Literature DB >> 31677010

The Enterprise Imaging Value Proposition.

Cheryl A Petersilge1.   

Abstract

As resources in the healthcare environment continue to wane, leaders are seeking ways to continue to provide quality care bounded by the constraints of a reduced budget. This manuscript synthesizes the experience from a number of institutions to provide the healthcare leadership with an understanding of the value of an enterprise imaging program. The value of such a program extends across the entire health system. It leads to operational efficiencies through infrastructure and application consolidation and the creation of focused support capabilities with increased depth of skill. An enterprise imaging program provides a centralized foundation for all phases of image management from every image-producing specialty. Through centralization, standardized image exchange functions can be provided to all image producers. Telehealth services can be more tightly integrated into the electronic medical record. Mobile platforms can be utilized for image viewing and sharing by patients and providers. Mobile tools can also be utilized for image upload directly into the centralized image repository. Governance and data standards are more easily distributed, setting the stage for artificial intelligence and data analytics. Increased exposure to all image producers provides opportunities for cybersecurity optimization and increased awareness.

Entities:  

Keywords:  Cybersecurity; Digital transformation; Enterprise imaging; Operational effectiveness; Strategy; Value-based care

Mesh:

Year:  2020        PMID: 31677010      PMCID: PMC7064696          DOI: 10.1007/s10278-019-00293-1

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  43 in total

1.  Digital imaging: an accurate and easy method of measuring foot ulcers.

Authors:  S M Rajbhandari; N D Harris; M Sutton; C Lockett; S Eaton; M Gadour; S Tesfaye; J D Ward
Journal:  Diabet Med       Date:  1999-04       Impact factor: 4.359

Review 2.  Informatics in radiology: Measuring and improving quality in radiology: meeting the challenge with informatics.

Authors:  Daniel L Rubin
Journal:  Radiographics       Date:  2011-10       Impact factor: 5.333

3.  A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task.

Authors:  Titus J Brinker; Achim Hekler; Alexander H Enk; Joachim Klode; Axel Hauschild; Carola Berking; Bastian Schilling; Sebastian Haferkamp; Dirk Schadendorf; Stefan Fröhling; Jochen S Utikal; Christof von Kalle
Journal:  Eur J Cancer       Date:  2019-03-08       Impact factor: 9.162

4.  Implementation and Benefits of a Vendor-Neutral Archive and Enterprise-Imaging Management System in an Integrated Delivery Network.

Authors:  Chen Sirota-Cohen; Beverly Rosipko; Daniel Forsberg; Jeffrey L Sunshine
Journal:  J Digit Imaging       Date:  2019-04       Impact factor: 4.056

5.  Evaluating the benefits of digital pathology implementation: time savings in laboratory logistics.

Authors:  Alexi Baidoshvili; Anca Bucur; Jasper van Leeuwen; Jeroen van der Laak; Philip Kluin; Paul J van Diest
Journal:  Histopathology       Date:  2018-08-13       Impact factor: 5.087

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

Authors:  Stephen Jones; Seán Cournane; Niall Sheehy; Lucy Hederman
Journal:  J Digit Imaging       Date:  2016-12       Impact factor: 4.056

Review 7.  Cybersecurity in healthcare: A systematic review of modern threats and trends.

Authors:  Clemens Scott Kruse; Benjamin Frederick; Taylor Jacobson; D Kyle Monticone
Journal:  Technol Health Care       Date:  2017       Impact factor: 1.285

8.  Tablets for Image Review and Communication in Daily Routine of Orthopedic Surgeons-An Evaluation Study.

Authors:  Sven Y Vetter; Svenja Schüler; Matthes Hackbusch; Michael Müller; Benedict Swartman; Marc Schnetzke; Paul Alfred Grützner; Jochen Franke
Journal:  J Digit Imaging       Date:  2018-02       Impact factor: 4.056

9.  Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology.

Authors:  Thomas George Olsen; B Hunter Jackson; Theresa Ann Feeser; Michael N Kent; John C Moad; Smita Krishnamurthy; Denise D Lunsford; Rajath E Soans
Journal:  J Pathol Inform       Date:  2018-09-27

10.  Using an Existing DICOM Infrastructure to Enhance the Availability, Quality, and Efficiency of Imaging Throughout the Healthcare Enterprise.

Authors:  Dan Kayhart
Journal:  J Digit Imaging       Date:  2019-02       Impact factor: 4.056

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