Lab Soft News

What Is Digital Health and How Does a Health System Get There?

Digital health is a term being bandied about currently so I thought it would be a good start for this note to define it. Below is a paragraph that, while very broad, conveys the complexity of the pursuit of digital health (see: Digital Medicine: A Primer on Measurement):

Digital medicine describes a field, concerned with the use of technologies as tools for measurement, and intervention in the service of human health. Digital medicine products are driven by high-quality hardware and software products that support health research and the practice of medicine broadly, including treatment, recovery, disease prevention, and health promotion for individuals and across populations.

Here is a brief sampling of some facets of digital medicine that I have blogged about recently:

• Artificial intelligence (see, for example: Using AI to Ensure Follow-Up of Suspicious Nodules in Radiology Reports)
• Health wearables (see, for example: Wearable Device Offering "Cuff-less" Blood Pressure Monitoring Approved by FDA)
• Apple ecosystem for research support (see, for example: Using the Apple Ecosystem to Support Health Research and Clinical Trials)
• Telemedicine (see, for example: Reducing Healthcare Costs with Virtual Visits; Tangible Evidence Provided)
• Natural language processing (see, for example: AI Allows Computers to "Read" EHR Records and Make Predictions)
• Image analysis and digital pathology (see, for example: Pathology Departments Should Consider Launching a Digital Pathology Outreach Program)
• Predictive analytics (see, for example: Three Examples of Predictive Analytics in Clinical Practice).

Let's now jump to a recent article about barriers to digital health transformation (see: 6 Health System Barriers to Digital Health Transformation). Below is an excerpt from it:

Diverging Perspectives on the Role of EHR Vendors in Digital Health

About 40.4% of surveyed CIOs believe enterprise EHR vendors will play the biggest role in their facility’s digital health journey in the next two years. However, this won’t be the only source for digital health tools. Approximately 26.9% expect niche healthcare startups to play the biggest role, 17.3% are relying on in-house resources and 9.6% are looking at big tech vendors like Google and Amazon. An additional 3.9% are looking toward niche vendors outside the healthcare industry.

I had to laugh when I learned that "40.4% of surveyed [healthcare] CIOs believe enterprise EHR vendors will play the biggest role in their facility’s digital health journey in the next two years." This is an embarrassing demonstration of the limited strategic vision of many healthcare CIOs. The correct answer, probably not offered to the CIOs' survey, is myriad health IT companies, including EHR vendors, who will begin to offer affordable solutions that increase the quality of care and decrease costs. Assembling a digital healthcare strategy will be both challenging and complex and will be arrived at incrementally in the upcoming years.

Increasing Interest in Merging the Specialties of Pathology and Radiology

In October, 2006, I posted a blog note suggesting that pathology should consider merging with radiology. The idea gave a number of people indigestion; one pathology chairman described it to me as a bridge too far (see: Ten Reasons for Merging Pathology/Lab Medicine with Radiology). In the interval of nearly 13 years a number of changes have occurred in the two fields and the idea "may" have gained some cautious supporters -- one of them perhaps may be the blogger Dimitri Merine who posted a note in the JACR Blog in December, 2018, that provided a balanced discussion of the idea (see: Radpathology/Pathradiology: The Information Specialty of The Future?). Below is an excerpt of his article:

Welcome to the Imaging 5.0 uber-super physician who is both radiologist and pathologist. The image centric disciplines of radiology and pathology (rad-path) are ripe for disruption. Analyzing images is a perfect fit for AI, potentially redefining the two disciplines. Artificial intelligence (AI) will change radiology and pathology, likely forcing them to consider merging. One audacious and futuristic proposal to survive the AI robopocalypse is the merger of the two specialties. In a 2016 JAMA editorial, Drs Saurabh Jha...and Eric Topol...proposed a fusion of diagnostic radiology/molecular imaging and pathology/laboratory medicine into a unified "information specialist" discipline. 

Surprisingly, this was not a new idea. Richard Friedberg MD championed the idea as far back as 1997 when he created a unified Diagnostic Medicine Service (combining pathology, radiology, and nuclear medicine) within the Department of Veteran Affairs, known as the VA Southeast Network. In 2006, Bruce Friedman, a prominent pathologist, made a compelling argument for unification and presented "Ten reasons for merging pathology/lab medicine with radiology." Among the benefits he envisioned are "integrated reports of pathologists and radiologists working collaboratively" and "higher levels of quality." A former executive of General Electric made a similar argument in 2007.

