Digital pathology as a concept and a technology has been in the market for more than a decade now; yet, still most vendors in this space are found struggling to get a strong customer buy-in. Although the evolution of whole slide scanners, workflow management solutions, and image analysis platforms has contributed to increased user acceptability of digital pathology systems, it still remains below the expected adoption level due to a variety of reasons.
Establishing ROI on Digital Pathology Investments
It is established that digital pathology systems support improvements in patient safety, diagnostic workflow, workforce factors, and overall service quality. However, questions remain related to implementation—how do labs pay for such expensive technologies and how do they integrate them into their current workflows? How will labs get reimbursed? What is the ideal return on investment (ROI)? In a nutshell, it has become more of an adoption business problem rather than a technology problem.
High cost and lack of established ROI measures have been the major reasons for lower than expected adoption of digital pathology solutions. This is especially true in clinical settings, where the adoption rate is extremely low. At present, 60% to 70% of the use cases for digital pathology come from research applications. According to a recent survey conducted by Frost & Sullivan, approximately 70% of respondents indicated high cost as major reason for not adopting the digital pathology systems. More than 65% of the respondents indicated insufficient workload as a key reason, and ~50% were unsure of ROI and benefits.
In general, there is a disconnect between the approaches of customers and digital pathology companies—lab managers look at any lab technology cost model based on a per test fee and a total cost of ownership model, while digital pathology solutions providers tend to look at their material, engineering, and product development costs.
Another major challenge for companies is that digital pathology is being outcompeted by other innovations such as Next Generation Sequencing (NGS). Moreover, limited capacity of labs has been another restraining factor as managers are not able to put additional efforts for digitalization efforts.
In such a market scenario, it becomes important for companies to become more creative with their business models and monetization approaches to overcome these adoption challenges.
The key question in this scenario is how do digital pathology companies get a buy-in on digital pathology systems?
Would Change in Business Model Help Overcome These Challenges?
A Florida-based digital pathology workflow solutions company, Inspirata, has taken an interesting approach to overcome these existing challenges. Inspirata is a Cancer Informatics company that provides digital pathology workflow management solutions to cancer centers and academic medical centers.
To overcome the slow pace of adoption of digital pathology, Inspirata has introduced a unique ”Solution-as-a-Service” (SaaS) business model to help its customers stay ahead of the digital pathology adoption curve. Under this model, Inspirata provides all the hardware, software, data storage, support services, and additional resources to run a high-throughput whole slide image scanning center. Inspirata charges its clients based on implementation milestones. As the cost of this service is spread over the life of the engagement, the client does not have to make any significant capital investments in whole slide image scanners and other hardware and software. On the vendor side, there are multiple advantages of this model. While vendors are likely to get higher business volume, they also get access to patient data, which can be used for clinical decision support, research, reporting, operational uses, drug discovery, and a myriad of other use cases.
One of the challenges with this model is that it is capital intensive. However, Inspirata is assured of long-term client engagements that often result in collaborative partnerships. These types of relationships can generate higher ROI for both the vendor and the customer. Another advantage is that high-volume scanning centers can be used to serve other affiliated and non-affiliated hospitals overcoming the challenge of low equipment utilization. Inspirata’s overall approach is interesting and appears to be overcoming adoption challenges. It remains to be seen how Inspirata will be able to replicate this model at a larger scale and achieve efficiency at the same time.
What Does the Future Look Like?
Other applications and customer segments will be critical for shaping the future digital pathology market. With approval of digital pathology solutions for primary diagnosis in the United States, adoption is likely to go up. However, lack of interoperability between different health information systems and workflow platforms might hinder growth.
Industry bodies and organization such as the College of American Pathologists (CAP), the U.S. and Canadian Academy of Pathologists (USCAP) and the Digital Pathology Association (DPA) are already working with regulatory authorities to establish clear guidelines for vendors. For the U.S. market, there has been a gradual increase in adoption NCI-designated Comprehensive Cancer Centers and Academic Medical Centers. However, there is extremely low adoption for small- to mid-sized hospitals and even at Independent Delivery Networks (IDNs). Different business propositions for different segments and sizes of customers will be helpful in gaining strong customer traction.
For the EU market, growth opportunities look more promising as a number of healthcare organizations and bodies have initiated public digital pathology adoption projects.
According to Frost & Sullivan’s latest report on the global? digital pathology market, machine learning and automated analysis solutions are likely to dominate the future of digital pathology informatics. To enable machine learning in digital pathology workflow and informatics, high slide volume for machine learning and refining algorithms is essential. Furthermore, having specific therapeutic focus will enable informatics organizations to focus on improving the outcomes.
So far we have also seen huge interest from pharmaceutical companies to leverage digital pathology in drug discovery and development processes, especially for precision medicine applications. Tissue and biomarker assays are critical in oncology drug discovery, and to enable personalized medicines. Multiple data sources (including patients’ history, multi-omics data, HER platforms, and so on) need to be integrated and analyzed. Large digital pathology companies need to consider building the required business framework to support the development of precision medicine.
Frost & Sullivan expects market entry of some large medical imaging companies such as Nikon, Canon, and Fujifilm, given their healthcare segment focus and imaging market expertise. This is likely to create more competitive pressure on the current digital pathology imaging vendors.
Conclusion
While the future of digital pathology market looks promising, a number of questions remain. The industry has seen a significant technology transformation, but now is the time for change in business models to enable higher adoption. Given the high capital expenditure requirements, companies have a first-mover advantage, and hence, there is a dire need for them to reach out to a large set of customers to avoid any competitive threats. As companies such as Inspirata have started taking different approaches to gain a foothold in this space, it will be interesting to see how this market pans out over the next few years.
