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.
Acknowledgements: I would like to sincerely thank Dr. Alex Towbin (@towbinaj), Ryan Fallon, and Jason Nagels (@jaynagels) for their time to review this information and provide valuable input.
Important Note:This article is in no way intended to dissuade organizations from sharing imaging exam data. As described within, there are many benefits to everyone, and sharing of imaging data should be consider a best practice. This article is intended to explore how vendors charge differently for storing imported imaging data in their systems.
The sharing of imaging exams across different enterprises has long been of value to patients and healthcare systems. From teleradiology, patient transfers, consults, treatment planning, to other scenarios, avoiding the repeat of an exam by sharing existing ones lowers costs, minimizes stress and frustration on the patient (and their family), and avoids any unnecessary radiation exposure.
As technology and reliable Internet has evolved, the norm of printing film, then exporting digital exams to portable media (CDs, DVDs, USB drives, etc.), has given way to cloud-based image sharing solutions. With these solutions acting as a secure broker among enterprises, imaging data can be easily moved from one organization to another. Patients can even upload their exam data using a webpage.
And while the managed movement of imaging data from one organization to another has become much easier and more common, the importation of the data to systems, like the PACS, at the destination enterprise often still requires some manual activity to get the exam data acquired at another enterprise to include all the unique data identifiers used at the destination enterprise.
The costs of the above activities are all well understood by imaging professionals, but there is another aspect that is less understood: Does the receiving imaging IT system vendor charge for imported exams?
Imaging Exam Acquisition Revenues and PACS Costs
In most jurisdictions (with private or public funded healthcare), a fee is paid by the payor for each imaging exam performed. This fee, commonly called the Technical Fee (or TechFee) in the U.S., is intended to compensate the health provider for the labor, equipment, consumables, facilities, IT systems, and other resources required to perform the exam and manage the resulting data. The TechFee amount will vary among different procedures and payors.
Note: A Professional Fee (ProFee) is also billed, but these funds may go to pay the Radiologist group, if they are not directly employed by the health system. So, even if an imported exam is read (for example, as an “over read”) and a ProFee bill generated, in some cases, the health system paying for the PACS does not receive any revenue (but do incur costs, as detailed below). Not to mention, ProFee billing for over reads can be unpopular among patients and referring physicians, as it increases overall cost of care.
It is common for PACS vendors to charge a software license for the use of the solution. The commercial model may vary between a licensed annual exam total allowed or a per use fee, but in most cases, the more exams that are acquired and stored by the organization, the more the vendor is paid. This is generally accepted as fair by both vendors and health system buyers.
Where there can be variability in vendor agreement terms is in deciding which exams are counted when determining compliance with the license terms or fees for usage. As the health system receives a TechFee for any exams they acquire, both vendor and health system provider are generally in agreement that these exams should count towards the software license.
But what about imported exams?
Some vendors treat all exams – acquired and imported – as equal and they are both included in the license compliance or usage calculations, even though the health system typically receives no compensation for imported exams. Some vendors may charge a lower fee for imported exams, while others include terms in the agreement that specify that only exams acquired by the health system, and not imported exams, are counted.
If multiple applications are involved – for example, a cloud-based image sharing solution, a local system for reconciling the imaging data with a local order, the PACS, and a Vendor Neutral Archive (VNA) – there may be the terms of solution’s software licenses to review and consider.
Do you Need to See the Images or Have the Images?
Before exploring how imaging IT vendors may count exams when determining system usage for software license calculations, let’s discuss some different ways in which imaging data are accessed and shared.
Some enterprises provide a web-based image viewer, often as part of a referring physician or patient portal, to allow authorized users to see and navigate images and results. Cloud-based image sharing solutions also allow images temporarily stored to the solution to be viewed by authorized users. Similarly, Health Information Exchanges (HIEs) that support image data exchange usually also provide an image viewer to see the images. Common among all of these approaches is the ability to see and interact with the images without having to move the DICOM objects to the information-consuming enterprise, or having to reconcile the data to local identifiers or terms.
