Arden Syntax on FHIR.

Arden Syntax, a medical knowledge representation and processing language for clinical decision support tasks supervised by Health Level Seven International (HL7), was extended with HL7's Fast Healthcare Interoperability Resources (FHIR) constructs to allow standardized data access. The new version, Arden Syntax version 3.0, was successfully balloted as part of the audited, consensus-based, iterative HL7 standards development process.

Categorizing metadata to help mobilize computable biomedical knowledge.

Introduction: Computable biomedical knowledge artifacts (CBKs) are digital objects conveying biomedical knowledge in machine-interpretable structures. As more CBKs are produced and their complexity increases, the value obtained from sharing CBKs grows. Mobilizing CBKs and sharing them widely can only be achieved if the CBKs are findable, accessible, interoperable, reusable, and trustable (FAIR+T).

Optimization of sepsis therapy based on patient-specific digital precision diagnostics using next generation sequencing (DigiSep-Trial)-study protocol for a randomized, controlled, interventional, open-label, multicenter trial.

Background: Sepsis is triggered by an infection and represents one of the greatest challenges of modern intensive care medicine. With regard to a targeted antimicrobial treatment strategy, the earliest possible pathogen detection is of crucial importance. Until now, culture-based detection methods represent the diagnostic gold standard, although they are characterized by numerous limitations.

CheXED: Comparison of a Deep Learning Model to a Clinical Decision Support System for Pneumonia in the Emergency Department.

Purpose: Patients with pneumonia often present to the emergency department (ED) and require prompt diagnosis and treatment. Clinical decision support systems for the diagnosis and management of pneumonia are commonly utilized in EDs to improve patient care. The purpose of this study is to investigate whether a deep learning model for detecting radiographic pneumonia and pleural effusions can improve functionality of a clinical decision support system (CDSS) for pneumonia management (ePNa) operating in 20 EDs.

Electronic pulmonary embolism clinical decision support and effect on yield of computerized tomographic pulmonary angiography: ePE-A pragmatic prospective cohort study.

Objective: Multiple professional societies recommend pre-test probability (PTP) assessment prior to imaging in the evaluation of patients with suspected pulmonary embolism (PE), however, PTP testing remains uncommon, with imaging occurring frequently and rates of confirmed PE remaining low. The goal of this study was to assess the impact of a clinical decision support tool embedded into the electronic health record to improve the diagnostic yield of computerized tomography pulmonary angiography (CTPA) in suspected patients with PE in the emergency department (ED).

Methods: Between July 24, 2014 and December 31, 2016, 4 hospitals from a healthcare system embedded an optional electronic clinical decision support system to assist in the diagnosis of pulmonary embolism (ePE).

Contemporary clinical decision support standards using Health Level Seven International Fast Healthcare Interoperability Resources.

Objective: To facilitate the development of standards-based clinical decision support (CDS) systems, we review the current set of CDS standards that are based on Health Level Seven International Fast Healthcare Interoperability Resources (FHIR). Widespread adoption of these standards may help reduce healthcare variability, improve healthcare quality, and improve patient safety.

Target Audience: This tutorial is designed for the broad informatics community, some of whom may be unfamiliar with the current, FHIR-based CDS standards.

Implementation of Real-Time Electronic Clinical Decision Support for Emergency Department Patients with Pneumonia Across a Healthcare System.

A real-time electronic CDS for pneumonia (ePNa) identifies possible pneumonia patients, measures severity and antimicrobial resistance risk, and then recommends disposition, antibiotics, and microbiology studies. Use is voluntary, and clinicians may modify treatment recommendations. ePNa was associated with lower mortality in emergency department (ED) patients versus usual care (Annals EM 66:511).

The design and evaluation of a Bayesian system for detecting and characterizing outbreaks of influenza.

The prediction and characterization of outbreaks of infectious diseases such as influenza remains an open and important problem. This paper describes a framework for detecting and characterizing outbreaks of influenza and the results of testing it on data from ten outbreaks collected from two locations over five years. We model outbreaks with compartment models and explicitly model non-influenza influenza-like illnesses.

Enhancing a Commercial EMR with an Open, Standards-Based Publish-Subscribe Infrastructure.

