Genomic considerations for FHIR®; eMERGE implementation lessons.
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ABSTRACT:
SUBMITTER: Murugan M
PROVIDER: S-EPMC8583906 | biostudies-literature | 2021 Jun
REPOSITORIES: biostudies-literature
Publications
Genomic considerations for FHIR®; eMERGE implementation lessons.
Murugan Mullai M Babb Lawrence J LJ Overby Taylor Casey C Rasmussen Luke V LV Freimuth Robert R RR Venner Eric E Yan Fei F Yi Victoria V Granite Stephen J SJ Zouk Hana H Aronson Samuel J SJ Power Kevin K Fedotov Alex A Crosslin David R DR Fasel David D Jarvik Gail P GP Hakonarson Hakon H Bangash Hana H Kullo Iftikhar J IJ Connolly John J JJ Nestor Jordan G JG Caraballo Pedro J PJ Wei WeiQi W Wiley Ken K Rehm Heidi L HL Gibbs Richard A RA
Journal of biomedical informatics 20210428
Structured representation of clinical genetic results is necessary for advancing precision medicine. The Electronic Medical Records and Genomics (eMERGE) Network's Phase III program initially used a commercially developed XML message format for standardized and structured representation of genetic results for electronic health record (EHR) integration. In a desire to move towards a standard representation, the network created a new standardized format based upon Health Level Seven Fast Healthcar ...[more]
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Project description:ObjectivesTo understand opinions and perceptions on the state of information resources specifically targeted to genomics, and approaches to delivery in clinical practice.MethodsWe conducted a survey of genomic content use and its clinical delivery from representatives across eight institutions in the electronic Medical Records and Genomics (eMERGE) network and two institutions in the Clinical Sequencing Exploratory Research (CSER) consortium in 2014.ResultsEleven responses representing distinct projects across ten sites showed heterogeneity in how content is being delivered, with provider-facing content primarily delivered via the electronic health record (EHR) (n=10), and paper/pamphlets as the leading mode for patient-facing content (n=9). There was general agreement (91%) that new content is needed for patients and providers specific to genomics, and that while aspects of this content could be shared across institutions there remain site-specific needs (73% in agreement).ConclusionThis work identifies a need for the improved access to and expansion of information resources to support genomic medicine, and opportunities for content developers and EHR vendors to partner with institutions to develop needed resources, and streamline their use - such as a central content site in multiple modalities while implementing approaches to allow for site-specific customization.
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Project description:BackgroundImplementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes - a process that can be labor intensive and error prone. To address the critical need for reducing the implementation efforts, it is important to develop portable algorithms.MethodsWe conducted a retrospective analysis of phenotype algorithms developed in the Electronic Medical Records and Genomics (eMERGE) network and identified common customization tasks required for implementation. A novel scoring system was developed to quantify portability from three aspects: Knowledge conversion, clause Interpretation, and Programming (KIP). Tasks were grouped into twenty representative categories. Experienced phenotype engineers were asked to estimate the average time spent on each category and evaluate time saving enabled by a common data model (CDM), specifically the Observational Medical Outcomes Partnership (OMOP) model, for each category.ResultsA total of 485 distinct clauses (phenotype criteria) were identified from 55 phenotype algorithms, corresponding to 1153 customization tasks. In addition to 25 non-phenotype-specific tasks, 46 tasks are related to interpretation, 613 tasks are related to knowledge conversion, and 469 tasks are related to programming. A score between 0 and 2 (0 for easy, 1 for moderate, and 2 for difficult portability) is assigned for each aspect, yielding a total KIP score range of 0 to 6. The average clause-wise KIP score to reflect portability is 1.37 ± 1.38. Specifically, the average knowledge (K) score is 0.64 ± 0.66, interpretation (I) score is 0.33 ± 0.55, and programming (P) score is 0.40 ± 0.64. 5% of the categories can be completed within one hour (median). 70% of the categories take from days to months to complete. The OMOP model can assist with vocabulary mapping tasks.ConclusionThis study presents firsthand knowledge of the substantial implementation efforts in phenotyping and introduces a novel metric (KIP) to measure portability of phenotype algorithms for quantifying such efforts across the eMERGE Network. Phenotype developers are encouraged to analyze and optimize the portability in regards to knowledge, interpretation and programming. CDMs can be used to improve the portability for some 'knowledge-oriented' tasks.