This is an efficient way to “browse” imaging data managed by an outside system and organization, but is often not efficient if advanced processing is required (if the image viewer lacks the features) or if the imaging data need to be closely compared to data stored in other systems (like the local PACS). While this method may suffice for some clinicians, Radiologists likely prefer the data to be presented in their primary system (linked to the local Patient ID and all other imaging records): the PACS.
In some cases, the DICOM data is moved to the local PACS and stored temporarily. These data are available for viewing using all the PACS tools, but are not stored to the long-term archive. The system purges the data from the PACS cache based on any configured retention rules. These exams are often reconciled to local identifiers, like the local enterprise’s Patient ID, to make comparison in the system easy, but not all organizations will localize these data (see note below).
Many organizations will apply a specific labelling, such as a prefix or suffix character or string in the Study Description, to denote that the exam was acquired elsewhere. This can preempt any confusion if the acquisition protocol or image parameters are not consistent with the local policies. When used consistently, this method may also help identify the number of imported exams within an imaging IT system.
This approach is common where images are shared with an organization, through an image sharing solution or portable media, and the local PACS is the preferred viewer (due to its advanced tools and/or system familiarity). It is also used where Radiologists need to review the imaging data for scenarios like teleradiology or consults, but the patient is not being transferred or admitted to the local organization.
Like Guest Studies above, these exams are received, stored, and (typically) reconciled to include local identifiers (for example, Patient ID and Accession Number) and terms (for example, an updated Study Description). They are archived to the PACS or VNA and made part of the local enterprise’s patient medical record.
This is common where the patient is going to be transferred to the local enterprise or will be getting diagnostic or clinical services, such as a follow-up imaging exam or surgery, and the enterprise wishes to retain these records for future reference.
Documented organizational policies and procedures that define when and how outside imaging exams are accessed and imported should provide guidance to staff and affiliates.
Determining whether an outside exam is beneficial or necessary to import requires significant knowledge. Often, a Radiologist is required to make this decision, but in some cases an experienced imaging analyst with deep Radiology knowledge, like a senior Technologist, may be able to make this determination in many cases.
For Guest Studies and Archived Exams, if the data are not reconciled to local identifiers, there are some risks that the outside identifiers match local ones, which can result in a potential patient risk (exam linked to wrong patient). It can also cause issues when searching for data (some demographic values as stored in the DICOM data need to be known), resulting in some clean-up activities during a data migration.
Exam Counting Methods
Many vendors run a query against the imaging IT system to collect a count of all imaging exams stored to the system over a period (for example, a year). They count each exam that has a unique Study Instance UID value (a required DICOM attribute). This method would include all stored exams, including imported exams.
Another method is to have the health system produce a report from their RIS that provides a count of all imaging exams performed by that enterprise. This list could exclude imported exams and would generally represent exams for which the health system received revenue in the form of TechFees. The vendor would have to trust the health provider’s report, but a sanity check against the imaging IT system’s database would suggest if the information were within reason.
How Much Difference Does this Make?
So, is this aspect worth looking into? Let’s look at some numbers.
Imagine a large healthcare enterprise that stores a total of 1 million imaging exams to their PACS each year. As an academic system with lots of affiliations, this enterprise has many patients in the area routinely referred for additional imaging and treatment.
Their vendor charges $1.50 per exam stored to the system.
If we assess different percentages of the total exam volume that is imported, we see that the cost to store imported exams could vary from $150K to $375K.
Keep in mind that the health system has no TechFee revenues to offset these costs (though there may be other clinical or diagnostic services that the patient receives for which the health system can bill).
The Model Matters
In the above case, if the vendor licenses the software based on a total number of annual exams, as long as the total annual exams stored does not exceed the amount currently licensed, no additional software license needs to be purchased. If the vendor counts imported exams in the total, and image sharing increases, it may require the health system to purchase additional licenses (for example, to allow another 50K to 100K annual exams to be stored), even though they receive no revenue for the imported exams.
