Imaging Informatics Education in Clinical Informatics Programs: Perspective from Imaging and Clinical Informatics Professionals

Abstract

Background Imaging and Clinical Informatics are domains of biomedical informatics. Imaging Informatics topics are often not covered in depth in most Clinical Informatics fellowships. While dedicated Imaging Informatics fellowships exist, they may not have the same rigor as ACGME (Accreditation Council for Graduate Medical Education) accredited Clinical Informatics fellowships and they do not provide a direct path toward subspecialty board certification.

Objectives We compared published curricula and test content between Clinical and Imaging Informatics fellowship programs. We then highlighted differences between training programs and identified overlapping topics and opportunities for additional education for each type of trainee.

Methods Published consensus curricula and topics were extracted for each specialty. Two informaticists compared topics as shared or not shared between specialties. Next, test content outlines were compared for each specialty exam, extracted, and classified as shared or not shared content. A Venn diagram was created to highlight areas unique to each specialty as well as areas of overlap.

Results There were 139 Clinical Informatics topics compared with 97 Imaging Informatics topics. Of the 139 Clinical Informatics topics, 115 (83%) were covered in the Imaging Informatics curriculum. Of the 97 Imaging Informatics topics, 74 (76%) were covered in the Clinical Informatics curriculum. When using test content outline data, 170 out of 397 (43%) Imaging Informatics topics matched to 64 out of 139 (46%) Clinical Informatics topics. We describe examples of overlapping topics and those unique to each program to identify potential areas to expand.

Conclusion Imaging Informatics and Clinical Informatics fellowship programs have some overlap with areas unique to each. Our review may help guide those seeking informatics education and potential certification. As enterprise imaging evolves, these differences may become more important and create knowledge gaps, if not systematically evaluated.

Keywords

fellowship training - academics - curriculum - imaging informatics - clinical informatics - medical education

Background and Significance

Enterprise imaging is expanding. Today, nearly every medical and surgical specialty uses imaging in some capacity, whether to diagnose disease, guide procedures or therapy, or to document a condition (referred to here as “enterprise imaging”[1]). Modalities such as visible light (medical photography[2]) and point-of-care ultrasound[3] [4] [5] [6] [7] [8] [9] [10] have turned nearly every healthcare worker into an imager. In this setting, good health information technology practices are required to ensure that healthcare workers are able to perform their tasks efficiently and that imaging data are incorporated into the electronic health record in a way that is viewable by all stakeholders.[11]

The domains of biomedical informatics were described in 2012 by Kulikowski et al and included bioinformatics, imaging informatics, clinical informatics, and public health informatics. They stated that the “depth of informatics is shared across the spectrum from the molecular to the population levels that define the core discipline of biomedical informatics,”[12] and while Clinical and Imaging Informatics are distinct domains, building common ground between the subspecialties is essential.

We believe that differences in training between Clinical and Imaging Informaticists as well as a relative lack of imaging knowledge among Clinical Informatics experts may also play a role in the slow adoption of enterprise imaging. Though the American Recovery and Reinvestment Act of 2009 and resultant Health Information Technology for Economic and Clinical Health provided incentive to accelerate acquisition, implementation, and use of electronic health records demonstrating their “meaningful use,” there was no equivalent for enterprise imaging implementation nor integration. In addition, given that Imaging Informatics arose from clinical radiology practice while Clinical Informatics largely grew out of direct patient care specialties, these historical differences have propagated the separation between the two fields both in curriculum and practice. As the field of informatics grows across the entire practice of medicine, these historical separations may create a barrier to expeditious integration of enterprise imaging tools within most clinical workflows.

Clinical Informatics is a more mature specialty than Imaging Informatics. According to the American Medical Informatics Association (AMIA) Web site,[13] there are approximately 54 Clinical Informatics programs compared with 11 Imaging Informatics programs (IIPs) listed by the Society of Imaging Informatics in Medicine (SIIM).[14] In addition to the number of programs, Clinical Informatics fellowship programs offer a direct path toward specialty board certification that is recognized by the American Board of Medical Specialties (ABMS).

The American Board of Imaging Informatics (ABII) allows qualifying information technology professionals (not just physicians) to test for certification in IIP. However, this certification is not recognized by ABMS. While each board provides information related to potential test content,[15] [16] [17] the differences in qualifications of potential diplomates make comparison challenging. Any effort to expose relevant content between these disciplines must begin with examining similarities. Thus, this work started with a review of curricula in both areas as a place to find commonality and provide a road map for future collaboration around education between these two fields.

