Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases (ICD) as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the ICD, which is currently under active development by the WHO contains nearly 50,000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding how these stakeholders collaborate will enable us to improve editing environments that support such collaborations. We uncover how large ontology-engineering projects, such as the ICD in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users subsequently change) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.Comment: Published in the Journal of Biomedical Informatic

The INCF Digital Atlasing Program: Report on Digital Atlasing Standards in the Rodent Brain

The goal of the INCF Digital Atlasing Program is to provide the vision and direction necessary to make the rapidly growing collection of multidimensional data of the rodent brain (images, gene expression, etc.) widely accessible and usable to the international research community. This Digital Brain Atlasing Standards Task Force was formed in May 2008 to investigate the state of rodent brain digital atlasing, and formulate standards, guidelines, and policy recommendations.

Our first objective has been the preparation of a detailed document that includes the vision and specific description of an infrastructure, systems and methods capable of serving the scientific goals of the community, as well as practical issues for achieving
the goals. This report builds on the 1st INCF Workshop on Mouse and Rat Brain Digital Atlasing Systems (Boline et al., 2007, _Nature Preceedings_, doi:10.1038/npre.2007.1046.1) and includes a more detailed analysis of both the current state and desired state of digital atlasing along with specific recommendations for achieving these goals

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches

Engineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements

Improving Clinical Communication and Collaboration Through Technology

Problem: Over the last 30 years, clinical communication methodologies in healthcare have evolved to become such disparate systems that they lead to confusion, wasted time, and clinician dissatisfaction. The Joint Commission (2016) reports up to 78% of sentinel events in hospitals are linked to communication failures, which have obvious implications for hospital systems in the quality and safety of their current communication systems. Context: The purpose of this project was to determine the effectiveness of implementing a unified clinical communication technology platform in an acute care hospital setting and to make recommendations from that implementation to the organization’s larger health system. Its purpose was also to determine if the creation of a clinical communication technology implementation guide for nurse leaders would positively impact future implementations of such platforms throughout the larger health system. Interventions: This project introduced smartphone communication technologies to inpatient nurses and other clinicians in a 352-bed hospital in California, which is part of a larger 39-hospital, multi-state system. Analysis was then performed by collecting data before and after implementation of the clinical communication platform. While not part of the original plan, elements of the platform were subsequently deployed to help with clinical communication during the height of the SARs CoV (COVID-19) pandemic, and this implementation was also analyzed for the project. The intention was also to determine if the creation of a clinically focused implementation guide for clinical leaders could positively impact the application of such a communication platform throughout the larger health system. Measures: Measures in this study included productivity, efficiency, quality of care, communication, and staff satisfaction with the newly implanted technology. Measurement regarding the usefulness of the implementation guide was gauged through the perceived satisfaction of nurse leaders who reviewed the guide and gave feedback. Results: Mixed results were realized from the implementation of this technology, but the work yielded valuable information for future implementations within the organization. Frontline staff and physician satisfaction with the whole platform was low, but leadership satisfaction with the elements implemented for COVID-19 was high. For the implementation guide, nurse leaders gave valuable feedback and determined it would be a highly useful document for facility implementation leads in the future. Conclusion: The implementation of new clinical communication technology and methodologies has the opportunity to improve productivity, efficiency, quality of care, communication, and staff satisfaction, but only if barriers to implementation are mitigated before, during, and immediately after go-live. A comprehensive implementation guide for nurse leaders can be the tool designed specifically to mitigate these barriers and prepare nurse leaders and facilities for the new technology and associated workflow changes that accompany the technology

ACR Accreditation for Utah Valley Hospital’s Radiation Oncology Center

Becoming an accredited clinic through the American College of Radiology (ACR) and their Radiation Oncology Practice Accreditation (ROPA) program will provide third-party evaluation of patient care to ensure the best treatment possible for patients. Talk of getting ACR accreditation has occurred in the past for Utah Valley Hospital/American Fork Hospital, but at the time it was seen as something that did not provide sufficient value vs. the cost. The recent One Intermountain restructuring is intended to unify all of the Intermountain Healthcare radiation oncology centers in Utah so the Radiation Oncology Director has set the goal that all Intermountain radiation oncology programs will be accredited. Intermountain Medical Center (IMC) and Dixie Regional Medical Center (DRMC) are currently ACR accredited and can be used as model programs. I started with an in-depth examination of our department’s workflow, documentation, and policies in order to determine where improvements to meet ACR accreditation standards could be made. I followed this up by working on implementing some of these improvements throughout the clinic and made sure they become routine and a standard in the department. An analysis of Dixie Regional Medical Center and Intermountain Medical Center’s ACR documents was performed to provide a baseline of an accredited-ACR program. Finally, a comprehensive checklist of everything that will need to be changed or implemented was presented in order to provide guidance for the future