Summary
Objective:
To discuss recent developments in clinical terminologies. SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) is the world's largest clinical terminology, developed ...by an international consortium. LOINC (Logical Observation Identifiers, Names, and Codes) is an international terminology widely used for clinical and laboratory observations. RxNorm is the standard drug terminology in the U.S.
Methods and results:
We present a brief review of the history, current state, and future development of SNOMED CT, LOINC and RxNorm. We also analyze their similarities and differences, and outline areas for greater interoperability among them.
Conclusions:
With different starting points, representation formalisms, funding sources, and evolutionary paths, SNOMED CT, LOINC, and RxNorm have evolved over the past few decades into three major clinical terminologies supporting key use cases in clinical practice. Despite their differences, partnerships have been created among their development teams to facilitate interoperability and minimize duplication of effort.
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.
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).
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.
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.
Organizational readiness for change is a key factor in success or failure of electronic health record (EHR) system implementations. Readiness is a multifaceted and multilevel abstract construct ...encompassing individual and organizational aspects, which makes it difficult to assess. Available tools for assessing readiness need to be tested in different contexts. To identify and assess relevant variables that determine readiness to implement an EHR in oncology in a low-and-middle income setting. At the Uganda Cancer Institute (UCI), a 100-bed tertiary oncology center in Uganda,we conducted a cross-sectional survey using the Paré model. This model has 39 indicator variables (Likert-scale items) for measuring 9 latent variables that contribute to readiness. We analyzed data using partial least squares structural equation modeling (PLS-SEM). In addition, we collected comments that we analyzed by qualitative content analysis and sentiment analysis as a way of triangulating the Likert-scale survey responses. One hundred and forty-six clinical and non-clinical staff completed the survey, and 116 responses were included in the model. The measurement model showed good indicator reliability, discriminant validity, and internal consistency. Path coefficients for 6 of the 9 latent variables (i.e. vision clarity, change appropriateness, change efficacy, presence of an effective champion, organizational flexibility, and collective self-efficacy) were statistically significant at p < 0.05. The R.sup.2 for the outcome variable (organizational readiness) was 0.67. The sentiments were generally positive and correlated well with the survey scores (Pearson's r = 0.73). Perceived benefits of an EHR included improved quality, security and accessibility of clinical data, improved care coordination, reduction of errors, and time and cost saving. Recommended considerations for successful implementation include sensitization, training, resolution of organizational conflicts and computer infrastructure. Change management during EHR implementation in oncology in low- and middle- income setting should focus on attributes of the change and the change targets, including vision clarity, change appropriateness, change efficacy, presence of an effective champion, organizational flexibility, and collective self-efficacy. Particularly, issues of training, computer skills of staff, computer infrastructure, sensitization and strategic implementation need consideration.
Healthcare data and the knowledge gleaned from it play a key role in improving the health of current and future patients. These knowledge sources are regularly represented as 'linked' resources based ...on the Resource Description Framework (RDF). Making resources 'linkable' to facilitate their interoperability is especially important in the rare-disease domain, where health resources are scattered and scarce. However, to benefit from using RDF, resources need to be of good quality. Based on existing metrics, we aim to assess the quality of RDF resources related to rare diseases and provide recommendations for their improvement.
Sixteen resources of relevance for the rare-disease domain were selected: two schemas, three metadatasets, and eleven ontologies. These resources were tested on six objective metrics regarding resolvability, parsability, and consistency. Any URI that failed the test based on any of the six metrics was recorded as an error. The error count and percentage of each tested resource were recorded. The assessment results were represented in RDF, using the Data Quality Vocabulary schema.
For three out of the six metrics, the assessment revealed quality issues. Eleven resources have non-resolvable URIs with proportion to all URIs ranging from 0.1% (6/6,712) in the Anatomical Therapeutic Chemical Classification to 13.7% (17/124) in the WikiPathways Ontology; seven resources have undefined URIs; and two resources have incorrectly used properties of the 'owl:ObjectProperty' type. Individual errors were examined to generate suggestions for the development of high-quality RDF resources, including the tested resources.
We assessed the resolvability, parsability, and consistency of RDF resources in the rare-disease domain, and determined the extent of these types of errors that potentially affect interoperability. The qualitative investigation on these errors reveals how they can be avoided. All findings serve as valuable input for the development of a guideline for creating high-quality RDF resources, thereby enhancing the interoperability of biomedical resources.
Background:
Patient-accessible electronic health records (PAEHRs) and associated national policies have increasingly been set up over the past two decades. Still little is known about the most ...effective strategy for developing and implementing PAEHRs. There are many stakeholders to take into account, and previous research focuses on the viewpoints of patients and healthcare professionals. Many known barriers and challenges could be solved by involving end-users in the development and implementation process. This study therefore compares barriers and facilitators for PAEHR development and implementation, both general and specific for patient involvement, that were present in Sweden and the Netherlands.
