The informed consent form navigator: a tool for producing readable and compliant consent documents.

Background/Objective: Informed consent forms (ICFs) and practices vary widely across institutions. This project expands on previous work at the University of Arkansas for Medical Sciences (UAMS) Center for Health Literacy to develop a plain language ICF template. Our interdisciplinary team of researchers, comprised of biomedical informaticists, health literacy experts, and stakeholders in the Institutional Review Board (IRB) process, has developed the ICF Navigator, a novel tool to facilitate the creation of plain language ICFs that comply with all relevant regulatory requirements. Methods: Our team first developed requirements for the ICF Navigator tool. The tool was then implemented by a technical team of informaticists and software developers, in consultation with an informed consent legal expert. We developed and formalized a detailed knowledge map modeling regulatory requirements for ICFs, which drives workflows within the tool. Results: The ICF Navigator is a web-based tool that guides researchers through creating an ICF as they answer questions about their project. The navigator uses those responses to produce a clear and compliant ICF, displaying a real-time preview of the final form as content is added. Versioning and edits can be tracked to facilitate collaborative revisions by the research team and communication with the IRB. The navigator helps guide the creation of study-specific language, ensures compliance with regulatory requirements, and ensures that the resulting ICF is easy to read and understand. Conclusion: The ICF Navigator is an innovative, customizable, open-source software tool that helps researchers produce custom readable and compliant ICFs for research studies involving human subjects.

Evaluating the temporal association between the recency of prescribed controlled substance acquisition and fatal and non-fatal opioid overdose.

BACKGROUND: Previous studies have explored psychosocial effects as possible triggers of opioid overdose (OOD). However, little is known about the temporal association between OOD and prescribed controlled substance (CS) acquisition. OBJECTIVE: The objective of this study was to evaluate the temporal relationship between OOD and acquiring prescribed CSs prior to OOD. METHODS: This study is an exploratory descriptive analysis using Arkansas Prescription Drug Monitoring Program (AR-PDMP) data linked to death certificate and statewide inpatient discharge records. All persons with ≥1 AR-PDMP prescription fill(s) between 1 January 2014 and 31 December 2017 were included (n = 1,946,686). For persons that experienced OOD and had ≥1 PDMP record(s), the difference in days between OOD and the most recent AR-PDMP prescription filled prior to an OOD was recorded. To account for censoring, a sensitivity analysis was conducted restricting the study group to "New AR-PDMP Entrants" that had at least a 180-day gap between consecutive AR-PDMP fill dates. RESULTS: 28,998,307 AR-PDMP records were analyzed for 1,946,686 individuals. 7195 persons experienced 9223 OODs and 414 (4.49%) of those were fatal. Of these, 6236 experienced ≥1 OOD and acquired prescribed CSs prior to or on the day of the first OOD. Of those that experienced ≥1 OOD(s), 2201 (30.59%) had an AR-PDMP record in the 0- to 5-day period prior to their overdose and 497 (6.91%) had an AR-PDMP record the day prior to their overdose. Among New AR-PDMP Entrants that experienced ≥1 OOD(s), 408 (27.38%) had an AR-PDMP record in the 0- to 5-day period prior to their overdose. CONCLUSION: Though the vast majority of persons accessing CSs in Arkansas did not experience an OOD, a sizable proportion of persons that experience an OOD(s) obtained prescribed CSs immediately prior.

The PRISM semantic cohort builder: a novel tool to search and access clinical data in TCIA imaging collections.

The cancer imaging archive (TICA) receives and manages an ever-increasing quantity of clinical (non-image) data containing valuable information about subjects in imaging collections. To harmonize and integrate these data, we have first cataloged the types of information occurring across public TCIA collections. We then produced mappings for these diverse instance data using ontology-based representation patterns and transformed the data into a knowledge graph in a semantic database. This repository combined the transformed instance data with relevant background knowledge from domain ontologies. The resulting repository of semantically integrated data is a rich source of information about subjects that can be queried across imaging collections. Building on this work we have implemented and deployed a REST API and a user-facing semantic cohort builder tool. This tool allows allow researchers and other users to search and identify groups of subject-level records based on non-image data that were not queryable prior to this work. The search results produced by this interface link to images, allowing users to quickly identify and view images matching the selection criteria, as well as allowing users to export the harmonized clinical data.

