nach oben

Journal of Imaging Informatics in Medicine

Erschienen in:

08.03.2016

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

verfasst von: Stephen Jones, Seán Cournane, Niall Sheehy, Lucy Hederman

Erschienen in: Journal of Imaging Informatics in Medicine | Ausgabe 6/2016

Einloggen, um Zugang zu erhalten

Abstract

Business analytics (BA) is increasingly being utilised by radiology departments to analyse and present data. It encompasses statistical analysis, forecasting and predictive modelling and is used as an umbrella term for decision support and business intelligence systems. The primary aim of this study was to determine whether utilising BA technologies could contribute towards improved decision support and resource management within radiology departments. A set of information technology requirements were identified with key stakeholders, and a prototype BA software tool was designed, developed and implemented. A qualitative evaluation of the tool was carried out through a series of semi-structured interviews with key stakeholders. Feedback was collated, and emergent themes were identified. The results indicated that BA software applications can provide visibility of radiology performance data across all time horizons. The study demonstrated that the tool could potentially assist with improving operational efficiencies and management of radiology resources.

Emery DJ, Forster AJ, Shojania KG, Magnan S, Tubman M, Feasby TE: Management of MRI wait lists in Canada. Health Policy 4(3):76–86, 2009

Silvester K, Lendon R, Bevan H, Steyn R, Walley P: Reducing waiting times in the NHS: is lack of capacity the problem? Clin Manag 12(3):105–111, 2004

Cosic R, Shanks G, Maynard S. Towards a business analytics capability maturity model. ACIS 2012 : Location, location, location : Proceedings of the 23rd Australasian Conference on Information Systems 2012. 1;1–11,2012

Brynjolfsson E, Hitt LM, Kim HH: Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance? [internet]. Social Science Research Network, Rochester, 2011. [cited 2013 Oct 13]. Report No.: ID 1819486. Available from: http://papers.ssrn.com/abstract=1819486

Nagy PG, Warnock MJ, Daly M, Toland C, Meenan CD, Mezrich RS: Informatics in Radiology: Automated Web-based Graphical Dashboard for Radiology Operational Business Intelligence1. Radiographics 29(7):1897–1906, 2009CrossRefPubMed

Morgan MB, Iv BFB, Lionetti DM, Richardson JS, Chang PJ: The Radiology Digital Dashboard: Effects on Report Turnaround Time. J Digit Imaging 21(1):50–58, 2008CrossRefPubMed

Honeyman-Buck J. Unlocking the Business-intelligence Vaults in Radiology. imagingBiz electronic journal for leaders in medical imaging services [Internet]. 2010 [cited 2014 Jan 3]; Available from: http://www.imagingbiz.com/articles/view/unlocking-the-business-intelligence-vaults-in-radiology

Mansoori B, Novak RD, Sivit CJ, Ros PR: Utilization of Dashboard Technology in Academic Radiology Departments: Results of a National Survey. J Am Coll Radiol 10(4):283–288, 2013. e3 CrossRefPubMed

Ondategui-Parra S, Bhagwat JG, Zou KH, Gogate A, Intriere LA, Kelly P, et al: Practice Management Performance Indicators in Academic Radiology Departments. Radiology 233(3):716–722, 2004CrossRefPubMed

10.

Jones SS, Evans RS, Allen TL, Thomas A, Haug PJ, Welch SJ, et al: A multivariate time series approach to modeling and forecasting demand in the emergency department. J Biomed Inform 42(1):123–139, 2009CrossRefPubMed

11.

Mills MD, Thornewill J, Esterhay RJ: Future trends in the supply and demand for radiation oncology physicists. J Appl Clin Med Phys 11(2):3005, 2010PubMed

12.

Ong MEH, Ho KK, Tan TP, Koh SK, Almuthar Z, Overton J, et al: Using demand analysis and system status management for predicting ED attendances and rostering. Am J Emerg Med 27(1):16–22, 2009CrossRefPubMed

13.

Prevedello LM, Andriole KP, Hanson R, Kelly P, Khorasani R: Business Intelligence Tools for Radiology: Creating a Prototype Model Using Open-Source Tools. J Digit Imaging 23(2):133–141, 2010CrossRefPubMed

14.

