Nutrigenomic Information in the openEHR Data Set

Nutrition Information in Oncology - Extending the Electronic Patient-Record Data Set

Electronic health records (EHRs) present extensive patient information and may be used as a tool to improve health care. However, the oncology context presents a complex content that increases the difficulties of EHR application. This study aimed at developing openEHR-archetypes representing clinical concepts in cancer nutrition-care, as well as to develop an openEHR-template including the aforementioned archetypes. The study involved the following stages: 1) a thorough literature review, followed by an expert’s (nutrition guideline authors) survey, aiming to identify the main statements of published clinical guidelines on nutrition in cancer patients that were not included on the Clinical Knowledge Manager (CKM) repository; 2) modelling of the archetypes using the Ocean Archetype Software and submission to the CKM repository; 3) creating an example template with Template Designer; and 4) automatic conversion of the openEHR-template into a readily usable EHR using VCIntegrator. The clinical concepts (among 17 clinical concepts not yet available in the CKM repository) chosen for further development were: body composition, diet plan, dietary nutrients, dietary supplements, dietary intake assessment, and Malnutrition Screening Tool (MST). So far, four archetypes were accepted for review in the CKM repository and a template was created and converted into an EHR. This study designed new openEHR-archetypes for nutrition management in cancer patients. These archetypes can be included in EHR. Future studies are needed to assess their applicability in other areas and their practical impact on data quality, system interoperability and, ultimately, on clinical practice and research.

Sync for Genes: Making Clinical Genomics Available for Precision Medicine at the Point-of-Care

BACKGROUND

Making genomic data available at the point-of-care and for research is critical for the success of the Precision Medicine Initiative (PMI), a research initiative which seeks to change health care by "tak(ing) into account individual differences in people's genes, environments, and lifestyles." The Office of the National Coordinator for Health Information Technology (ONC) led Sync for Genes, a program to develop standards that make genomic data available when and where it matters most. This article discusses lessons learned from recent Sync for Genes activities.

OBJECTIVES

The goals of Sync for Genes were to (1) demonstrate exchange of genomic data using health data standards, (2) provide feedback for refinement of health data standards, and (3) synthesize project experiences to support the integration of genomic data at the point-of-care and for research.

METHODS

Four organizations participated in a program to test the Health Level Seven International (HL7^®) Fast Healthcare Interoperability Resources (FHIR^®) standard, which supports sharing genomic data. ONC provided access to subject matter experts, resources, tools, and technical guidance to support testing activities. Three of the four organizations participated in HL7 FHIR Connectathons to test FHIR's ability to exchange genomic diagnostic reports.

RESULTS

The organizations successfully demonstrated exchange of genomic diagnostic reports using FHIR. The feedback and artifacts that resulted from these activities were shared with HL7 and made publicly available. Four areas were identified as important considerations for similar projects: (1) FHIR proficiency, (2) developer support, (3) project scope, and (4) bridging health information technology and genomic expertise.

CONCLUSION

Precision medicine is a rapidly evolving field, and there is opportunity to continue maturing health data standards for the exchange of necessary genomic data, increasing the likelihood that the standard supports the needs of users.

Identification and validation of common molecular targets of hydroxytyrosol

Hydroxytyrosol (HT) is involved in healthful activities and is beneficial to lipid metabolism. Many investigations focused on finding tissue-specific targets of HT through the use of different omics approaches such as transcriptomics and proteomics. However, it is not clear which (if any) of the potential molecular targets of HT reported in different studies are concurrently affected in various tissues. Following the bioinformatic analyses of publicly available data from a selection of in vivo studies involving HT-supplementation, we selected differentially expressed lipid metabolism-related genes and proteins common to more than one study, for validation in rodent liver samples from the entire selection. Four miRNAs (miR-802-5p, miR-423-3p, miR-30a-5p, and miR-146b-5p) responded to HT supplementation. Of note, miR-802-5p was commonly regulated in the liver and intestine. Our premise was that, in an organ crucial for lipid metabolism such as the liver, consistent modulation should be found for a specific target of HT even if different doses and duration of HT supplementation were used in vivo. Even though our results show inconsistency regarding differentially expressed lipid metabolism-related genes and proteins across studies, we found Fgf21 and Rora as potential novel targets of HT. Omics approaches should be fine-tuned to better exploit the available databases.

Discovering Clinical Information Models Online to Promote Interoperability of Electronic Health Records: A Feasibility Study of OpenEHR

Background

Clinical information models (CIMs) enabling semantic interoperability are crucial for electronic health record (EHR) data use and reuse. Dual model methodology, which distinguishes the CIMs from the technical domain, could help enable the interoperability of EHRs at the knowledge level. How to help clinicians and domain experts discover CIMs from an open repository online to represent EHR data in a standard manner becomes important.

Objective

This study aimed to develop a retrieval method to identify CIMs online to represent EHR data.

Methods

We proposed a graphical retrieval method and validated its feasibility using an online CIM repository: openEHR Clinical Knowledge Manager (CKM). First, we represented CIMs (archetypes) using an extended Bayesian network. Then, an inference process was run in the network to discover relevant archetypes. In the evaluation, we defined three retrieval tasks (medication, laboratory test, and diagnosis) and compared our method with three typical retrieval methods (BM25F, simple Bayesian network, and CKM), using mean average precision (MAP), average precision (AP), and precision at 10 (P@10) as evaluation metrics.

Results

We downloaded all available archetypes from the CKM. Then, the graphical model was applied to represent the archetypes as a four-level clinical resources network. The network consisted of 5513 nodes, including 3982 data element nodes, 504 concept nodes, 504 duplicated concept nodes, and 523 archetype nodes, as well as 9867 edges. The results showed that our method achieved the best MAP (MAP=0.32), and the AP was almost equal across different retrieval tasks (AP=0.35, 0.31, and 0.30, respectively). In the diagnosis retrieval task, our method could successfully identify the models covering “diagnostic reports,” “problem list,” “patients background,” “clinical decision,” etc, as well as models that other retrieval methods could not find, such as “problems and diagnoses.”

Conclusions

The graphical retrieval method we propose is an effective approach to meet the uncertainty of finding CIMs. Our method can help clinicians and domain experts identify CIMs to represent EHR data in a standard manner, enabling EHR data to be exchangeable and interoperable.