RSNA 2012 - The Use of Business Analytics for Improving Radiology Operations, Quality, and Clinical Performance

Data needed for business analytics for radiology is distributed over many different source systems (RIS, PACS, IHE, etc, etc). The data in those systems can provide vital information. However, to use this information we have to be able to gather and combine this information in an easy and fast manner.
Leveraging informatics tools such as business analytics can help a practice transform its service delivery to improve performance, productivity and quality. BA tools can be used for quality assurance, maintenance of certification (MOC) and pracitce quality improvement (PQI).
The general steps involved in business analytics, include extract, transform, load (ETL) and key performance indicators (KPI). BI involves: Aggregation, Integration (ETL), Storage (data warehouse), Analysis(OLAP, data mining), and Presentation. Relational databases are concerted to an OLAP configuration to allow fast data searching and analytics.
Before starting with Business Analytics, the goals and metrics needed to achieve those goals need to be defined.

Business intelligence maturity from least to most mature:
1. Standard reports
2. Ad hoc reports
3. Drilldown
4. Alerts
5. Statistical analysis
6. Forecasting
7. Pre modeling

- Business intelligence tools can leverage radiology systems' utility and empower executive decisions.
- The selection of the most appropriate KPIs are critical.

RNSA 2012 - Decision Support in Clinical Practice

Decision support systems use knowledge -- ranging from books, to web sites, to real-time artificial intelligence systems -- to help physicians improve their decision making.
Passive decision support leav responsibility to search the data with the radiologist while active decision support uses artificial intelligence and computer software to actively provide relevant information and guidance to the radiologist.
In the US a lot of the work in decision support in order entry is stimulated by the appropriateness criteria for radiological examinations.
Decision support systems have to be real time and integrated to be able to be a success in clinical practice.
Recipe for success of decision support depends on the system being:
- available
- easy to use
- integrated
- collaborative
- good medicine
- aware of limits

Predicting Diagnosis and Outcome
Currently there is a clearly identified role for informatics in predicting diagnosis and outcome based on variables derived from imaging. However, there are important trade-offs that exist when developing or using predictive models.
The goal of decision suppprt is to take predictive information and assist the physician in quantifying risk of disease or probability.
An important trade-off is that In most cases the correctness of the outcome of decision support heavily depends on the dataset used to construct or train the decision support system.

Quantitative Image Analysis for Image Retrieval, Decision Support, and Knowledge Discovery
Quantitative image analysis is to characterize images and or parts of images with rich features that can be accessed by computers for comparison or decision support.
Image characterization can be done by annotation and computing of image features. This characterization (among others based on quantitative image analysis) can be used for content based image retrieval by defining a vector of different features. The acknowledged features in this vector should be weighted to determine their relative importance. This weighting can either be done by human definition or by computer learning or training and depends on what you are interested in. In radiology this can be used to retrieve similar images from the PACS or, more challenging, find images with similar lesions. Standardization using e.g. RadLex and identifying exact locations is vital to obtain usefull desciptions to use as image features.

Key points
1) Evolving technologies provide new ways to integrate advanced decision support into routine clinical practice, and decision support systems can improve outcomes in patient care.
2) Decision support gives radiology a chance to transform to a more proactive role in the managing of patients and examinations.

RSNA 2012 - New and Improved Features and Functionality of the RSNAMIRC Teaching File Software(LL-INE1201)

New and Improved Features and Functionality of the RSNA MIRC Teaching File Software
William Weadock, MD , Sarah Abate, BS

Over the past few years, the RSNA MIRC system has undergone significant improvements in functionality. Installation, creation and display of teaching file, and conferencing have been significantly improved. The MIRC system has become the standard format for teaching file software. The software is free to download and use and is supported by the RSNA. This exhibit will show how the system can be incorporated in daily workflow.

Key inprovements in functionality shown at the demonstration at RSNA are the ability to further anonymize or de-identify the DICOM data not only by de-identification of DICOM fields but also by removing parts of the image that could possibly contain patient related information such as in ultrasound captures or secondary captures. Another feature is the ability to export directly to powerpoint of one or more cases to construct a presentation on a certain topic.

