Making Science Computable Using Evidence-Based Medicine on Fast Healthcare Interoperability Resources: Standards Development Project.

BACKGROUND: Evidence-based medicine (EBM) has the potential to improve health outcomes, but EBM has not been widely integrated into the systems used for research or clinical decision-making. There has not been a scalable and reusable computer-readable standard for distributing research results and synthesized evidence among creators, implementers, and the ultimate users of that evidence. Evidence that is more rapidly updated, synthesized, disseminated, and implemented would improve both the delivery of EBM and evidence-based health care policy. OBJECTIVE: This study aimed to introduce the EBM on Fast Healthcare Interoperability Resources (FHIR) project (EBMonFHIR), which is extending the methods and infrastructure of Health Level Seven (HL7) FHIR to provide an interoperability standard for the electronic exchange of health-related scientific knowledge. METHODS: As an ongoing process, the project creates and refines FHIR resources to represent evidence from clinical studies and syntheses of those studies and develops tools to assist with the creation and visualization of FHIR resources. RESULTS: The EBMonFHIR project created FHIR resources (ie, ArtifactAssessment, Citation, Evidence, EvidenceReport, and EvidenceVariable) for representing evidence. The COVID-19 Knowledge Accelerator (COKA) project, now Health Evidence Knowledge Accelerator (HEvKA), took this work further and created FHIR resources that express EvidenceReport, Citation, and ArtifactAssessment concepts. The group is (1) continually refining FHIR resources to support the representation of EBM; (2) developing controlled terminology related to EBM (ie, study design, statistic type, statistical model, and risk of bias); and (3) developing tools to facilitate the visualization and data entry of EBM information into FHIR resources, including human-readable interfaces and JSON viewers. CONCLUSIONS: EBMonFHIR resources in conjunction with other FHIR resources can support relaying EBM components in a manner that is interoperable and consumable by downstream tools and health information technology systems to support the users of evidence.

Making science computable: Developing code systems for statistics, study design, and risk of bias.

The COVID-19 crisis led a group of scientific and informatics experts to accelerate development of an infrastructure for electronic data exchange for the identification, processing, and reporting of scientific findings. The Fast Healthcare Interoperability Resources (FHIR®) standard which is overcoming the interoperability problems in health information exchange was extended to evidence-based medicine (EBM) knowledge with the EBMonFHIR project. A 13-step Code System Development Protocol was created in September 2020 to support global development of terminologies for exchange of scientific evidence. For Step 1, we assembled expert working groups with 55 people from 26 countries by October 2020. For Step 2, we identified 23 commonly used tools and systems for which the first version of code systems will be developed. For Step 3, a total of 368 non-redundant concepts were drafted to become display terms for four code systems (Statistic Type, Statistic Model, Study Design, Risk of Bias). Steps 4 through 13 will guide ongoing development and maintenance of these terminologies for scientific exchange. When completed, the code systems will facilitate identifying, processing, and reporting research results and the reliability of those results. More efficient and detailed scientific communication will reduce cost and burden and improve health outcomes, quality of life, and patient, caregiver, and healthcare professional satisfaction. We hope the achievements reached thus far will outlive COVID-19 and provide an infrastructure to make science computable for future generations. Anyone may join the effort at https://www.gps.health/covid19_knowledge_accelerator.html.

Striking a match between FHIR-based patient data and FHIR-based eligibility criteria.

Inputs and Outputs: The Strike-a-Match Function, written in JavaScript version ES6+, accepts the input of two datasets (one dataset defining eligibility criteria for research studies or clinical decision support, and one dataset defining characteristics for an individual patient). It returns an output signaling whether the patient characteristics are a match for the eligibility criteria. Purpose: Ultimately, such a system will play a "matchmaker" role in facilitating point-of-care recognition of patient-specific clinical decision support. Specifications: The eligibility criteria are defined in HL7 FHIR (version R5) EvidenceVariable Resource JSON structure. The patient characteristics are provided in an FHIR Bundle Resource JSON including one Patient Resource and one or more Observation and Condition Resources which could be obtained from the patient's electronic health record. Application: The Strike-a-Match Function determines whether or not the patient is a match to the eligibility criteria and an Eligibility Criteria Matching Software Demonstration interface provides a human-readable display of matching results by criteria for the clinician or patient to consider. This is the first software application, serving as proof of principle, that compares patient characteristics and eligibility criteria with all data exchanged using HL7 FHIR JSON. An Eligibility Criteria Matching Software Library at https://fevir.net/110192 provides a method for sharing functions using the same information model.

Pseudomonas aeruginosa: host defence in lung diseases.

Lung infections caused by the opportunistic pathogen Pseudomonas aeruginosa can present as a spectrum of clinical entities from a rapidly fatal pneumonia in a neutropenic patient to a multi-decade bronchitis in patients with cystic fibrosis. P. aeruginosa is ubiquitous in our environment, and one of the most versatile pathogens studied, capable of infecting a number of diverse life forms and surviving harsh environmental factors. It is also able to quickly adapt to new environments, including the lung, where it orchestrates virulence factors to acquire necessary nutrients, and if necessary, turn them off to prevent immune recognition. Despite these capabilities, P. aeruginosa rarely infects healthy human lungs. This is secondary to a highly evolved host defence mechanism that efficiently removes inhaled or aspirated pseudomonads. Many arms of the respiratory host defence have been elucidated using P. aeruginosa as a model pathogen. Human infections with P. aeruginosa have demonstrated the importance of the mechanical barrier functions including mucus clearance, and the innate immune system, including the critical role of the neutrophilic response. As more models of persistent or biofilm P. aeruginosa infections are developed, the role of the adaptive immune response will likely become more evident. Understanding the pathogenesis of P. aeruginosa, and the respiratory host defence response to it has, and will continue to, lead to novel therapeutic strategies to help patients.

Interventions for Supporting and Assessing Science Writing Communication: Cases of Asian English Language Learners.

In seeking to support diversity, one challenge lies in adequately supporting and assessing science cognitions in a writing-intensive Biochemistry laboratory course when highly engaged Asian English language learners (Asian ELLs) struggle to communicate and make novice errors in English. Because they may understand advanced science concepts, but are not being adequately assessed for their deeper scientific understanding, we sought and examined interventions. We hypothesized that inquiry strategies, scaffolded learning through peer evaluation, and individualized tools that build writing communication skills would increase confidence. To assess scientific thinking, Linguistic Inquiry Word Count (LIWC) software measured underlying analytic and cognitive features of writing despite grammatical errors. To determine whether interventions improved student experience or learning outcomes, we investigated a cross-sectional sample of cases within experimental groups (n = 19) using a mixed-methods approach. Overall trends of paired t-tests from Asian ELLs' pre/post surveys showed gains in six measures of writing confidence, with some statistically significant gains in confidence in writing skill (p=0.025) and in theory (p≤0.05). LIWC scores for Asian ELL and native-English-speaking students were comparable except for increased cognitive scores for Asian ELLs and detectable individual differences. An increase in Asian ELLs' cognitive scores in spring/summer over fall was observed (p = 0.04), likely as a result of greater cognitive processes with language use, inquiry-related interventions, and peer evaluation. Individual cases further elucidated challenges faced by Asian ELL students. LIWC scores of student writing may be useful in determining underlying understanding. Interventions designed to provide support and strengthen the writing of Asian ELL students may also improve their confidence in writing, even if improvement is gradual.