Here is a list of some new ideas and considerations regarding the possible merger of pathology and radiology into one specialty, perhaps called Diagnostic Medicine:

• Digital pathology has become much more of a reality in the intervening years. I would predict that the majority of academic and large hospital pathology departments will deploy this technology in the next five years. Therefore, much of surgical pathology (to come) and all of radiology imaging (now) will be digital.
• Serous discussion in the past has focused on the development of enterprise image servers that would support both pathology and radiology images (see: PACS (picture archiving and communication system)). I can't find much discussion about this on the web so maybe this idea is not yet practical. 
• Radiology and pathology will be the first medical specialties to broadly deploy AI systems closely integrated with workflow, more specifically deploying image analysis software. For pathology, I think that the most promising systems will identify fields of interest in each surgical pathology case being reviewed by a pathologist, thus reducing the time spent per slide.
• For those health systems wanting to "dip a toe" into closer collaboration between radiology and pathology, I previously suggested in a blog note that a concordance conference should be launched in most hospitals for discussion of complex cases with a review of both radiology and surgical pathology reports (see: Pathology and Radiology Collaborate with a Concordance Conference). The goal is to identify those cases where complete concordance (i.e., agreement) in reporting is not achieved and discuss why this has occurred. In the process, mistakes will inevitably be uncovered.

A New Tool for Obtaining Crowd-Sourced Diagnoses for Pathology Images

Below is an announcement from Dr. Ulysses Balis, Professor of Pathology and Director of Pathology Informatics at the University of Michigan Medical School in Ann Arbor, Michigan:

The University of Michigan Department of Pathology, Division of Pathology Informatics is very pleased to introduce a new educational tool for the global Pathology Community: www.Histoquery.org.

With use of this new crowd-sourcing tool, following a simple and fast user registration process, users will be able to post educational cases to the site (comprised of one or more pathology images and an associated diagnostic question), and then be able to receive a crowd-sourced answer in a matter of seconds. Similarly, users can log on to the site as a crowd-sourcing participant, with the option of either providing a new answer or providing a vote on an already-submitted answer. Every respondent gets one vote, which may be changed as needed (but, no ballot stuffing is possible).

An example of how this educational tool might be helpful would be: seeking consultation from your colleagues on a particularly difficult case in a weekly slide teaching set. Simply post an image or two along with any salient history (but no protected health information, please) and simply wait for the flow of answers and their associated histogram to appear, in real time. Result answers and counts are tabulated in real time for all to see, as a dynamically updated histogram and consequently, no web page updates or refreshes are ever required.

Just as much fun as submitting questions is the opportunity to respond to them! Users may assist in the answering of as many questions as they would like, in the sub-specialty or specialties of their interest. In responding to submitted questions, you become an important and essential component of the crowd-sourced equation! Background: The site is designed with the latest audience-participation, real-time web architecture and will easily accommodate hundreds of concurrent users without skipping a beat. When registering, you will have the choice of selecting as many pathology sub-specialties as you would like, for case monitoring purposes.

Cases may be submitted at any time to any available sub-specialty board, independent of the monitoring board elections that you have previously logged. An all-board-monitoring category is also available, for those who feel confident in assisting with any type of inbound question. Please forward all user support questions to me directly, at: [email protected] There is no fee to use this educational service and I very much look forward to hearing back from you on your initial experiences with the use of this new class of educational tool.

I think that this is an incredibly useful idea by Ul and his colleagues. We all know about the value of crowd-sourced knowledge. As noted in the note above, the web site will serve as a valuable education tool for residents and fellows as well as practicing pathologists.

Pathology and Radiology Collaborate with a Concordance Conference

The University Hospitals Cleveland Medical Center (UHCMC) is in the process of deploying a digital pathology system and therefore exploring ways to educate and prepare their pathologists for this new technology (see: Digital Pathology Pilot Predicts Prosperity: Pondering Pathology’s Pivot). As part of the process, a pilot study was undertaken with a focus on frozen sections. In the study, pathologists’ diagnoses using digitized slide images were compared with diagnoses using standard microscopy. Frozen sections were chosen as the focus of this study because digital pathology allows remote access by a specialized pathologist if necessary. The results showed an average overall concordance rate of 95% for the two methods across 11 organ systems.