RSNA 2012 - ISP: Informatics (Image Sharing, Management and Display)

ISP: Informatics (Image Sharing, Management and Display)

SSA11-01 • 10:45 AM
Informatics Keynote Speaker: Image Sharing

Network based sharing, probably using cloud services, is mainly the replacement of the sneaker net (CD transfer) and comes with the same challenges. One has to be careful not to use tools that are used daily like Dropbox for patient related data sharing. Standards like XDR and XDM should be used to ensure safe transfer of patient data. Cloud services have to be standards based (IHE XDS.i) as implemented in the RSNA image sharing project. The exchange is based in a money transfer system where the patient gets an eight digit code and password. With these codes the patient can access the data and grant access to healthcare providers.

SSA11-02 • 10:55 AM
The RSNA’s Image Sharing Project: Initial Challenges from the Patients’ Perspective
Yousaf Awan et al

Traditionally patients are not involved in data sharing. The push to patient centered healthcare lead to the implementation of the RSNA image sharing project where the patient has control over his/her own data. Inability to access their images was the biggest issue with the current system looking at the helpdesk calls. In general, not many problems occurred.

SSA11-03 • 11:05 AM
Outcomes of an Automated System of Importation of Outside Imaging Studies into the Radiology Information System
Jonelle Marie Petscavage et al 

Patient data from CDs is uploaded decentrally and recorded in RIS and PACS. Sending AE title is set to 'OUTSIDE CD'. The error rate was less than 1%. interesting report, check our publications by van Ooijen et al in the Journal of Digital Imaging where we demonstrated something similar quite some years ago (free fulltext available)

SSA11-04 • 11:15 AM
Introducing Intuitive Simplicity in a New Type of Browser-based Dicom-Viewer for Non-Radiologists' Daily Clinical Routine
Sebastian Bickelhaupt et al

Many users are presented with non intuitive viewers that either have too much or too little functionality. The new viewer presented by this group is based on the body regions on an anatomic picture of a human. A model of human anatomy is given with colored dots on the model where the color indicates the modality and the datasets are shown next to it. After selecting the dataset of interest the DICOM data is shown and direclt connected to a 3D anatomical atlas. They showed that the new prototype speeds up and simplifies DICOM image viewing.

SSA11-05 • 11:25 AM
Influence of Medical Display System on Productivity and Eye-strain of Radiologists
Mony Weschler et al

This group compared setups with two 3MP screens to one 6MP screen both from Barco. Factors like eyestrain are evaluated using a questionnaire. It is show by this study that 6MP provides significantly less eyestrain and higher reading speeds. Although this is only based on manual registration of findings by the radiologists on a questionnaire.

RSNA 2012 - Cloud Computing for Radiologists—A Primer

Cloud computing session. See below for the learning objectives of this session.
Cloud computing involves both applications and services delivered over the internet using hardware and servers in a data center.
Virtual systems and cloud based systems can be used both within radiology as well as outside. Cloud computing provide referring clinicians with both image interpretation as well as teaching capabilities.

Cloud Computing can be applied for
1) Using virtualized systems to improve access to advanced image processing tools.
2) Using cloud based systems to provide access to advanced imaging tools.
3) Getting hands on experience using 2D / 3D / 4D tools to process data in near realtime in a virtual environment.

advantages of cloud computing
1) should lower IT costs
2) automate storage management
3) improve scalability
4) improve accessibility (e.g. improve access to advanced image processing tools)
5) fast deployment of services

Currently, people are net centered and used to being online and connected at all times. This is one of the main drivers behind cloud computing since the investment in having the best technology on the serverside is the responsiblity of the service provider and not the user.

Problems or challenges in cloud computing are:
1) can the networks available service all the cloud based applications?
2) how do we ensure patient data security and privacy?
3) is it really saving money?

when using Citrix like solutions, a major problem is that Citrix will not notify that the displayed information is compressed. If the software you use is not Citrix aware then it says that diagnostic quality data without compression is displayed while it actually is compressed by Citrix to preserve bandwidth.

Trade-offs of local storage versus cloud storage should be considered in terms of reliability, latency, costs, and scalability.
Keep in mind that the bandwidth of the network is not all, the latency is dictating how much you can transfer over that network. Therefore, testing or computing the transfer time based on the latency is essential.