Intermountain Healthcare has designed and implemented a publish-subscribe (PubSub) infrastructure to support essential event processing workflows across our organization. A recent implementation of a commercial EMR highlighted the need to provide this capability on top of the EMR to support external applications and services that require access to triggering events within the EMR. A description of the PubSub architecture is presented.

Promoting national and international standards to build interoperable clinical applications.

During the last decade, software supporting healthcare delivery has proliferated. This software can be divided into electronic medical record (EHR) systems and applications that treat EHRs as platforms. These collect, manage, and interpret medical data, thereby adding value to associated EHRs.

CDS in a Learning Health Care System: Identifying Physicians' Reasons for Rejection of Best-Practice Recommendations in Pneumonia through Computerized Clinical Decision Support.

Background: Local implementation of guidelines for pneumonia care is strongly recommended, but the context of care that affects implementation is poorly understood. In a learning health care system, computerized clinical decision support (CDS) provides an opportunity to both improve and track practice, providing insights into the implementation process.

Objectives: This article examines physician interactions with a CDS to identify reasons for rejection of guideline recommendations.

Experience with Integrating Diagnostic Decision Support Software with Electronic Health Records: Benefits versus Risks of Information Sharing.

Introduction: Reducing misdiagnosis has long been a goal of medical informatics. Current thinking has focused on achieving this goal by integrating diagnostic decision support into electronic health records.

Methods: A diagnostic decision support system already in clinical use was integrated into electronic health record systems at two large health systems, after clinician input on desired capabilities.

Discovery of N-arylpyrroles as agonists of GPR120 for the treatment of type II diabetes.

The discovery of a novel series of N-arylpyrroles as agonists of GPR120 (FFAR4) is discussed. One lead compound is a potent GPR120 agonist, has good selectivity for related receptor GPR40 (FFAR1), has acceptable PK properties, and is active in 2 models of Type 2 Diabetes in mice.

Discovery of an Isothiazole-Based Phenylpropanoic Acid GPR120 Agonist as a Development Candidate for Type 2 Diabetes.

We have discovered a novel series of isothiazole-based phenylpropanoic acids as GPR120 agonists. Extensive structure-activity relationship studies led to the discovery of a potent GPR120 agonist , which displayed good EC values in both calcium and β-arrestin assays. It also presented good pharmaceutical properties and a favorable PK profile.

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods.

Background: To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes.

A Bayesian system to detect and characterize overlapping outbreaks.

Outbreaks of infectious diseases such as influenza are a significant threat to human health. Because there are different strains of influenza which can cause independent outbreaks, and influenza can affect demographic groups at different rates and times, there is a need to recognize and characterize multiple outbreaks of influenza. This paper describes a Bayesian system that uses data from emergency department patient care reports to create epidemiological models of overlapping outbreaks of influenza.

Design, synthesis and SAR of a novel series of heterocyclic phenylpropanoic acids as GPR120 agonists.

A novel series of 5-membered heterocycle-containing phenylpropanoic acid derivatives was discovered as potent GPR120 agonists with low clearance, high oral bioavailability and in vivo antidiabetic activity in rodents.

The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance.

Objectives: This study evaluates the accuracy and portability of a natural language processing (NLP) tool for extracting clinical findings of influenza from clinical notes across two large healthcare systems. Effectiveness is evaluated on how well NLP supports downstream influenza case-detection for disease surveillance.

Methods: We independently developed two NLP parsers, one at Intermountain Healthcare (IH) in Utah and the other at University of Pittsburgh Medical Center (UPMC) using local clinical notes from emergency department (ED) encounters of influenza.

Using classification models for the generation of disease-specific medications from biomedical literature and clinical data repository.

Objective: Mining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based applications. Many association mining techniques have been exploited. However, the challenge remains when those extracted associations contained much noise.

A study of the transferability of influenza case detection systems between two large healthcare systems.

Objectives: This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases.

Methods: A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients' diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH).

Generating disease-pertinent treatment vocabularies from MEDLINE citations.

Objective: Healthcare communities have identified a significant need for disease-specific information. Disease-specific ontologies are useful in assisting the retrieval of disease-relevant information from various sources. However, building these ontologies is labor intensive.