Collecting data on organizational structures of trauma centers: the CAFE web service.

BACKGROUND: During the past several decades, the American College of Surgeons has led efforts to standardize trauma care through their trauma center verification process and Trauma Quality Improvement Program. Despite these endeavors, great variability remains among trauma centers functioning at the same level. Little research has been conducted on the correlation between trauma center organizational structure and patient outcomes. We are attempting to close this knowledge gap with the Comparative Assessment Framework for Environments of Trauma Care (CAFE) project. METHODS: Our first action was to establish a shared terminology that we then used to build the Ontology of Organizational Structures of Trauma centers and Trauma systems (OOSTT). OOSTT underpins the web-based CAFE questionnaire that collects detailed information on the particular organizational attributes of trauma centers and trauma systems. This tool allows users to compare their organizations to an aggregate of other organizations of the same type, while collecting their data. RESULTS: In collaboration with the American College of Surgeons Committee on Trauma, we tested the system by entering data from three trauma centers and four trauma systems. We also tested retrieval of answers to competency questions. DISCUSSION: The data we gather will be made available to public health and implementation science researchers using visualizations. In the next phase of our project, we plan to link the gathered data about trauma center attributes to clinical outcomes.

Development and Validation of a Controlled Vocabulary: An OWL Representation of Organizational Structures of Trauma Centers and Trauma Systems.

In trauma care and trauma care research there exists an implementation gap regarding a consistent controlled vocabulary to describe organizational aspects of trauma centers and trauma systems. This paper describes the development and evaluation of a controlled vocabulary for trauma care organizations. We give a detailed description of the involvement of domain experts in the domain analysis workflow and the authoring of definitions and additional term descriptions. Finally, the paper details the evaluation methodology to assess the initial version of the controlled vocabulary. The results of the evaluation show that our development process yields terms most of which find approval from domain experts not involved in the development. In addition, our evaluation tools resulted in valuable domain expert input to optimize the controlled vocabulary.

Formalizing Evidence Type Definitions for Drug-Drug Interaction Studies to Improve Evidence Base Curation.

In this research we aim to demonstrate that an ontology-based system can categorize potential drug-drug interaction (PDDI) evidence items into complex types based on a small set of simple questions. Such a method could increase the transparency and reliability of PDDI evidence evaluation, while also reducing the variations in content and seriousness ratings present in PDDI knowledge bases. We extended the DIDEO ontology with 44 formal evidence type definitions. We then manually annotated the evidence types of 30 evidence items. We tested an RDF/OWL representation of answers to a small number of simple questions about each of these 30 evidence items and showed that automatic inference can determine the detailed evidence types based on this small number of simpler questions. These results show proof-of-concept for a decision support infrastructure that frees the evidence evaluator from mastering relatively complex written evidence type definitions.

Easy Extraction of Terms and Definitions with OWL2TL.

Facilitating good communication between semantic web specialists and domain experts is necessary to efficient ontology development. This development may be hindered by the fact that domain experts tend to be unfamiliar with tools used to create and edit OWL files. This is true in particular when changes to definitions need to be reviewed as often as multiple times a day. We developed "OWL to Term List" (OWL2TL) with the goal of allowing domain experts to view the terms and definitions of an OWL file organized in a list that is updated each time the OWL file is updated. The tool is available online and currently generates a list of terms, along with additional annotation properties that are chosen by the user, in a format that allows easy copying into a spreadsheet.

OOSTT: a Resource for Analyzing the Organizational Structures of Trauma Centers and Trauma Systems.

Organizational structures of healthcare organizations has increasingly become a focus of medical research. In the CAFÉ project we aim to provide a web-service enabling ontology-driven comparison of the organizational characteristics of trauma centers and trauma systems. Trauma remains one of the biggest challenges to healthcare systems worldwide. Research has demonstrated that coordinated efforts like trauma systems and trauma centers are key components of addressing this challenge. Evaluation and comparison of these organizations is essential. However, this research challenge is frequently compounded by the lack of a shared terminology and the lack of effective information technology solutions for assessing and comparing these organizations. In this paper we present the Ontology of Organizational Structures of Trauma systems and Trauma centers (OOSTT) that provides the ontological foundation to CAFÉ's web-based questionnaire infrastructure. We present the usage of the ontology in relation to the questionnaire and provide the methods that were used to create the ontology.