scholarly journals Getting There: Evidence-Based Decision-Making in Road Trauma Prehospital Transport and Care in Queensland

2019 ◽  
Vol 34 (s1) ◽  
pp. s64-s65
Author(s):  
Robert Andrews ◽  
Moe Wynn ◽  
Arthur ter Hofstede ◽  
Kirsten Vallmuur ◽  
Emma Bosley ◽  
...  
Keyword(s):  
Emergency Department ◽  
Data Quality ◽  
Data Science ◽  
Hospital Admissions ◽  
Process Mining ◽  
Transport Processes ◽  
Process Models ◽  
Actual Behavior ◽  
Patient Pathways ◽  
Quality Issues

Introduction:Process mining, a branch of data science, aims at deriving an understanding of process behaviors from data collected during executions of the process. In this study, we apply process mining techniques to examine retrieval and transport of road trauma patients in Queensland. Specifically, we use multiple datasets collected from ground and air ambulance, emergency department, and hospital admissions to investigate the various patient pathways and transport modalities from accident to definitive care.Aim:The project aims to answer the question, “Are we providing the right level of care to patients?” We focus on (i) automatically discovering, from historical records, the different care and transport processes, and (ii) identifying and quantifying factors influencing deviance from standard processes, e.g. mechanisms of injury and geospatial (crash and trauma facility) considerations.Methods:We adapted the Cross-Industry Standard Process for Data Mining methodology to Queensland Ambulance Service, Retrieval Services Queensland (aero-medical), and Queensland Health (emergency department and hospital admissions) data. Data linkage and “case” definition emerged as particular challenges. We developed detailed data models, conduct a data quality assessment, and preliminary process mining analyses.Results:Preliminary results only with full results are presented at the conference. A collection of process models, which revealed multiple transport pathways, were automatically discovered from pilot data. Conformance checking showed some variations from expected processing. Systematic analysis of data quality allowed us to distinguish between systemic and occasional quality issues, and anticipate and explain certain observable features in process mining analyses. Results will be validated with domain experts to ensure insights are accurate and actionable.Discussion:Preliminary analysis unearthed challenging data quality issues that impact the use of historical retrieval data for secondary analysis. The automatically discovered process models will facilitate comparison of actual behavior with existing guidelines.

scholarly journals Leveraging Data Quality to Better Prepare for Process Mining: An Approach Illustrated Through Analysing Road Trauma Pre-Hospital Retrieval and Transport Processes in Queensland

2019 ◽  
Vol 16 (7) ◽  
pp. 1138 ◽  
Author(s):  
Robert Andrews ◽  
Moe Wynn ◽  
Kirsten Vallmuur ◽  
Arthur ter Hofstede ◽  
Emma Bosley ◽  
...  
Keyword(s):  
Data Quality ◽  
Process Mining ◽  
Data Extraction ◽  
Case Study Research ◽  
Poor Quality ◽  
Transport Processes ◽  
Quality Data ◽  
Trauma Patients ◽  
Quality Issues

While noting the importance of data quality, existing process mining methodologies (i) do not provide details on how to assess the quality of event data (ii) do not consider how the identification of data quality issues can be exploited in the planning, data extraction and log building phases of any process mining analysis, (iii) do not highlight potential impacts of poor quality data on different types of process analyses. As our key contribution, we develop a process-centric, data quality-driven approach to preparing for a process mining analysis which can be applied to any existing process mining methodology. Our approach, adapted from elements of the well known CRISP-DM data mining methodology, includes conceptual data modeling, quality assessment at both attribute and event level, and trial discovery and conformance to develop understanding of system processes and data properties to inform data extraction. We illustrate our approach in a case study involving the Queensland Ambulance Service (QAS) and Retrieval Services Queensland (RSQ). We describe the detailed preparation for a process mining analysis of retrieval and transport processes (ground and aero-medical) for road-trauma patients in Queensland. Sample datasets obtained from QAS and RSQ are utilised to show how quality metrics, data models and exploratory process mining analyses can be used to (i) identify data quality issues, (ii) anticipate and explain certain observable features in process mining analyses, (iii) distinguish between systemic and occasional quality issues, and (iv) reason about the mechanisms by which identified quality issues may have arisen in the event log. We contend that this knowledge can be used to guide the data extraction and pre-processing stages of a process mining case study to properly align the data with the case study research questions.

