RESULT AND DISCUSSION: The best-match-average aggregation technique performed better in terms of clustering similar templates than the average aggregation technique. No difference could be seen in terms of the choice of similarity estimate in this study, but the finding may be different for other datasets. The dendrograms resulting from the hierarchical clustering gave an overview of the templates and a basis of further analysis. CONCLUSION: Hierarchical clustering of templates based on SNOMED CT and semantic similarity estimation with best-match-average aggregation technique can be used for comparison and summarization of multiple templates. Consequently, it can provide a valuable tool for harmonization and standardization of clinical models.

A structured approach to recording AIDS-defining illnesses in Kenya: A SNOMED CT based solution.

INTRODUCTION: Several studies conducted in sub-Saharan Africa (SSA) have shown that routine clinical data in HIV clinics often have errors. Lack of structured and coded documentation of diagnosis of AIDS defining illnesses (ADIs) can compromise data quality and decisions made on clinical care. METHODS: We used a structured framework to derive a reference set of concepts and terms used to describe ADIs. The four sources used were: (i) CDC/Accenture list of opportunistic infections, (ii) SNOMED Clinical Terms (SNOMED CT), (iii) Focus Group Discussion (FGD) among clinicians and nurses attending to patients at a referral provincial hospital in western Kenya, and (iv) chart abstraction from the Maternal Child Health (MCH) and HIV clinics at the same hospital. Using the January 2014 release of SNOMED CT, concepts were retrieved that matched terms abstracted from approach iii & iv, and the content coverage assessed. Post-coordination matching was applied when needed. RESULTS: The final reference set had 1054 unique ADI concepts which were described by 1860 unique terms. Content coverage of SNOMED CT was high (99.9% with pre-coordinated concepts; 100% with post-coordination). The resulting reference set for ADIs was implemented as the interface terminology on OpenMRS data entry forms. CONCLUSION: Different sources demonstrate complementarity in the collection of concepts and terms for an interface terminology. SNOMED CT provides a high coverage in the domain of ADIs. Further work is needed to evaluate the effect of the interface terminology on data quality and quality of care.

Influence of data quality on computed Dutch hospital quality indicators: a case study in colorectal cancer surgery.

BACKGROUND: Our study aims to assess the influence of data quality on computed Dutch hospital quality indicators, and whether colorectal cancer surgery indicators can be computed reliably based on routinely recorded data from an electronic medical record (EMR). METHODS: Cross-sectional study in a department of gastrointestinal oncology in a university hospital, in which a set of 10 indicators is computed (1) based on data abstracted manually for the national quality register Dutch Surgical Colorectal Audit (DSCA) as reference standard and (2) based on routinely collected data from an EMR. All 75 patients for whom data has been submitted to the DSCA for the reporting year 2011 and all 79 patients who underwent a resection of a primary colorectal carcinoma in 2011 according to structured data in the EMR were included. Comparison of results, investigating the causes for any differences based on data quality analysis. Main outcome measures are the computability of quality indicators, absolute percentages of indicator results, data quality in terms of availability in a structured format, completeness and correctness. RESULTS: All indicators were fully computable based on the DSCA dataset, but only three based on EMR data, two of which were percentages. For both percentages, the difference in proportions computed based on the two datasets was significant.All required data items were available in a structured format in the DSCA dataset. Their average completeness was 86%, while the average completeness of these items in the EMR was 50%. Their average correctness was 87%. CONCLUSIONS: Our study showed that data quality can significantly influence indicator results, and that our EMR data was not suitable to reliably compute quality indicators. EMRs should be designed in a way so that the data required for audits can be entered directly in a structured and coded format.

Assessing the use of HL7 FHIR for implementing the FAIR guiding principles: a case study of the MIMIC-IV Emergency Department module.

