Closed loop medication administration using mobile nursing information system

Through this long journey of PhD study including a research on ‘Closed Loop Medication Administration Using Mobile Nursing Information System’ and the thesis writing, I obtained a lot of knowledge and experience about research method and writing. I really very appreciate the help of all my supervisors

Electronic Health Record Summarization over Heterogeneous and Irregularly Sampled Clinical Data

The increasing adoption of electronic health records (EHRs) has led to an unprecedented amount of patient health information stored in an electronic format. The ability to comb through this information is imperative, both for patient care and computational modeling. Creating a system to minimize unnecessary EHR data, automatically distill longitudinal patient information, and highlight salient parts of a patient’s record is currently an unmet need. However, summarization of EHR data is not a trivial task, as there exist many challenges with reasoning over this data. EHR data elements are most often obtained at irregular intervals as patients are more likely to receive medical care when they are ill, than when they are healthy. The presence of narrative documentation adds another layer of complexity as the notes are riddled with over-sampled text, often caused by the frequent copy-and-pasting during the documentation process. This dissertation synthesizes a set of challenges for automated EHR summarization identified in the literature and presents an array of methods for dealing with some of these challenges. We used hybrid data-driven and knowledge-based approaches to examine abundant redundancy in clinical narrative text, a data-driven approach to identify and mitigate biases in laboratory testing patterns with implications for using clinical data for research, and a probabilistic modeling approach to automatically summarize patient records and learn computational models of disease with heterogeneous data types. The dissertation also demonstrates two applications of the developed methods to important clinical questions: the questions of laboratory test overutilization and cohort selection from EHR data

Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities

Recent advancements in AI applications to healthcare have shown incredible promise in surpassing human performance in diagnosis and disease prognosis. With the increasing complexity of AI models, however, concerns regarding their opacity, potential biases, and the need for interpretability. To ensure trust and reliability in AI systems, especially in clinical risk prediction models, explainability becomes crucial. Explainability is usually referred to as an AI system's ability to provide a robust interpretation of its decision-making logic or the decisions themselves to human stakeholders. In clinical risk prediction, other aspects of explainability like fairness, bias, trust, and transparency also represent important concepts beyond just interpretability. In this review, we address the relationship between these concepts as they are often used together or interchangeably. This review also discusses recent progress in developing explainable models for clinical risk prediction, highlighting the importance of quantitative and clinical evaluation and validation across multiple common modalities in clinical practice. It emphasizes the need for external validation and the combination of diverse interpretability methods to enhance trust and fairness. Adopting rigorous testing, such as using synthetic datasets with known generative factors, can further improve the reliability of explainability methods. Open access and code-sharing resources are essential for transparency and reproducibility, enabling the growth and trustworthiness of explainable research. While challenges exist, an end-to-end approach to explainability in clinical risk prediction, incorporating stakeholders from clinicians to developers, is essential for success

COVID-19 and Environment: Impacts of a Global Pandemic

This is a reprint of the MDPI IJERPH Special Issue entitled "COVID-19 and Environment: Impacts of a Global Pandemic". The reprint consists of 17 papers with different topics related to the COVID-19 pandemic and environmental impacts using data from different countries all over the globe

A Human Factors Approach for Identifying Latent Failures in Healthcare Settings

INTRODUCTION: The purpose of the current research was to assess the utility of the Human Factors Analysis and Classification System (HFACS), a tool that has historically been used reactively to look at accidents and incidents, for classifying observational data from various healthcare venues. METHOD: Three studies are presented to investigate the reliability of HFACS for classifying observational data. In Study I, HFACS was applied to observational human factors data collected from the cardiovascular operating room (CVOR) at an academic medical university. Three trained analysts categorized the data using HFACS and several approaches were used to evaluate its reliability during the categorization task. The same method was repeated for Study II, which utilized CVOR data collected from a non-academic hospital. To investigate the ability of HFACS for differentiating between hospitals, the data from the academic and non-academic hospitals were compared. Finally, to explore the utility of HFACS in another venue, Study III employed the same approach as Study I and II however, observational data from a trauma center was utilized. RESULTS: Results of the three studies revealed that the framework was substantially reliable (k=0.635 (95% CI, .611-.659), p = 0.000; k =0.642 (95% CI, .633-.652), p = 0.000; k=0.680 (95% CI, .662 to .698), p = 0.000) for classifying observational healthcare data. In all three data sets, preconditions for unsafe acts were the most common area of systemic weakness. However, differences in the distributions of these categories did exist when data-sets were compared. CONCLUSION: This study is a first step in establishing the reliability of the HFACS framework as a tool for classifying observational human factors data. As HFACS appears to be a reliable observation tool, findings associated with its use could help to identify where errors and adverse events are likely to occur. Therefore, the proactive identification of human factors issues associated with patient harm represents the next step in the evolution of patient safety. Predictably, hospital administrators could put in place targeted interventions to help mitigate human factors issues before they manifest and become harmful events in the future

Human experience in the natural and built environment : implications for research policy and practice

22nd IAPS conference. Edited book of abstracts. 427 pp. University of Strathclyde, Sheffield and West of Scotland Publication. ISBN: 978-0-94-764988-3

SPATIAL TRANSFORMATION PATTERN DUE TO COMMERCIAL ACTIVITY IN KAMPONG HOUSE

ABSTRACT Kampung houses are houses in kampung area of the city. Kampung House oftenly transformed into others use as urban dynamics. One of the transfomation is related to the commercial activities addition by the house owner. It make house with full private space become into mixused house with more public spaces or completely changed into full public commercial building. This study investigate the spatial transformation pattern of the kampung houses due to their commercial activities addition. Site observations, interviews and questionnaires were performed to study the spatial transformation. This study found that in kampung houses, the spatial transformation pattern was depend on type of commercial activities and owner perceptions, and there are several steps of the spatial transformation related the commercial activity addition. Keywords: spatial transformation pattern; commercial activity; owner perception, kampung house; adaptabilit

Naval Aviation Squadron Risk Analysis Predictive Bayesian Network Modeling Using Maintenance Climate Assessment Survey Results

Associated risks in flying have resulted in injury or death to aircrew and passengers, and damage or destruction of the aircraft and its surroundings. Although the Naval Aviation\u27s flight mishap rate declined over the past 60 years, the proportion of human error causal factors has stayed relatively constant at about 80%. Efforts to reduce human errors have focused attention on understanding the aircrew and maintenance actions occurring in complex systems. One such tool has been the Naval Aviation squadrons\u27 regular participation in survey questionnaires deigned to measure respondent ratings related to personal judgments or perceptions of organizational climate for meeting the extent to which a particular squadron achieved the High Reliability Organization (HRO) criteria of achieving safe and reliable operations and maintenance practices while working in hazardous environments. Specifically, the Maintenance Climate Assessment Survey (MCAS) is completed by squadron maintainers to enable leadership to assess their unit\u27s aggregated responses against those from other squadrons. Bayesian Network Modeling and Simulation provides a potential methodology to represent the relationships of MCAS results and mishap occurrences that can be used to derive and calculate probabilities of incurring a future mishap. Model development and simulation analysis was conducted to research a causal relationship through quantitative analysis of conditional probabilities based upon observed evidence of previously occurred mishaps. This application would enable Navy and Marine Corps aviation squadron leadership to identify organizational safety risks, apply focused proactive measures to mitigate related hazards characterized by the MCAS results, and reduce organizational susceptibility to future aircraft mishaps

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance: Building the Future of Public Health Surveillance

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub1117