If the vendor licenses the imaging IT solution on a per-exam usage fee, and they count imported exams, the health system would have to pay the vendor for each exam imported (at whatever rate they specify in the agreement, which may be the same as an acquired exam, or at a lower rate).
Other Costs and the Cloud
Today, most healthcare providers procure and manage their own hardware (for example, servers, storage, and network equipment) and the solution vendor supplies the software, technical and professional services, along with ongoing support. So, in addition to any software license costs paid to the imaging IT vendor, the health system also must supply the hardware to store and manage the imported exams, and the labor to perform the importation. If a data migration is performed sometime in the future, the health system will have to pay for any imported exams that are included in the migration.
This is all in addition to any fees paid for an image-sharing solution.
The above details are important to consider if a cloud-based PACS solution is considered.
When a system is on-premises (for example, hosted in the health system’s data center), the vendor may be willing to acknowledge that the imported exams do not count towards the total exam volume when calculating the software license, but when they also provide all the hardware infrastructure, they will incur costs for all data stored. This may result in some fee being charged to the health system.
Sidenote: Enterprise Imaging
While beyond the focus of this article, many of the same questions should be posed when storing clinical images, also called Enterprise Imaging (EI) content, to the imaging IT system. Most of these data sets are not captured in response to an ordered service and have no billing associated with their capture or storage. If the imaging IT solution vendor counts these data sets (which may be one or more image, video, audio file, document, etc.) as an exam when determining software license compliance or system usage, costs (with no associated revenues) could be higher than expected.
Sidenote: Labor for Importing and Reconciling Exam Data
Another significant cost of image-sharing is the labor involved in reviewing, importing, and reconciling the imaging data to use local identifiers. This cost is not hidden, but is not measured by most enterprises, and these costs can be larger than many people understand. Industry vendors have worked on solutions to auto-generate the necessary order information to perform reconciliation automatically. New data standards, like HL7 FHIR® (https://www.hl7.org/fhir/), and APIs that support them developed and deployed in EHRs make this approach more feasible. Careful attention needs to be paid to how data elements like Study Description are applied in the reconciled data, as how procedures are labelled vary among enterprises. Inconsistent Study Description values can affect relevancy rules for pre-fetching and routing and may necessitate maintaining complex terminology mapping tables (if supported by the imaging IT solution). Series descriptions, which often affect features like display protocols, also vary among modalities and enterprises. Terminology normalization is a complex challenge involving many unmanaged data variables that some believe may be solved more effectively through the application of Artificial Intelligence (AI).
Tips for Minimizing Costs
The first step is understanding your current situation with the following factors to consider:
Do an inventory of acquired (check the RIS) and imported exams for each year for the past three years. Understand how many exams you are importing (with no offsetting revenue) and look for any trends. Are exam imports increasing year-over-year?
Read the agreement with your current vendor to understand the terms around how exams totals are counted. Understand whether they differentiate between acquired and imported exams. Understand whether Guest Studies (as described in this article) are counted, even though they are not archived.
If your imaging IT vendor does count imported exams when calculating the software license or usage, make sure you account for these costs when budgeting capital and operating budgets.
Understand the fees for any image-sharing solution you use. Does the vendor charge a different amount for exams that are viewed from the cloud, but not imported to the local PACS? Do they charge both the sender of the data and the receiver, or just one of the parties?
Review your policies and procedures for exam importation. Are they clear so that the necessary exams get imported, but ones that are not relevant are not?
If your organization is looking to replace an imaging IT system, like a PACS or Vendor Neutral Archive (VNA):
Ask the bidding vendors how they determine which exams are included in the count for software license calculations. If some do count imported exams and some do not, include an estimate of the additional costs for those that do in your Total Cost of Ownership (TCO) analysis.