Methods:
There were a total of 14 semi-structured interviews with 16 key informants from both countries, on which content analysis was performed. The Consolidated Framework for Implementation Research was used to guide both the construction of the interview guides and the content analysis.
Outcomes:
The main barriers present in both countries are resistance from healthcare professionals and technical barriers regarding electronic health record systems and vendors. Facilitators varied across the two contexts, where the national infrastructure and program management were highlighted as facilitators in Sweden and stakeholder engagement (including patients and healthcare professionals) was described as a facilitator in both contexts. Strong leadership was also described as a critical success factor, especially when faced with healthcare professional resistance.
Conclusion:
Most of the major barriers and facilitators from both countries are covered in existing literature. This study, however, identified factors that can be seen as more practical and that would not have arisen from interviews with patients or physicians. Recommendations for policymakers include keeping the mentioned barriers in mind from the start of development and paving the way for facilitators, mainly strict policies, learning from peer implementers, and patient involvement, when possible. Implementers should focus on strong decision-making and project management and on preparing the healthcare organization for the PAEHR.
Free-text descriptions in electronic health records (EHRs) can be of interest for clinical research and care optimization. However, free text cannot be readily interpreted by a computer and, ...therefore, has limited value. Natural Language Processing (NLP) algorithms can make free text machine-interpretable by attaching ontology concepts to it. However, implementations of NLP algorithms are not evaluated consistently. Therefore, the objective of this study was to review the current methods used for developing and evaluating NLP algorithms that map clinical text fragments onto ontology concepts. To standardize the evaluation of algorithms and reduce heterogeneity between studies, we propose a list of recommendations.
Two reviewers examined publications indexed by Scopus, IEEE, MEDLINE, EMBASE, the ACM Digital Library, and the ACL Anthology. Publications reporting on NLP for mapping clinical text from EHRs to ontology concepts were included. Year, country, setting, objective, evaluation and validation methods, NLP algorithms, terminology systems, dataset size and language, performance measures, reference standard, generalizability, operational use, and source code availability were extracted. The studies' objectives were categorized by way of induction. These results were used to define recommendations.
Two thousand three hundred fifty five unique studies were identified. Two hundred fifty six studies reported on the development of NLP algorithms for mapping free text to ontology concepts. Seventy-seven described development and evaluation. Twenty-two studies did not perform a validation on unseen data and 68 studies did not perform external validation. Of 23 studies that claimed that their algorithm was generalizable, 5 tested this by external validation. A list of sixteen recommendations regarding the usage of NLP systems and algorithms, usage of data, evaluation and validation, presentation of results, and generalizability of results was developed.
We found many heterogeneous approaches to the reporting on the development and evaluation of NLP algorithms that map clinical text to ontology concepts. Over one-fourth of the identified publications did not perform an evaluation. In addition, over one-fourth of the included studies did not perform a validation, and 88% did not perform external validation. We believe that our recommendations, alongside an existing reporting standard, will increase the reproducibility and reusability of future studies and NLP algorithms in medicine.
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.
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.
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.
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.
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.
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)).
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.
The FAIR Data Principles are being rapidly adopted by many research institutes and funders worldwide. This study aimed to assess the awareness and attitudes of clinical researchers and research ...support staff regarding data FAIRification. A questionnaire was distributed to researchers and support staff in six Dutch University Medical Centers and Electronic Data Capture platform users. 164 researchers and 21 support staff members completed the questionnaire. 62.8% of the researchers and 81.0% of the support staff are currently undertaking at least some effort to achieve any aspect of FAIR, 11.0% and 23.8%, respectively, address all aspects. Only 46.6% of the researchers add metadata to their datasets, 39.7% add metadata to data elements, and 35.9% deposit their data in a repository. 94.7% of the researchers are aware of the usefulness of their data being FAIR for others and 89.3% are, given the right resources and support, willing to FAIRify their data. Institutions and funders should, therefore, develop FAIRification training and tools and should (financially) support researchers and staff throughout the process.
New primary renal diagnosis codes for the ERA-EDTA VENKAT-RAMAN, Gopalakrishnan; TOMSON, Charles R. V; CASINO, Francesco ...
Nephrology, dialysis, transplantation,
12/2012, Letnik:
27, Številka:
12
Journal Article
Recenzirano
Odprti dostop
The European Renal Association-European Dialysis and Transplant Association (ERA-EDTA) Registry has produced a new set of primary renal diagnosis (PRD) codes that are intended for use by affiliated ...registries. It is designed specifically for use in renal centres and registries but is aligned with international coding standards supported by the WHO (International Classification of Diseases) and the International Health Terminology Standards Development Organization (SNOMED Clinical Terms). It is available as supplementary material to this paper and free on the internet for non-commercial, clinical, quality improvement and research use, and by agreement with the ERA-EDTA Registry for use by commercial organizations. Conversion between the old and the new PRD codes is possible. The new codes are very flexible and will be actively managed to keep them up-to-date and to ensure that renal medicine can remain at the forefront of the electronic revolution in medicine, epidemiology research and the use of decision support systems to improve the care of patients.
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