Developing a sampling method and preliminary taxonomy for classifying COVID-19 public health guidance for healthcare organizations and the general public

Abstract

Background Development and dissemination of public health (PH) guidance to healthcare organizations and the general public (e.g., businesses, schools, individuals) during emergencies like the COVID-19 pandemic is vital for policy, clinical, and public decision-making. Yet, the rapidly evolving nature of these events poses significant challenges for guidance development and dissemination strategies predicated on well-understood concepts and clearly defined access and distribution pathways. Taxonomies are an important but underutilized tool for guidance authoring, dissemination and updating in such dynamic scenarios. Objective To design a rapid, semi-automated method for sampling and developing a PH guidance taxonomy using widely available Web crawling tools and streamlined manual content analysis. Methods Iterative samples of guidance documents were taken from four state PH agency websites, the US Center for Disease Control and Prevention, and the World Health Organization. Documents were used to derive and refine a preliminary taxonomy of COVID-19 PH guidance via content analysis. Results Eight iterations of guidance document sampling and taxonomy revisions were performed, with a final corpus of 226 documents. The preliminary taxonomy contains 110 branches distributed between three major domains: stakeholders (24 branches), settings (25 branches) and topics (61 branches). Thematic saturation measures indicated rapid saturation (≤5% change) for the domains of “stakeholders” and “settings”, and “topic”-related branches for clinical decision-making. Branches related to business reopening and economic consequences remained dynamic throughout sampling iterations. Conclusion The PH guidance taxonomy can support public health agencies by aligning guidance development with curation and indexing strategies; supporting targeted dissemination; increasing the speed of updates; and enhancing public-facing guidance repositories and information retrieval tools. Taxonomies are essential to support knowledge management activities during rapidly evolving scenarios such as disease outbreaks and natural disasters.

A Systematic Literature Review on Transparencyand Interpretability of AI models in Healthcare:Taxonomies, Tools, Techniques, Datasets, OpenResearch Challenges, and Future Trends

“…They guide the ethical design, informed consent, data privacy, and the explainability of AI decisions, which are paramount in safety-critical contexts [71]. Classifications also help establish standardized protocols for continuous monitoring and post-market surveillance, ensuring that AI systems remain safe and effective throughout their lifecycle [72], [73], [74]. As AI systems learn and evolve, these frameworks support the systematic collection and analysis of performance data, essential for maintaining AI applications' reliability in dynamic clinical environments [75].…”

Hints of Earlier and Other Creation: Unsupervised Machine Learning in Financial Time-Series Analysis

“…In the absence of p-values and other conventional indicators of statistical significance, unsupervised learning converts raw data into mathematical outputs that can, in turn, enable more fruitful applications of economic domain knowledge and expert judgment. Other applications of machine learning and artificial intelligence, particularly in natural language processing, readily accommodate a blend of formal mathematics and subjective but mathematically informed analyst judgment [73][74][75].…”

Novel informatics approaches to COVID-19 Research: From methods to applications

“…As COVID-19 is a new disease, quickly defining standard representations (e.g., codes in medical terminologies) becomes the first step of informatics research. To address this gap, four papers present work on ontologies and information models related to COVID-19: (1) A group of researchers from the COVID-19 Knowledge Accelerator (COKA) initiative proposed the development of a code system for electronic data exchange for the identification, processing, and reporting of scientific findings of COVID-19 [8] ; (2) To facilitate COVID-19 clinical research using EHRs data, Pedrera-Jimenez et al [9] designed and implemented a flexible methodology based on detailed clinical models (DCM), which can quickly generate derived research datasets from ERHs without loss of meaning; (3) To better classify public health guidelines on COVID-19, Dr. Taber and team developed a taxonomy using a novel sampling method [10] ; and (4) Zheng et al [11] introduced an innovative visualization of the weighted aggregate taxonomy for better orientation and comprehension of CIDO (Coronavirus Infectious Disease Ontology), the largest COVID ontology. The lessons described above apply to both current and future infectious disease outbreaks.…”

“… Methodological Review [7] Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance Shahid, O. Methodological Review Clinical research and practice (13) [8] Making science computable: Developing code systems for statistics, study design, and risk of bias Alper, B. S. Special Communication [9] Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models Pedrera-Jimenez, M. Original Research [10] Developing a sampling method and preliminary taxonomy for classifying COVID-19 public health guidance for healthcare organizations and the general public Taber, P. Original Research [11] Visual comprehension and orientation into the COVID-19 CIDO ontology Zheng, L. Original Research [12] Phenotyping coronavirus disease 2019 during a global health pandemic: Lessons learned from the characterization of an early cohort DeLozier, S. Special Communication [13] Extracting COVID-19 diagnoses and symptoms from clinical text: A new annotated corpus and neural event extraction framework Lybarger, K. Original Research [14] ELII: A novel inverted index for fast temporal query, with application to a large Covid-19 EHR dataset Huang, Y. Original Research [15] Creating and implementing a COVID-19 recruitment Data Mart Helmer, T. T. Special Communication [16] Critical carE Database for Advanced Research (CEDAR): An automated method to support intensive care units with electronic health record data Schenck, E. J.…”