The purpose of this article is not to dissuade enterprises from sharing imaging exam data. As stated, there are many benefits of this activity to all stakeholders, most notably the patient. The purpose here is to explore variability in how imaging IT solution vendors count exams for software licensing, and how this can add up to represent significant costs, over time.
It would be unwise to select a new imaging IT solution simply based on whether they count imported exams toward the software license or not, but it should be considered in your cost projection calculations.
And it may be worthwhile to educate your imaging IT vendor partner on the economics of providing diagnostic imaging services and to align their compensation to reflect the scale of the enterprise’s revenues.
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.
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!
AI Strategy of CAR – Roger Tam will enlighten us on the Canadian Association of Radiologists’ strategy for AI.
Cloud Services for Machine Learning and Analytics – Patrick Kling will reveal how cloud-based solutions can address the challenge of managing large volumes of data.
Patient-Centered Radiology – Dr. Tessa Cook (@asset25)will provide insight into their progress on this topic at UPenn.
Collecting Data to Facilitate Change – Dr. Alex Towbin of Cincinnati Children’s Hospital (@CincyKidsRad) will show us how to use data to support change management.
Panel on the Future of DIRs in Canada – In this interactive session, we will discover what has been accomplished with Diagnostic Imaging Repositories (DIRs) in Ontario, and what’s next. I will moderate a panel with leaders from SWODIN and HDIRS.
Practical Guide to making AI a Reality – Brad Genereaux (@IntegratorBrad), with broad experience working in hospitals, industry, standards committees, and technology, will help attendees prepare for this new area.
Healthcare IT Standards – Kevin O’Donnell, a veteran of healthcare standards development and MIIT, will provide an overview of developments within the DICOM and HL7 standards, and IHE.
ClinicalConnect – Dale Anderson will provide an update on this application (@ClinicalConnect), used by many organizations in the local region.
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.
As health systems acquire or partner with previously independent facilities to form Consolidated Enterprises, and implement a Shared Electronic Medical Record (EMR) system, they often consolidate legacy diagnostic imaging IT systems to a shared solution. Facilities, data centers, identity management, networking equipment, interface engines, and other IT infrastructure and communications components are also often consolidated and managed centrally. Often, a program to capture and manage clinical imaging records follows.
Whether the health system deploys a Vendor Neutral Archive (VNA), an Enterprise PACS, or a combination of both, some investment is made to reduce the overall number of imaging IT systems installed and the number of interfaces to maintain. An enterprise-wide radiation dose monitoring solution may also be implemented.
While much has been written on strategies to achieve this type of shared, integrated, enterprise-wide imaging IT solution, there are several other opportunities for improvement beyond this vision.
In addition to imaging and information record management systems, enterprise-wide solutions for system monitoring, audit record management, and data analytics can also provide significant value.
Organizations often have some form of enterprise-level host monitoring solution, which provides basic information on the operational status of the computers, operating systems and (sometimes) databases. However, even when the hosts are operating normally, there are many conditions that can cause a solution or workflow to be impeded.
In imaging, there are many transaction dependencies that, if they are not all working as expected, can cause workflow to be delayed or disabled. Often, troubleshooting these workflow issues can be a challenge, especially in a high-transaction enterprise.
Having a solution that monitors all the involved systems and the transactions between them can help detect, prevent, and correct workflow issues.
Audit Record Management
Many jurisdictions have laws and regulations that require a comprehensive audit trail to be made available on demand. Typically, this audit trail provides a time-stamped record of all accesses and changes to a patient’s record, including their medical images, indexed by the users and systems involved.
Generating this audit trail from the myriad of logs in each involved system, each with its own record format and schema, can be a costly manual effort.
The Audit Trail and Node Authentication integration profile (ATNA), part of Integrating the Healthcare Enterprise (IHE), provides a framework for publishing, storing, and indexing audit records from different systems. It defines triggering events, along with a record format, and communication protocol.