Application of process mining to assess the data quality of routinely collected time-based performance data sourced from electronic health records by validating process conformance

2016 ◽  
Vol 22 (4) ◽  
pp. 1017-1029 ◽  
Author(s):  
Lua Perimal-Lewis ◽  
David Teubner ◽  
Paul Hakendorf ◽  
Chris Horwood
Keyword(s):  
Electronic Health Records ◽  
Data Quality ◽  
Performance Metrics ◽  
Process Mining ◽  
Hospital Performance ◽  
Process Models ◽  
Patient Journey ◽  
Health Records ◽  
Electronic Health

Effective and accurate use of routinely collected health data to produce Key Performance Indicator reporting is dependent on the underlying data quality. In this research, Process Mining methodology and tools were leveraged to assess the data quality of time-based Emergency Department data sourced from electronic health records. This research was done working closely with the domain experts to validate the process models. The hospital patient journey model was used to assess flow abnormalities which resulted from incorrect timestamp data used in time-based performance metrics. The research demonstrated process mining as a feasible methodology to assess data quality of time-based hospital performance metrics. The insight gained from this research enabled appropriate corrective actions to be put in place to address the data quality issues.

scholarly journals The assessment of data quality issues for process mining in healthcare using Medical Information Mart for Intensive Care III, a freely available e-health record database

2018 ◽  
Vol 25 (4) ◽  
pp. 1878-1893 ◽  
Author(s):  
Angelina Prima Kurniati ◽  
Eric Rojas ◽  
David Hogg ◽  
Geoff Hall ◽  
Owen A Johnson
Keyword(s):  
Intensive Care ◽  
Electronic Health Record ◽  
Data Quality ◽  
Medical Information ◽  
Process Mining ◽  
Data Access ◽  
Health Record ◽  
Healthcare Process ◽  
Quality Issues ◽  
Electronic Health

There is a growing body of literature on process mining in healthcare. Process mining of electronic health record systems could give benefit into better understanding of the actual processes happened in the patient treatment, from the event log of the hospital information system. Researchers report issues of data access approval, anonymisation constraints, and data quality. One solution to progress methodology development is to use a high-quality, freely available research dataset such as Medical Information Mart for Intensive Care III, a critical care database which contains the records of 46,520 intensive care unit patients over 12 years. Our article aims to (1) explore data quality issues for healthcare process mining using Medical Information Mart for Intensive Care III, (2) provide a structured assessment of Medical Information Mart for Intensive Care III data quality and challenge for process mining, and (3) provide a worked example of cancer treatment as a case study of process mining using Medical Information Mart for Intensive Care III to illustrate an approach and solution to data quality challenges. The electronic health record software was upgraded partway through the period over which data was collected and we use this event to explore the link between electronic health record system design and resulting process models.

scholarly journals STUDY OF DESCRIPTIVE PROCESS MODEL OF SHIP HANDLING AT AN OIL TERMINAL BASED ON TIMESHEETS

2017 ◽  
pp. 7-15 ◽  
Author(s):  
Julia Jurjevna Rudnitckaia
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Behavioral Model ◽  
Transport Processes ◽  
Process Models ◽  
Actual Process ◽  
Existing Problems ◽  
Whole Process ◽  
Mathematical Algorithms ◽  
Transport Logistics

Modern information systems and technologies make it possible to store a large amount of data on transport-logistics processes. Using various mathematical algorithms and meth-ods of the data mining and machine learning, knowledge can be obtained and expressed as a behavioral model of the system. The process mining method is one of the relatively new methods and it is already widely used in different areas. Due to this method, one can simulate the whole process as it is and lately work with a valid re-flection of the actual operations. Theoretically, transport-logistics processes are an example of reg-ulated and structured processes. However, in practice, normative and descriptive models often have a lot of differences and such deviations cause bottlenecks in the workflow, and thus lead to temporal and financial losses. With the help of Process Mining algorithms, the actual process of ship entry processing at the oil terminal is represented as an undirected graph. The existing problems of port systems are marked, as well as the need to work with the actual process models. The possible options to optimize processes and, consequently, improve the competitiveness of the port were considered. The schemes of transport processes, in which sea port is involved, were presented. The place of the analysed processes in the overall process scheme is defined. The methods and documents involved in the process of creating the model were identified. Two received models of vessel’s treatment were reflected and the main results of the analysis were described, problems and possible solutions were listed.