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Project description:BACKGROUND:Genomic risk information, based on common genomic susceptibility variants associated with risk of complex diseases such as cancer, may be incorporated into personalised prevention and screening strategies. We aimed to engage with members of the public, who are important stakeholders in this process, to further inform program development and other implementation outcomes such as acceptability and appropriateness. METHODS:Semi-structured interviews were undertaken with 30 participants (aged 24-69?years, 50% female) recruited from a pilot trial in which they received personalised genomic risk information for melanoma. We explored participants' views and attitudes towards offering general personal genomic risk information to the broader population. The data were analysed thematically. RESULTS:Two overarching themes relevant to implementation considerations were identified. Firstly, participants' preferences for accepting an offer of genomic risk information were based on family history, disease incidence and the possibility of prevention. Secondly, participants felt that the processes for offering risk information should be based on individual preferences, triaged according to risk and be supported by a health professional trained in genomics. CONCLUSIONS:Participants felt that offering personal genomic risk information to the general population to inform prevention and early detection recommendations is acceptable, particularly for common, complex conditions such as cancer. Understanding participants' preferences for receiving genomic risk information will assist with communication strategies and health workforce planning. We anticipate that these findings will contribute to the development of implementation strategies for incorporating genomic risk information into routine clinical practice.
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Project description:ObjectiveTo describe the development of the Pediatric Epilepsy Outcome-Informatics Project (PEOIP) at Alberta Children's Hospital (ACH), which was created to provide standardized, point-of-care data entry; near-time data analysis; and availability of outcome dashboards as a baseline on which to pursue quality improvement.MethodsStakeholders involved in the PEOIP met weekly to determine the most important outcomes for patients diagnosed with epilepsy, create a standardized electronic note with defined fields (patient demographics, seizure and syndrome type and frequency and specific outcomes- seizure type and frequency, adverse effects, emergency department visits, hospitalization, and care pathways for clinical decision support. These were embedded in the electronic health record from which the fields were extracted into a data display platform that provided patient- and population-level dashboards updated every 36 hours. Provider satisfaction and family experience surveys were performed to assess the impact of the standardized electronic note.ResultsIn the last 5 years, 3,245 unique patients involving 13, 831 encounters had prospective, longitudinal, standardized epilepsy data accrued via point-of-care data entry into an electronic note as part of routine clinical care. A provider satisfaction survey of the small number of users involved indicated that the vast majority believed that the note makes documentation more efficient. A family experience survey indicated that being provided with the note was considered "valuable" or "really valuable" by 86% of respondents and facilitated communication with family members, school, and advocacy organizations.SignificanceThe PEOIP serves as a proof of principle that information obtained as part of routine clinical care can be collected in a prospective, standardized, efficient manner and be used to construct filterable process/outcome dashboards, updated in near time (36 hours). This information will provide the necessary baseline data on which multiple of QI projects to improve meaningful outcomes for children with epilepsy will be based.
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Project description:The Electronic Medical Records and Genomics (eMERGE) Network, established in 2007, is a consortium of academic and integrated health systems conducting discovery and implementation research in translational genomics. Here, we outline the history of the network, highlight major impacts and lessons learned, and present the tools and resources developed for large-scale genomic analyses and translation into a clinical setting. The network developed methods to extract phenotypes from the electronic medical record to perform genome-wide and phenome-wide association studies. Recruited cohorts were clinically sequenced off a custom panel for targeted sequencing of variants and monogenic disease risks and returned to participants to investigate the impact of return of genomic results. After generating a 105,000 participant-imputed genome-wide association study (GWAS) dataset for discovery, the network enrolled and sequenced 24,998 participants. Integration of these results into the medical record and the effects of results on participants provided key lessons to the field. These learned lessons inform genetic research in diverse populations and provide insights into the clinical impact of return and implementation of genomic medicine using the electronic medical record. The lessons produced by the eMERGE Network can be utilized by other consortia as translational genomic medicine research evolves.