In the article referenced above, brief mention was made about a routine rad-path conference in the hospital that was greatly improved by the use of digital images. The article went on to discuss the purpose of this rad-path concordance conference: to resolve issues of concordance between imaging findings and the microscopic findings to also determine whether or not patients need additional surgical intervention or can be followed with imaging. In the support of this goal, it was deemed necessary for all conference participants to examine both the radiologic images and the microscopic images. 

In October, 2006, I first proposed close collaboration (or even, shockingly, merger) of the two medical specialties of pathology and radiology into a new one called "diagnostic medicine" (see: Ten Reasons for Merging Pathology/Lab Medicine with Radiology). I think that a concurrence conference similar to the one at the UHCMC would be an important first step in promoting collaboration between radiology and pathology in any hospital. One major goal that could be accomplished by such a conference would be to determine whether reports generated for a particular patient by both departments were in agreement and/or consistent with each other.

To put the matter more bluntly, the continuing goal of the conference would be to determine whether a significant pathologic finding by one group of diagnostic specialists was overlooked or deemphasized by the other group. Another important goal would be for pathologists and radiologists to better understand the reporting conventions and medical vocabulary of the other group and, in so doing, promote better concurrence in the future. In this same spirit, it would be possible to determine which pathologic changes in an image or specimen noted by one group would prompt a telephone call to to the other group rather than communicating solely by "formal" reports that may not always highlight subtleties or uncertainties.

Healthcare AI Market to Surge; Already Commonplace in Radiology

The healthcare artificial intelligence (AI) market is going to grow enormously and has the potential to increase the efficiency of all healthcare processes and procedures; it may even reduce physician burnout (see: Cerner Offers AI Tool to Combat Physician EHR Burnout). A recent article discussed how the healthcare AI market will increase in value (see: Healthcare AI market expected to surge from $2.1 to $36.1 billion by 2025). It's a long and complex article so read it in its entirely if you're interested. Below is a small excerpt from it:

The healthcare artificial intelligence market is expected to grow from $2.1 billion in 2018 to $36.1 billion by 2025, at a compound annual growth rate of 50.2 percent during the forecast period....Increasingly large and complex data sets available in the form of big data, and a growing need to reduce increasing healthcare costs, are driving the growth of the market. Improving computing power and the declining cost of hardware are other key factors in the projected market growth. Despite the emerging data/technology picture, challenges remain. There's a reluctance among medical practitioners to adopt AI-based technologies, and a lack of skilled workforce and ambiguous regulatory guidelines for medical software also conspire to restrain the growth of the healthcare AI market....AI has already penetrated healthcare in areas such as radiology and cancer detection....Perhaps due to this, some are worried that AI is coming to replace their jobs -- fears that experts say are largely unfounded. Artificial intelligence and machine learning algorithms tend to rely on large quantities of data to be effective, and that data needs human hands to collect it and human eyes to analyze it. And since AI in healthcare is currently utilized mainly to aggregate and organize data -- looking for trends and patterns and making recommendations -- a human component is very much needed.

I have recently blogged about how a relatively high percentage of jobs in healthcare will be immune from automation (i.e., obsolescence on the basis of AI). This is because many of them are related to personal services that only humans can perform (see: Which Healthcare Jobs Are Safe from Replacement by Automation or Robots?). One of the reasons that AI has been deployed relatively early in radiology is that image analysis software was already highly developed and radiologists were also attune to the idea of using automation to relieve some of the more tedious tasks in the field. One would think that pathology would be a close second in automated image analysis but progress has been hampered by the slow deployment of digital pathology due, in part, to regulatory constraints.

The earliest AI deployments in healthcare will undoubtedly take place in areas where we already know roughly how to provide better care and reduce expenses. One example would be the goal of reducing the number of premature patient discharges, resulting in readmissions for which hospitals are financially penalized (see: Talking to Patients Helps Reduce Hospital Readmissions; Keeping Tabs on Patients Post-Discharge Via Telemonitoring; Home Monitoring of Discharged Patients by Hospitals for Cost Savings). For the future, AI systems will be constantly monitoring the status of hospitalized patients, calculating in the background when they can be discharged and what type of services they will require at home.

Which Healthcare Jobs Are Safe from Replacement by Automation or Robots?

