A5050577357- Business Process Modeling and Analysis
- Semantic Web and Ontologies
- Electronic Health Records Systems
- Data Quality and Management
- Biomedical Text Mining and Ontologies
- Topic Modeling
- Healthcare Systems and Technology
- Machine Learning in Healthcare
- Clinical practice guidelines implementation
- Artificial Intelligence in Healthcare
- Healthcare Operations and Scheduling Optimization
- Primary Care and Health Outcomes
- Big Data and Business Intelligence
- Advanced Text Analysis Techniques
- Health Policy Implementation Science
- Cancer survivorship and care
- Flexible and Reconfigurable Manufacturing Systems
- Healthcare Policy and Management
- Natural Language Processing Techniques
- Chronic Disease Management Strategies
- Mobile Health and mHealth Applications
- Artificial Intelligence in Healthcare and Education
- Healthcare Technology and Patient Monitoring
- Ethics in Clinical Research
- Scientific Computing and Data Management
University of Leeds
2016-2025
Pondicherry Institute of Medical Sciences
2024
Institut d'Etudes Politiques de Paris
2024
Bradford Royal Infirmary
2022-2023
Indiana University School of Medicine
2023
Indiana University – Purdue University Indianapolis
2023
National Institute for Health Research
2023
Leeds Teaching Hospitals NHS Trust
2021
Queen Mary University of London
2018
Massey University
2018
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...
Background There are many proposed benefits of using learning health systems (LHSs), including improved patient outcomes. has been little adoption LHS in practice due to challenges and barriers that limit new data-driven technologies healthcare. We have identified a more fundamental explanation: the majority developments not as LHS. The absence unifying namespace framework brings lack consistency how is classified. As result, ‘community’ fragmented, with groups working on similar being...
Reliable research demands data of known quality. This can be very challenging for electronic health record (EHR) based where quality issues complex and often unknown. Emerging technologies such as process mining reveal insights into how to improve care pathways but only if technological advances are matched by strategies methods The aim this work was develop a pathway framework (CP-DQF) identify, manage mitigate EHR in the context mining, using dental EHRs an example. Objectives: To: 1)...
There is a growing body of literature on process mining in healthcare. Process electronic health record systems could give benefit into better understanding the actual processes happened patient treatment, from event log hospital information system. Researchers report issues data access approval, anonymisation constraints, and quality. One solution to progress methodology development use high-quality, freely available research dataset such as Medical Information Mart for Intensive Care III,...
Learning health systems (LHS) are one of the major computing advances in care. However, no prior research has systematically analysed barriers and facilitators for LHS. This paper presents an investigation into barriers, benefits, facilitating factors LHS order to create a basis their successful implementation adoption.First, ITPOSMO-BBF framework was developed based on established ITPOSMO (information, technology, processes, objectives, staffing, management, other factors) framework,...
Knowledge of post-myocardial infarction (MI) disease risk to date is limited-yet the number survivors MI has increased dramatically in recent decades. We investigated temporally ordered sequences all conditions following nationwide electronic health record data through application process mining.
Summary Surgical decision‐making after SARS‐CoV‐2 infection is influenced by the presence of comorbidity, severity and whether surgical problem time‐sensitive. Contemporary policy to delay surgery informed highly heterogeneous country‐specific guidance. We evaluated provision in England during COVID‐19 pandemic assess real‐world practice deferral remains necessary. Using OpenSAFELY platform, we adapted COVIDSurg protocol for a service evaluation procedures that took place within English NHS...
Low- and lower-middle-income countries account for a higher percentage of global epidemics chronic diseases. In most low- countries, there is limited access to health care. The implementation open-source electronic records (EHRs) can be understood as powerful enabler because it transform the way care technology delivered. Open-source EHRs enhance delivery in by improving collection, management, analysis data needed inform delivery, policy, planning. While EHR systems are cost-effective...
Introduction: International labor migrants are crucial to the global workforce in Gulf Cooperation Council (GCC) countries, which host over 11.7% of world's migrant workforce, posing significant healthcare challenges. This systematic review aims evaluate whether international GCC countries have effective access for work-related diseases and injuries propose evidence-based recommendations policy interventions. Methods: will include studies from 2013 2023 published peer-reviewed journals...
Understanding the psychosocial challenges of cancer survivorship, and identifying which patients experience ongoing difficulties, is a key priority. The ePOCS (electronic patient-reported outcomes from survivors) project aims to develop evaluate cost-efficient, UK-scalable electronic system for collecting outcome measures (PROMs), at regular post-diagnostic timepoints, linking these with clinical data in registries. A multidisciplinary team developed using agile methods. Design entailed...
Abstract Background Global evidence suggests a range of benefits for introducing electronic health record (EHR) systems to improve patient care. However, implementing EHR within healthcare organisations is complex and, in the United Kingdom (UK), uptake has been slow. More research needed explore factors influencing successful implementation. This study explored staff expectations change and outcome following procurement commercial system by large academic acute NHS hospital UK. Methods...
Process mining is a data analytics approach to discover and analyse process models based on the real activities captured in information systems. There growing body of literature healthcare, including oncology, study cancer. In earlier work we found 37 peer-reviewed papers describing research oncology with regular complaint being limited availability accessibility datasets suitable for mining. Publicly available are one option this paper describes potential use MIMIC-III, oncology. MIMIC-III...
Studies of Functional Neurological Disorders (FND) are usually outpatient-based. To inform service development, we aimed to describe patient pathways through healthcare events, and factors affecting risk emergency department (ED) reattendance, for people presenting acutely with FND.Acute neurology/stroke teams at a UK city hospital were contacted regularly over 8 months log FND referrals. Electronic documentation was then reviewed events the preceding years. Patient time mapped, mixed...
Oesophago-gastric cancer is difficult to diagnose in the early stages given its typical non-specific initial manifestation. We hypothesise that machine learning can improve upon diagnostic performance of current primary care risk-assessment tools by using advanced analytical techniques exploit wealth evidence available electronic health record. used a record dataset derived from UK General Practice Research Database (7471 cases; 32,877 controls) and developed five probabilistic classifiers:...