Enterprises are encouraged to look for systems that support the appropriate actors in the ATNA integration profiles during procurement of new IT systems and equipment. Implementing an Audit Record Repository with tools that make audit trail generation easy is also important.
Capturing and analyzing operational data is key to identifying issues and trends. As each system generates logs in different formats and using different methods, it often takes significant effort to normalize data records to get reliable analytics reports.
Periodic (for example, daily, weekly, or monthly) reports, common in imaging departments for decades, are often not considered enough in today’s on-demand, real-time world. Interactive dashboards that allow stakeholders to examine the data through different “lenses”, by changing the query parameters, are increasingly being implemented.
Getting reliable analytics results using data from both information (for example, the EMR and RIS) and imaging (for example, modalities, PACS, VNA, and Viewers) systems often requires significant effort, tools to extract/transform/load (ETL) the data, and a deep understanding of the “meaning” of the data.
Implementing solutions that continuously and efficiently manage the health of your systems, the records accessed, and operational metrics are important aspects in today’s Consolidated Enterprise. Evaluating any new system as to their ability to integrate with, and provide information to, these systems is recommended.
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.
As existing healthcare provider organizations merge and affiliate to create Consolidated Enterprises, image acquisition workflows are often found to be different across the various facilities. Often, the different facilities that comprise the Consolidated Enterprise had different procedures and standard of practice for image acquisition and Quality Control (QC), along with different information and imaging systems.
Standardizing and harmonizing enterprise-wide policies, especially for imaging exam QC, can have significant benefits. A failure to standardize these workflows in a Consolidated Enterprise may result in inconsistent or inaccurate imaging records, which can lead to reading and viewing workflow challenges. These are compounded with a shared imaging system, such as an enterprise PACS or VNA, and can result in delays in care and patient safety risks.
There are generally two areas worth evaluating for optimization:
Imaging record Quality Control workflow (QC Workflow)
Here, we will explore Tech Workflow. QC Workflow will be covered in a subsequent post.
Throughout this discussion the term Radiology Information System (RIS) is used, which can be a standalone system or a module of an EMR.
The use of DICOM Modality Worklist (DMWL) for the management of image acquisition is well-understood and broadly adopted. However, the process of marking an exam as “complete” (or “closed”) following acquisition is less standardized and varies across different vendors and healthcare enterprises. The subsequent QC and diagnostic reading workflows rely on the “completion” of the exam before they can begin. For example, an exam that is never marked as “complete” may not appear on a Radiologist Reading Worklist, and an imaging exam that is marked as “complete” when it isn’t will be available for Radiologists to read with only a partial set of images.
Imaging Technologists typically interact with the following applications on a daily-basis.
Modality Console – a comprehensive set of tools, attached to the modality, to perform image acquisition activities (such as DMWL queries, exam protocoling, post-processing, etc.).
Radiology Information System (RIS) – a specific view into the enterprise RIS application, allowing Technologists to look up patient/procedure information, a set of tools to document the acquisition and mark exam as “complete”, etc.
Image Manager/Archive (IM/A) QC – a comprehensive set of imaging exam Quality Control (QC) tools, provided by the Image Manager/Archive (IM/A), such as PACS or VNA, or a dedicated application, to make any necessary corrections to ensure the quality of acquired imaging exam records.
As stated above, there is significant variability among healthcare providers with respect to instituting Tech Workflow policies and procedures. The following diagram illustrates the steps involved in a common Tech Workflow.
In some cases, Technologists validate the quality of the image and confirm that the number of images in the IM/A is correct for multiple studies at a time instead of each one independently due to the high-volume of exams being acquired.
An ability to assess the quality of the imaging exam and correct it (if needed) in a quick and user-friendly manner is critical for an efficient exam completion workflow.
PACS-driven Reading Workflow
In this scenario, the PACS Client provides a Reading Worklist and it is typically responsible for launching (in-context, through a desktop integration) the Report Creator application. There are several methods used across provider organizations to communicate study complete status updates to the PACS.