One facet of modernity that is getting more attention is that many of today's jobs will be encroached upon (i.e., replaced) by automation and robots. As one current example, you will not find many people working in modern hospital clinical labs because much of this human labor has been replaced by automated equipment. That's why lab testing is one of the biggest bargains in healthcare today. A recent article addressed the question of whether healthcare jobs in particular are safe from replacement by AI (See: Are healthcare jobs safe from AI?). Below is an excerpt from the it:

Across the country and across industries, workers are nervous that automation and artificial intelligence will eventually take over their jobs. For some, those fears may be grounded in reality. Healthcare, however, looks like it will be largely safe from that trend, a new report from the Brookings Metropolitan Policy Program finds. Examining a chunk of time from the 1980s to 2016, the piece tracks the historical evolution of the technology and uses those findings to project forward to 2030. The verdict? AI will replace jobs in various industries, but not so much in healthcare.... Overall, though, only about 25 percent of U.S. jobs are at a high risk of replacement by automation. That translates to about 36 million jobs, based on 2016 data. A higher percentage, 36 percent, are at medium risk (52 million jobs) while the largest group is the low-risk group, at 39 percent (57 million jobs). Most of healthcare belongs in the medium-to-low categories, largely driven by the complexity of healthcare jobs....Medical assistants have what the report calls "automation potential" of 54 percent, but home health aids have just an 8 percent automation potential. Registered nurses sit somewhere in between, at 54 percent.

The emerging growth areas in pathology and the clinical labs, from my perspective, will be based on the conversion to digital pathology (see: Seeking Funding from Hospital Executives for Digital Pathology Platforms; Integration and Interoperability Are Essential for Growth of Digital Pathology), the automation of anatomic pathology, and the explosion of analytics in all of the clinical labs. The topic of analytics in healthcare was discussed in more detail in a recent post (see: Diagnostic and Predictive Analytics and Their Possible Link to the Future of the LIS). Analytics was described there as bring comprised of the following: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. I have also blogged about what I envision as the future of LISs as being highly related to analytics such that I think they should be referred to LIS-A's (see: Predicting the Future Functions of the LIS-Analytic (LIS-A)).

Registration Almost Full for the Second Annual API Digital Pathology Workshop

The second annual API Digital Pathology and AI Workshop will be presented on December 7-8. 2018 (see: Digital Pathology and AI Workshop II). The location will be Ohio State University College of Medicine in Columbus, Ohio. You can register for the workshop here. The capacity for the workshop has been doubled over last year's event, now with a maximum of 80 people. Only nine spaces remain open at this time. A waiting list will be created when these places are filled.

Available on the API web site are the schedule and the sponsoring vendors to date for the workshop. Here is a list of the workshop faculty and moderators: Ulysses J. Balis, M.D,; Michael D. Feldman, M.D; Wendy L. Frankel, M.D.; Chris Garcia, M.D.; Stephen M. Hewitt, M.D., Ph.D.;  Keith J. Kaplan, M.D.; Anant Madabhushi, Ph.D. Robert McGonnagle; Liron Pantanowitz, M.D.; Anil Parwani, M.D.;S. Joseph Sirintrapun, M.D.; and J. Mark Tuthill, M.D. Below is a quote from the workshop summary page on the API web site describing the workshop in more detail:

This workshop will focus on the practical issues surrounding the deployment of digital pathology and AI in a hospital setting. Ten lectures by national experts will be punctuated by Q and A sessions including industry representatives and a guided tour of the Ohio State University's Department of Pathology. Topics covered will include the fundamentals of digital pathology, the integration of the technology with LISs, the enhancement of AP workflow, the development of an RFP for purchasing digital pathology equipment, and current perspectives about AI within pathology.

This workshop has been planned with busy surgical pathologists and departmental leadership in mind. It lasts only one and a half days and takes place on a Friday and Saturday morning. The pathology department at OSU is the first department in the country providing primary surgical pathology diagnoses using digital pathology so a tour of the imaging facility will be highly educational. A dinner is planned for the Friday night for networking and relaxation and is included in the registration fee.  

The second annual API Digital Pathology and AI Workshop will be presented on December 7-8. 2018 (see: Digital Pathology and AI Workshop II). The location will be Ohio State University College of Medicine in Columbus, Ohio. You can register for the workshop here. The capacity for the workshop has been doubled over last year's event, now with a maximum of 80 people. Only nine spaces remain open at this time. A waiting list will be created when these places are filled.

