Lessons learned from the eMERGE Network: balancing genomics in discovery and practice.
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ABSTRACT:
SUBMITTER: eMERGE Consortiumdavid.crosslin@gmail.com
PROVIDER: S-EPMC8756524 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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:The DAta Tag Suite (DATS) is a model supporting dataset description, indexing, and discovery. It is available as an annotated serialization with schema.org, a vocabulary used by major search engines, thus making the datasets discoverable on the web. DATS underlies DataMed, the National Institutes of Health Big Data to Knowledge Data Discovery Index prototype, which aims to provide a "PubMed for datasets." The experience gained while indexing a heterogeneous range of >60 repositories in DataMed helped in evaluating DATS's entities, attributes, and scope. In this work, 3 additional exemplary and diverse data sources were mapped to DATS by their representatives or experts, offering a deep scan of DATS fitness against a new set of existing data. The procedure, including feedback from users and implementers, resulted in DATS implementation guidelines and best practices, and identification of a path for evolving and optimizing the model. Finally, the work exposed additional needs when defining datasets for indexing, especially in the context of clinical and observational information.
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Project description:IntroductionDuring the past 2 decades, basic research deciphering the underlying mechanisms of nociception and chronic pain was thought to finally step beyond opioids and nonsteroidals and provide patients with new analgesics. But apart from calcitonin gene-related peptide antagonists, nothing arrived in hands of clinicians.ObjectivesTo present existing evidence of 3 representative target molecules in the development of novel pain treatment that, so far, did not result in approved drugs.MethodsThis Clinical Update aligns with the 2022 IASP Global Year Translating Pain Knowledge into Practice and selectively reviews best available evidence and practice.ResultsWe highlight 3 targets: a ion channel, a neuronal growth factor, and a neuropeptide to explore why these drug targets have been dropped in clinical phase II-III trials. Antibodies to nerve growth factor had very good effects in musculoskeletal pain but resulted into more patients requiring joint replacements. Blockers of NaV1.7 were often not effective enough-at least if patients were not stratified. Blockers of neurokinin receptor were similarly not successful enough. In general, failure was most often to the result of a lack of effect and to a lesser extend because of unexpected severe side effects. However, all studies and trials lead to an enormous move in the scientific community to better preclinical models and testing as well as revised methods to molecularly phenotype and stratify patients.ConclusionAll stakeholders in the process can help in the future: better preclinical studies, phenotyping and stratifying patients, and participation in clinical trials to move the discovery of analgesics forward.
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Project description:ObjectivesImplementing new programs to support precision medicine in clinical settings is a complex endeavor. We describe challenges and potential solutions based on the Indiana GENomics Implementation: an Opportunity for the Underserved (INGenious) program at Eskenazi Health-one of six sites supported by the Implementing GeNomics In pracTicE network grant of the National Institutes of Health/National Human Genome Research Institute. INGenious is an implementation of a panel of genomic tests.MethodsWe conducted a descriptive case study of the implementation of this pharmacogenomics program, which has a wide scope (14 genes, 27 medications) and a diverse population (patients who often have multiple chronic illnesses, in a large urban safety-net hospital and its outpatient clinics).ChallengesWe placed the clinical pharmacogenomics implementation challenges into six categories: patient education and engagement in care decision making; clinician education and changes in standards of care; integration of technology into electronic health record systems; translational and implementation sciences in real-world clinical environments; regulatory and reimbursement considerations, and challenges in measuring outcomes. A cross-cutting theme was the need for careful attention to workflow. Our clinical setting, a safety-net health care system, presented some distinctive challenges. Patients often had multiple chronic illnesses and sometimes were taking more than one pharmacogenomics-relevant medication. Reaching patients for recruitment or follow-up was another challenge.ConclusionsNew, large-scale endeavors in health care are challenging. A description of the challenges that we encountered and the approaches that we adopted to address them may provide insights for those who implement and study innovations in other health care systems.
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Project description:Many drugs, or their antecedents, were discovered through observation of their effects on normal or disease physiology. For the past generation, this phenotypic drug discovery approach has been largely supplanted by the powerful but reductionist approach of modulating specific molecular targets of interest. Nevertheless, modern phenotypic drug discovery, which combines the original concept with modern tools and strategies, has re-emerged over the past decade to systematically pursue drug discovery based on therapeutic effects in realistic disease models. Here, we discuss recent successes with this approach, as well as consider ongoing challenges and approaches to address them. We also explore how innovation in this area may fuel the next generation of successful projects.
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Project description:A primary barrier to translation of clinical research discoveries into care delivery and population health is the lack of sustainable infrastructure bringing researchers, policymakers, practitioners, and communities together to reduce silos in knowledge and action. As National Institutes of Health's (NIH) mechanism to advance translational research, Clinical and Translational Science Award (CTSA) awardees are uniquely positioned to bridge this gap. Delivering on this promise requires sustained collaboration and alignment between research institutions and public health and healthcare programs and services. We describe the collaboration of seven CTSA hubs with city, county, and state healthcare and public health organizations striving to realize this vision together. Partnership representatives convened monthly to identify key components, common and unique themes, and barriers in academic-public collaborations. All partnerships aligned the activities of the CTSA programs with the needs of the city/county/state partners, by sharing resources, responding to real-time policy questions and training needs, promoting best practices, and advancing community-engaged research, and dissemination and implementation science to narrow the knowledge-to-practice gap. Barriers included competing priorities, differing timelines, bureaucratic hurdles, and unstable funding. Academic-public health/health system partnerships represent a unique and underutilized model with potential to enhance community and population health.
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Project description:BackgroundDrug overdose has become a leading cause of accidental death in the United States. Between 2000 and 2015, the rate of deaths from drug overdoses increased 137%, including a 200% increase in the rate of overdose deaths involving opioids (including opioid pain relievers and heroin). Unnecessary opioid prescribing is one of the factors driving this epidemic.ObjectivesThe primary objective of this paper is to share lessons learned while conducting a randomized trial to de-implement opioids for post-extraction pain management utilizing clinical decision support (CDS) with and without patient education. The lessons learned from conducting this trial in a real-world setting can be applied to future dissemination and implementation oral health research.MethodsThe sources informing lessons learned were generated from qualitative interviews conducted with 20 of the forty-nine dental providers involved in the study following the implementation phase of the trial. Ongoing policy, social and environmental factors were tracked throughout the study.ResultsDental providers in the trial identified the impact of training that involved health professionals sharing information about the personal impact of pain and opioid use. Additionally, they found utility in being presented with a dashboard detailing their prescribing patterns related to other dentists. For the 30 general dentists with access to the CDS, use of its portal varied widely, with most using it 10%-49% of the time related to extractions.ConclusionsIn the context of a downward trend in opioid prescribing and considering the influence of the COVID pandemic during the trial, dental providers indicated benefit in training about negative personal impacts of prescribing opioids, and personally relevant feedback about their prescribing patterns. Only modest use of the CDS was realized. Implementation of this trial was impacted by governmental and health system policies and the COVID pandemic, prompt the consideration of implications regarding continuing ways to limit opioid prescribing among dental providers.
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