Socioeconomic Determinants of Remote Patient Monitoring Implementation Among Rural and Urban Hospitals.

Introduction: Remote patient monitoring (RPM) is a form of telehealth that improves quality of care for chronic disease treatment and reduces hospital readmission rates. Geographical proximity to health care is important for individuals of low socioeconomic status (SES) who face additional financial and transportation barriers. The goal of this study was to assess the association between social determinants of health and adoption of RPM. Methods: This cross-sectional study analyzed data from hospitals that responded to the American Hospital Association's Annual Survey (2018) and spatially linked census tract-level environmental and social determinants of health obtained from the Social Vulnerability Index (2018). Results: A total of 4,206 hospitals (1,681 rural and 2,525 urban hospitals) met study criteria. Rural hospitals near households in the lower middle quartile SES were associated with a 33.5% lower likelihood of having adopted RPM for chronic care management compared with rural hospitals near households in the highest quartile SES (adjusted odds ratios [aOR] = 0.665; 95% confidence interval [CI]: 0.453-0.977). Urban hospitals near households in the lowest quartile SES were associated with a 41.9% lower likelihood of having adopted RPM for chronic care management compared with urban hospitals near households in the highest quartile SES (aOR = 0.581; 95% CI: 0.435-0.775). Similar trends in accessibility were found with RPM for postdischarge services among urban hospitals. Conclusion: Our findings highlight the importance of hospital responsibility and state and federal policy approaches toward ensuring equitable access to RPM services for patients characterized by lower SES.

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.

Analysis of Caregiver Burden Expressed in Social Media Discussions.

Almost 40% of US adults provide informal caregiving, yet research gaps remain around what burdens affect informal caregivers. This study uses a novel social media site, Reddit, to mine and better understand what online communities focus on as their caregiving burdens. These forums were accessed using an application programming interface, a machine learning classifier was developed to remove low information posts, and topic modeling was applied to the corpus. An expert panel summarized the forums' themes into ten categories. The largest theme extracted from Reddit's forums discussed the personal emotional toll of being a caregiver. This was followed by logistic issues while caregiving and caring for parents who have cancer. Smaller themes included approaches to end-of-life care, physical equipment needs when caregiving, and the use of wearables or technology to help monitor care recipients. The platform often discusses caregiving for parents which may reflect the age of Reddit's users. This study confirms that Reddit forums are used for caregivers to discuss the burdens associated with their role and the types of stress that can result from informal caregiving.

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.

Characteristics and Network Influence of Providers Involved in the Treatment of Patients With Chronic Back, Neck or Joint Pain in Arkansas.

Increasing emphasis on guidelines and prescription drug monitoring programs highlight the role of healthcare providers in pain treatment. Objectives of this study were to identify characteristics of key players and influence of opioid prescribers through construction of a referral network of patients with chronic pain. A retrospective cohort study was performed and patients with commercial or Medicaid coverage with chronic back, neck, or joint pain were identified using the Arkansas All-Payer Claims-Database. A social network comprised of providers connected by patient referrals based on 12-months of healthcare utilization following chronic pain was constructed. Network measures evaluated were indegree and eigen (referrals obtained), betweenness (involvement), and closeness centrality (reach). Outcomes included influence of providers, opioid prescribers, and brokerage status. Exposures included provider demographics, specialties and network characteristics. There were 51,941 chronic pain patients who visited 8,110 healthcare providers. Primary care providers showed higher betweenness and closeness whereas specialists had higher indegree. Opioid providers showed higher centrality compared to non-opioid providers, which decreased with increasing volume of opioid prescribing. Non-pharmacologic providers showed significant brokerage scores. Findings from this study such as primary care providers having better reach, non-central positions of high-volume prescribers and non-pharmacologic providers having higher brokerage can aid interventional physician detailing. PERSPECTIVE: Opioid providers held central positions in the network aiding provider-directed interventions. However, high-volume opioid providers were at the borders making them difficult targets for interventions. Primary care providers had the highest reach, specialists received the most referrals and non-pharmacological providers and specialists acted as brokers between non-opioid and opioid prescribers.

Enhancing the drug ontology with semantically-rich representations of National Drug Codes and RxNorm unique concept identifiers.