Available on the API web site are the schedule and the sponsoring vendors to date for the workshop. Here is a list of the workshop faculty and moderators: Ulysses J. Balis, M.D,; Michael D. Feldman, M.D; Wendy L. Frankel, M.D.; Chris Garcia, M.D.; Stephen M. Hewitt, M.D., Ph.D.;  Keith J. Kaplan, M.D.; Anant Madabhushi, Ph.D. Robert McGonnagle; Liron Pantanowitz, M.D.; Anil Parwani, M.D.;S. Joseph Sirintrapun, M.D.; and J. Mark Tuthill, M.D. Below is a quote from the workshop summary page on the API web site describing the workshop in more detail:

This workshop will focus on the practical issues surrounding the deployment of digital pathology and AI in a hospital setting. Ten lectures by national experts will be punctuated by Q and A sessions including industry representatives and a guided tour of the Ohio State University's Department of Pathology. Topics covered will include the fundamentals of digital pathology, the integration of the technology with LISs, the enhancement of AP workflow, the development of an RFP for purchasing digital pathology equipment, and current perspectives about AI within pathology.

This workshop has been planned with busy surgical pathologists and departmental leadership in mind. It lasts only one and a half days and takes place on a Friday and Saturday morning. The pathology department at OSU is the first department in the country providing primary surgical pathology diagnoses using digital pathology so a tour of the imaging facility will be highly educational. A dinner is planned for the Friday night for networking and relaxation and is included in the registration fee.  

The Critical Role of Pathology Informatics in the Practice of Pathology

On October 3, 2018, I delivered a lecture at the ASCP Annual Meeting in Baltimore entitled: Using Informatics to Address Five Major Challenges in Pathology. You can view a PDF version of the lecture here. At the beginning of the lecture, I listed five major trends in healthcare. They are listed below:

1. This is golden era of diagnostics, affecting primarily complex diseases like cancer
2. Decentralization of healthcare away from hospitals & toward consumers
3. Rising tide of consumerism; patients will need to be treated more like customers
4. Computer-driven analytics are changing the style and content of pathology reports
5. For-profit companies like CVS, Apple, & Google beginning to reshape healthcare

In the summation of the lecture, I presented five major challenges flowing from the above trends that relate specifically to the practice of pathology and I list them below:

1. Translation of cancer genomic & molecular pathology test results into actionable information for clinicians
2. Deployment of predictive diagnostics results into most pathology reports
3. Total automation of anatomic pathology with integration of digital pathology into AP workflow
4. Conversion of most healthcare processes, including surgery, to ambulatory settings with the majority of interactions supported by telemedicine
5. Integration of lab data from "wearables” & home testing devices into LIS & EHR

As you review this list of challenges for pathology, you will notice that each item is at least partly or almost totally intertwined with information technology (IT). This should not come as a surprise because healthcare delivery and research is information rich and IT is about managing information. The inescapable conclusion from all of this is that all pathologists need to be well versed in IT and all pathology teaching programs need to introduce IT as a major component. If this does not occur quickly, the informatics portion of the practice of pathology will be ceded to other physicians in healthcare systems and/or to the central IT units in these organizations that support the EHR.

Controversy at MSK Cancer Center Regarding the Pathology Archive and Database

A controversy has erupted at prestigious cancer center Memorial Sloan Kettering (MSK) about the granting of exclusive access to its surgical pathology slide archive and database to a startup company, Paige.AI. It was been revealed in a recent NYT article that the hospital holds an equity stake in the company. There is also participation in the startup by some hospital insiders (see: Sloan Kettering’s Cozy Deal With Start-Up Ignites a New Uproar). Below is an excerpt from the article: 

An artificial intelligence start-up founded by three insiders at Memorial Sloan Kettering Cancer Center debuted with great fanfare in February, with $25 million in venture capital and the promise that it might one day transform how cancer is diagnosed. The company, Paige.AI, is one in a burgeoning field of start-ups that are applying artificial intelligence to health care, yet it has an advantage over many competitors: The company has an exclusive deal to use the cancer center’s vast archive of 25 million patient tissue slides, along with decades of work by its world-renowned pathologists. Memorial Sloan Kettering holds an equity stake in Paige.AI, as does a member of the cancer center’s executive board, the chairman of its pathology department and the head of one of its research laboratories. Three other board members are investors....

Doctors [at MSK} said they were concerned about a lack of communication from hospital leadership, and one said patients were nervous that their health data was being commercialized by the institution. Hospital pathologists have strongly objected to the Paige.AI deal, saying it is unfair that the founders received equity stakes in a company that relies on the pathologists’ expertise and work amassed over 60 years. They also questioned the use of patients’ data — even if it is anonymous — without their knowledge in a profit-driven venture. In addition, experts in nonprofit law and corporate governance have questioned whether Memorial Sloan Kettering, one of the nation’s leading cancer centers, complied with federal and state law governing nonprofits when it set up the deal. The experts pointed out that charitable institutions like Memorial Sloan Kettering must show that they didn’t provide assets to insiders for less than the fair market value. 

