A Business Analytics Software Tool for Monitoring and Predicting Radiology Throughput Performance

Jones, Stephen T.; Cournane, Seán; Sheehy, Niall; Hederman, Lucy

doi:10.1007/s10278-016-9871-3

Cited by 14 publications

(15 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Decision-making tools are being increasingly used with promising results in the management of imaging departments 1 . However, these tools have not been used in selecting medical technologies.…”

Section: General Objective and Literature Reviewmentioning

confidence: 99%

Application of Data Envelopment Analysis (DEA) in choosing the proper Magnetic Resonance Imaging (MRI) machine

Dehghan

Rajaei

Zandi

et al. 2021

Medico Research Chronicles

View full text Add to dashboard Cite

This study is aimed to apply one of the decision-making tools, Data Envelopment Analysis (DEA) in the field of imaging in health care for choosing the most efficient model of Siemens MRI machines for clinical purposes. A list of Siemens MRI machines with their corresponding details such as price and technical characteristics were collected as mentioned in the machine booklets and through consultation with Siemens representative in the country. Variables were defined and categorized as input and output and the linear mathematical model for each machine was written and calculated using the super-efficiency model to find the most efficient Siemens MRI machine and rank the available models using DEA. The results showed that the most efficient model of Siemens MRI is Prisma (Super-efficiency score = 2.009302) followed by Skyra (Super-efficiency score = 1.697531) and Sola (Superefficiency score = 1.683571). Data Envelopment Analysis (DEA) is recommended as the decisionmaking tool for selecting advanced technologies in healthcare since it can handle a substantial number of variables as input and output and unlike other decision-making tools such as Analytic Hierarchy Process (AHP) which is widely used in this industry, the weight of each variable

show abstract

Section: General Objective and Literature Reviewmentioning

confidence: 99%

Application of Data Envelopment Analysis (DEA) in choosing the proper Magnetic Resonance Imaging (MRI) machine

Dehghan

Rajaei

Zandi

et al. 2021

Medico Research Chronicles

View full text Add to dashboard Cite

show abstract

“…Another example is the business analytics software for monitoring and predicting radiology throughput performance (Jones, Cournane, Sheehy, & Hederman, 2016).…”

Section: Radiology Workflow Management Dashboardmentioning

confidence: 99%

RadStream: An interactive visual display of radiology workflow for delay detection in the clinical imaging process

Abukhodair

Khashoggi

O’Connell

et al. 2017

2017 IEEE Workshop on Visual Analytics in Healthcare (VAHC)

View full text Add to dashboard Cite

Given the high caseload most radiology departments face on a daily basis, workflow optimization becomes a necessity to avoid delays and poor health outcomes. This requires detailed analysis of workflow data to identify problem areas in the process. Analysis of the clinical imaging process demands an understanding of temporal intervals and temporal event sequences and relationships. Working with radiology staff, we seek to provide a tool to help monitor and improve radiology department workflow in order to increase efficiency and productivity and ensure the delivery of timely clinical imaging reports. In this thesis, I present RadStream: a web-based retrospective, exploratory, interactive data visualization tool that provides a comprehensive overview of the radiology department's daily activities. I worked closely with radiology staff to analyze the department workflow and classify the analytical tasks required by domain experts in order to inform the design of the tool. Together, we abstracted the steps involved in the clinical imaging process. We also identified factors affecting the workflow (such as personnel, machine availability, and resources) and noted the relationship between the different factors as it plays an important role in increasing productivity. RadStream depicts the steps involved in the process of clinical imaging and shows the flow of processes from one step to the next. The visual representation emphasizes the time intervals between the different steps and uses colour coding to denote the status of a process (on time, acceptably late, late) in compliance with standard radiology turnaround times (TATs). The main focus of RadStream is on monitoring performance with special attention to duration, delays, and compliance with standard TATs. RadStream was evaluated by radiology staff using hospital data and real scenarios to evaluate its effectiveness, efficiency, and usability. The initial feedback received was very promising. And based on results collected from the evaluation studies, I sensed a general acceptance and excitement about the system as a quality assurance tool. I have also collected some constructive feedback to build upon for future releases. Finally, I reflect on lessons l learned from iteratively designing RadStream, and present design guidelines for the design of visual analytics tools for health care.

show abstract

Section: Introductionmentioning

confidence: 99%

“…The authors presented practical applications of analysis and of reports provided to all departments of the organization in the last 5 years, including nances, IT, human resources, billing, coding, clinical operations, and juridical operations. Furthermore, they state that reliable data now inform the aspects of organizational decision making in daily operations, growth, strategic planning, and e ciency [8][9]. The objective of this research is to apply and document a BI process that provides support to the decision making of managers, so the use of ICA can be better managed, allowing for the identi cation of situations in which this material was wasted in CT exams in the General Hospital of Porto Alegre.…”

Section: Introductionmentioning

confidence: 99%

Business Intelligence Applied to The Consumption of Iodinated Contrast Agents in Computed Tomography Scans

Andrade

Blomberg

2021

Preprint

View full text Add to dashboard Cite

Background: The management of the use of iodinated contrast agents (ICA) in the computed tomography (CT) has clinical and financial impacts; however, the approaches in the current research setting have limitations with regard to their exploration of the theme. This work describes the application of the stages of a process of business intelligence (BI), from the formulation of business questions, the building of a research database, and the adaptation of a multidimensional model, to the creation of dashboards to give support to the decision-making process in a hospital. This research aims to apply and document a BI process that provides support to the decision making of managers, so the use of ICA can be better managed, allowing for the identification of situations in which the material was wasted using a study applied to the hospital field.Methods: An applied exploratory research with a quantitative approach in a database made up by 24 variables and 35,388 records extracted from the RIS (Radiology Information System) that is used by the General Hospital of Porto Alegre - HCPA. The software used, supplied by AGFA Healthcare, were the Qdoc system (version 6.2.0) and the Impax BI (Version 11.1.1) for, respectively, data entry and data exploration. At the end of the process, a total of 48 variables was considered.Results: The BI process applied allowed for the identification of situations in which ICA was being wasted during the operationalization of the volume/mass ratio of the agent injected in the patient. It also offered the necessary substantiation for the managers to formulate plans, actions, and controls associated to the use of the material. This work made it possible to diminish in 15.65% the total consumption of ICA injected in the patients who underwent the CTAB1 exam (full CT scan of the abdomen), with a projected economy of US$ 10,039.95, for the performance of this exam from 2020 on. The measuring of the impact and the relevance of the process was 99.6% positive, according to the evaluation of the managers. Conclusions: This research generated clinical and financial benefits for the HCPA, a positive evaluation by the managers and the generation of new knowledge, which can be shared with other public or private health organizations.

show abstract

A Business Analytics Software Tool for Monitoring and Predicting Radiology Throughput Performance

Cited by 14 publications

References 17 publications

Application of Data Envelopment Analysis (DEA) in choosing the proper Magnetic Resonance Imaging (MRI) machine

Application of Data Envelopment Analysis (DEA) in choosing the proper Magnetic Resonance Imaging (MRI) machine

RadStream: An interactive visual display of radiology workflow for delay detection in the clinical imaging process

Business Intelligence Applied to The Consumption of Iodinated Contrast Agents in Computed Tomography Scans

Contact Info

Product

Resources

About