| Literature DB >> 34221633 |
Giovanni Lujan1, Jennifer C Quigley2, Douglas Hartman3, Anil Parwani1, Brian Roehmholdt4, Bryan Van Meter5, Orly Ardon6, Matthew G Hanna6, Dan Kelly7, Chelsea Sowards8, Michael Montalto9, Marilyn Bui10, Mark D Zarella11, Victoria LaRosa12, Gerard Slootweg13, Juan Antonio Retamero13, Mark C Lloyd14, James Madory15, Doug Bowman16.
Abstract
We believe the switch to a digital pathology (DP) workflow is imminent and it is essential to understand the economic implications of conversion. Many aspects of the adoption of DP will be disruptive and have a direct financial impact, both in short term costs, such as investment in equipment and personnel, and long term revenue potential, such as improved productivity and novel tests. The focus of this whitepaper is to educate pathologists, laboratorians and other stakeholders about the business and monetary considerations of converting to a digital pathology workflow. The components of a DP business plan will be thoroughly summarized, and guidance will be provided on how to build a case for adoption and implementation as well as a roadmap for transitioning from an analog to a digital pathology workflow in various laboratory settings. It is important to clarify that this publication is not intended to list prices although some financials will be mentioned as examples. The authors encourage readers who are evaluating conversion to a DP workflow to use this paper as a foundational guide for conducting a thorough and complete assessment while incorporating in current market pricing. Contributors to this paper analyzed peer-reviewed literature and data collected from various institutions, some of which are mentioned. Digital pathology will change the way we practice through facilitating patient access to expert pathology services and enabling image analysis tools and assays to aid in diagnosis, prognosis, risk stratification and therapeutic selection. Together, they will result in the delivery of valuable information from which to make better decisions and improve the health of patients. Copyright:Entities:
Keywords: Business case; clinical laboratory; digital pathology; implementation; patient care
Year: 2021 PMID: 34221633 PMCID: PMC8240548 DOI: 10.4103/jpi.jpi_67_20
Source DB: PubMed Journal: J Pathol Inform
Figure 1Information Technology infrastructure components
Storage comparison
| Tier | Type of storage | Purpose |
|---|---|---|
| Tier 1 | SSD/flash drives | Highest performance |
| Tier 2 | 15 k rpm hard drives | Balance of performance/capacity |
| Archive Tier 3 | 7.2 k/10 k rpm hard drives | High capacity/low performance |
SSD: Solid state drive
Common types of image analysis software (courtesy of Joseph Johnson, Moffitt Cancer Center)
| Image format compatibility | Technical knowledge level | Customization level | Features | |
|---|---|---|---|---|
| Basic science image analysis | Most image formats | Moderate | High | Variety of measurement tools |
| Access to image processing tools | ||||
| Some automation | ||||
| Slide scanner based | Limited image formats | Low | Lowmoderate | Direct access to images |
| Access to common algorithms | ||||
| US IVD for HER2/ER available | ||||
| Pattern recognition | ||||
| Batch processing | ||||
| Designed for digital pathology | ||||
| Digital pathology inspired | Most image formats | Moderate | Moderate | Workflow based |
| Easily adjustableparameters | ||||
| Batch processing | ||||
| Pattern recognition | ||||
| Access more feature data | ||||
| Designed for digital pathology | ||||
| Algorithm based | Most image formats | High | High | Fully customizable |
| Unique algorithms | ||||
| Even more feature data | ||||
| Batch processing |
IVD: In vitro diagnostics, ER: Estrogen receptor, HER2: Human epidermal growth factor receptor 2
Figure 2Digital imaging added value proposition (Courtesy of Orly Ardon, Memorial Sloan Kettering)
Figure 3Projected shortage of practicing pathologists in the next 5 years Digital Pathology and Image Analysis Can Help Alleviate Pathologist Shortage (Courtesy of Orly Ardon, Memorial Sloan Kettering)
Figure 4Current trends in diagnostic laboratories. The case for improved productivity (Courtesy of Orly Ardon, Memorial Sloan Kettering)
Differences in regulatory governance of diagnostic tests
| Government agency via regulatory authority | CMS via CLIA program | FDA via code of regulations (21 CFR) |
|---|---|---|
| Has regulatory authority over… | Clinical laboratories | Medical device manufacturers |
| Intent of regulations | Ensure accurate test results are performed and delivered | Ensure safety and efficacy of marketed devices and tests distributed to laboratories |
| Scope of analytical validity | Single laboratory where test is performed | All laboratories that use the device/test as intended in device labeling |
| Regulates IVD test | Yes – through laboratory oversight | Yes – through device manufacturer oversight |
| Regulates LDTs | Yes – through laboratory oversight | No – FDA has exercised ‘enforcement discretion’* thus far, which may change in future |
*Enforcement discretion is a form of regulation which allows the FDA to formally choose not to enforce the regulations. The FDA may decide to reverse this decision and regulate LDT in the future. CFR: Code of Federal Regulations, CMS: Centers for Medicare and Medicaid Services, CLIA: Clinical Laboratory Improvement Amendments, FDA: Food and Drug Administration, IVD: In vitro diagnostic, LDTs: Laboratory developed tests