Computerized analysis of the scanned images of surgical pathology slides is an important and ongoing research area that will lead to more accurate and faster diagnosis of specimens. Algorithms will not replace pathologists any time soon but will certainly improve the accuracy and speed of diagnoses. An essential requirement for such research is access to images of specimens in addition to the diagnoses assigned by pathologists. Hence, exclusive access to superb cancer databases such as the one at MSK is enormously valuable. Here is a short but revealing quote about this process copied from the Paige.AI web site suggesting that Paige is also scanning the MSK slides:

Medical artificial intelligence at an unprecedented scale: Memorial Sloan Kettering Cancer Center scans up to 30K pathology slides each month, an enormous treasure of training data for powerful predictive models. PAIGE is ramping up to scan 100K archive slides per month.

The issue under consideration here is not whether artificial intelligence (AI) can and should be used to improve surgical pathology performance but rather whether it is appropriate for MSK to provide exclusive access to its pathology database to Paige.AI. I can't improve on the following two comments about this issue that were posted on the API listserve on 9/20/2018 by Dr. Brian Jackson. He is an informatician of note and also Medical Director of Support Services, IT and Business Development, at ARUP Laboratories. Boldface emphasis is mine.

1. In my opinion, tissue archives (ideally having accompanying patient consent, even if they’re de-identified) should be treated as public resources for the advancement of science broadly. Exclusive licenses for tissue archives, just like patents on DNA sequences, are just flat out immoral. Even if you ignore the embarrassing conflicts of interest. [side note: the pathology department chair gave up his equity stake after the NYTimes reporters started asking questions.]....
2. The problem is not commercialization per se. The problem is when scientists and physicians naively act as if commercialization excuses them from basic ethical principles. It’s completely possible to set up companies, even for-profit ones, that are fully transparent and respect scientific and medical ethical values. And that don’t have glaring conflicts of interest. In this case, I’m guessing that eliminating the exclusivity and the personal equity stakes for faculty members might have solved most of the problems. If that meant that certain investors wouldn’t have wanted to invest, then that’s what STTR grants are for. Or maybe a philanthropist. Or bootstrapping using departmental funds. But you don’t prostitute resources that you have stewardship over and then pretend that it’s ok just because it’s “innovation”. 

Addendum added on 11/1/2018: (see: Controversy Surrounding Memorial Sloan Kettering Cancer Center and Paige.AI Highlights Risks of Data Sharing and Monetization in Anatomic Pathology)

Pathology Departments Should Consider Launching a Digital Pathology Outreach Program

For about five years, some academic pathology programs have launched surgical pathology consulting programs, often serving countries like China with a shortage of highly trained pathologists (see: China: Pathology Consults and Health Care Collaboration; China Struggling to Keep Up with Demand for Anatomic Pathologists). These initiatives were based on less robust digital pathology technology than now exists. They were also acceptable in the eyes of the FDA because they were secondary consultations to pathologists working abroad who were responsible for the primary diagnosis. This is in contrast to the current situation with FDA approval for primary consultation for one particular vendor digital pathology platform (see: FDA Approves the Philips Digital Pathology WSI Solution for Primary Diagnosis). It is expected that similar approval will be forthcoming shortly for digital pathology platforms from other vendors.

There is growing enthusiasm and momentum for the deployment of digital pathology systems as the result of the recent FDA decision. This is also partly due to the advantages of working with portable image files and the fact that the recruitment of pathology residents and fellows is enhanced by the presence of as robust digital pathology program in pathology departments. There are also a growing number of conferences and workshops available with a digital pathology theme, particularly those emphasizing practical issues and the rapid adoption of the technology (see: API Digital Pathology and AI Workshop II).

By way of this note, I want to call attention to the opportunities for pathology departments for what I will refer to here as digital pathology outreach. I am using this new term for the digital pathology consulting of prior years but with whole slide imaging (WSI) and the latest technology platforms. I have chosen this specific term because it so closely aligns with the clinical pathology outreach programs that have been offered by pathology programs for many decades. In short, I am suggesting that pathology departments should graft surgical pathology consulting (i.e., digital pathology outreach) on top of their existing CP outreach programs. Because such initiatives will be revenue generating if successful, they constitute an additional justification for starting a digital pathology program.