In what is becoming an annual tradition, I have written an article on Imaging IT trends for 2023, which is published in HealthCareBusiness.
Chronicling changes in both technology and industry with those in healthcare provider organizations, the article covers everything from Cloud, AI, Multimedia Reporting, to expanding expectations from healthcare provider system leadership from their staff.
On Friday, November 18, 2022, 12:00pm – 1:00pm ET, I will be participating in a webinar with the esteemed Dr. Chris Roth, MD, MMCI, FSIIM.
Covering a wide range of topics related to enterprise imaging, PACS, and trends in imaging informatics, Jason Nagels and David Kwan will moderate.
There is no cost to attend, but you must register. More information here.
Enterprise Imaging Webinar
Friday November 18, 12-1 pm ET
HealthCareBusiness asked me to provide an article on some of the trends that I have been seeing in Imaging IT, including RIS, Reporting, and PACS (including Enterprise Imaging, Cloud, AI, and Pricing Models), along with some other general market trends.
If you prefer to read it in the magazine’s reader app (runs in your browser), you can use this link.
When IT professionals from Radiology and other image-generating departments merge with the enterprise IT team that provides IT infrastructure and support for enterprise-wide solutions, employee angst and culture friction can occur.
The same thing can happen when imaging IT teams historically working at different sites are merged to a single group, or when imaging informatics professionals (IIPs) from different healthcare service lines, like Radiology and Cardiology, are put into the same team.
In this paper published in Journal of Digital Imaging (JDI), all these topics are covered, along with things to consider when managing (or undergoing) these team consolidations, and tips for success.
As someone that has mostly worked from home for over seven years, I understand the value of digital communication and collaboration. In healthcare, we have had many digital tools for more virtual experiences, but have been slow to adopt them. The COVID-19 pandemic has caught many organizations with paper-based and analog processes flat footed.
Education is no different than healthcare and other organizations that have heavily depended on in-person settings. While there is no doubt an in-person conference is more personal and provides social networking opportunities, content can easily be delivered online (the slides and audio are all digital, right?).
This year’s Medical Imaging Informatics and Teleradiology (MIIT) meeting was scheduled to take place in-person in Hamilton, Ontario, just like it has for well over a dozen years. Given the current situation we are all facing, my Co-Chair Dr. David Koff (@koff14) and I were faced with a choice: cancel or try to continue with an online version. As we believe strongly in the importance of imaging informatics education, and the value in this year’s excellent program, we chose the latter.
The first virtual MIIT will take place on Friday May 15, 2020. We have lowered attendee and sponsor rates in consideration of the lower costs of operating a virtual meeting.
We have a great program this year with Dr. Tessa Cook from UPenn, Dr. Vamsi Narra from BJC, Dr. Cree Gaskin from UVa, Les Folio from the NIH, Dr. Adam Prater from Emory, Michael Toland from UMMS, Ted Scott from HHSC, along with Kevin O’Donnell providing an update on the DICOM standard at MIIT 2020 and MIIT’s own Britt Tomlin providing an update on Alberta’s province-wide Connect Care CIS program.
Review the full program here.
At only CAD$80 per person (which, at the current exchange rate, is only US$57 for our American friends), and with accreditation for CE credits, it is very high value. There are no travel costs and imaging informatics professionals can join from anywhere in the world.
Register using the “Register Now” button at miit.ca.
Sponsorship of MIIT is still available, starting as CAD$1,000 (~US$715 with today’s exchange rate). Higher tier sponsors are given high visibility with attendees and the opportunity to give a brief talk during the lunch hour.
Be sure to stay top-of-mind among imaging IT decision makers and influencers (from anywhere now) to make up for lost contact at cancelled meetings, conferences, and trade shows!
More information is available here.
If you have any questions, email us at info@miit.ca.
In less than two weeks, the SIIM Annual Meeting will be taking place at the newly opened Gaylord Rockies Resort in Aurora, Colorado. I am looking forward to catching up with old friends and meeting new ones, along with learning lots of new information.
This year, I am (co-)chairing three sessions and participating in a fourth. Here is a summary (my role). All times are in local MT.
If it is your first annual meeting, I recommend you go to the First Time Attendee Meet-up on Tue 25-Jun at 6:15 pm in the Adams C/D Lobby room and attend the New Member Orientation: Intro to SIIM session on Wed 26-Jun at 9:45 am. Jim and Rick are great educators and mentors.
Also, be sure to check out the pre-conference sessions, the Hackathon and Innovation Challenge events, as well as the #AskIndustry, Learning Lab, and Scientific sessions. And be sure to put the SIIM 2019 Reception on your calendar. It is great to connect with peers and review the scientific posters.
While you are on-site, don’t forget to share information and activities (and selfies!) using the SIIM Twitter handle @SIIM_Tweets (remember to follow SIIM for great info on imaging informatics throughout the year!) and the official 2019 Annual Meeting hashtag: #SIIM19.
I hope to see you there!
On Friday, May 10, I once again have the pleasure of co-chairing the Medical Imaging Informatics and Teleradiology (MIIT) conference at Liuna Station in Hamilton, ON.
The program for the 14th annual MIIT meeting is stellar, we have a record number of sponsors, and—thanks to lower registration fees and new group discounts—many people are already signed up to attend.
Program Highlights:
If you can attend, I am sure you will find the event educational. There are lots of opportunities to interact with our speakers and sponsors. If you are not from the region, you may find a weekend getaway to the nearby Niagara on the Lake wine region enjoyable.
And don’t forget to follow MIIT (@MIIT_Canada) on Twitter!
For those not following me on Twitter (@DonKDennison), here is a recent article on AI adoption to which I contributed. Enjoy!
