Lessons Learned from Vendor Neutral Archive (VNA) Solutions
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.
What is a Vendor Neutral Artificial Intelligence (VNAi) Solution?
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.
Do we want to repeat the historic challenges and costs associated with managing disparate image archives when implementing AI capabilities in an enterprise?
Characteristics of a Good VNAi Solution
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.
Data Format Support
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.
Processor Plug-in Framework
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.
Quality Control (QC) Tools
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.
Data Persistence Rules
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.
High Availability (HA) and Business Continuity (BC) and System Monitoring
Like any critical system, uptime is important. The ability for the VNAi to be deployed in an HA/BC configuration will be essential.
Multi-tenant Data Segmentation and Access Controls
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.
Effective Technical Support
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.
In just less than a month from today on Friday May 4, 2018 (Star Wars day!), the annual MIIT (Medical Imaging Informatics and Teleradiology) conference will once again be held at the beautiful Liuna Station in Hamilton, Ontario, Canada.
Talks will cover EMR implementation, Radiology Outreach, the link between Quality and Informatics, Highly Automated Radiology (using AI), an update on IHE, and a comparison of PACS+VNA vs. Regional PACS. It will also have a panel on the impact of EMRs and AI on Radiology and a talk on AI by a speaker from IBM Watson Health.
The annual update to the free, online IHE Buyers’ Guide tool has been completed.
Not many changes to the tool content itself for this year, but several important edits to the notes about pending integration profiles were made.
“All the king’s horses and all the king’s men…”
Deconstructing a PACS into discrete, enterprise-scale components seems to be all the rage for many organizations. But, like many things in life, taking something apart is often far easier than putting the pieces back together (and getting something that works).
At this year’s RSNA meeting, I will chair a session on PACS Reconstruction (RCC24) on Mon 27-Nov-2017 from 2:30 to 4:00 pm CT that will focus on the challenges and opportunities of building an integrated enterprise-wide imaging solution for diagnostic review and clinical access.
Following my introduction of core concepts, we will hear from Charlene Tomaselli, Director of Medical Imaging IT at Johns Hopkins and Bob Coleman, Senior Director of Enterprise Imaging Informatics at MaineHealth on their progress and vision to providing an integrated imaging solution for their enterprises.
We will have a panel Q&A with the audience to share lessons learned and discuss how to best prepare for changes.
I recently contributed an article to HealthCareBusiness that explored the scenarios whereby the use of an Enterprise PACS—defined as a system serving multiple organizations and facilities across an enterprise—or the use of a VNA may be the right approach for an organization seeking to consolidate their image archive and provide a longitudinal patient imaging record. It also covers some scenarios where both may be required.
To some vendors, this can be an ideological debate. It can also lead to discussions about the definition of what is “vendor-neutral” or not.
What is important is understanding what problems you are trying to solve, what requirements exist for the overall solution, what benefits you expect (and a plan to measure them), and having a feasible plan to get there.