Time out – this is the most typical approach, which considers a study to be complete after a defined period of time has passed (for example, five minutes) since the receipt (by PACS) of the last DICOM object from the modality.
Easy to implement
If the time-out is too long, the creation of the corresponding Reading Worklist item will be delayed. Alternatively, a short time-out may result in a Radiologist reporting an incomplete study, which requires follow-up review and potentially an addendum to the report once the missing images are stored to PACS.
HL7 ORM – some organizations release HL7 ORM messages to the Report Creator only after the order status is updated (to study complete) in the RIS.
Easy to implement
Prevents reporting of incomplete exams (although relies on Technologists to validate the completeness of the study structure in the PACS)
There are scenarios where PACS has received DICOM studies, but their statuses in the RIS application has not yet been updated (for example, as can happen with mobile modalities). The Reading Worklist is unaware of the HL7 message flow between the RIS and the Report Creator and, therefore, allows the Radiologist to start reviewing cases. However, these cases have no corresponding procedure information in the Report Creator. When the Radiologists tries to launch the reporting application in the context of the current study, the Report Creator is unable to comply.
DICOM MPPS – Once an exam is complete, a DICOM MPPS N-Set message (issued by the modality) informs the PACS (and/or RIS) about the structure of the study and the fact that it is completed (along with other useful exam information).
Prevents reporting of incomplete exams
Automatic confirmation of the structure of the study
The adoption of DICOM Modality Performed Procedure Step (MPPS) is still limited in most enterprises, even though some modalities, RIS, and PACS support it.
Somewhat complex to implement (requires integration and testing between each modality and the MPPS server) coupled with a lack of understanding as to the benefits of this approach in many healthcare provider organizations.
Some modality vendors charge an additional fee for a license to enable MPPS integration.
Can be disconnected from the “completion” of the exam in RIS (i.e. can ensure the Report Creator’s readiness), provided only the PACS receives and processes the MPPS messages.
DICOM Storage Commitment – Once the exam is complete, a series of DICOM messages (N-Action, N-Event-Report) between modalities and PACS can determine whether a complete study was stored to PACS.
Prevents reporting of incomplete exams
Automatic confirmation of the structure of the study
Although most PACS and many modalities support this DICOM transaction, it is not widely implemented by healthcare providers.
Somewhat complex to implement (requires integration and testing between each modality and the PACS server) coupled with a lack of understanding as to the benefits of this approach in many healthcare provider organizations.
Can be disconnected from the “completion” of the exam in RIS (i.e. can ensure the Report Creator’s readiness).
RIS-driven Reading Workflow
In this scenario, the RIS provides the Reading Worklist and it is implicitly aware of the status of the exam (assuming the same system is used by Techs and Rads). It creates the worklist item that corresponds to the exam once it reaches the “complete” status. As the Reading Worklist launches both the Report Creator and the Diagnostic Viewer (PACS Client) applications, it does not face the informatics challenges inherent to the PACS-driven Reading Workflow described above.
Some organizations use an enterprise-wide Reading Worklist that is a separate application from the PACS and RIS to orchestrate enterprise-wide diagnostic reading (and other imaging related) tasks across all their Radiologists using fine-grained task-allocation rules. Similar to the RIS-driven Reading Workflow, the worklist launches both the Report Creator and the Diagnostic Viewer applications once a worklist item is selected.
To prevent the complexity of the PACS-driven Reading Workflow described above, some organizations choose to release an HL7 ORM message from the RIS application to the worklist only when the status of the corresponding exam in that system is updated. Alternatively, organizations that choose to send all ORM messages to the worklist application as soon as procedures are scheduled, need to deal with ensuring that the PACS has a complete study prior to allowing it to be reported.
It is important for healthcare provider organizations to understand the relationship between the Tech Workflow and the Reading Worklist approach they adopt. If a RIS-driven approach is not chosen, then there should be a clear integration strategy in place to ensure that studies are not reported too soon or missed.
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.
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.
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).
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.