Objectives: To provide a real-world example on how and to what extent Health Level Seven Fast Healthcare Interoperability Resources (FHIR) implements the Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles for scientific data. Additionally, presents a list of FAIR implementation choices for supporting future FAIR implementations that use FHIR. Materials and methods: A case study was conducted on the Medical Information Mart for Intensive Care-IV Emergency Department (MIMIC-ED) dataset, a deidentified clinical dataset converted into FHIR. The FAIRness of this dataset was assessed using a set of common FAIR assessment indicators. Results: The FHIR distribution of MIMIC-ED, comprising an implementation guide and demo data, was more FAIR compared to the non-FHIR distribution. The FAIRness score increased from 60 to 82 out of 95 points, a relative improvement of 37%. The most notable improvements were observed in interoperability, with a score increase from 5 to 19 out of 19 points, and reusability, with a score increase from 8 to 14 out of 24 points. A total of 14 FAIR implementation choices were identified. Discussion: Our work examined how and to what extent the FHIR standard contributes to FAIR data. Challenges arose from interpreting the FAIR assessment indicators. This study stands out for providing a real-world example of a dataset that was made more FAIR using FHIR. Conclusion: To the best of our knowledge, this is the first study that formally assessed the conformance of a FHIR dataset to the FAIR principles. FHIR improved the accessibility, interoperability, and reusability of MIMIC-ED. Future research should focus on implementing FHIR in research data infrastructures.

Development of the CDISC Pediatrics User Guide: a CDISC and conect4children collaboration.

Introduction: The conect4children (c4c) project aims to facilitate efficient planning and delivery of paediatric clinical trials. One objective of c4c is data standardization and reuse. Interoperability and reusability of paediatric clinical trial data is challenging due to a lack of standardization. The Clinical Data Interchange Standards Consortium (CDISC) standards that are required or recommended for regulatory submissions in several countries lack paediatric specificity with limited awareness within academic institutions. To address this, c4c and CDISC collaborated to develop the Pediatrics User Guide (PUG) consisting of cross-cutting data items that are routinely collected in paediatric clinical trials, factoring in all paediatric age ranges. Methods and Results: The development of the PUG consisted of six stages. During the scoping phase, subtopics (each containing several clinically relevant concepts) were suggested and debated for inclusion in the PUG. Ninety concepts were selected for the modelling phase. Concept maps describing the Research Topic and representation procedure were developed for the 19 concepts that had no (or partial) previous modelling in CDISC. Next, metadata and implementation examples were developed for concepts. This was followed by a CDISC internal review and a public review. For both these review stages, the feedback comments were either implemented or rejected based on budget, timelines, expert review, and scope. The PUG was published on the CDISC website on February 23, 2023. Discussion: The PUG is a first step in bridging the lack of child specific CDISC standards, particularly within academia. Several academic and industrial partners were involved in the development of the PUG, and c4c has undertaken multiple steps to publicize the PUG within its academic partner organizations - in particular, the European Reference Networks (ERNs) that are developing registries and dictionaries in 24 disease areas. In the long term, continued use of the PUG in paediatric clinical trials will enable the pooling of data from multiple trials, which is particularly important for medical domains with small populations.

Renal replacement therapy registries--time for a structured data quality evaluation programme.

Registries in the area of renal replacement therapy (RRT) are intended to be a tool for epidemiological research, health care planning and improvement of quality of care. In this perspective, the value of a population-based RRT registry and its ability to achieve its goals rely heavily on the quality of its data and on quality control procedures. Data quality is of crucial importance to prevent 'garbage coming out from garbage going in'. Regular evaluations of the data quality within these registries are needed to assess various aspects of data quality like completeness, prevention of duplicates, data validity, comparability, timeliness, usefulness and responsiveness of different items and accuracy of reporting and interpretation. In this technical note, we will discuss the purposes of RRT registries and the importance of different aspects of data quality in relation to those purposes. In addition, we discuss how these data quality aspects can be assessed and, where possible, how data quality may be improved.

A survey of SNOMED CT implementations.

The Systematised Nomenclature of Medicine Clinical Terms (SNOMED CT) has been designated as the recommended clinical reference terminology for use in clinical information systems around the world and is reported to be used in over 50 countries. However, there are still few implementation details. This study examined the implementation of SNOMED CT in terms of design, use and maintenance issues involved in 13 healthcare organisations across eight countries through a series of interviews with 14 individuals. While a great deal of effort has been spent on developing and refining SNOMED CT, there is still much work ahead to bring SNOMED CT into routine clinical use.