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Project description:The 3D organisation of the genome in interphase cells is not a randomly folded polymer. Rather, experiments show that chromosomes arrange into a network of 3D compartments that correlate with biological processes, such as transcription, chromatin modifications and protein binding. However, these compartments do not exist during cell division when the DNA is condensed, and it is unclear how and when they emerge. In this paper, we focus on the early stages after cell division as the chromosomes start to decondense. We use a simple polymer model to understand the types of 3D structures that emerge from local unfolding of a compact initial state. From simulations, we recover 3D compartments, such as TADs and A/B compartments that are consistently detected in chromosome capture experiments across cell types and organisms. This suggests that the large-scale 3D organisation is a result of an inflation process.
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Project description:BackgroundClinical trial registries increase transparency in medical research by making information and results of planned, ongoing, and completed studies publicly available. However, the registration of clinical trials remains a time-consuming manual task complicated by the fact that the same studies often need to be registered in different registries with different data entry requirements and interfaces.ObjectiveThis study investigates how Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) may be used as a standardized format for exchanging and storing clinical trial records.MethodsWe designed and prototypically implemented an open-source central trial registry containing records from university hospitals, which are automatically exported and updated by local study management systems.ResultsWe provided an architecture and implementation of a multisite clinical trials registry based on HL7 FHIR as a data storage and exchange format.ConclusionsThe results show that FHIR resources establish a harmonized view of study information from heterogeneous sources by enabling automated data exchange between trial centers and central study registries.
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Project description:BackgroundClinical decision support (CDS) has the potential to improve clinical decision-making consistent with evidence-based care. CDS can be designed to save health care providers time and help them provide safe and personalized analgesic prescribing.ObjectiveThe aim of this report is to describe the development of a CDS system designed to provide dentists with personalized pain management recommendations to reduce opioid prescribing following extractions. The use of CDS is also examined.MethodsThis study was conducted in HealthPartners, which uses an electronic health record (EHR) system that integrates both medical and dental information upon which the CDS application was developed based on SMART (Substitutable Medical Applications and Reusable Technologies) on FHIR (Fast Healthcare Interoperability Resources). The various tools used to bring relevant medical conditions, medications, patient history, and other relevant data into the CDS interface are described. The CDS application runs a drug interaction algorithm developed by our organization and provides patient-specific recommendations. The CDS included access to the state Prescription Monitoring Program database.Implementation (results)The pain management CDS was implemented as part of a study examining opioid prescribing among patients undergoing dental extraction procedures from February 17, 2020, to May 14, 2021. Provider-level use of CDS at extraction encounters ranged from 0% to 87.4% with 12.1% of providers opening the CDS for no encounters, 39.4% opening the CDS for 1%-20% of encounters, 36.4% opening it for 21%-50% of encounters, and 12.1% opening it for 51%-87% of encounters.ConclusionsThe pain management CDS is an EHR-embedded, provider-facing tool to help dentists make personalized pain management recommendations following dental extractions. The SMART on FHIR-based pain management CDS adapted well to the point-of-care dental setting and led to the design of a scalable CDS tool that is EHR vendor agnostic.Trial registrationClinicalTrials.gov NCT03584789; https://clinicaltrials.gov/study/NCT03584789.
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Project description:In 2011, the Veterans Health Administration (VHA) implemented electronic consults (e-consults) as an alternative to in-person specialty visits to improve access and reduce travel for veterans. We conducted an evaluation to understand variation in the use of the new e-consult mechanism and the causes of variable implementation, guided by the Consolidated Framework for Implementation Research (CFIR).Qualitative case studies of 3 high- and 5 low-implementation e-consult pilot sites. Participants included e-consult site leaders, primary care providers, specialists, and support staff identified using a modified snowball sample.We used a 3-step approach, with a structured survey of e-consult site leaders to identify key constructs, based on the CFIR. We then conducted open-ended interviews, focused on key constructs, with all participants. Finally, we produced structured, site-level ratings of CFIR constructs and compared them between high- and low-implementation sites.Site leaders identified 14 initial constructs. We conducted 37 interviews, from which 4 CFIR constructs distinguished high implementation e-consult sites: compatibility, networks and communications, training, and access to knowledge and information. For example, illustrating compatibility, a specialist at a high-implementation site reported that the site changed the order of consult options so that all specialties listed e-consults first to maintain consistency. High-implementation sites also exhibited greater agreement on constructs.By using the CFIR to analyze results, we facilitate future synthesis with other findings, and we better identify common patterns of implementation determinants common across settings.
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