Risk factors of hospital admissions and recurrent emergency department visits among pediatric asthma patients

2020 ◽  
pp. 1011-1015
Author(s):  
Abdullah Aldamigh ◽  
Afaf Alnefisah ◽  
Abdulrahman Almutairi ◽  
Fatima Alturki ◽  
Suhailah Alhtlany ◽  
...  
Keyword(s):  
Risk Factors ◽  
Emergency Department ◽  
Hospital Admissions ◽  
Pediatric Asthma ◽  
Emergency Department Visits ◽  
Asthma Patients

scholarly journals Cohort profile: CROSS-TRACKS: a population-based open cohort across healthcare sectors in Denmark

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e039996
Author(s):  
Anders Hammerich Riis ◽  
Pia Kjær Kristensen ◽  
Matilde Grøndahl Petersen ◽  
Ninna Hinchely Ebdrup ◽  
Simon Meyer Lauritsen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Hospital Admissions ◽  
Smoking Status ◽  
Population Based ◽  
Fertility Treatment ◽  
Study Patient ◽  
Transfer Payment ◽  
Sociodemographic Characteristics ◽  
Patient Pathways

PurposeThis paper describes the open cohort CROSS-TRACKS, which comprises population-based data from primary care, secondary care and national registries to study patient pathways and transitions across sectors while adjusting for sociodemographic characteristics.ParticipantsA total of 221 283 individuals resided in the four Danish municipalities that constituted the catchment area of Horsens Regional Hospital in 2012–2018. A total of 96% of the population used primary care, 35% received at least one transfer payment and 66% was in contact with a hospital at least once in the period. Additional clinical information is available for hospital contacts (eg, alcohol intake, smoking status, body mass index and blood pressure). A total of 23% (n=8191) of individuals aged ≥65 years had at least one potentially preventable hospital admission, and 73% (n=5941) of these individuals had more than one.Findings to dateThe cohort is currently used for research projects in epidemiology and artificial intelligence. These projects comprise a prediction model for potentially preventable hospital admissions, a clinical decision support system based on artificial intelligence, prevention of medication errors in the transition between sectors, health behaviour and sociodemographic characteristics of men and women prior to fertility treatment, and a recently published study applying machine learning methods for early detection of sepsis.Future plansThe CROSS-TRACKS cohort will be expanded to comprise the entire Central Denmark Region consisting of 1.3 million residents. The cohort can provide new knowledge on how to best organise interventions across healthcare sectors and prevent potentially preventable hospital admissions. Such knowledge would benefit both the individual citizen and society as a whole.

scholarly journals First Surveillance of Violence against Women during COVID-19 Lockdown: Experience from “Niguarda” Hospital in Milan, Italy

2021 ◽  
Vol 18 (7) ◽  
pp. 3801
Author(s):  
Giulio Nittari ◽  
Getu Gamo Sagaro ◽  
Alessandro Feola ◽  
Mattia Scipioni ◽  
Giovanna Ricci ◽  
...  
Keyword(s):  
Emergency Department ◽  
Violence Against Women ◽  
Social Problem ◽  
Medical Records ◽  
Hospital Admissions ◽  
Daily News ◽  
The People ◽  
Notable Increase

Violence against women emerges with tragic regularity in the daily news. It is now an evident trace of a dramatic social problem, the characteristics of which are not attributable to certain economic, cultural, or religious conditions of the people involved but affect indiscriminately, in a unanimous way, our society. The study is a survey about the number of hospital admissions due to episodes attributable to violence against women, recorded by the Niguarda Hospital in Milan in the period 1 March–30 May from 2017 to 2020. This period, in 2020, corresponds to the coronavirus Lockdown in Italy. All the medical records of the Emergency department were reviewed, and the extracted data classified in order to identify the episodes of violence against women and the features of the reported injuries and the characteristics of the victims. The data did not show an increase in the number of cases in 2020 compared to previous years, but we did find a notable increase in the severity of injuries.

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