A5074297656- Business Process Modeling and Analysis
- Machine Learning in Healthcare
- Mobile Health and mHealth Applications
- Electronic Health Records Systems
- Data Quality and Management
- Telemedicine and Telehealth Implementation
- Healthcare Systems and Technology
- Semantic Web and Ontologies
- Medical Coding and Health Information
- Health Policy Implementation Science
- Health Systems, Economic Evaluations, Quality of Life
- Primary Care and Health Outcomes
- Service-Oriented Architecture and Web Services
- Biomedical Text Mining and Ontologies
- AI in cancer detection
- Radiomics and Machine Learning in Medical Imaging
- Chronic Disease Management Strategies
- Digital Mental Health Interventions
- Cancer survivorship and care
- Social Media in Health Education
- Sepsis Diagnosis and Treatment
- Topic Modeling
- Emergency and Acute Care Studies
- Interdisciplinary Research and Collaboration
- Focus Groups and Qualitative Methods
The University of Melbourne
2019-2025
The Royal Melbourne Hospital
2024-2025
Bavarian Research Institute for Digital Transformation
2020-2024
Universidad de La Frontera
2024
Pontificia Universidad Católica de Chile
2011-2020
University of Washington
2011-2017
University of Washington Medical Center
2012-2014
Seattle University
2013
Washington Hospital
2013
Institute of Translational Health Sciences
2013
Process mining techniques can be used to analyse business processes using the data logged during their execution. These are leveraged in a wide range of domains, including healthcare, where it focuses mainly on analysis diagnostic, treatment, and organisational processes. Despite huge amount generated hospitals by staff machinery involved healthcare processes, there is no evidence systematic uptake process beyond targeted case studies research context. When developing distinguishing...
In this overview, we describe theObservational Medical Outcomes Partnership Common Data Model (OMOP-CDM), the established governance processes employed in EMR data repositories, and demonstrate how OMOP transformed provides a lever for more efficient secure access to electronic medical record (EMR) by health service providers researchers.
In order to improve the efficiency and effectiveness of Emergency Rooms (ER), it is important provide answers frequently-posed questions regarding all relevant processes executed therein. Process mining provides different techniques tools that help obtain insights into analyzed answer these questions. However, ER experts require certain guidelines in carry out process effectively. This article proposes a number solutions, including classification about processes, data reference model guide...
Abstract Background Systematic reviews allow health decisions to be informed by the best available research evidence. However, their number is proliferating quickly, and many skills are required identify all relevant for a specific question. Methods findings We screen 10 bibliographic databases on daily or weekly basis, systematic decision-making. Using machine-based approach developed this project we select reviews, which then validated network of more than 1000 collaborators. After...
Background As the COVID-19 pandemic disrupted medical practice, telemedicine emerged as an alternative to outpatient visits. However, it is not known how patients and physicians responded accelerated implementation of this model care. Objective The aim study report system-wide telemedicine, compare patient satisfaction between in-person visits, provider perceptions. Methods This was conducted at UC Christus Health Network, a large private academic health network in Santiago, Chile. receiving...
Background: Effective communication of public health messages is a key strategy for promotion by agencies. Creating effective materials requires careful message design and feedback from representatives target populations. This particularly true when the audiences are hard to reach as limited English proficiency groups. Traditional methods soliciting feedback—such focus groups convenience sample interviews—are expensive time consuming. As result, adequate populations often insufficient due...
A key attribute of a learning health care system is the ability to collect and analyze routinely collected clinical data in order quickly generate new evidence, monitor quality provided. To achieve this vision, must be easy extract stored computer readable formats. We conducted study across multiple organizations assess availability such specifically for comparative effectiveness research (CER) improvement (QI) on surgical procedures.This was context needed already established Surgical Care...
The application of Value-based Healthcare requires not only the identification key processes in clinical domain but also an adequate analysis value chain delivered to patient. Data Science and Big approaches are technologies that enable creation accurate systems model reality. However, classical Mining techniques presented by professionals as black boxes. This evokes a lack trust those medical domain. Process human-understandable tools can fill this gap support Value-Based real domains. aim...
As healthcare providers receive fixed amounts of reimbursement for given services under DRG (Diagnosis-Related Groups) payment, codes are valuable cost monitoring and resource allocation. However, coding is typically performed retrospectively post-discharge. We seek to predict DRGs DRG-based case mix index (CMI) at early inpatient admission using routine clinical text estimate hospital in an acute setting. examined a deep learning-based natural language processing (NLP) model automatically...
Purpose Healthcare supply chains (HSCs) have been adopting Industry 4.0 (I4.0) as a means to boost their resilience. The first objective of this study is identify the effect contextual variables HSCs on resilience development and I4.0 adoption. Second, paper examines pervasiveness relationship between across different characteristics. Design/methodology/approach 179 organizations from HSC in Brazil India were surveyed. Responses analyzed using multivariate data techniques. Findings Large...
One unintended consequence of the Electronic Health Records (EHR) implementation is overuse content-importing technology, such as copy-and-paste, that creates "bloated" notes containing large amounts textual redundancy. Despite rising interest in applying machine learning models to learn from real-patient data, it unclear how phenomenon note bloat might affect Natural Language Processing (NLP) derived these notes. Therefore, this work we examine impact redundancy on deep learning-based NLP...
With the increasing amount and growing variety of healthcare data, multimodal machine learning supporting integrated modeling structured unstructured data is an increasingly important tool for clinical tasks. However, it non-trivial to manage differences in dimensionality, volume, temporal characteristics modalities context a shared target task. Furthermore, patients can have substantial variations availability while existing methods typically assume completeness lack mechanism handle...
Background Gonorrhoea notification rates in Australia have more than doubled between 2014 and 2019. We explored gonorrhoea testing patterns management of infection general practice. Methods analysed de-identified electronic medical record data for individuals who attended 73 Australian practices (72 the state Victoria) January 2018 December 2020. The ‘care cascade’ model was utilised to explore detection management. Descriptive analysis logistic regression were used investigate factors...
Introduction This protocol outlines a mixed methods study evaluating new Digital Coordination Centre (DCC) at the Royal Melbourne Hospital (RMH), Melbourne, Australia. While coordination centres show potential for impact, evidence on effective implementation in Australian context remains scarce. aims to address this gap. Methods and analysis The evaluation involves two-stage approach: process clarify DCC design identify factors, an initial outcome assess short medium term outcomes. A...