Objective

The purpose of this work was to compare topic areas within Clinical and Imaging Informatics training programs in the United States. We hypothesized that there would be significant overlap of foundational concepts. We also anticipated that each specialty would have topics unique to their discipline.

Methods

Fellowship curricula were identified for each specialty using the Core Topics and Potential Interprogram Areas of Collaboration by Makeeva et al and the Clinical Informatics Subspecialty Delineation of Practice (CIS DoP).[18] [19] Interprogram variability across Clinical and Imaging Informatics fellowship programs was not compared and is beyond the scope of this article. These topics were converted from outline form and entered into Google Sheets (Alphabet Inc., Menlo Park, California, United States) using VS code and ChatGPT-3.5 (OpenAI, San Francisco, California, United States) to assist with data cleansing (i.e., punctuation and formatting).

Two reviewers, with 1 and 16 years of experience in informatics, independently examined the Clinical and Imaging Informatics topics and categorized each topic as “shared” or “not shared” between the two curricula. Discordant categorizations were reviewed and adjudicated in a consensus fashion by the same reviewers. Clinical Informatics topics were first compared with Imaging Informatics topics, then Imaging Informatics topics were compared with Clinical Informatics topics. The scores were then aggregated. Differences in total scores between the two reviewers were then averaged and reported with less than 5% difference between raters.

A mind map was created where selected Imaging Informatics topics were used as “nodes” or branches and the Clinical Informatics topics were mapped distally in a nonhierarchical manner. Due to the size of the lists, topics that had a clear mapping and used similar terms were selected. Topics that did not match were not mapped. The comparison was challenging in that despite efforts to standardize clinical terms to include the National Library of Medicine's Unified Medical Language System, attempts at reconciliation, descriptions, and relations continue to vary across professional informatics fields.

Next, we identified topics for Clinical and Imaging Informatics board examinations using each specialty board's published test content outline.[18] [19] [20] The concepts important for each examination were extracted similarly to the method used for topics on the fellowship curriculum. However, the reviewers found the two datasets were not compatible for pairwise review and instead, we leveraged a collaboration tool, Miro board (Realtime Board, Inc., San Francisco, California, United States) to create a Venn diagram. This approach is similar to a collaborative affinity diagram exercise that can continue to be updated in a crowdsourcing approach. Matched topic areas were deleted from each outline, pasted, and grouped together as sticky notes in the overlapped area (visually depicting the areas in common), with the deletions leaving gaps in the topic areas in the nonoverlapped on the Venn diagram in the Miro board ([Fig. 1]). The overlapped area of the Venn is analogous to the logical AND operator, and the remaining topic areas not deleted represent the unique topic areas to each discipline (also known as the “exclusive or” in natural language).

Fig. 1 (A) Screenshot of Miro board showing the common ground overlap with digital sticky notes showing Imaging Informatics outline (yellow circle and yellow sticky notes) and Clinical Informatics content in blue (matched blue sticky notes). Note that this figure is high level for reference only and not meant to be read in detail. Each group is outlined to ease matching, e.g., the red rectangle is magnified in (B) to zoom in on the detail of a set of sticky notes of common concepts. This zoomed-in view is what is available on the Miro board that can be read easily. The Miro board results can be reviewed in detail in the following read-only link at: https://miro.com/app/board/uXjVNuyhgMk=/?share_link_id=435966513821 . (B) Magnified portion of red rectangle in (A) demonstrating capability of zooming in on digital collaborative platforms. This shows the blue sticky note topics (Clinical Informatics) related to quality improvement compared with related yellow sticky note topics (Imaging Informatics). These areas are common to both Imaging and Clinical Informatics and include key performance indicators, root cause analysis, and Lean/Six Sigma. Note the blue rounded rectangle grouping the common topic areas within the Venn.

Results

Of the 139 topics identified in the Clinical Informatics fellowship curriculum, 115 (83%) are covered in the Imaging Informatics curriculum ([Fig. 2]). Examples of topics not covered include social determinants of health, clinical registries, analytics to include Bayesian and statistical analysis, in-depth computational principles including logic gates, and structured query language joins.

Fig. 2 High-level bar chart displaying common and uncommon topics between Clinical Informatics and Imaging Informatics fellowships. This highlights the large number of topics that are shared between the two specialties.