RSNA 2012 - RadSnap - A Free iPhone Application for Cloud Based Consultation Between Referring Physicians and Radiologists(LL-INE1245)

Interesting poster at RSNA of a iPhone application for Cloud Based Consultation. It is freely available from the appstores.

RadSnap - A Free iPhone Application for Cloud Based Consultation Between Referring Physicians and Radiologists

Roland Talanow, MD,PhD

PURPOSE/AIM

Referring physicians are often in a situation where they need a quick professional consultation for images at hand. Also radiologists in poor areas of this world may have not the expensive equipment to exchange studies with professionals. A solution is desired where radiologists or referring physicians who are in need for a quick case consultation receive such in a timely manner.

CONTENT ORGANIZATION

We developed an iPhone app that allows sending images of an indeterminate case into a dedicated section of a protected community of over 10.000 Radiology professionals. Case consultations and opinions are usually provided within a few minutes to hours. The program mask is short and intuitive and after taking the picture, it provides several options for a title, short description where a question can be placed and optional case relevant parameters. The user may choose to display their name or stay anonymous. As soon as a peer provides an opinion, the user will be notified via email. The user may also discuss this case with peers in the protected community to get more insight.

SUMMARY

RadSnap is a free clinical tool that allows receiving quickly and easily professional consultations for difficult cases sent via iPhone. This tool helps especially referring physicians and radiologists in areas of the world who cannot afford expensive PACS software.

RSNA 2012 - Ensuring Research Subject Privacy: Anonymization andObscuring of Facial Features of Shared Head Volumes Using Open SourceOnline Tools in XNAT (LL-INE1243)

One of the tricky things about anonymization of image data for scientific research is the fact that although we de-identify the DICOM header and textual content of the images, an additional issue is involved in CT and MR of the head. This issue involves the fact that based on the data acquired a three dimensional visualization of the face can be reconstructed on which the patient could be identified. This work provides a solution to deface MR data in order to tackle this problem.

Ensuring Research Subject Privacy: Anonymization and Obscuring of Facial Features of Shared Head Volumes Using Open Source Online Tools in XNAT
Mikhail Milchenko, PhD , Kevin Archie , Daniel Marcus, PhD
PURPOSE/AIM
1. To understand anonymization requirements for DICOM tags;
2. To understand whether a 3D rendering of head anatomy scan (e.g. MR) can identify a person;
3. To understand the difference between de-identified and original head scans for the identification purposes, and for the purposes of automatic post-processing.
4. To learn to share MR head data using automatic online de-identification tools based on pipelines in XNAT (open source research image sharing database);
CONTENT ORGANIZATION
1. 3D head visualizations based on high resolution MR scan vs. photographs for identification purposes.
2. Obscuring facial features: registering the image into atlas space combined with anatomical surface obscuring
3. Sharing data using the open source research database engine (XNAT) with DICOM anonymization and face obscuring pipeline
4. Comparison of original and face-obscured MR head renderings.
5. Outcomes of post-processing tools (gain field correction, skull stripping, registration) on original and face-obscured MR images
SUMMARY
The exhibit will review the de-identification technique that consists of DICOM anonymization and obscuring of face anatomy. The emphasis will be made on learning the impact of face obscuring algorithm on visual change in a 3D rendering appearance, and of examining the outcomes of post-processing tools used on obscured images.

TEDxToronto 2012 Talk - Dr. Joseph Cafazzo

Dr. Cafazzo is a biomedical engineer who has spent his entire career in a hospital setting. By observing healthcare delivery from the inside, he works on ways to keep people out of hospital by creating technologies that allow for self-care at home. At the same time, Joe and his team are the biggest critics of poorly designed health technologies and their ineffectiveness. He surrounds himself with whip-smart, and passionate engineers and designers who are creating technologies that are spirited, modern, people-focused, and hopefully, suck less.

Computer Magazine November 2012 - The move toward electronic health records

Check out Computer Magazine November 2012 for a whole series on the move toward electronic health records. Selected articles are also available Bericht publicerenfor free at the computer magazine website.

Use of Mobile Devices in Healthcare institutions

Amcom Software surveyed nearly 300 healthcare organizations to better understand how healthcare facilities are addressing questions about the use of mobile devices and to see how far along hospitals are in devising their strategies. This white paper report titles "The Role of Mobility Strategies in Healthcare" examines the top three topics and discusses solution ideas.