For well over a decade, VNA solutions have been available to provide a shared multi-department, multi-facility repository and integration point for healthcare enterprises. Organizations employing these systems, often in conjunction with an enterprise-wide Electronic Medical Record (EMR) system, typically benefit from a reduction in complexity, compared to managing disparate archives for each site and department. These organizations can invest their IT dollars in ensuring that the system is fast and provides maximum uptime, using on-premises or cloud deployments. And it can act as a central, managed broker for interoperability with other enterprises.
The ability to standardize on the format, metadata structure, quality of data (completeness and consistency of data across records, driven by organizational policy), and interfaces for storage, discovery, and access of records is much more feasible with a single, centrally-managed system. Ensuring adherence to healthcare IT standards, such as HL7 and DICOM, for all imaging records across the enterprise is possible with a shared repository that has mature data analytics capabilities and Quality Control (QC) tools.
The same benefits of centralization and standardization of interfaces and data structures that VNA solutions provide are applicable to Artificial Intelligence (AI) solutions. This is not to say that a VNAi solution must also be a VNA (though it could be), just that they are both intended to be open and shared resources that provide services to several connected systems.
Without a shared, centrally managed solution, healthcare enterprises run the risk of deploying a multitude of vendor-proprietary systems, each with a narrow set of functions. Each of these systems would require integration with data sources and consumer systems, user interfaces to configure and support it, and potentially varying platforms to operate on.
The following capabilities are important for a VNAi solution.
Flexible, well-documented, and supported interfaces for both imaging and clinical data are required. Standards should be supported, where they exist. Where standards do not exist, good design principles, such as the use of REST APIs and support for IT security best practices, should be adhered to.
Note: Connections to, or inclusion of, other sub-processes—such as Optical Character Recognition (OCR) and Natural Language Processing (NLP)—may be necessary to extract and preprocess unstructured data before use by AI algorithms.
The data both coming in and going out will vary and a VNAi will need to support all kinds of data formats (including multimedia ones) with the ability to process this data for use in its algorithms. The more the VNAi can perform data parsing and preprocessing, the less each algorithm will need to deal with this.
Note: It may be required to have a method to anonymize some inbound and/or outbound data, based on configurable rules.
To provide consistent and reliable services to algorithms, which could be written in different programming languages or run on different hosts, the VNAi needs a well-documented, tested, and supported framework for plugging in algorithms for use by connected systems. Methods to manage the state of a plug-in—from test, production, and disabled, as well as revision controls—will be valuable.
Automated and manual correction of data inputs and outputs will be required to address inaccurate or incomplete data sets.
Capturing the logic and variables used in AI processes will be important to retrospectively assess their success and to identify data generated by processes that prove over time to be flawed.
For both business stakeholders (people) and connected applications (software), the ability to use data to measure success and predict outcomes will be essential.
Much like other data processing applications that rely on data as input, the VNAi will need to have configurable rules that determine how long defined sets of data are persisted, and when they are purged.
The VNAi will need to be able to quickly process large data sets at peak loads, even with highly complex algorithms. Dynamically assigning IT resources (compute, network, storage, etc.) within minutes, not hours or days, may be necessary.
Some organizations will want their VNAi in the cloud, others will want it on-premises. Perhaps some organizations want a hybrid approach, where learning and testing is on-premises, but production processing is done in the cloud.
Like any critical system, uptime is important. The ability for the VNAi to be deployed in an HA/BC configuration will be essential.
A shared VNAi reduces the effort to build and maintain the system, but its use and access to the data it provides will require data access controls to ensure that data is accessed only by authorized parties and systems.
Though this is not a technical characteristic, the VNAi solution likely requires the ability to share the system build and operating costs among participating organizations. Methods to identify usage of specific functions and algorithms to allocate licensing revenues would be very helpful.
A VNAi can be a complex ecosystem with variable uses and data inputs and outputs. If the system is actively learning, how it behaves on one day may be different than on another. Supporting such a system will require developer-level profile support staff in many cases.
Without some form of VNAi (such the one described here), we risk choosing between monolithic, single-vendor platforms, or a myriad of different applications, each with their own vendor agreements, hosting and interface requirements, and management tools.
Special thanks to @kinsonho for his wisdom in reviewing this post prior to publication.
The SIIM 2018 Annual Meeting in Washington D.C. is just around the corner (May 31 to June 2). I look forward to seeing many friends, sharing ideas, and learning. I will be involved in number of sessions this year. Here is a preview.
Thursday, May 31 | 9:45 am – 10:45 am | Annapolis 1
In this roundtable session, participants will discuss how to best prepare for, develop, and issue an RFP, as well as how to analyze and grade the responses. We will also discuss how to best prepare for, and support, contract negotiations with a vendor.
Friday, June 1 | 9:45 am – 10:45 am | Cherry Blossom Ballroom
Depending on your organization’s goals and scale of enterprise, the options available to you for an image archive can vary. In this debate-style session, we will explore the merits of using a Vendor Neutral Archive (VNA) vs. an archive provided as part of an Enterprise PACS. I am moderating the session.
Saturday, June 2 | 12:45 pm – 2:45 pm | Baltimore 3/4/5
Participants that sign up for this learning lab (limited seats available) will work hands-on with experts to learn how to perform clear and compelling financial analysis. Two lab exercises—one focused on assessing cloud-based vs. on-premises image archive storage, and another on the IT investment required for rolling out the enterprise imaging solution to a newly acquired facility—will be worked on in teams. Each team will share their work with the other near the end of the session. Lab assistants will be on-hand to assist. Participants must bring a laptop or tablet with Microsoft Excel installed.
Don K Dennison 