Of the 97 topics in the Imaging Informatics curriculum, 74 (76%) are covered in the Clinical Informatics curriculum. Examples of topics that are not covered in Clinical Informatics include dealing with vendors, web services, ergonomics, workstation environment, and Picture Archiving and Communication Systems (PACS) infrastructure. A mind map is shown which depicts Imaging Informatics topics mapped to Clinical Informatics topics ([Fig. 3]).

Fig. 3 (A) This represents a collapsed view of selected Imaging Informatics topics without the related Clinical Informatics topics seen in (B) and (C). When comparing large datasets, it can be helpful to have selected topics broken down and mapped to see the connections. Colors are for visualization purposes only. (B) Selective examples of Imaging Informatics topics mapped to Clinical Informatics topics as represented by a mind map. Some of the Imaging Informatics nodes map to multiple Clinical Informatics topics, while others map to a single topic. Note the blue box is magnified in (C). See the following link for more details: https://mm.tt/app/map/3074230591?t=XpwuJTe1Gi . (C) Example showing Imaging Informatics topic “Integrating Healthcare Enterprise” mapped to multiple related Clinical Informatics topics. This is one example of the different granularity between the datasets with broad and specific references.

There are approximately 397 topics within the ABII test content outline. Initial attempts to review and compare the specialties were performed by one reviewer, but the topics were too granular and not easily comparable. Instead, a Miro board ([Fig. 1]) was created where the topics were matched, with 170/397 (43%) Imaging Informatics topics matched to 64/139 (46%) Clinical Informatics topics.

Discussion

Clinical and Imaging Informatics are subspecialties of Biomedical Informatics. Historically, these specialties have worked independently developing solutions to problems unique to their domains. These siloes began to change as electronic health records helped to consolidate institutional data, bringing specialty information systems into the larger electronic medical record. More recently, topics like artificial intelligence and enterprise imaging have forced Clinical and Imaging informaticists to work together. As we noticed this trend at our local level, we wondered if fellowship trainees were taught the foundational knowledge needed to collaborate in their future careers.

To that end, our study explored the overlap of topics within Clinical and Imaging Informatics fellowship curricula and board certification test content outlines. We found considerable overlap in topics in both documents. Approximately three-quarters of the topics within each fellowship curriculum with 20 major groupings (boxed in shared topic areas in the Miro Venn diagram) and approximately 230 of the test content topics were shared across both specialties. While the overlap is impressive, the differences require more study. For example, some of the differences represent key concepts for cross-specialty work, whereas other concepts represent domain-specific knowledge.

Selected examples of key concepts needed for cross-specialty work include clinical registries, electronic medical records details (not included in the Imaging Informatics curriculum), and PACS infrastructure (not included in the Clinical Informatics curriculum). It should be noted that while we believe that these items should be included in each curriculum, we recognize that the depth of knowledge required for each specialty differs. Examples of topics that may be more domain-specific include Bayesian and other analytics in Clinical Informatics and exam protocolling in Imaging Informatics.

This is not the first attempt at comparing the two programs; Makeeva et al surveyed imaging informatics fellowship directors to identify areas of potential improvement.[20] Others have expressed a need for overlap in Clinical and Imaging Informatics training.[21] We expand on prior efforts by systematically comparing the similarities and differences in three distinct approaches: manually comparing the curricula and indicating whether a topic was shared or not shared, creating a mind map of selected relations between curricula, and applying a Venn diagram of test content outlines.

There are several limitations of our work. First, the curricula and content outlines differed in specificity. Namely, the Imaging Informatics curriculum[19] was less detailed than the one used for Clinical Informatics.[21] This difference in detail made it challenging to directly compare the lists as a topic in one list might cover several topics in the other list. This difference in terminology and domain-specific terms was especially apparent when considering the overlap on the Miro board with only 43 and 46% of Imaging and Clinical Informatics topics within the intersection of the Venn diagram. Also, the ABII's stated purpose is toward imaging informatics professionals and not clinical fellows. Lastly, we should also keep in mind that Clinical Informatics fellowships have a more standardized curriculum structure (as these are ACGME [Accreditation Council for Graduate Medical Education] accredited), whereas IIPs do not.

We should point out that our review is comparing each program's curricula and not the details of instruction in the fellowships and review courses since they vary. For example, there is currently no accreditation process for Imaging Informatics fellowships.