BACKGROUND: The Drug Ontology (DrOn) is a modular, extensible ontology of drug products, their ingredients, and their biological activity created to enable comparative effectiveness and health services researchers to query National Drug Codes (NDCs) that represent products by ingredient, by molecular disposition, by therapeutic disposition, and by physiological effect (e.g., diuretic). It is based on the RxNorm drug terminology maintained by the U.S. National Library of Medicine, and on the Chemical Entities of Biological Interest ontology. Both national drug codes (NDCs) and RxNorm unique concept identifiers (RXCUIS) can undergo changes over time that can obfuscate their meaning when these identifiers occur in historic data. We present a new approach to modeling these entities within DrOn that will allow users of DrOn working with historic prescription data to more easily and correctly interpret that data. RESULTS: We have implemented a full accounting of national drug codes and RxNorm unique concept identifiers as information content entities, and of the processes involved in managing their creation and changes. This includes an OWL file that implements and defines the classes necessary to model these entities. A separate file contains an instance-level prototype in OWL that demonstrates the feasibility of this approach to representing NDCs and RXCUIs and the processes of managing them by retrieving and representing several individual NDCs, both active and inactive, and the RXCUIs to which they are connected. We also demonstrate how historic information about these identifiers in DrOn can be easily retrieved using a simple SPARQL query. CONCLUSIONS: An accurate model of how these identifiers operate in reality is a valuable addition to DrOn that enhances its usefulness as a knowledge management resource for working with historic data.

Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective.

OBJECTIVES: There exists a communication gap between the biomedical informatics community on one side and the computer science/artificial intelligence community on the other side regarding the meaning of the terms "semantic integration" and "knowledge representation". This gap leads to approaches that attempt to provide one-to-one mappings between data elements and biomedical ontologies. Our aim is to clarify the representational differences between traditional data management and semantic-web-based data management by providing use cases of clinical data and clinical research data re-representation. We discuss how and why one-to-one mappings limit the advantages of using Semantic Web Technologies (SWTs). METHODS: We employ commonly used SWTs, such as Resource Description Framework (RDF) and Ontology Web Language (OWL). We reuse pre-existing ontologies and ensure shared ontological commitment by selecting ontologies from a framework that fosters community-driven collaborative ontology development for biomedicine following the same set of principles. RESULTS: We demonstrate the results of providing SWT-compliant re-representation of data elements from two independent projects managing clinical data and clinical research data. Our results show how one-to-one mappings would hinder the exploitation of the advantages provided by using SWT. CONCLUSIONS: We conclude that SWT-compliant re-representation is an indispensable step, if using the full potential of SWT is the goal. Rather than providing one-to-one mappings, developers should provide documentation that links data elements to graph structures to specify the re-representation.

Mismatches between major subhierarchies and semantic tags in SNOMED CT.

The fully specified name of a concept in SNOMED CT is formed by a term to which in the typical case is added a semantic tag. The latter is meant to disambiguate homonymous terms and to indicate in which major subhierarchy of SNOMED CT that concept fits. We have developed a method to determine whether a concept's tag correctly identifies its place in the hierarchy, and applied this method to an analysis of all active concepts in every SNOMED CT release from January 2003 to January 2017. Our results show (1) that there are concepts in almost every release whose semantic tag does not match their placement in the hierarchy, (2) that it is primarily disorder concepts that are involved, and (3) that the number of such mismatches increase since the July 2012 version. Our analysis determined that it is primarily the absence of a mechanism in the SNOMED CT authoring environment to suggest stated relationships for very similar concepts that is responsible for the mismatches. We argue that the SNOMED CT authoring environment should treat the semantic tags as part of the formal structure so that methods can be implemented to keep the sub-hierarchies in sync with the semantic tags.

Analyzing SNOMED CT's Historical Data: Pitfalls and Possibilities.

SNOMED CT's Release Format 2 (RF2) has been announced as an improvement over its predecessor, for instance because of its more consistent and almost formal approach towards describing changes in components over different versions, as well as changes in the structure of SNOMED CT itself. We explore two sorts of changes that are only partially formalized in RF2: the relationships between associative relations and reasons for inactivations as expressed in Association Reference Sets and Attribute Value Reference Sets on the one hand, and the various patterns according to which semantic tags appearing in fully specified names change over subsequent versions with or without being related to inactivations. We propose a data conversion methodology that combines assertions about SNOMED CT components into history profiles and use elements of these profiles to build Formal Concept Analysis contexts to discover valid implications that can render implicit assumptions hidden in SNOMED CT's structure explicit.