Jones S: A Business Analytics Software Tool for Monitoring and Predicting Radiology Throughput Performance (Master’s Thesis). Trinity College Dublin, Dublin, 2014

15.

Qlikview [Internet]. [cited 2015 Jan 19]. Available from: http://www.qlik.com/

16.

Fotiadou A: Choosing and visualizing waiting time indicators in diagnostic medical imaging department for different purposes and audiences (Unpublished Master’s Thesis). Karolinska Institutet, Stockholm, 2013

17.

Breil B, Fritz F, Thiemann V, Dugas M: Mapping Turnaround Times (TAT) to a Generic Timeline: A Systematic Review of TAT Definitions in Clinical Domains. BMC Med Inform Decis Mak 11(1):34, 2011CrossRefPubMedPubMedCentral

18.

Ward MJ, Marsolo KA, Froehle CM: Applications of Business Analytics in Healthcare. Bus Horiz 57(5):571–582, 2014CrossRefPubMedPubMedCentral

19.

Health Information and Quality Authority: Report of the investigation into the quality, safety and governance of the care provided by the Adelaide and Meath Hospital, Dublin incorporating the National Children’s Hospital (AMNCH) for patients who require acute admission - Recommendations [Internet]. HIQA, Dublin, 2012. [cited 2014 Jan 1]. Available from: http://www.hiqa.ie/publications/report-investigation-quality-safety-and-governance-care-provided-adelaide-and-meath-hos

20.

Cook TS, Nagy P: Business Intelligence for the Radiologist: Making Your Data Work for You. J Am Coll Radiol 11(12, Part B):1238–1240, 2014CrossRefPubMed

21.

Golfarelli M, Rizzi S: What-if Simulation Modeling in Business Intelligence. Int J Data Warehouse Min 5(4):24–43, 2009CrossRef

22.

Korbage AC, Bedi HS: Mobile Technology in Radiology Resident Education. J Am Coll Radiol 9(6):426–429, 2012CrossRefPubMed

23.

Morris H: The Financial Impact of Business Analytics: Build vs Buy. DM Rev 13:40–41, 2003

Titel: A Business Analytics Software Tool for Monitoring and Predicting Radiology Throughput Performance
verfasst von: Stephen Jones
Seán Cournane
Niall Sheehy
Lucy Hederman
Publikationsdatum: 08.03.2016
Verlag: Springer International Publishing
Erschienen in: Journal of Imaging Informatics in Medicine / Ausgabe 6/2016
Print ISSN: 2948-2925
Elektronische ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-016-9871-3

Neu im Fachgebiet Radiologie

18.04.2024 | Pädiatrische Notfallmedizin | Nachrichten

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Springer Medizin

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

Abstract

Neu im Fachgebiet Radiologie

Fünf Dinge, die im Kindernotfall besser zu unterlassen sind

„Nur wer sich gut aufgehoben fühlt, kann auch für Patientensicherheit sorgen“

Wie toxische Männlichkeit der Gesundheit von Männern schadet

Studie bestätigt Potenzial von KI in der Radiologie

Update Radiologie

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 6/2016

Automatic Classification of Ultrasound Screening Examinations of the Abdominal Aorta

Vascular Access Tracking System: a Web-Based Clinical Tracking Tool for Identifying Catheter Related Blood Stream Infections in Interventional Radiology Placed Central Venous Catheters

Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research

Integration of Computed Tomography and Three-Dimensional Echocardiography for Hybrid Three-Dimensional Printing in Congenital Heart Disease

A IHE-Like Approach Method for Quantitative Analysis of PACS Usage

Factors Affecting Radiologist’s PACS Usage

Neu im Fachgebiet Radiologie

Fünf Dinge, die im Kindernotfall besser zu unterlassen sind

„Nur wer sich gut aufgehoben fühlt, kann auch für Patientensicherheit sorgen“

Wie toxische Männlichkeit der Gesundheit von Männern schadet

Studie bestätigt Potenzial von KI in der Radiologie

Update Radiologie