Physicians seeking an informatics career have several choices where we have provided two major options. A third that we have not discussed is a new certification called the AMIA Health Informatics Certification that also has five knowledge domains with very similar delineations of practice as the Clinical Informatics board exam, however, less extensive.

Although we refrain from making recommendations, this review may provide guidance when pursuing informatics educational advancement. Potential trainees are encouraged to leverage this review in assessing their own pros and cons as informatics becomes more pervasive in medicine and enterprise knowledge is much more valuable. Differences in terminology for the same or similar topic were noted between documents to describe the same or similar topics. To reconcile these differences, reviewers were forced to infer if a topic was covered within a particular curriculum and oftentimes referring to details in board reviews and texts. While there may be differences in how other reviewers would make these inferences, we believe that it is unlikely that the results would substantially change if different reviewers were used. The involvement of a senior reviewer who is board-certified in both specialties lends credibility to the analysis.

Conclusion

Clinical and Imaging Informatics fellowship programs are governed independently and have a different history of development. Yet, both fellowship programs teach and test common health information technology topics. While there is overlap, 20 to 25% of topics are unique to each specialty. We believe this represents an opportunity to enhance each training program. As training programs evolve, we believe the shared understanding of Clinical and Imaging Informatics will allow informaticists to build better, more efficient workflows. Our systematic review may also help those seeking informatics education and potential certification to further career development. Finally, we hope that showing the commonality in the curricula between Clinical and Imaging Informatics will lead to a new era of collaboration, understanding, and a brave new future in medicine.

Clinical Relevance Statement

Our comparison of Clinical and Imaging Informatics has potential benefits for clinicians when deciding which pathway to follow in their careers. Additionally, an increased understanding of informatics curricula may lead to increased collaboration and respect for the commonalities and differences of the two fellowship pathways.

Multiple-Choice Questions

1. What are two areas covered in Imaging Informatics that are not covered in Clinical Informatics?

   • Human computer interface and Fitts law.

   • Lean Six Sigma and social determinants of health.

   • Tiered storage and Bridges Change theory.

   • Ergonomics and Reading Room Environment.

   Correct Answer: The correct answer is option d. While there are many topics covered in both Imaging and Clinical Informatics, Ergonomics and Reading Room Environment are dedicated subjects in the Imaging Informatics curriculum.

2. Which of the following is TRUE?

   • Imaging Informatics board exam is managed by the American Board of Preventive Medicine.

   • Clinical Informatics board exam is managed by the American Medical Informatics Association.

   • Clinical Informatics has 5 core content areas and 139 knowledge and skills topics.

   • Imaging Informatics fellowships are more numerous than Clinical Informatics fellowship programs.

   Correct Answer: The correct answer is option c. Clinical Informatics has five core content areas, which are fundamental knowledge and skills, improving care delivery and outcomes, enterprise information systems, data governance and data analytics, and leadership and professionalism. A detailed list of these domains and topics can be found here: https://www.mhsinformatics.org/wp-content/uploads/2020/03/2019-05-14_Supplementary_File_-_CIS_Practice_Analysis_APPENDIX_B.pdf.

Conflict of Interest

A.J.T. reports author royalties from Elsevier, is a consultant for Applied Radiology, and has had funded travel from Merative. L.R.F. reports the following patents (no royalties since National Institutes of Health [NIH] and military-owned): “Radiographic marker that displays upright angle on portable x-rays.” U.S. Patent 9,541,822 B2, “Multigrayscale Universal CT Window.” US Patent 8,406,493 B2 as well as author royalties from Springer for “Combat Radiology.” He reports several additional published books while in the U.S. government for which there are no royalties. He reports an NIH Grant: for “Automated De-Identification of Pathology and Radiology Data,” which is sponsored by the NIH (PI: Rasool, G., Co-PI: Folio, L., Co-PI: Bui, M., CO-PI: Farinhas, J.). 'N.A.B., P.G.-F., J.W., and T.C. otherwise report no relevant conflict of interest. N.A.B. has a disclosure statement that “The views expressed are those of the author and do not necessarily reflect the official policy or position of the U.S. Air Force, Department of Defense, or the U.S. Government.”

Protection of Human and Animal Subjects

This work was exempt from the Institutional Review Board, as it did not involve human subject research.

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Publication History

Received: 29 March 2024

Accepted: 24 June 2024

Article published online:
18 September 2024